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coverbench
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The percentage decline in recorded international slot and route authorities from $736 million to $708 million as of December 31, 2009 and 2010, respectively, was 3.9%.
|
[
"american airlines , inc .\nnotes to consolidated financial statements 2014 ( continued ) temporary , targeted funding relief ( subject to certain terms and conditions ) for single employer and multiemployer pension plans that suffered significant losses in asset value due to the steep market slide in 2008 .\nunder the relief act , the company 2019s 2010 minimum required contribution to its defined benefit pension plans was reduced from $ 525 million to approximately $ 460 million .\nthe following benefit payments , which reflect expected future service as appropriate , are expected to be paid : retiree medical pension and other .\n\n[{\"\":\"2011\",\"pension\":\"574\",\"retiree medical and other\":\"173\"},{\"\":\"2012\",\"pension\":\"602\",\"retiree medical and other\":\"170\"},{\"\":\"2013\",\"pension\":\"665\",\"retiree medical and other\":\"169\"},{\"\":\"2014\",\"pension\":\"729\",\"retiree medical and other\":\"170\"},{\"\":\"2015\",\"pension\":\"785\",\"retiree medical and other\":\"173\"},{\"\":\"2016 2014 2020\",\"pension\":\"4959\",\"retiree medical and other\":\"989\"}]\n\nduring 2008 , amr recorded a settlement charge totaling $ 103 million related to lump sum distributions from the company 2019s defined benefit pension plans to pilots who retired .\npursuant to u.s .\ngaap , the use of settlement accounting is required if , for a given year , the cost of all settlements exceeds , or is expected to exceed , the sum of the service cost and interest cost components of net periodic pension expense for a plan .\nunder settlement accounting , unrecognized plan gains or losses must be recognized immediately in proportion to the percentage reduction of the plan 2019s projected benefit obligation .\n11 .\nintangible assets the company has recorded international slot and route authorities of $ 708 million and $ 736 million as of december 31 , 2010 and 2009 , respectively .\nthe company considers these assets indefinite life assets and as a result , they are not amortized but instead are tested for impairment annually or more frequently if events or changes in circumstances indicate that the asset might be impaired .\nsuch triggering events may include significant changes to the company 2019s network or capacity , or the implementation of open skies agreements in countries where the company operates flights .\nin the fourth quarter of 2010 , the company performed its annual impairment testing on international slots and routes , at which time the net carrying value was reassessed for recoverability .\nit was determined through this annual impairment testing that the fair value of certain international routes in latin america was less than the carrying value .\nthus , the company incurred an impairment charge of $ 28 million to write down the values of these and certain other slots and routes .\nas there is minimal market activity for the valuation of routes and international slots and landing rights , the company measures fair value with inputs using the income approach .\nthe income approach uses valuation techniques , such as future cash flows , to convert future amounts to a single present discounted amount .\nthe inputs utilized for these valuations are unobservable and reflect the company 2019s assumptions about market participants and what they would use to value the routes and accordingly are considered level 3 in the fair value hierarchy .\nthe company 2019s unobservable inputs are developed based on the best information available as of december 31 ."
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NS
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FinQA
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coverbench
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The recorded net losses decreased by 50.7% from 2007 to 2008 due to the sales of certain non-core towers and other assets.
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[
"american tower corporation and subsidiaries notes to consolidated financial statements 2014 ( continued ) 3.00% ( 3.00 % ) convertible notes 2014during the years ended december 31 , 2008 and 2007 , the company issued an aggregate of approximately 8.9 million and 973 shares of common stock , respectively , upon conversion of $ 182.8 million and $ 0.02 million principal amount , respectively , of 3.00% ( 3.00 % ) notes .\npursuant to the terms of the indenture , holders of the 3.00% ( 3.00 % ) notes are entitled to receive 48.7805 shares of common stock for every $ 1000 principal amount of notes converted .\nin connection with the conversions in 2008 , the company paid such holders an aggregate of approximately $ 4.7 million , calculated based on the discounted value of the future interest payments on the notes , which is reflected in loss on retirement of long-term obligations in the accompanying consolidated statement of operations for the year ended december 31 , 2008 .\n14 .\nimpairments , net loss on sale of long-lived assets , restructuring and merger related expense the significant components reflected in impairments , net loss on sale of long-lived assets , restructuring and merger related expense in the accompanying consolidated statements of operations include the following : impairments and net loss on sale of long-lived assets 2014during the years ended december 31 , 2008 , 2007 and 2006 , the company recorded impairments and net loss on sale of long-lived assets ( primarily related to its rental and management segment ) of $ 11.2 million , $ 9.2 million and $ 2.6 million , respectively .\nduring the years ended december 31 , 2008 , 2007 and 2006 respectively , the company recorded net losses associated with the sales of certain non-core towers and other assets , as well as impairment charges to write-down certain assets to net realizable value after an indicator of impairment had been identified .\nas a result , the company recorded net losses and impairments of approximately $ 10.5 million , $ 7.1 million and $ 2.0 million for the years ended december 31 , 2008 , 2007 and 2006 , respectively .\nthe net loss for the year ended december 31 , 2008 is comprised of net losses from asset sales and other impairments of $ 10.7 million , offset by gains from asset sales of $ 0.2 million .\nthe net loss for the year ended december 31 , 2007 is comprised of net losses from asset sales and other impairments of $ 7.8 million , offset by gains from asset sales of $ 0.7 million .\nmerger related expense 2014during the year ended december 31 , 2005 , the company assumed certain obligations , as a result of the merger with spectrasite , inc. , primarily related to employee separation costs of former spectrasite employees .\nseverance payments made to former spectrasite , inc .\nemployees were subject to plans and agreements established by spectrasite , inc .\nand assumed by the company in connection with the merger .\nthese costs were recognized as an assumed liability in the purchase price allocation .\nin addition , the company also incurred certain merger related costs for additional employee retention and separation costs incurred during the year ended december 31 , 2006 .\nthe following table displays the activity with respect to this accrued liability for the years ended december 31 , 2008 , 2007 and 2006 ( in thousands ) : liability december 31 , expense 2006 cash payments other liability december 31 , expense 2007 cash payments other liability december 31 , expense 2008 cash payments other liability december 31 , employee separations .\n.\n.\n.\n$ 20963 $ 496 $ ( 12389 ) $ ( 1743 ) $ 7327 $ 633 $ ( 6110 ) $ ( 304 ) $ 1546 $ 284 $ ( 1901 ) $ 71 2014 as of december 31 , 2008 , the company had paid all of these merger related liabilities. .\n\n 0 1 2 3 4 5 6 7 8 9 10 11 12 13\n0 employee separations liability as of december 31 2005 $ 20963 2006 expense $ 496 2006 cash payments $ -12389 ( 12389 ) other $ -1743 ( 1743 ) liability as of december 31 2006 $ 7327 2007 expense $ 633 2007 cash payments $ -6110 ( 6110 ) other $ -304 ( 304 ) liability as of december 31 2007 $ 1546 2008 expense $ 284 2008 cash payments $ -1901 ( 1901 ) other $ 71 liability as of december 31 2008 2014\n\namerican tower corporation and subsidiaries notes to consolidated financial statements 2014 ( continued ) 3.00% ( 3.00 % ) convertible notes 2014during the years ended december 31 , 2008 and 2007 , the company issued an aggregate of approximately 8.9 million and 973 shares of common stock , respectively , upon conversion of $ 182.8 million and $ 0.02 million principal amount , respectively , of 3.00% ( 3.00 % ) notes .\npursuant to the terms of the indenture , holders of the 3.00% ( 3.00 % ) notes are entitled to receive 48.7805 shares of common stock for every $ 1000 principal amount of notes converted .\nin connection with the conversions in 2008 , the company paid such holders an aggregate of approximately $ 4.7 million , calculated based on the discounted value of the future interest payments on the notes , which is reflected in loss on retirement of long-term obligations in the accompanying consolidated statement of operations for the year ended december 31 , 2008 .\n14 .\nimpairments , net loss on sale of long-lived assets , restructuring and merger related expense the significant components reflected in impairments , net loss on sale of long-lived assets , restructuring and merger related expense in the accompanying consolidated statements of operations include the following : impairments and net loss on sale of long-lived assets 2014during the years ended december 31 , 2008 , 2007 and 2006 , the company recorded impairments and net loss on sale of long-lived assets ( primarily related to its rental and management segment ) of $ 11.2 million , $ 9.2 million and $ 2.6 million , respectively .\nduring the years ended december 31 , 2008 , 2007 and 2006 respectively , the company recorded net losses associated with the sales of certain non-core towers and other assets , as well as impairment charges to write-down certain assets to net realizable value after an indicator of impairment had been identified .\nas a result , the company recorded net losses and impairments of approximately $ 10.5 million , $ 7.1 million and $ 2.0 million for the years ended december 31 , 2008 , 2007 and 2006 , respectively .\nthe net loss for the year ended december 31 , 2008 is comprised of net losses from asset sales and other impairments of $ 10.7 million , offset by gains from asset sales of $ 0.2 million .\nthe net loss for the year ended december 31 , 2007 is comprised of net losses from asset sales and other impairments of $ 7.8 million , offset by gains from asset sales of $ 0.7 million .\nmerger related expense 2014during the year ended december 31 , 2005 , the company assumed certain obligations , as a result of the merger with spectrasite , inc. , primarily related to employee separation costs of former spectrasite employees .\nseverance payments made to former spectrasite , inc .\nemployees were subject to plans and agreements established by spectrasite , inc .\nand assumed by the company in connection with the merger .\nthese costs were recognized as an assumed liability in the purchase price allocation .\nin addition , the company also incurred certain merger related costs for additional employee retention and separation costs incurred during the year ended december 31 , 2006 .\nthe following table displays the activity with respect to this accrued liability for the years ended december 31 , 2008 , 2007 and 2006 ( in thousands ) : liability december 31 , expense 2006 cash payments other liability december 31 , expense 2007 cash payments other liability december 31 , expense 2008 cash payments other liability december 31 , employee separations .\n.\n.\n.\n$ 20963 $ 496 $ ( 12389 ) $ ( 1743 ) $ 7327 $ 633 $ ( 6110 ) $ ( 304 ) $ 1546 $ 284 $ ( 1901 ) $ 71 2014 as of december 31 , 2008 , the company had paid all of these merger related liabilities. ."
] |
NS
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FinQA
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coverbench
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The cash held on behalf of GE as a percentage of cash and equivalents in 2017 was 14.34%.
|
[
"36 | bhge 2017 form 10-k liquidity and capital resources our objective in financing our business is to maintain sufficient liquidity , adequate financial resources and financial flexibility in order to fund the requirements of our business .\nat december 31 , 2017 , we had cash and equivalents of $ 7.0 billion compared to $ 981 million of cash and equivalents at december 31 , 2016 .\ncash and equivalents includes $ 997 million of cash held on behalf of ge at december 31 , 2017 .\nat december 31 , 2017 , approximately $ 3.2 billion of our cash and equivalents was held by foreign subsidiaries compared to approximately $ 878 million at december 31 , 2016 .\na substantial portion of the cash held by foreign subsidiaries at december 31 , 2017 has been reinvested in active non-u.s .\nbusiness operations .\nat december 31 , 2017 , our intent is , among other things , to use this cash to fund the operations of our foreign subsidiaries , and we have not changed our indefinite reinvestment decision as a result of u.s .\ntax reform but will reassess this during the course of 2018 .\nif we decide at a later date to repatriate those funds to the u.s. , we may be required to provide taxes on certain of those funds , however , due to the enactment of u.s .\ntax reform , repatriations of foreign earnings will generally be free of u.s .\nfederal tax but may incur other taxes such as withholding or state taxes .\non july 3 , 2017 , in connection with the transactions , bhge llc entered into a new five-year $ 3 billion committed unsecured revolving credit facility ( 2017 credit agreement ) with commercial banks maturing in july 2022 .\nas of december 31 , 2017 , there were no borrowings under the 2017 credit agreement .\non november 3 , 2017 , bhge llc entered into a commercial paper program under which it may issue from time to time up to $ 3 billion in commercial paper with maturities of no more than 397 days .\nat december 31 , 2017 , there were no borrowings outstanding under the commercial paper program .\nthe maximum combined borrowing at any time under both the 2017 credit agreement and the commercial paper program is $ 3 billion .\non november 6 , 2017 , we announced that our board of directors authorized bhge llc to repurchase up to $ 3 billion of its common units from the company and ge .\nthe proceeds of such repurchase that are distributed to the company will be used to repurchase class a shares of the company on the open market or in privately negotiated transactions .\non december 15 , 2017 , we filed a shelf registration statement on form s-3 with the sec to give us the ability to sell up to $ 3 billion in debt securities in amounts to be determined at the time of an offering .\nany such offering , if it does occur , may happen in one or more transactions .\nthe specific terms of any securities to be sold will be described in supplemental filings with the sec .\nthe registration statement will expire in 2020 .\nduring the year ended december 31 , 2017 , we used cash to fund a variety of activities including certain working capital needs and restructuring costs , capital expenditures , business acquisitions , the payment of dividends and share repurchases .\nwe believe that cash on hand , cash flows generated from operations and the available credit facility will provide sufficient liquidity to manage our global cash needs .\ncash flows cash flows provided by ( used in ) each type of activity were as follows for the years ended december 31: .\n\n[{\"0\":\"( in millions )\",\"1\":\"2017\",\"2\":\"2016\",\"3\":\"2015\"},{\"0\":\"operating activities\",\"1\":\"$ -799 ( 799 )\",\"2\":\"$ 262\",\"3\":\"$ 1277\"},{\"0\":\"investing activities\",\"1\":\"-4130 ( 4130 )\",\"2\":\"-472 ( 472 )\",\"3\":\"-466 ( 466 )\"},{\"0\":\"financing activities\",\"1\":\"10919\",\"2\":\"-102 ( 102 )\",\"3\":\"-515 ( 515 )\"}]\n\noperating activities our largest source of operating cash is payments from customers , of which the largest component is collecting cash related to product or services sales including advance payments or progress collections for work to be performed .\nthe primary use of operating cash is to pay our suppliers , employees , tax authorities and others for a wide range of material and services. ."
] |
NS
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FinQA
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coverbench
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The percentage change in the weighted average fair value on the date of the award of the common stock was 37.3%.
|
[
"the fair value of options that vested during the years ended december 31 , 2017 , 2016 and 2015 was $ 6.8 million , $ 6.0 million and $ 7.8 million , respectively .\nthe intrinsic value of fortune brands stock options exercised in the years ended december 31 , 2017 , 2016 and 2015 was $ 70.6 million , $ 88.1 million and $ 78.0 million , respectively .\nperformance awards performance share awards were granted to officers and certain employees of the company under the plans and represent the right to earn shares of company common stock based on the achievement of or company-wide performance conditions , including cumulative diluted earnings per share , average return on invested capital , average return on net tangible assets and ebitda during the three-year performance period .\ncompensation cost is amortized into expense over the performance period , which is generally three years , and is based on the probability of meeting performance targets .\nthe fair value of each performance share award is based on the average of the high and low stock price on the date of grant .\nthe following table summarizes information about performance share awards as of december 31 , 2017 , as well as activity during the year then ended .\nthe number of performance share awards granted are shown below at the target award amounts : number of performance share awards weighted-average grant-date fair value .\n\n number of performance share awards weighted-averagegrant-datefair value\n0 non-vestedat december 31 2016 421600 $ 48.00\n1 granted 160196 58.02\n2 vested -95183 ( 95183 ) 45.13\n3 forfeited -58285 ( 58285 ) 48.22\n4 non-vestedat december 31 2017 428328 $ 52.35\n\nthe remaining unrecognized pre-tax compensation cost related to performance share awards at december 31 , 2017 was approximately $ 6.8 million , and the weighted-average period of time over which this cost will be recognized is 1.3 years .\nthe fair value of performance share awards that vested during 2017 was $ 5.6 million ( 100580 shares ) .\ndirector awards stock awards are used as part of the compensation provided to outside directors under the plan .\nawards are issued annually in the second quarter .\nin addition , outside directors can elect to have director fees paid in stock or can elect to defer payment of stock .\ncompensation cost is expensed at the time of an award based on the fair value of a share at the date of the award .\nin 2017 , 2016 and 2015 , we awarded 15311 , 16471 and 19695 shares of company common stock to outside directors with a weighted average fair value on the date of the award of $ 63.43 , $ 57.37 and $ 46.21 , respectively .\n14 .\ndefined benefit plans we have a number of pension plans in the united states , covering many of the company 2019s employees , however these plans have been closed to new hires .\nthe plans provide for payment of retirement benefits , mainly commencing between the ages of 55 and 65 .\nafter meeting certain qualifications , an employee acquires a vested right to future benefits .\nthe benefits payable under the plans are generally determined on the basis of an employee 2019s length of service and/or earnings .\nemployer contributions to the plans are made , as necessary , to ensure legal funding requirements are satisfied .\nalso , from time to time , we may make contributions in excess of the legal funding requirements .\nservice cost for 2017 relates to benefit accruals in an hourly union defined benefit plan in our security segment .\nbenefit accruals under all other defined benefit pension plans were frozen as of december 31 , 2016. ."
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S
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FinQA
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coverbench
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Pension fundings represent 13.43% of the total expected cash outflow to satisfy contractual obligations and commitments as of December 31, 2007.
|
[
"providing a revolving credit facility of $ 7.0 billion and expiring on october 17 , 2008 .\ninterest on any amounts we borrow under these facilities would be charged at 90-day libor plus 15 basis points .\nat december 31 , 2007 , there were no outstanding borrowings under these facilities .\nour existing debt instruments and credit facilities do not have cross-default or ratings triggers , however these debt instruments and credit facilities do subject us to certain financial covenants .\ncovenants in our credit facilities generally require us to maintain a $ 3.0 billion minimum net worth and limit the amount of secured indebtedness that may be incurred by the company .\nthe notes issued in january 2008 include limitations on secured indebtedness and on sale-leaseback transactions .\nthese covenants are not considered material to the overall financial condition of the company , and all applicable covenant tests were satisfied as of december 31 , commitments we have contractual obligations and commitments in the form of capital leases , operating leases , debt obligations , purchase commitments , and certain other liabilities .\nwe intend to satisfy these obligations through the use of cash flow from operations .\nthe following table summarizes the expected cash outflow to satisfy our contractual obligations and commitments as of december 31 , 2007 ( in millions ) : capital leases operating leases principal interest purchase commitments pension fundings liabilities .\n\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>year</td>\n <td>capital leases</td>\n <td>operating leases</td>\n <td>debt principal</td>\n <td>debt interest</td>\n <td>purchase commitments</td>\n <td>pension fundings</td>\n <td>other liabilities</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2008</td>\n <td>$ 108</td>\n <td>$ 378</td>\n <td>$ 3426</td>\n <td>$ 329</td>\n <td>$ 1306</td>\n <td>$ 101</td>\n <td>$ 78</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2009</td>\n <td>73</td>\n <td>325</td>\n <td>83</td>\n <td>384</td>\n <td>791</td>\n <td>824</td>\n <td>74</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2010</td>\n <td>91</td>\n <td>237</td>\n <td>40</td>\n <td>380</td>\n <td>729</td>\n <td>630</td>\n <td>71</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2011</td>\n <td>31</td>\n <td>166</td>\n <td>33</td>\n <td>379</td>\n <td>698</td>\n <td>717</td>\n <td>69</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2012</td>\n <td>31</td>\n <td>116</td>\n <td>26</td>\n <td>377</td>\n <td>304</td>\n <td>859</td>\n <td>67</td>\n </tr>\n <tr>\n <th>6</th>\n <td>after 2012</td>\n <td>285</td>\n <td>560</td>\n <td>6919</td>\n <td>6177</td>\n <td>2014</td>\n <td>334</td>\n <td>203</td>\n </tr>\n <tr>\n <th>7</th>\n <td>total</td>\n <td>$ 619</td>\n <td>$ 1782</td>\n <td>$ 10527</td>\n <td>$ 8026</td>\n <td>$ 3828</td>\n <td>$ 3465</td>\n <td>$ 562</td>\n </tr>\n </tbody>\n</table>\n\nour capital lease obligations relate primarily to leases on aircraft .\ncapital leases , operating leases , and purchase commitments , as well as our debt principal obligations , are discussed further in note 8 to our consolidated financial statements .\nthe amount of interest on our debt was calculated as the contractual interest payments due on our fixed-rate debt , in addition to interest on variable rate debt that was calculated based on interest rates as of december 31 , 2007 .\nthe calculations of debt interest do not take into account the effect of interest rate swap agreements .\nthe maturities of debt principal and interest include the effect of the january 2008 issuance of $ 4.0 billion in senior notes that were used to reduce the commercial paper balance .\npurchase commitments represent contractual agreements to purchase goods or services that are legally binding , the largest of which are orders for aircraft , engines , and parts .\nin february 2007 , we announced an order for 27 boeing 767-300er freighters to be delivered between 2009 and 2012 .\nwe also have firm commitments to purchase nine boeing 747-400f aircraft scheduled for delivery between 2008 and 2010 , and two boeing 747-400bcf aircraft scheduled for delivery during 2008 .\nthese aircraft purchase orders will provide for the replacement of existing capacity and anticipated future growth .\nin july 2007 , we formally cancelled our previous order for ten airbus a380-800 freighter aircraft , pursuant to the provisions of an agreement signed with airbus in february 2007 .\nas a result of our cancellation of the airbus a380-800 order , we received cash in july 2007 representing the return of amounts previously paid to airbus as purchase contract deposits and accrued interest on those balances .\nadditionally , we received a credit memorandum to be used by ups for the purchase of parts and services from airbus .\nthe cancellation of the airbus order did not have a material impact on our financial condition , results of operations , or liquidity. ."
] |
NS
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FinQA
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coverbench
|
The total number of tap water analyses conducted per year is 1800.
|
[
"<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Water Amount</th>\n <th>Result</th>\n <th>Action</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>750 million gallons</td>\n <td>Met/exceeded EPA standards</td>\n <td>600,000 analyses/year, corrosion control, water testing</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Almost 1 billion gallons</td>\n <td>Met/exceeded EPA standards</td>\n <td>600,000 analyses/year, corrosion control, water testing</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Almost 1 billion gallons</td>\n <td>Met/exceeded EPA standards</td>\n <td>600,000 analyses/year, corrosion control, water testing</td>\n </tr>\n </tbody>\n</table>"
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NS
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TACT
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coverbench
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The average number of floors in buildings constructed between 2015 and 2027 is 107.
|
[
"| | Building Name | Height (m/ft) | City | Country | Number of Floors | Completion Year |\n|---:|:---------------------------|:-------------------|:-------------|:----------|-------------------:|------------------:|\n| 0 | Merdeka 118 | 678.9 m / 2,227 ft | Kuala Lumpur | Malaysia | 118 | 2021 |\n| 1 | Tianjin CTF Finance Center | 530 m / 1,740 ft | Tianjin | China | 97 | 2019 |"
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S
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TACT
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coverbench
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On average, 12% of people who work in Manhattan come from places starting with "Br".
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[
"| | Commute Origin | Commute Destination | Percentage |\n|---:|:-------------------------------|:-------------------------------|:-------------|\n| 0 | Manhattan | Manhattan | 30% |\n| 1 | Queens | Manhattan | 17% |\n| 2 | Brooklyn | Manhattan | 16% |\n| 3 | The Bronx | Manhattan | 8% |\n| 4 | Staten Island | Manhattan | 2.5% |\n| 5 | Nassau County (Manhattan) | Manhattan (Nassau County) | 7.5% |\n| 6 | Westchester County (Manhattan) | Manhattan (Westchester County) | 5.5% |\n| 7 | Suffolk County (Manhattan) | Manhattan (Suffolk County) | 2.5% |\n| 8 | Bergen/Hudson NJ (Manhattan) | Manhattan (Bergen/Hudson NJ) | 6.3% |"
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S
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TACT
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coverbench
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There are 0 viruses starting with "East".
|
[
"<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Symptoms</th>\n <th>Country</th>\n <th>Virus</th>\n <th>Prevention</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Fever, nausea, headache, muscle aches</td>\n <td>USA</td>\n <td>West Nile Virus</td>\n <td>Wearing insect repellent, eliminating standing water, monitoring and reporting dead or sick birds</td>\n </tr>\n </tbody>\n</table>"
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S
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TACT
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coverbench
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The lower bound of the 90% confidence interval of the difference for the survival rates in the CPR study, rounded to the nearest thousandth, is -0.026.
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[
"We consider an experiment for patients who underwent cardiopulmonary resuscitation (CPR) for a heart attack and were subsequently admitted to a hospital. These patients were randomly divided into a treatment group where they received a blood thinner or the control group where they did not receive a blood thinner. The outcome variable of interest was whether the patients survived for at least 24 hours. The data is shown in cpr.csv.\n\ncpr.csv\n| | group | outcome |\n|---:|:----------|:----------|\n| 0 | control | survived |\n| 1 | control | survived |\n| 2 | control | survived |\n| 3 | control | survived |\n| 4 | control | survived |\n| 5 | control | survived |\n| 6 | control | survived |\n| 7 | control | survived |\n| 8 | control | survived |\n| 9 | control | survived |\n| 10 | control | survived |\n| 11 | control | died |\n| 12 | control | died |\n| 13 | control | died |\n| 14 | control | died |\n| 15 | control | died |\n| 16 | control | died |\n| 17 | control | died |\n| 18 | control | died |\n| 19 | control | died |\n| 20 | control | died |\n| 21 | control | died |\n| 22 | control | died |\n| 23 | control | died |\n| 24 | control | died |\n| 25 | control | died |\n| 26 | control | died |\n| 27 | control | died |\n| 28 | control | died |\n| 29 | control | died |\n| 30 | control | died |\n| 31 | control | died |\n| 32 | control | died |\n| 33 | control | died |\n| 34 | control | died |\n| 35 | control | died |\n| 36 | control | died |\n| 37 | control | died |\n| 38 | control | died |\n| 39 | control | died |\n| 40 | control | died |\n| 41 | control | died |\n| 42 | control | died |\n| 43 | control | died |\n| 44 | control | died |\n| 45 | control | died |\n| 46 | control | died |\n| 47 | control | died |\n| 48 | control | died |\n| 49 | control | died |\n| 50 | treatment | survived |\n| 51 | treatment | survived |\n| 52 | treatment | survived |\n| 53 | treatment | survived |\n| 54 | treatment | survived |\n| 55 | treatment | survived |\n| 56 | treatment | survived |\n| 57 | treatment | survived |\n| 58 | treatment | survived |\n| 59 | treatment | survived |\n| 60 | treatment | survived |\n| 61 | treatment | survived |\n| 62 | treatment | survived |\n| 63 | treatment | survived |\n| 64 | treatment | died |\n| 65 | treatment | died |\n| 66 | treatment | died |\n| 67 | treatment | died |\n| 68 | treatment | died |\n| 69 | treatment | died |\n| 70 | treatment | died |\n| 71 | treatment | died |\n| 72 | treatment | died |\n| 73 | treatment | died |\n| 74 | treatment | died |\n| 75 | treatment | died |\n| 76 | treatment | died |\n| 77 | treatment | died |\n| 78 | treatment | died |\n| 79 | treatment | died |\n| 80 | treatment | died |\n| 81 | treatment | died |\n| 82 | treatment | died |\n| 83 | treatment | died |\n| 84 | treatment | died |\n| 85 | treatment | died |\n| 86 | treatment | died |\n| 87 | treatment | died |\n| 88 | treatment | died |\n| 89 | treatment | died |"
] |
S
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QRData
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coverbench
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The lower bound of the 90% confidence interval of the difference for the survival rates in the CPR study, rounded to the nearest thousandth, is -0.026.
|
[
"We consider an experiment for patients who underwent cardiopulmonary resuscitation (CPR) for a heart attack and were subsequently admitted to a hospital. These patients were randomly divided into a treatment group where they received a blood thinner or the control group where they did not receive a blood thinner. The outcome variable of interest was whether the patients survived for at least 24 hours. The data is shown in cpr.csv.\n\ncpr.csv\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>group</th>\n <th>outcome</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>control</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>1</th>\n <td>control</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>2</th>\n <td>control</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>3</th>\n <td>control</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>4</th>\n <td>control</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>5</th>\n <td>control</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>6</th>\n <td>control</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>7</th>\n <td>control</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>8</th>\n <td>control</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>9</th>\n <td>control</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>10</th>\n <td>control</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>11</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>12</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>13</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>14</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>15</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>16</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>17</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>18</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>19</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>20</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>21</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>22</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>23</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>24</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>25</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>26</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>27</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>28</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>29</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>30</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>31</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>32</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>33</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>34</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>35</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>36</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>37</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>38</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>39</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>40</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>41</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>42</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>43</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>44</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>45</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>46</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>47</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>48</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>49</th>\n <td>control</td>\n <td>died</td>\n </tr>\n <tr>\n <th>50</th>\n <td>treatment</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>51</th>\n <td>treatment</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>52</th>\n <td>treatment</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>53</th>\n <td>treatment</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>54</th>\n <td>treatment</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>55</th>\n <td>treatment</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>56</th>\n <td>treatment</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>57</th>\n <td>treatment</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>58</th>\n <td>treatment</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>59</th>\n <td>treatment</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>60</th>\n <td>treatment</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>61</th>\n <td>treatment</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>62</th>\n <td>treatment</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>63</th>\n <td>treatment</td>\n <td>survived</td>\n </tr>\n <tr>\n <th>64</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>65</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>66</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>67</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>68</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>69</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>70</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>71</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>72</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>73</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>74</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>75</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>76</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>77</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>78</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>79</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>80</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>81</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>82</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>83</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>84</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>85</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>86</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>87</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>88</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n <tr>\n <th>89</th>\n <td>treatment</td>\n <td>died</td>\n </tr>\n </tbody>\n</table>"
] |
S
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QRData
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coverbench
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The lower bound of the 90% confidence interval of the difference for the survival rates in the CPR study, rounded to the nearest thousandth, is -0.026.
|
[
"We consider an experiment for patients who underwent cardiopulmonary resuscitation (CPR) for a heart attack and were subsequently admitted to a hospital. These patients were randomly divided into a treatment group where they received a blood thinner or the control group where they did not receive a blood thinner. The outcome variable of interest was whether the patients survived for at least 24 hours. The data is shown in cpr.csv.\n\ncpr.csv\n{\"group\":{\"0\":\"control\",\"1\":\"control\",\"2\":\"control\",\"3\":\"control\",\"4\":\"control\",\"5\":\"control\",\"6\":\"control\",\"7\":\"control\",\"8\":\"control\",\"9\":\"control\",\"10\":\"control\",\"11\":\"control\",\"12\":\"control\",\"13\":\"control\",\"14\":\"control\",\"15\":\"control\",\"16\":\"control\",\"17\":\"control\",\"18\":\"control\",\"19\":\"control\",\"20\":\"control\",\"21\":\"control\",\"22\":\"control\",\"23\":\"control\",\"24\":\"control\",\"25\":\"control\",\"26\":\"control\",\"27\":\"control\",\"28\":\"control\",\"29\":\"control\",\"30\":\"control\",\"31\":\"control\",\"32\":\"control\",\"33\":\"control\",\"34\":\"control\",\"35\":\"control\",\"36\":\"control\",\"37\":\"control\",\"38\":\"control\",\"39\":\"control\",\"40\":\"control\",\"41\":\"control\",\"42\":\"control\",\"43\":\"control\",\"44\":\"control\",\"45\":\"control\",\"46\":\"control\",\"47\":\"control\",\"48\":\"control\",\"49\":\"control\",\"50\":\"treatment\",\"51\":\"treatment\",\"52\":\"treatment\",\"53\":\"treatment\",\"54\":\"treatment\",\"55\":\"treatment\",\"56\":\"treatment\",\"57\":\"treatment\",\"58\":\"treatment\",\"59\":\"treatment\",\"60\":\"treatment\",\"61\":\"treatment\",\"62\":\"treatment\",\"63\":\"treatment\",\"64\":\"treatment\",\"65\":\"treatment\",\"66\":\"treatment\",\"67\":\"treatment\",\"68\":\"treatment\",\"69\":\"treatment\",\"70\":\"treatment\",\"71\":\"treatment\",\"72\":\"treatment\",\"73\":\"treatment\",\"74\":\"treatment\",\"75\":\"treatment\",\"76\":\"treatment\",\"77\":\"treatment\",\"78\":\"treatment\",\"79\":\"treatment\",\"80\":\"treatment\",\"81\":\"treatment\",\"82\":\"treatment\",\"83\":\"treatment\",\"84\":\"treatment\",\"85\":\"treatment\",\"86\":\"treatment\",\"87\":\"treatment\",\"88\":\"treatment\",\"89\":\"treatment\"},\"outcome\":{\"0\":\"survived\",\"1\":\"survived\",\"2\":\"survived\",\"3\":\"survived\",\"4\":\"survived\",\"5\":\"survived\",\"6\":\"survived\",\"7\":\"survived\",\"8\":\"survived\",\"9\":\"survived\",\"10\":\"survived\",\"11\":\"died\",\"12\":\"died\",\"13\":\"died\",\"14\":\"died\",\"15\":\"died\",\"16\":\"died\",\"17\":\"died\",\"18\":\"died\",\"19\":\"died\",\"20\":\"died\",\"21\":\"died\",\"22\":\"died\",\"23\":\"died\",\"24\":\"died\",\"25\":\"died\",\"26\":\"died\",\"27\":\"died\",\"28\":\"died\",\"29\":\"died\",\"30\":\"died\",\"31\":\"died\",\"32\":\"died\",\"33\":\"died\",\"34\":\"died\",\"35\":\"died\",\"36\":\"died\",\"37\":\"died\",\"38\":\"died\",\"39\":\"died\",\"40\":\"died\",\"41\":\"died\",\"42\":\"died\",\"43\":\"died\",\"44\":\"died\",\"45\":\"died\",\"46\":\"died\",\"47\":\"died\",\"48\":\"died\",\"49\":\"died\",\"50\":\"survived\",\"51\":\"survived\",\"52\":\"survived\",\"53\":\"survived\",\"54\":\"survived\",\"55\":\"survived\",\"56\":\"survived\",\"57\":\"survived\",\"58\":\"survived\",\"59\":\"survived\",\"60\":\"survived\",\"61\":\"survived\",\"62\":\"survived\",\"63\":\"survived\",\"64\":\"died\",\"65\":\"died\",\"66\":\"died\",\"67\":\"died\",\"68\":\"died\",\"69\":\"died\",\"70\":\"died\",\"71\":\"died\",\"72\":\"died\",\"73\":\"died\",\"74\":\"died\",\"75\":\"died\",\"76\":\"died\",\"77\":\"died\",\"78\":\"died\",\"79\":\"died\",\"80\":\"died\",\"81\":\"died\",\"82\":\"died\",\"83\":\"died\",\"84\":\"died\",\"85\":\"died\",\"86\":\"died\",\"87\":\"died\",\"88\":\"died\",\"89\":\"died\"}}"
] |
S
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QRData
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coverbench
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The correlation between the perceived competence of the Democratic candidate and the vote share differential of the Democratic candidate minus the Republican candidate is 0.433, rounded to the nearest thousandth.
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[
"Several psychologists have reported the intriguing result of an experiment showing that facial appearance predicts election outcomes better than chance. In their experiment, the researchers briefly showed student subjects the black-and-white head shots of two candidates from a US congressional election (winner and runner-up). The exposure of subjects to facial pictures lasted less than a second, and the subjects were then asked to evaluate the two candidates in terms of their perceived competence.\nThe researchers used these competence measures to predict election outcomes. The key hypothesis is whether or not a within-a-second evaluation of facial appearance can predict election outcomes. The CSV data set, face.csv, contains the data from the experiment. Note that we include data only from subjects who did not know the candidates’ political parties, their policies, or even which candidate was the incumbent or challenger. They were simply making snap judgments about which candidate appeared more competent based on their facial expression alone.\n\nVariable Description\ncongress: session of Congress\nyear: year of the election\nstate: state of the election\nwinner: name of the winner\nloser: name of the runner-up\nw.party: party of the winner\nl.party: party of the loser\nd.votes: number of votes for the Democratic candidate\nr.votes: number of votes for the Republican candidate\nd.comp: competence measure for the Democratic candidate\nr.comp: competence measure for the Republican candidate\n\nface.csv\n| | year | state | winner | loser | w.party | l.party | d.comp | r.comp | d.votes | r.votes |\n|----:|-------:|:--------|:----------|:-----------|:----------|:----------|----------:|---------:|----------:|----------:|\n| 0 | 2000 | CA | Feinstein | Campbell | D | R | 0.564568 | 0.435432 | 5790154 | 3779325 |\n| 1 | 2000 | DE | Carper | Roth | D | R | 0.341912 | 0.658088 | 181387 | 142683 |\n| 2 | 2000 | FL | Nelson | McCollum | D | R | 0.612368 | 0.387632 | 2987644 | 2703608 |\n| 3 | 2000 | GA | Miller | Mattingly | D | R | 0.541533 | 0.458467 | 1390428 | 933698 |\n| 4 | 2000 | HI | Akaka | Carroll | D | R | 0.680232 | 0.319768 | 251130 | 84657 |\n| 5 | 2000 | IN | Lugar | Johnson | R | D | 0.320502 | 0.679498 | 684242 | 1419629 |\n| 6 | 2000 | MA | Kennedy | Robinson | D | R | 0.403756 | 0.596244 | 1877439 | 334721 |\n| 7 | 2000 | MD | Sarbanes | Rappaport | D | R | 0.603015 | 0.396985 | 1171151 | 678376 |\n| 8 | 2000 | ME | Snowe | Lawrence | R | D | 0.538573 | 0.461427 | 197742 | 431727 |\n| 9 | 2000 | MI | Stabenow | Abraham | D | R | 0.869215 | 0.130785 | 2034342 | 1991507 |\n| 10 | 2000 | MN | Dayton | Grams | D | R | 0.565335 | 0.434665 | 1180335 | 1048224 |\n| 11 | 2000 | MO | Lott | Brown | R | D | 0.594791 | 0.405209 | 296149 | 621500 |\n| 12 | 2000 | MT | Burns | Schweitzer | R | D | 0.248399 | 0.751601 | 194567 | 208026 |\n| 13 | 2000 | ND | Conrad | Sand | D | R | 0.649669 | 0.350331 | 177661 | 111376 |\n| 14 | 2000 | NE | Nelson | Stenberg | D | R | 0.245012 | 0.754988 | 330366 | 318368 |\n| 15 | 2000 | NM | Bingaman | Redmond | D | R | 0.754391 | 0.245609 | 363279 | 225040 |\n| 16 | 2000 | NV | Ensign | Bernstein | R | D | 0.3904 | 0.6096 | 238243 | 330663 |\n| 17 | 2000 | OH | DeWine | Celeste | R | D | 0.325593 | 0.674407 | 1539001 | 2590952 |\n| 18 | 2000 | PA | Santorum | Klink | R | D | 0.578942 | 0.421058 | 2134734 | 2473118 |\n| 19 | 2000 | RI | Chafee | Weygand | R | D | 0.437047 | 0.562953 | 165367 | 226592 |\n| 20 | 2000 | TN | Frist | Clark | R | D | 0.114507 | 0.885493 | 617684 | 1247436 |\n| 21 | 2000 | TX | Hutchison | Kelly | R | D | 0.204603 | 0.795397 | 2026184 | 4080582 |\n| 22 | 2000 | UT | Hatch | Howell | R | D | 0.442465 | 0.557535 | 241129 | 501925 |\n| 23 | 2000 | VA | Allen | Robb | R | D | 0.741696 | 0.258304 | 1289087 | 1414577 |\n| 24 | 2000 | VT | Jeffords | Flanagan | R | D | 0.457816 | 0.542184 | 72909 | 188070 |\n| 25 | 2000 | WA | Cantwell | Gorton | D | R | 0.614515 | 0.385485 | 1199437 | 1197208 |\n| 26 | 2000 | WI | Kohl | Gillespie | D | R | 0.614357 | 0.385643 | 1563565 | 941132 |\n| 27 | 2000 | WV | Byrd | Gallaher | D | R | 0.683657 | 0.316343 | 462566 | 119958 |\n| 28 | 2000 | WY | Thomas | Logan | R | D | 0.224063 | 0.775937 | 47039 | 157316 |\n| 29 | 2002 | AK | Stevens | Vondersaar | R | D | 0.333592 | 0.666408 | 20466 | 155054 |\n| 30 | 2002 | AL | Sessions | Parker | R | D | 0.551095 | 0.448905 | 537882 | 790757 |\n| 31 | 2002 | AR | Pryor | Hutchinson | D | R | 0.273883 | 0.726117 | 435346 | 372909 |\n| 32 | 2002 | CO | Allard | Strickland | R | D | 0.401537 | 0.598463 | 634227 | 707349 |\n| 33 | 2002 | DE | Biden | Clatworthy | D | R | 0.639578 | 0.360422 | 135170 | 94716 |\n| 34 | 2002 | GA | Chambliss | Cleland | R | D | 0.246164 | 0.753836 | 928905 | 1068902 |\n| 35 | 2002 | IA | Harkin | Ganske | D | R | 0.710124 | 0.289876 | 550156 | 446209 |\n| 36 | 2002 | ID | Craig | Blinken | R | D | 0.457524 | 0.542476 | 132845 | 265849 |\n| 37 | 2002 | IL | Durbin | Durkin | D | R | 0.428035 | 0.571965 | 2080411 | 1320621 |\n| 38 | 2002 | KY | McConnell | Weinberg | R | D | 0.562735 | 0.437265 | 400818 | 726396 |\n| 39 | 2002 | LA | Landrieu | Terrell | D | R | 0.766916 | 0.233084 | 563400 | 327975 |\n| 40 | 2002 | ME | Collins | Pingree | R | D | 0.327268 | 0.672732 | 205901 | 290266 |\n| 41 | 2002 | MI | Levin | Raczkowski | D | R | 0.703998 | 0.296002 | 1893788 | 1184548 |\n| 42 | 2002 | MN | Coleman | Mondale | R | D | 0.665846 | 0.334154 | 1029982 | 1091253 |\n| 43 | 2002 | MO | Talent | Carnahan | R | D | 0.396744 | 0.603256 | 911507 | 934093 |\n| 44 | 2002 | MT | Baucus | Taylor | D | R | 0.897228 | 0.102772 | 202908 | 102766 |\n| 45 | 2002 | NC | Dole | Bowles | R | D | 0.313285 | 0.686715 | 1034941 | 1238203 |\n| 46 | 2002 | NE | Hagel | Matulka | R | D | 0.213806 | 0.786194 | 68657 | 391648 |\n| 47 | 2002 | NH | Sununu | Shaheen | R | D | 0.526114 | 0.473886 | 206689 | 225506 |\n| 48 | 2002 | NJ | Lautenber | Forrester | D | R | 0.636229 | 0.363771 | 1112499 | 909383 |\n| 49 | 2002 | NM | Domenici | Tristani | R | D | 0.346114 | 0.653886 | 161409 | 296935 |\n| 50 | 2002 | OK | Inhofe | Walters | R | D | 0.449049 | 0.550951 | 369789 | 578579 |\n| 51 | 2002 | OR | Smith | Bradbury | R | D | 0.402232 | 0.597768 | 487995 | 695345 |\n| 52 | 2002 | RI | Reed | Tingle | D | R | 0.711679 | 0.288321 | 241315 | 66613 |\n| 53 | 2002 | SC | Graham | Sanders | R | D | 0.573504 | 0.426496 | 484798 | 597789 |\n| 54 | 2002 | SD | Johnson | Thune | D | R | 0.318235 | 0.681765 | 167481 | 166954 |\n| 55 | 2002 | TN | Alexander | Clement | R | D | 0.522373 | 0.477627 | 726510 | 888223 |\n| 56 | 2002 | TX | Cornyn | Kirk | R | D | 0.430373 | 0.569627 | 1946681 | 2480991 |\n| 57 | 2002 | WV | Rockefell | Wolfe | D | R | 0.638702 | 0.361298 | 271314 | 158211 |\n| 58 | 2002 | WY | Enzi | Corcoran | R | D | 0.175816 | 0.824184 | 49587 | 133615 |\n| 59 | 2004 | AL | Shelby | Sowell | R | D | 0.322314 | 0.677686 | 593302 | 1240061 |\n| 60 | 2004 | AK | Murkowski | Knowles | R | D | 0.419355 | 0.580645 | 110699 | 121027 |\n| 61 | 2004 | AR | Lincoln | Holt | D | R | 0.736 | 0.264 | 573793 | 454132 |\n| 62 | 2004 | CA | Boxer | Jones | D | R | 0.598361 | 0.401639 | 5599219 | 3642281 |\n| 63 | 2004 | CO | Salazar | Coors | D | R | 0.512605 | 0.487395 | 1023803 | 944520 |\n| 64 | 2004 | CT | Dodd | Orchulli | D | R | 0.618644 | 0.381356 | 923836 | 452874 |\n| 65 | 2004 | FL | Martinez | Castor | R | D | 0.441667 | 0.558333 | 3544602 | 3622823 |\n| 66 | 2004 | GA | Isakson | Majette | R | D | 0.617886 | 0.382114 | 1268529 | 1839069 |\n| 67 | 2004 | HI | Inouye | Cavasso | D | R | 0.731707 | 0.268293 | 313269 | 87119 |\n| 68 | 2004 | IL | Obama | Keyes | D | R | 0.164835 | 0.835165 | 3524702 | 1371882 |\n| 69 | 2004 | IN | Bayh | Scott | D | R | 0.55 | 0.45 | 1488782 | 902108 |\n| 70 | 2004 | IA | Grassley | Small | R | D | 0.516949 | 0.483051 | 403434 | 1025566 |\n| 71 | 2004 | KY | Brownback | Jones | R | D | 0.357143 | 0.642857 | 307968 | 777198 |\n| 72 | 2004 | KZ | Bunning | Mongiardo | R | D | 0.696721 | 0.303279 | 850756 | 873596 |\n| 73 | 2004 | LA | Vitter | JohnKenne | R | D | 0.352 | 0.648 | 275494 | 942755 |\n| 74 | 2004 | MD | Mikulski | Pipkin | D | R | 0.508621 | 0.491379 | 1385009 | 725898 |\n| 75 | 2004 | MS | Bond | Farmer | R | D | 0.621849 | 0.378151 | 1153422 | 1514793 |\n| 76 | 2004 | NC | Burr | Bowles | R | D | 0.27 | 0.73 | 1586968 | 1742182 |\n| 77 | 2004 | ND | Dorgan | Liffrig | D | R | 0.758621 | 0.241379 | 211503 | 98244 |\n| 78 | 2004 | NV | Reid | Zizer | D | R | 0.747967 | 0.252033 | 490232 | 282255 |\n| 79 | 2004 | NH | Gregg | Haddock | R | D | 0.064 | 0.936 | 221011 | 434292 |\n| 80 | 2004 | NY | Schumer | Mills | D | R | 0.318182 | 0.681818 | 4409162 | 1535871 |\n| 81 | 2004 | OH | Voinovich | Fingerhut | R | D | 0.581967 | 0.418033 | 1907852 | 3380364 |\n| 82 | 2004 | OK | Coburn | Carson | R | D | 0.292683 | 0.707317 | 596672 | 763332 |\n| 83 | 2004 | OR | Wyden | King | D | R | 0.6 | 0.4 | 1072079 | 536506 |\n| 84 | 2004 | PA | Spekter | Hoeffel | R | D | 0.712 | 0.288 | 2295305 | 2890818 |\n| 85 | 2004 | SC | Demint | Tenenbaum | R | D | 0.464 | 0.536 | 691918 | 843884 |\n| 86 | 2004 | SD | Thune | Daschle | R | D | 0.367925 | 0.632075 | 193279 | 197814 |\n| 87 | 2004 | UT | Bennett | VanDam | R | D | 0.761905 | 0.238095 | 237415 | 564260 |\n| 88 | 2004 | VT | Leahy | McMullen | D | R | 0.660714 | 0.339286 | 212850 | 74704 |\n| 89 | 2004 | WA | Murray | Nethercutt | D | R | 0.264463 | 0.735537 | 1215647 | 935992 |\n| 90 | 2004 | WI | Feingold | Michels | D | R | 0.54918 | 0.45082 | 1632562 | 1301305 |\n| 91 | 2006 | AZ | Kyl,Jon | Pederson, | R | D | 0.206349 | 0.793651 | 505136 | 605266 |\n| 92 | 2006 | CA | Feinstein | Mountjoy, | D | R | 0.719298 | 0.280702 | 3889327 | 2275304 |\n| 93 | 2006 | DE | Carper,T | Ting,Jan | D | R | 0.83871 | 0.16129 | 170544 | 69732 |\n| 94 | 2006 | FL | Nelson,B | Harris,Ka | D | R | 0.548387 | 0.451613 | 2844459 | 1797229 |\n| 95 | 2006 | ME | Snowe,Ol | Bright,Je | R | D | 0.442623 | 0.557377 | 108796 | 393230 |\n| 96 | 2006 | MD | Cardin,B | Steele,Mi | D | R | 0.278689 | 0.721311 | 846709 | 682641 |\n| 97 | 2006 | MA | Kennedy, | Chase,Ken | D | R | 0.673469 | 0.326531 | 1497304 | 658374 |\n| 98 | 2006 | MI | Stabenow, | Vouchard, | D | R | 0.52381 | 0.47619 | 2146538 | 1558483 |\n| 99 | 2006 | MN | Klobuchar | Kennedy,M | D | R | 0.290323 | 0.709677 | 1279515 | 839173 |\n| 100 | 2006 | MS | Lott,Tre | Fleming,E | R | D | 0.436364 | 0.563636 | 205518 | 375307 |\n| 101 | 2006 | MO | McCaskill | Talent,Ji | D | R | 0.525424 | 0.474576 | 1028215 | 987077 |\n| 102 | 2006 | MT | Tester,J | Burns,Con | D | R | 0.163934 | 0.836066 | 198302 | 195455 |\n| 103 | 2006 | NE | Nelson,B | Ricketts, | D | R | 0.612903 | 0.387097 | 371777 | 211111 |\n| 104 | 2006 | NV | Ensign,J | Carter,Ja | R | D | 0.174603 | 0.825397 | 237875 | 321186 |\n| 105 | 2006 | NJ | Menendez, | Kean,Tom | D | R | 0.683333 | 0.316667 | 1159642 | 973895 |\n| 106 | 2006 | NM | Bingaman, | McCulloch, | D | R | 0.616667 | 0.383333 | 371068 | 156314 |\n| 107 | 2006 | ND | Conrad,K | Grotberg, | D | R | 0.539683 | 0.460317 | 149317 | 64133 |\n| 108 | 2006 | OH | Brown,Sh | DeWine,Mi | D | R | 0.590164 | 0.409836 | 2131741 | 1680177 |\n| 109 | 2006 | PA | Casey,Bo | Santorum, | D | R | 0.0847458 | 0.915254 | 2341170 | 1650139 |\n| 110 | 2006 | RI | Whitehous | Chafee,Li | D | R | 0.786885 | 0.213115 | 205274 | 178548 |\n| 111 | 2006 | TN | Corker,B | Ford,Haro | R | D | 0.264151 | 0.735849 | 877716 | 927343 |\n| 112 | 2006 | TX | Hutchison | Radnofsky, | R | D | 0.416667 | 0.583333 | 1550950 | 2654004 |\n| 113 | 2006 | UT | Hatch,Or | Ashdown,P | R | D | 0.0892857 | 0.910714 | 168551 | 342901 |\n| 114 | 2006 | VA | Webb,Jam | Allen,Geo | D | R | 0.114754 | 0.885246 | 1172671 | 1165440 |\n| 115 | 2006 | WA | Cantwell, | McGavick, | D | R | 0.396825 | 0.603175 | 652515 | 445395 |\n| 116 | 2006 | WV | Byrd,Rob | JohnRaese | D | R | 0.327869 | 0.672131 | 291058 | 152315 |\n| 117 | 2006 | WI | Kohl,Her | Lorge,Rob | D | R | 0.57377 | 0.42623 | 1436157 | 628879 |\n| 118 | 2006 | WY | Thomas,C | Groutage, | R | D | 0.25 | 0.75 | 57640 | 134942 |"
] |
S
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QRData
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coverbench
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The probability that a randomly chosen male respondent with blue eyes has a partner with blue eyes, rounded to the nearest hundredth, is 0.68.
|
[
"Assortative mating is a nonrandom mating pattern where individuals with similar genotypes and/or phenotypes mate with one another more frequently than what would be expected under a random mating pattern. Researchers studying this topic collected data on eye colors of 204 Scandinavian men and their female partners. The data is in the CSV file assortative_mating.csv\n\nassortative_mating.csv\n| | self_male | partner_female |\n|----:|:------------|:-----------------|\n| 0 | blue | blue |\n| 1 | blue | blue |\n| 2 | blue | blue |\n| 3 | blue | blue |\n| 4 | blue | blue |\n| 5 | blue | blue |\n| 6 | blue | blue |\n| 7 | blue | blue |\n| 8 | blue | blue |\n| 9 | blue | blue |\n| 10 | blue | blue |\n| 11 | blue | blue |\n| 12 | blue | blue |\n| 13 | blue | blue |\n| 14 | blue | blue |\n| 15 | blue | blue |\n| 16 | blue | blue |\n| 17 | blue | blue |\n| 18 | blue | blue |\n| 19 | blue | blue |\n| 20 | blue | blue |\n| 21 | blue | blue |\n| 22 | blue | blue |\n| 23 | blue | blue |\n| 24 | blue | blue |\n| 25 | blue | blue |\n| 26 | blue | blue |\n| 27 | blue | blue |\n| 28 | blue | blue |\n| 29 | blue | blue |\n| 30 | blue | blue |\n| 31 | blue | blue |\n| 32 | blue | blue |\n| 33 | blue | blue |\n| 34 | blue | blue |\n| 35 | blue | blue |\n| 36 | blue | blue |\n| 37 | blue | blue |\n| 38 | blue | blue |\n| 39 | blue | blue |\n| 40 | blue | blue |\n| 41 | blue | blue |\n| 42 | blue | blue |\n| 43 | blue | blue |\n| 44 | blue | blue |\n| 45 | blue | blue |\n| 46 | blue | blue |\n| 47 | blue | blue |\n| 48 | blue | blue |\n| 49 | blue | blue |\n| 50 | blue | blue |\n| 51 | blue | blue |\n| 52 | blue | blue |\n| 53 | blue | blue |\n| 54 | blue | blue |\n| 55 | blue | blue |\n| 56 | blue | blue |\n| 57 | blue | blue |\n| 58 | blue | blue |\n| 59 | blue | blue |\n| 60 | blue | blue |\n| 61 | blue | blue |\n| 62 | blue | blue |\n| 63 | blue | blue |\n| 64 | blue | blue |\n| 65 | blue | blue |\n| 66 | blue | blue |\n| 67 | blue | blue |\n| 68 | blue | blue |\n| 69 | blue | blue |\n| 70 | blue | blue |\n| 71 | blue | blue |\n| 72 | blue | blue |\n| 73 | blue | blue |\n| 74 | blue | blue |\n| 75 | blue | blue |\n| 76 | blue | blue |\n| 77 | blue | blue |\n| 78 | blue | brown |\n| 79 | blue | brown |\n| 80 | blue | brown |\n| 81 | blue | brown |\n| 82 | blue | brown |\n| 83 | blue | brown |\n| 84 | blue | brown |\n| 85 | blue | brown |\n| 86 | blue | brown |\n| 87 | blue | brown |\n| 88 | blue | brown |\n| 89 | blue | brown |\n| 90 | blue | brown |\n| 91 | blue | brown |\n| 92 | blue | brown |\n| 93 | blue | brown |\n| 94 | blue | brown |\n| 95 | blue | brown |\n| 96 | blue | brown |\n| 97 | blue | brown |\n| 98 | blue | brown |\n| 99 | blue | brown |\n| 100 | blue | brown |\n| 101 | blue | green |\n| 102 | blue | green |\n| 103 | blue | green |\n| 104 | blue | green |\n| 105 | blue | green |\n| 106 | blue | green |\n| 107 | blue | green |\n| 108 | blue | green |\n| 109 | blue | green |\n| 110 | blue | green |\n| 111 | blue | green |\n| 112 | blue | green |\n| 113 | blue | green |\n| 114 | brown | blue |\n| 115 | brown | blue |\n| 116 | brown | blue |\n| 117 | brown | blue |\n| 118 | brown | blue |\n| 119 | brown | blue |\n| 120 | brown | blue |\n| 121 | brown | blue |\n| 122 | brown | blue |\n| 123 | brown | blue |\n| 124 | brown | blue |\n| 125 | brown | blue |\n| 126 | brown | blue |\n| 127 | brown | blue |\n| 128 | brown | blue |\n| 129 | brown | blue |\n| 130 | brown | blue |\n| 131 | brown | blue |\n| 132 | brown | blue |\n| 133 | brown | brown |\n| 134 | brown | brown |\n| 135 | brown | brown |\n| 136 | brown | brown |\n| 137 | brown | brown |\n| 138 | brown | brown |\n| 139 | brown | brown |\n| 140 | brown | brown |\n| 141 | brown | brown |\n| 142 | brown | brown |\n| 143 | brown | brown |\n| 144 | brown | brown |\n| 145 | brown | brown |\n| 146 | brown | brown |\n| 147 | brown | brown |\n| 148 | brown | brown |\n| 149 | brown | brown |\n| 150 | brown | brown |\n| 151 | brown | brown |\n| 152 | brown | brown |\n| 153 | brown | brown |\n| 154 | brown | brown |\n| 155 | brown | brown |\n| 156 | brown | green |\n| 157 | brown | green |\n| 158 | brown | green |\n| 159 | brown | green |\n| 160 | brown | green |\n| 161 | brown | green |\n| 162 | brown | green |\n| 163 | brown | green |\n| 164 | brown | green |\n| 165 | brown | green |\n| 166 | brown | green |\n| 167 | brown | green |\n| 168 | green | blue |\n| 169 | green | blue |\n| 170 | green | blue |\n| 171 | green | blue |\n| 172 | green | blue |\n| 173 | green | blue |\n| 174 | green | blue |\n| 175 | green | blue |\n| 176 | green | blue |\n| 177 | green | blue |\n| 178 | green | blue |\n| 179 | green | brown |\n| 180 | green | brown |\n| 181 | green | brown |\n| 182 | green | brown |\n| 183 | green | brown |\n| 184 | green | brown |\n| 185 | green | brown |\n| 186 | green | brown |\n| 187 | green | brown |\n| 188 | green | green |\n| 189 | green | green |\n| 190 | green | green |\n| 191 | green | green |\n| 192 | green | green |\n| 193 | green | green |\n| 194 | green | green |\n| 195 | green | green |\n| 196 | green | green |\n| 197 | green | green |\n| 198 | green | green |\n| 199 | green | green |\n| 200 | green | green |\n| 201 | green | green |\n| 202 | green | green |\n| 203 | green | green |"
] |
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QRData
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coverbench
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The chi-square distribution used for X2 has 3 degrees of freedom, given the null hypothesis that the jurors are a random sample.
|
[
"The data file jury.csv contains a random sample of 275 jurors in a small county. Jurors identified their racial group, and we would like to determine if these jurors are racially representative of the population. In the population, 72% are white, 7% are black, 12% are hispanic, and 9% are others.\n\njury.csv\n{\"race\":{\"0\":\"white\",\"1\":\"white\",\"2\":\"white\",\"3\":\"white\",\"4\":\"white\",\"5\":\"white\",\"6\":\"white\",\"7\":\"white\",\"8\":\"white\",\"9\":\"white\",\"10\":\"white\",\"11\":\"white\",\"12\":\"white\",\"13\":\"white\",\"14\":\"white\",\"15\":\"white\",\"16\":\"white\",\"17\":\"white\",\"18\":\"white\",\"19\":\"white\",\"20\":\"white\",\"21\":\"white\",\"22\":\"white\",\"23\":\"white\",\"24\":\"white\",\"25\":\"white\",\"26\":\"white\",\"27\":\"white\",\"28\":\"white\",\"29\":\"white\",\"30\":\"white\",\"31\":\"white\",\"32\":\"white\",\"33\":\"white\",\"34\":\"white\",\"35\":\"white\",\"36\":\"white\",\"37\":\"white\",\"38\":\"white\",\"39\":\"white\",\"40\":\"white\",\"41\":\"white\",\"42\":\"white\",\"43\":\"white\",\"44\":\"white\",\"45\":\"white\",\"46\":\"white\",\"47\":\"white\",\"48\":\"white\",\"49\":\"white\",\"50\":\"white\",\"51\":\"white\",\"52\":\"white\",\"53\":\"white\",\"54\":\"white\",\"55\":\"white\",\"56\":\"white\",\"57\":\"white\",\"58\":\"white\",\"59\":\"white\",\"60\":\"white\",\"61\":\"white\",\"62\":\"white\",\"63\":\"white\",\"64\":\"white\",\"65\":\"white\",\"66\":\"white\",\"67\":\"white\",\"68\":\"white\",\"69\":\"white\",\"70\":\"white\",\"71\":\"white\",\"72\":\"white\",\"73\":\"white\",\"74\":\"white\",\"75\":\"white\",\"76\":\"white\",\"77\":\"white\",\"78\":\"white\",\"79\":\"white\",\"80\":\"white\",\"81\":\"white\",\"82\":\"white\",\"83\":\"white\",\"84\":\"white\",\"85\":\"white\",\"86\":\"white\",\"87\":\"white\",\"88\":\"white\",\"89\":\"white\",\"90\":\"white\",\"91\":\"white\",\"92\":\"white\",\"93\":\"white\",\"94\":\"white\",\"95\":\"white\",\"96\":\"white\",\"97\":\"white\",\"98\":\"white\",\"99\":\"white\",\"100\":\"white\",\"101\":\"white\",\"102\":\"white\",\"103\":\"white\",\"104\":\"white\",\"105\":\"white\",\"106\":\"white\",\"107\":\"white\",\"108\":\"white\",\"109\":\"white\",\"110\":\"white\",\"111\":\"white\",\"112\":\"white\",\"113\":\"white\",\"114\":\"white\",\"115\":\"white\",\"116\":\"white\",\"117\":\"white\",\"118\":\"white\",\"119\":\"white\",\"120\":\"white\",\"121\":\"white\",\"122\":\"white\",\"123\":\"white\",\"124\":\"white\",\"125\":\"white\",\"126\":\"white\",\"127\":\"white\",\"128\":\"white\",\"129\":\"white\",\"130\":\"white\",\"131\":\"white\",\"132\":\"white\",\"133\":\"white\",\"134\":\"white\",\"135\":\"white\",\"136\":\"white\",\"137\":\"white\",\"138\":\"white\",\"139\":\"white\",\"140\":\"white\",\"141\":\"white\",\"142\":\"white\",\"143\":\"white\",\"144\":\"white\",\"145\":\"white\",\"146\":\"white\",\"147\":\"white\",\"148\":\"white\",\"149\":\"white\",\"150\":\"white\",\"151\":\"white\",\"152\":\"white\",\"153\":\"white\",\"154\":\"white\",\"155\":\"white\",\"156\":\"white\",\"157\":\"white\",\"158\":\"white\",\"159\":\"white\",\"160\":\"white\",\"161\":\"white\",\"162\":\"white\",\"163\":\"white\",\"164\":\"white\",\"165\":\"white\",\"166\":\"white\",\"167\":\"white\",\"168\":\"white\",\"169\":\"white\",\"170\":\"white\",\"171\":\"white\",\"172\":\"white\",\"173\":\"white\",\"174\":\"white\",\"175\":\"white\",\"176\":\"white\",\"177\":\"white\",\"178\":\"white\",\"179\":\"white\",\"180\":\"white\",\"181\":\"white\",\"182\":\"white\",\"183\":\"white\",\"184\":\"white\",\"185\":\"white\",\"186\":\"white\",\"187\":\"white\",\"188\":\"white\",\"189\":\"white\",\"190\":\"white\",\"191\":\"white\",\"192\":\"white\",\"193\":\"white\",\"194\":\"white\",\"195\":\"white\",\"196\":\"white\",\"197\":\"white\",\"198\":\"white\",\"199\":\"white\",\"200\":\"white\",\"201\":\"white\",\"202\":\"white\",\"203\":\"white\",\"204\":\"white\",\"205\":\"black\",\"206\":\"black\",\"207\":\"black\",\"208\":\"black\",\"209\":\"black\",\"210\":\"black\",\"211\":\"black\",\"212\":\"black\",\"213\":\"black\",\"214\":\"black\",\"215\":\"black\",\"216\":\"black\",\"217\":\"black\",\"218\":\"black\",\"219\":\"black\",\"220\":\"black\",\"221\":\"black\",\"222\":\"black\",\"223\":\"black\",\"224\":\"black\",\"225\":\"black\",\"226\":\"black\",\"227\":\"black\",\"228\":\"black\",\"229\":\"black\",\"230\":\"black\",\"231\":\"hispanic\",\"232\":\"hispanic\",\"233\":\"hispanic\",\"234\":\"hispanic\",\"235\":\"hispanic\",\"236\":\"hispanic\",\"237\":\"hispanic\",\"238\":\"hispanic\",\"239\":\"hispanic\",\"240\":\"hispanic\",\"241\":\"hispanic\",\"242\":\"hispanic\",\"243\":\"hispanic\",\"244\":\"hispanic\",\"245\":\"hispanic\",\"246\":\"hispanic\",\"247\":\"hispanic\",\"248\":\"hispanic\",\"249\":\"hispanic\",\"250\":\"hispanic\",\"251\":\"hispanic\",\"252\":\"hispanic\",\"253\":\"hispanic\",\"254\":\"hispanic\",\"255\":\"hispanic\",\"256\":\"other\",\"257\":\"other\",\"258\":\"other\",\"259\":\"other\",\"260\":\"other\",\"261\":\"other\",\"262\":\"other\",\"263\":\"other\",\"264\":\"other\",\"265\":\"other\",\"266\":\"other\",\"267\":\"other\",\"268\":\"other\",\"269\":\"other\",\"270\":\"other\",\"271\":\"other\",\"272\":\"other\",\"273\":\"other\",\"274\":\"other\"}}"
] |
S
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QRData
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coverbench
|
The correlation between the perceived competence of the Democratic candidate and the vote share differential of the Democratic candidate minus the Republican candidate is 0.433, rounded to the nearest thousandth.
|
[
"Several psychologists have reported the intriguing result of an experiment showing that facial appearance predicts election outcomes better than chance. In their experiment, the researchers briefly showed student subjects the black-and-white head shots of two candidates from a US congressional election (winner and runner-up). The exposure of subjects to facial pictures lasted less than a second, and the subjects were then asked to evaluate the two candidates in terms of their perceived competence.\nThe researchers used these competence measures to predict election outcomes. The key hypothesis is whether or not a within-a-second evaluation of facial appearance can predict election outcomes. The CSV data set, face.csv, contains the data from the experiment. Note that we include data only from subjects who did not know the candidates’ political parties, their policies, or even which candidate was the incumbent or challenger. They were simply making snap judgments about which candidate appeared more competent based on their facial expression alone.\n\nVariable Description\ncongress: session of Congress\nyear: year of the election\nstate: state of the election\nwinner: name of the winner\nloser: name of the runner-up\nw.party: party of the winner\nl.party: party of the loser\nd.votes: number of votes for the Democratic candidate\nr.votes: number of votes for the Republican candidate\nd.comp: competence measure for the Democratic candidate\nr.comp: competence measure for the Republican candidate\n\nface.csv\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>year</th>\n <th>state</th>\n <th>winner</th>\n <th>loser</th>\n <th>w.party</th>\n <th>l.party</th>\n <th>d.comp</th>\n <th>r.comp</th>\n <th>d.votes</th>\n <th>r.votes</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2000</td>\n <td>CA</td>\n <td>Feinstein</td>\n <td>Campbell</td>\n <td>D</td>\n <td>R</td>\n <td>0.564568</td>\n <td>0.435432</td>\n <td>5790154</td>\n <td>3779325</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2000</td>\n <td>DE</td>\n <td>Carper</td>\n <td>Roth</td>\n <td>D</td>\n <td>R</td>\n <td>0.341912</td>\n <td>0.658088</td>\n <td>181387</td>\n <td>142683</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2000</td>\n <td>FL</td>\n <td>Nelson</td>\n <td>McCollum</td>\n <td>D</td>\n <td>R</td>\n <td>0.612368</td>\n <td>0.387632</td>\n <td>2987644</td>\n <td>2703608</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2000</td>\n <td>GA</td>\n <td>Miller</td>\n <td>Mattingly</td>\n <td>D</td>\n <td>R</td>\n <td>0.541533</td>\n <td>0.458467</td>\n <td>1390428</td>\n <td>933698</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2000</td>\n <td>HI</td>\n <td>Akaka</td>\n <td>Carroll</td>\n <td>D</td>\n <td>R</td>\n <td>0.680232</td>\n <td>0.319768</td>\n <td>251130</td>\n <td>84657</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2000</td>\n <td>IN</td>\n <td>Lugar</td>\n <td>Johnson</td>\n <td>R</td>\n <td>D</td>\n <td>0.320502</td>\n <td>0.679498</td>\n <td>684242</td>\n <td>1419629</td>\n </tr>\n <tr>\n <th>6</th>\n <td>2000</td>\n <td>MA</td>\n <td>Kennedy</td>\n <td>Robinson</td>\n <td>D</td>\n <td>R</td>\n <td>0.403756</td>\n <td>0.596244</td>\n <td>1877439</td>\n <td>334721</td>\n </tr>\n <tr>\n <th>7</th>\n <td>2000</td>\n <td>MD</td>\n <td>Sarbanes</td>\n <td>Rappaport</td>\n <td>D</td>\n <td>R</td>\n <td>0.603015</td>\n <td>0.396985</td>\n <td>1171151</td>\n <td>678376</td>\n </tr>\n <tr>\n <th>8</th>\n <td>2000</td>\n <td>ME</td>\n <td>Snowe</td>\n <td>Lawrence</td>\n <td>R</td>\n <td>D</td>\n <td>0.538573</td>\n <td>0.461427</td>\n <td>197742</td>\n <td>431727</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2000</td>\n <td>MI</td>\n <td>Stabenow</td>\n <td>Abraham</td>\n <td>D</td>\n <td>R</td>\n <td>0.869215</td>\n <td>0.130785</td>\n <td>2034342</td>\n <td>1991507</td>\n </tr>\n <tr>\n <th>10</th>\n <td>2000</td>\n <td>MN</td>\n <td>Dayton</td>\n <td>Grams</td>\n <td>D</td>\n <td>R</td>\n <td>0.565335</td>\n <td>0.434665</td>\n <td>1180335</td>\n <td>1048224</td>\n </tr>\n <tr>\n <th>11</th>\n <td>2000</td>\n <td>MO</td>\n <td>Lott</td>\n <td>Brown</td>\n <td>R</td>\n <td>D</td>\n <td>0.594791</td>\n <td>0.405209</td>\n <td>296149</td>\n <td>621500</td>\n </tr>\n <tr>\n <th>12</th>\n <td>2000</td>\n <td>MT</td>\n <td>Burns</td>\n <td>Schweitzer</td>\n <td>R</td>\n <td>D</td>\n <td>0.248399</td>\n <td>0.751601</td>\n <td>194567</td>\n <td>208026</td>\n </tr>\n <tr>\n <th>13</th>\n <td>2000</td>\n <td>ND</td>\n <td>Conrad</td>\n <td>Sand</td>\n <td>D</td>\n <td>R</td>\n <td>0.649669</td>\n <td>0.350331</td>\n <td>177661</td>\n <td>111376</td>\n </tr>\n <tr>\n <th>14</th>\n <td>2000</td>\n <td>NE</td>\n <td>Nelson</td>\n <td>Stenberg</td>\n <td>D</td>\n <td>R</td>\n <td>0.245012</td>\n <td>0.754988</td>\n <td>330366</td>\n <td>318368</td>\n </tr>\n <tr>\n <th>15</th>\n <td>2000</td>\n <td>NM</td>\n <td>Bingaman</td>\n <td>Redmond</td>\n <td>D</td>\n <td>R</td>\n <td>0.754391</td>\n <td>0.245609</td>\n <td>363279</td>\n <td>225040</td>\n </tr>\n <tr>\n <th>16</th>\n <td>2000</td>\n <td>NV</td>\n <td>Ensign</td>\n <td>Bernstein</td>\n <td>R</td>\n <td>D</td>\n <td>0.390400</td>\n <td>0.609600</td>\n <td>238243</td>\n <td>330663</td>\n </tr>\n <tr>\n <th>17</th>\n <td>2000</td>\n <td>OH</td>\n <td>DeWine</td>\n <td>Celeste</td>\n <td>R</td>\n <td>D</td>\n <td>0.325593</td>\n <td>0.674407</td>\n <td>1539001</td>\n <td>2590952</td>\n </tr>\n <tr>\n <th>18</th>\n <td>2000</td>\n <td>PA</td>\n <td>Santorum</td>\n <td>Klink</td>\n <td>R</td>\n <td>D</td>\n <td>0.578942</td>\n <td>0.421058</td>\n <td>2134734</td>\n <td>2473118</td>\n </tr>\n <tr>\n <th>19</th>\n <td>2000</td>\n <td>RI</td>\n <td>Chafee</td>\n <td>Weygand</td>\n <td>R</td>\n <td>D</td>\n <td>0.437047</td>\n <td>0.562953</td>\n <td>165367</td>\n <td>226592</td>\n </tr>\n <tr>\n <th>20</th>\n <td>2000</td>\n <td>TN</td>\n <td>Frist</td>\n <td>Clark</td>\n <td>R</td>\n <td>D</td>\n <td>0.114507</td>\n <td>0.885493</td>\n <td>617684</td>\n <td>1247436</td>\n </tr>\n <tr>\n <th>21</th>\n <td>2000</td>\n <td>TX</td>\n <td>Hutchison</td>\n <td>Kelly</td>\n <td>R</td>\n <td>D</td>\n <td>0.204603</td>\n <td>0.795397</td>\n <td>2026184</td>\n <td>4080582</td>\n </tr>\n <tr>\n <th>22</th>\n <td>2000</td>\n <td>UT</td>\n <td>Hatch</td>\n <td>Howell</td>\n <td>R</td>\n <td>D</td>\n <td>0.442465</td>\n <td>0.557535</td>\n <td>241129</td>\n <td>501925</td>\n </tr>\n <tr>\n <th>23</th>\n <td>2000</td>\n <td>VA</td>\n <td>Allen</td>\n <td>Robb</td>\n <td>R</td>\n <td>D</td>\n <td>0.741696</td>\n <td>0.258304</td>\n <td>1289087</td>\n <td>1414577</td>\n </tr>\n <tr>\n <th>24</th>\n <td>2000</td>\n <td>VT</td>\n <td>Jeffords</td>\n <td>Flanagan</td>\n <td>R</td>\n <td>D</td>\n <td>0.457816</td>\n <td>0.542184</td>\n <td>72909</td>\n <td>188070</td>\n </tr>\n <tr>\n <th>25</th>\n <td>2000</td>\n <td>WA</td>\n <td>Cantwell</td>\n <td>Gorton</td>\n <td>D</td>\n <td>R</td>\n <td>0.614515</td>\n <td>0.385485</td>\n <td>1199437</td>\n <td>1197208</td>\n </tr>\n <tr>\n <th>26</th>\n <td>2000</td>\n <td>WI</td>\n <td>Kohl</td>\n <td>Gillespie</td>\n <td>D</td>\n <td>R</td>\n <td>0.614357</td>\n <td>0.385643</td>\n <td>1563565</td>\n <td>941132</td>\n </tr>\n <tr>\n <th>27</th>\n <td>2000</td>\n <td>WV</td>\n <td>Byrd</td>\n <td>Gallaher</td>\n <td>D</td>\n <td>R</td>\n <td>0.683657</td>\n <td>0.316343</td>\n <td>462566</td>\n <td>119958</td>\n </tr>\n <tr>\n <th>28</th>\n <td>2000</td>\n <td>WY</td>\n <td>Thomas</td>\n <td>Logan</td>\n <td>R</td>\n <td>D</td>\n <td>0.224063</td>\n <td>0.775937</td>\n <td>47039</td>\n <td>157316</td>\n </tr>\n <tr>\n <th>29</th>\n <td>2002</td>\n <td>AK</td>\n <td>Stevens</td>\n <td>Vondersaar</td>\n <td>R</td>\n <td>D</td>\n <td>0.333592</td>\n <td>0.666408</td>\n <td>20466</td>\n <td>155054</td>\n </tr>\n <tr>\n <th>30</th>\n <td>2002</td>\n <td>AL</td>\n <td>Sessions</td>\n <td>Parker</td>\n <td>R</td>\n <td>D</td>\n <td>0.551095</td>\n <td>0.448905</td>\n <td>537882</td>\n <td>790757</td>\n </tr>\n <tr>\n <th>31</th>\n <td>2002</td>\n <td>AR</td>\n <td>Pryor</td>\n <td>Hutchinson</td>\n <td>D</td>\n <td>R</td>\n <td>0.273883</td>\n <td>0.726117</td>\n <td>435346</td>\n <td>372909</td>\n </tr>\n <tr>\n <th>32</th>\n <td>2002</td>\n <td>CO</td>\n <td>Allard</td>\n <td>Strickland</td>\n <td>R</td>\n <td>D</td>\n <td>0.401537</td>\n <td>0.598463</td>\n <td>634227</td>\n <td>707349</td>\n </tr>\n <tr>\n <th>33</th>\n <td>2002</td>\n <td>DE</td>\n <td>Biden</td>\n <td>Clatworthy</td>\n <td>D</td>\n <td>R</td>\n <td>0.639578</td>\n <td>0.360422</td>\n <td>135170</td>\n <td>94716</td>\n </tr>\n <tr>\n <th>34</th>\n <td>2002</td>\n <td>GA</td>\n <td>Chambliss</td>\n <td>Cleland</td>\n <td>R</td>\n <td>D</td>\n <td>0.246164</td>\n <td>0.753836</td>\n <td>928905</td>\n <td>1068902</td>\n </tr>\n <tr>\n <th>35</th>\n <td>2002</td>\n <td>IA</td>\n <td>Harkin</td>\n <td>Ganske</td>\n <td>D</td>\n <td>R</td>\n <td>0.710124</td>\n <td>0.289876</td>\n <td>550156</td>\n <td>446209</td>\n </tr>\n <tr>\n <th>36</th>\n <td>2002</td>\n <td>ID</td>\n <td>Craig</td>\n <td>Blinken</td>\n <td>R</td>\n <td>D</td>\n <td>0.457524</td>\n <td>0.542476</td>\n <td>132845</td>\n <td>265849</td>\n </tr>\n <tr>\n <th>37</th>\n <td>2002</td>\n <td>IL</td>\n <td>Durbin</td>\n <td>Durkin</td>\n <td>D</td>\n <td>R</td>\n <td>0.428035</td>\n <td>0.571965</td>\n <td>2080411</td>\n <td>1320621</td>\n </tr>\n <tr>\n <th>38</th>\n <td>2002</td>\n <td>KY</td>\n <td>McConnell</td>\n <td>Weinberg</td>\n <td>R</td>\n <td>D</td>\n <td>0.562735</td>\n <td>0.437265</td>\n <td>400818</td>\n <td>726396</td>\n </tr>\n <tr>\n <th>39</th>\n <td>2002</td>\n <td>LA</td>\n <td>Landrieu</td>\n <td>Terrell</td>\n <td>D</td>\n <td>R</td>\n <td>0.766916</td>\n <td>0.233084</td>\n <td>563400</td>\n <td>327975</td>\n </tr>\n <tr>\n <th>40</th>\n <td>2002</td>\n <td>ME</td>\n <td>Collins</td>\n <td>Pingree</td>\n <td>R</td>\n <td>D</td>\n <td>0.327268</td>\n <td>0.672732</td>\n <td>205901</td>\n <td>290266</td>\n </tr>\n <tr>\n <th>41</th>\n <td>2002</td>\n <td>MI</td>\n <td>Levin</td>\n <td>Raczkowski</td>\n <td>D</td>\n <td>R</td>\n <td>0.703998</td>\n <td>0.296002</td>\n <td>1893788</td>\n <td>1184548</td>\n </tr>\n <tr>\n <th>42</th>\n <td>2002</td>\n <td>MN</td>\n <td>Coleman</td>\n <td>Mondale</td>\n <td>R</td>\n <td>D</td>\n <td>0.665846</td>\n <td>0.334154</td>\n <td>1029982</td>\n <td>1091253</td>\n </tr>\n <tr>\n <th>43</th>\n <td>2002</td>\n <td>MO</td>\n <td>Talent</td>\n <td>Carnahan</td>\n <td>R</td>\n <td>D</td>\n <td>0.396744</td>\n <td>0.603256</td>\n <td>911507</td>\n <td>934093</td>\n </tr>\n <tr>\n <th>44</th>\n <td>2002</td>\n <td>MT</td>\n <td>Baucus</td>\n <td>Taylor</td>\n <td>D</td>\n <td>R</td>\n <td>0.897228</td>\n <td>0.102772</td>\n <td>202908</td>\n <td>102766</td>\n </tr>\n <tr>\n <th>45</th>\n <td>2002</td>\n <td>NC</td>\n <td>Dole</td>\n <td>Bowles</td>\n <td>R</td>\n <td>D</td>\n <td>0.313285</td>\n <td>0.686715</td>\n <td>1034941</td>\n <td>1238203</td>\n </tr>\n <tr>\n <th>46</th>\n <td>2002</td>\n <td>NE</td>\n <td>Hagel</td>\n <td>Matulka</td>\n <td>R</td>\n <td>D</td>\n <td>0.213806</td>\n <td>0.786194</td>\n <td>68657</td>\n <td>391648</td>\n </tr>\n <tr>\n <th>47</th>\n <td>2002</td>\n <td>NH</td>\n <td>Sununu</td>\n <td>Shaheen</td>\n <td>R</td>\n <td>D</td>\n <td>0.526114</td>\n <td>0.473886</td>\n <td>206689</td>\n <td>225506</td>\n </tr>\n <tr>\n <th>48</th>\n <td>2002</td>\n <td>NJ</td>\n <td>Lautenber</td>\n <td>Forrester</td>\n <td>D</td>\n <td>R</td>\n <td>0.636229</td>\n <td>0.363771</td>\n <td>1112499</td>\n <td>909383</td>\n </tr>\n <tr>\n <th>49</th>\n <td>2002</td>\n <td>NM</td>\n <td>Domenici</td>\n <td>Tristani</td>\n <td>R</td>\n <td>D</td>\n <td>0.346114</td>\n <td>0.653886</td>\n <td>161409</td>\n <td>296935</td>\n </tr>\n <tr>\n <th>50</th>\n <td>2002</td>\n <td>OK</td>\n <td>Inhofe</td>\n <td>Walters</td>\n <td>R</td>\n <td>D</td>\n <td>0.449049</td>\n <td>0.550951</td>\n <td>369789</td>\n <td>578579</td>\n </tr>\n <tr>\n <th>51</th>\n <td>2002</td>\n <td>OR</td>\n <td>Smith</td>\n <td>Bradbury</td>\n <td>R</td>\n <td>D</td>\n <td>0.402232</td>\n <td>0.597768</td>\n <td>487995</td>\n <td>695345</td>\n </tr>\n <tr>\n <th>52</th>\n <td>2002</td>\n <td>RI</td>\n <td>Reed</td>\n <td>Tingle</td>\n <td>D</td>\n <td>R</td>\n <td>0.711679</td>\n <td>0.288321</td>\n <td>241315</td>\n <td>66613</td>\n </tr>\n <tr>\n <th>53</th>\n <td>2002</td>\n <td>SC</td>\n <td>Graham</td>\n <td>Sanders</td>\n <td>R</td>\n <td>D</td>\n <td>0.573504</td>\n <td>0.426496</td>\n <td>484798</td>\n <td>597789</td>\n </tr>\n <tr>\n <th>54</th>\n <td>2002</td>\n <td>SD</td>\n <td>Johnson</td>\n <td>Thune</td>\n <td>D</td>\n <td>R</td>\n <td>0.318235</td>\n <td>0.681765</td>\n <td>167481</td>\n <td>166954</td>\n </tr>\n <tr>\n <th>55</th>\n <td>2002</td>\n <td>TN</td>\n <td>Alexander</td>\n <td>Clement</td>\n <td>R</td>\n <td>D</td>\n <td>0.522373</td>\n <td>0.477627</td>\n <td>726510</td>\n <td>888223</td>\n </tr>\n <tr>\n <th>56</th>\n <td>2002</td>\n <td>TX</td>\n <td>Cornyn</td>\n <td>Kirk</td>\n <td>R</td>\n <td>D</td>\n <td>0.430373</td>\n <td>0.569627</td>\n <td>1946681</td>\n <td>2480991</td>\n </tr>\n <tr>\n <th>57</th>\n <td>2002</td>\n <td>WV</td>\n <td>Rockefell</td>\n <td>Wolfe</td>\n <td>D</td>\n <td>R</td>\n <td>0.638702</td>\n <td>0.361298</td>\n <td>271314</td>\n <td>158211</td>\n </tr>\n <tr>\n <th>58</th>\n <td>2002</td>\n <td>WY</td>\n <td>Enzi</td>\n <td>Corcoran</td>\n <td>R</td>\n <td>D</td>\n <td>0.175816</td>\n <td>0.824184</td>\n <td>49587</td>\n <td>133615</td>\n </tr>\n <tr>\n <th>59</th>\n <td>2004</td>\n <td>AL</td>\n <td>Shelby</td>\n <td>Sowell</td>\n <td>R</td>\n <td>D</td>\n <td>0.322314</td>\n <td>0.677686</td>\n <td>593302</td>\n <td>1240061</td>\n </tr>\n <tr>\n <th>60</th>\n <td>2004</td>\n <td>AK</td>\n <td>Murkowski</td>\n <td>Knowles</td>\n <td>R</td>\n <td>D</td>\n <td>0.419355</td>\n <td>0.580645</td>\n <td>110699</td>\n <td>121027</td>\n </tr>\n <tr>\n <th>61</th>\n <td>2004</td>\n <td>AR</td>\n <td>Lincoln</td>\n <td>Holt</td>\n <td>D</td>\n <td>R</td>\n <td>0.736000</td>\n <td>0.264000</td>\n <td>573793</td>\n <td>454132</td>\n </tr>\n <tr>\n <th>62</th>\n <td>2004</td>\n <td>CA</td>\n <td>Boxer</td>\n <td>Jones</td>\n <td>D</td>\n <td>R</td>\n <td>0.598361</td>\n <td>0.401639</td>\n <td>5599219</td>\n <td>3642281</td>\n </tr>\n <tr>\n <th>63</th>\n <td>2004</td>\n <td>CO</td>\n <td>Salazar</td>\n <td>Coors</td>\n <td>D</td>\n <td>R</td>\n <td>0.512605</td>\n <td>0.487395</td>\n <td>1023803</td>\n <td>944520</td>\n </tr>\n <tr>\n <th>64</th>\n <td>2004</td>\n <td>CT</td>\n <td>Dodd</td>\n <td>Orchulli</td>\n <td>D</td>\n <td>R</td>\n <td>0.618644</td>\n <td>0.381356</td>\n <td>923836</td>\n <td>452874</td>\n </tr>\n <tr>\n <th>65</th>\n <td>2004</td>\n <td>FL</td>\n <td>Martinez</td>\n <td>Castor</td>\n <td>R</td>\n <td>D</td>\n <td>0.441667</td>\n <td>0.558333</td>\n <td>3544602</td>\n <td>3622823</td>\n </tr>\n <tr>\n <th>66</th>\n <td>2004</td>\n <td>GA</td>\n <td>Isakson</td>\n <td>Majette</td>\n <td>R</td>\n <td>D</td>\n <td>0.617886</td>\n <td>0.382114</td>\n <td>1268529</td>\n <td>1839069</td>\n </tr>\n <tr>\n <th>67</th>\n <td>2004</td>\n <td>HI</td>\n <td>Inouye</td>\n <td>Cavasso</td>\n <td>D</td>\n <td>R</td>\n <td>0.731707</td>\n <td>0.268293</td>\n <td>313269</td>\n <td>87119</td>\n </tr>\n <tr>\n <th>68</th>\n <td>2004</td>\n <td>IL</td>\n <td>Obama</td>\n <td>Keyes</td>\n <td>D</td>\n <td>R</td>\n <td>0.164835</td>\n <td>0.835165</td>\n <td>3524702</td>\n <td>1371882</td>\n </tr>\n <tr>\n <th>69</th>\n <td>2004</td>\n <td>IN</td>\n <td>Bayh</td>\n <td>Scott</td>\n <td>D</td>\n <td>R</td>\n <td>0.550000</td>\n <td>0.450000</td>\n <td>1488782</td>\n <td>902108</td>\n </tr>\n <tr>\n <th>70</th>\n <td>2004</td>\n <td>IA</td>\n <td>Grassley</td>\n <td>Small</td>\n <td>R</td>\n <td>D</td>\n <td>0.516949</td>\n <td>0.483051</td>\n <td>403434</td>\n <td>1025566</td>\n </tr>\n <tr>\n <th>71</th>\n <td>2004</td>\n <td>KY</td>\n <td>Brownback</td>\n <td>Jones</td>\n <td>R</td>\n <td>D</td>\n <td>0.357143</td>\n <td>0.642857</td>\n <td>307968</td>\n <td>777198</td>\n </tr>\n <tr>\n <th>72</th>\n <td>2004</td>\n <td>KZ</td>\n <td>Bunning</td>\n <td>Mongiardo</td>\n <td>R</td>\n <td>D</td>\n <td>0.696721</td>\n <td>0.303279</td>\n <td>850756</td>\n <td>873596</td>\n </tr>\n <tr>\n <th>73</th>\n <td>2004</td>\n <td>LA</td>\n <td>Vitter</td>\n <td>JohnKenne</td>\n <td>R</td>\n <td>D</td>\n <td>0.352000</td>\n <td>0.648000</td>\n <td>275494</td>\n <td>942755</td>\n </tr>\n <tr>\n <th>74</th>\n <td>2004</td>\n <td>MD</td>\n <td>Mikulski</td>\n <td>Pipkin</td>\n <td>D</td>\n <td>R</td>\n <td>0.508621</td>\n <td>0.491379</td>\n <td>1385009</td>\n <td>725898</td>\n </tr>\n <tr>\n <th>75</th>\n <td>2004</td>\n <td>MS</td>\n <td>Bond</td>\n <td>Farmer</td>\n <td>R</td>\n <td>D</td>\n <td>0.621849</td>\n <td>0.378151</td>\n <td>1153422</td>\n <td>1514793</td>\n </tr>\n <tr>\n <th>76</th>\n <td>2004</td>\n <td>NC</td>\n <td>Burr</td>\n <td>Bowles</td>\n <td>R</td>\n <td>D</td>\n <td>0.270000</td>\n <td>0.730000</td>\n <td>1586968</td>\n <td>1742182</td>\n </tr>\n <tr>\n <th>77</th>\n <td>2004</td>\n <td>ND</td>\n <td>Dorgan</td>\n <td>Liffrig</td>\n <td>D</td>\n <td>R</td>\n <td>0.758621</td>\n <td>0.241379</td>\n <td>211503</td>\n <td>98244</td>\n </tr>\n <tr>\n <th>78</th>\n <td>2004</td>\n <td>NV</td>\n <td>Reid</td>\n <td>Zizer</td>\n <td>D</td>\n <td>R</td>\n <td>0.747967</td>\n <td>0.252033</td>\n <td>490232</td>\n <td>282255</td>\n </tr>\n <tr>\n <th>79</th>\n <td>2004</td>\n <td>NH</td>\n <td>Gregg</td>\n <td>Haddock</td>\n <td>R</td>\n <td>D</td>\n <td>0.064000</td>\n <td>0.936000</td>\n <td>221011</td>\n <td>434292</td>\n </tr>\n <tr>\n <th>80</th>\n <td>2004</td>\n <td>NY</td>\n <td>Schumer</td>\n <td>Mills</td>\n <td>D</td>\n <td>R</td>\n <td>0.318182</td>\n <td>0.681818</td>\n <td>4409162</td>\n <td>1535871</td>\n </tr>\n <tr>\n <th>81</th>\n <td>2004</td>\n <td>OH</td>\n <td>Voinovich</td>\n <td>Fingerhut</td>\n <td>R</td>\n <td>D</td>\n <td>0.581967</td>\n <td>0.418033</td>\n <td>1907852</td>\n <td>3380364</td>\n </tr>\n <tr>\n <th>82</th>\n <td>2004</td>\n <td>OK</td>\n <td>Coburn</td>\n <td>Carson</td>\n <td>R</td>\n <td>D</td>\n <td>0.292683</td>\n <td>0.707317</td>\n <td>596672</td>\n <td>763332</td>\n </tr>\n <tr>\n <th>83</th>\n <td>2004</td>\n <td>OR</td>\n <td>Wyden</td>\n <td>King</td>\n <td>D</td>\n <td>R</td>\n <td>0.600000</td>\n <td>0.400000</td>\n <td>1072079</td>\n <td>536506</td>\n </tr>\n <tr>\n <th>84</th>\n <td>2004</td>\n <td>PA</td>\n <td>Spekter</td>\n <td>Hoeffel</td>\n <td>R</td>\n <td>D</td>\n <td>0.712000</td>\n <td>0.288000</td>\n <td>2295305</td>\n <td>2890818</td>\n </tr>\n <tr>\n <th>85</th>\n <td>2004</td>\n <td>SC</td>\n <td>Demint</td>\n <td>Tenenbaum</td>\n <td>R</td>\n <td>D</td>\n <td>0.464000</td>\n <td>0.536000</td>\n <td>691918</td>\n <td>843884</td>\n </tr>\n <tr>\n <th>86</th>\n <td>2004</td>\n <td>SD</td>\n <td>Thune</td>\n <td>Daschle</td>\n <td>R</td>\n <td>D</td>\n <td>0.367925</td>\n <td>0.632075</td>\n <td>193279</td>\n <td>197814</td>\n </tr>\n <tr>\n <th>87</th>\n <td>2004</td>\n <td>UT</td>\n <td>Bennett</td>\n <td>VanDam</td>\n <td>R</td>\n <td>D</td>\n <td>0.761905</td>\n <td>0.238095</td>\n <td>237415</td>\n <td>564260</td>\n </tr>\n <tr>\n <th>88</th>\n <td>2004</td>\n <td>VT</td>\n <td>Leahy</td>\n <td>McMullen</td>\n <td>D</td>\n <td>R</td>\n <td>0.660714</td>\n <td>0.339286</td>\n <td>212850</td>\n <td>74704</td>\n </tr>\n <tr>\n <th>89</th>\n <td>2004</td>\n <td>WA</td>\n <td>Murray</td>\n <td>Nethercutt</td>\n <td>D</td>\n <td>R</td>\n <td>0.264463</td>\n <td>0.735537</td>\n <td>1215647</td>\n <td>935992</td>\n </tr>\n <tr>\n <th>90</th>\n <td>2004</td>\n <td>WI</td>\n <td>Feingold</td>\n <td>Michels</td>\n <td>D</td>\n <td>R</td>\n <td>0.549180</td>\n <td>0.450820</td>\n <td>1632562</td>\n <td>1301305</td>\n </tr>\n <tr>\n <th>91</th>\n <td>2006</td>\n <td>AZ</td>\n <td>Kyl,Jon</td>\n <td>Pederson,</td>\n <td>R</td>\n <td>D</td>\n <td>0.206349</td>\n <td>0.793651</td>\n <td>505136</td>\n <td>605266</td>\n </tr>\n <tr>\n <th>92</th>\n <td>2006</td>\n <td>CA</td>\n <td>Feinstein</td>\n <td>Mountjoy,</td>\n <td>D</td>\n <td>R</td>\n <td>0.719298</td>\n <td>0.280702</td>\n <td>3889327</td>\n <td>2275304</td>\n </tr>\n <tr>\n <th>93</th>\n <td>2006</td>\n <td>DE</td>\n <td>Carper,T</td>\n <td>Ting,Jan</td>\n <td>D</td>\n <td>R</td>\n <td>0.838710</td>\n <td>0.161290</td>\n <td>170544</td>\n <td>69732</td>\n </tr>\n <tr>\n <th>94</th>\n <td>2006</td>\n <td>FL</td>\n <td>Nelson,B</td>\n <td>Harris,Ka</td>\n <td>D</td>\n <td>R</td>\n <td>0.548387</td>\n <td>0.451613</td>\n <td>2844459</td>\n <td>1797229</td>\n </tr>\n <tr>\n <th>95</th>\n <td>2006</td>\n <td>ME</td>\n <td>Snowe,Ol</td>\n <td>Bright,Je</td>\n <td>R</td>\n <td>D</td>\n <td>0.442623</td>\n <td>0.557377</td>\n <td>108796</td>\n <td>393230</td>\n </tr>\n <tr>\n <th>96</th>\n <td>2006</td>\n <td>MD</td>\n <td>Cardin,B</td>\n <td>Steele,Mi</td>\n <td>D</td>\n <td>R</td>\n <td>0.278689</td>\n <td>0.721311</td>\n <td>846709</td>\n <td>682641</td>\n </tr>\n <tr>\n <th>97</th>\n <td>2006</td>\n <td>MA</td>\n <td>Kennedy,</td>\n <td>Chase,Ken</td>\n <td>D</td>\n <td>R</td>\n <td>0.673469</td>\n <td>0.326531</td>\n <td>1497304</td>\n <td>658374</td>\n </tr>\n <tr>\n <th>98</th>\n <td>2006</td>\n <td>MI</td>\n <td>Stabenow,</td>\n <td>Vouchard,</td>\n <td>D</td>\n <td>R</td>\n <td>0.523810</td>\n <td>0.476190</td>\n <td>2146538</td>\n <td>1558483</td>\n </tr>\n <tr>\n <th>99</th>\n <td>2006</td>\n <td>MN</td>\n <td>Klobuchar</td>\n <td>Kennedy,M</td>\n <td>D</td>\n <td>R</td>\n <td>0.290323</td>\n <td>0.709677</td>\n <td>1279515</td>\n <td>839173</td>\n </tr>\n <tr>\n <th>100</th>\n <td>2006</td>\n <td>MS</td>\n <td>Lott,Tre</td>\n <td>Fleming,E</td>\n <td>R</td>\n <td>D</td>\n <td>0.436364</td>\n <td>0.563636</td>\n <td>205518</td>\n <td>375307</td>\n </tr>\n <tr>\n <th>101</th>\n <td>2006</td>\n <td>MO</td>\n <td>McCaskill</td>\n <td>Talent,Ji</td>\n <td>D</td>\n <td>R</td>\n <td>0.525424</td>\n <td>0.474576</td>\n <td>1028215</td>\n <td>987077</td>\n </tr>\n <tr>\n <th>102</th>\n <td>2006</td>\n <td>MT</td>\n <td>Tester,J</td>\n <td>Burns,Con</td>\n <td>D</td>\n <td>R</td>\n <td>0.163934</td>\n <td>0.836066</td>\n <td>198302</td>\n <td>195455</td>\n </tr>\n <tr>\n <th>103</th>\n <td>2006</td>\n <td>NE</td>\n <td>Nelson,B</td>\n <td>Ricketts,</td>\n <td>D</td>\n <td>R</td>\n <td>0.612903</td>\n <td>0.387097</td>\n <td>371777</td>\n <td>211111</td>\n </tr>\n <tr>\n <th>104</th>\n <td>2006</td>\n <td>NV</td>\n <td>Ensign,J</td>\n <td>Carter,Ja</td>\n <td>R</td>\n <td>D</td>\n <td>0.174603</td>\n <td>0.825397</td>\n <td>237875</td>\n <td>321186</td>\n </tr>\n <tr>\n <th>105</th>\n <td>2006</td>\n <td>NJ</td>\n <td>Menendez,</td>\n <td>Kean,Tom</td>\n <td>D</td>\n <td>R</td>\n <td>0.683333</td>\n <td>0.316667</td>\n <td>1159642</td>\n <td>973895</td>\n </tr>\n <tr>\n <th>106</th>\n <td>2006</td>\n <td>NM</td>\n <td>Bingaman,</td>\n <td>McCulloch,</td>\n <td>D</td>\n <td>R</td>\n <td>0.616667</td>\n <td>0.383333</td>\n <td>371068</td>\n <td>156314</td>\n </tr>\n <tr>\n <th>107</th>\n <td>2006</td>\n <td>ND</td>\n <td>Conrad,K</td>\n <td>Grotberg,</td>\n <td>D</td>\n <td>R</td>\n <td>0.539683</td>\n <td>0.460317</td>\n <td>149317</td>\n <td>64133</td>\n </tr>\n <tr>\n <th>108</th>\n <td>2006</td>\n <td>OH</td>\n <td>Brown,Sh</td>\n <td>DeWine,Mi</td>\n <td>D</td>\n <td>R</td>\n <td>0.590164</td>\n <td>0.409836</td>\n <td>2131741</td>\n <td>1680177</td>\n </tr>\n <tr>\n <th>109</th>\n <td>2006</td>\n <td>PA</td>\n <td>Casey,Bo</td>\n <td>Santorum,</td>\n <td>D</td>\n <td>R</td>\n <td>0.084746</td>\n <td>0.915254</td>\n <td>2341170</td>\n <td>1650139</td>\n </tr>\n <tr>\n <th>110</th>\n <td>2006</td>\n <td>RI</td>\n <td>Whitehous</td>\n <td>Chafee,Li</td>\n <td>D</td>\n <td>R</td>\n <td>0.786885</td>\n <td>0.213115</td>\n <td>205274</td>\n <td>178548</td>\n </tr>\n <tr>\n <th>111</th>\n <td>2006</td>\n <td>TN</td>\n <td>Corker,B</td>\n <td>Ford,Haro</td>\n <td>R</td>\n <td>D</td>\n <td>0.264151</td>\n <td>0.735849</td>\n <td>877716</td>\n <td>927343</td>\n </tr>\n <tr>\n <th>112</th>\n <td>2006</td>\n <td>TX</td>\n <td>Hutchison</td>\n <td>Radnofsky,</td>\n <td>R</td>\n <td>D</td>\n <td>0.416667</td>\n <td>0.583333</td>\n <td>1550950</td>\n <td>2654004</td>\n </tr>\n <tr>\n <th>113</th>\n <td>2006</td>\n <td>UT</td>\n <td>Hatch,Or</td>\n <td>Ashdown,P</td>\n <td>R</td>\n <td>D</td>\n <td>0.089286</td>\n <td>0.910714</td>\n <td>168551</td>\n <td>342901</td>\n </tr>\n <tr>\n <th>114</th>\n <td>2006</td>\n <td>VA</td>\n <td>Webb,Jam</td>\n <td>Allen,Geo</td>\n <td>D</td>\n <td>R</td>\n <td>0.114754</td>\n <td>0.885246</td>\n <td>1172671</td>\n <td>1165440</td>\n </tr>\n <tr>\n <th>115</th>\n <td>2006</td>\n <td>WA</td>\n <td>Cantwell,</td>\n <td>McGavick,</td>\n <td>D</td>\n <td>R</td>\n <td>0.396825</td>\n <td>0.603175</td>\n <td>652515</td>\n <td>445395</td>\n </tr>\n <tr>\n <th>116</th>\n <td>2006</td>\n <td>WV</td>\n <td>Byrd,Rob</td>\n <td>JohnRaese</td>\n <td>D</td>\n <td>R</td>\n <td>0.327869</td>\n <td>0.672131</td>\n <td>291058</td>\n <td>152315</td>\n </tr>\n <tr>\n <th>117</th>\n <td>2006</td>\n <td>WI</td>\n <td>Kohl,Her</td>\n <td>Lorge,Rob</td>\n <td>D</td>\n <td>R</td>\n <td>0.573770</td>\n <td>0.426230</td>\n <td>1436157</td>\n <td>628879</td>\n </tr>\n <tr>\n <th>118</th>\n <td>2006</td>\n <td>WY</td>\n <td>Thomas,C</td>\n <td>Groutage,</td>\n <td>R</td>\n <td>D</td>\n <td>0.250000</td>\n <td>0.750000</td>\n <td>57640</td>\n <td>134942</td>\n </tr>\n </tbody>\n</table>"
] |
S
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QRData
| |
coverbench
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The betweenness of the BISCHERI family, rounded to the nearest thousandth, is 9.500.
|
[
"The CSV file, florentine.csv, contains an adjacency matrix whose entries represent the existence of relationships between two units (one unit represented by the row and the\nother represented by the column). Specifically, there are 16 elite Florentine families in the data, leading to a 16×16 adjacency matrix. If the (i, j) entry of this adjacency matrix is 1, then it implies that the ith and jth Florentine families had a marriage relationship. In contrast, a value of 0 indicates the absence of a marriage.\n\nflorentine.csv\n| | FAMILY | ACCIAIUOL | ALBIZZI | BARBADORI | BISCHERI | CASTELLAN | GINORI | GUADAGNI | LAMBERTES | MEDICI | PAZZI | PERUZZI | PUCCI | RIDOLFI | SALVIATI | STROZZI | TORNABUON |\n|---:|:----------|------------:|----------:|------------:|-----------:|------------:|---------:|-----------:|------------:|---------:|--------:|----------:|--------:|----------:|-----------:|----------:|------------:|\n| 0 | ACCIAIUOL | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 1 | ALBIZZI | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 2 | BARBADORI | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 3 | BISCHERI | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 |\n| 4 | CASTELLAN | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 |\n| 5 | GINORI | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 6 | GUADAGNI | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |\n| 7 | LAMBERTES | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 8 | MEDICI | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 |\n| 9 | PAZZI | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |\n| 10 | PERUZZI | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |\n| 11 | PUCCI | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 12 | RIDOLFI | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |\n| 13 | SALVIATI | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |\n| 14 | STROZZI | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |\n| 15 | TORNABUON | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |"
] |
NS
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QRData
| |
coverbench
|
The betweenness of the BISCHERI family, rounded to the nearest thousandth, is 9.500.
|
[
"The CSV file, florentine.csv, contains an adjacency matrix whose entries represent the existence of relationships between two units (one unit represented by the row and the\nother represented by the column). Specifically, there are 16 elite Florentine families in the data, leading to a 16×16 adjacency matrix. If the (i, j) entry of this adjacency matrix is 1, then it implies that the ith and jth Florentine families had a marriage relationship. In contrast, a value of 0 indicates the absence of a marriage.\n\nflorentine.csv\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>FAMILY</th>\n <th>ACCIAIUOL</th>\n <th>ALBIZZI</th>\n <th>BARBADORI</th>\n <th>BISCHERI</th>\n <th>CASTELLAN</th>\n <th>GINORI</th>\n <th>GUADAGNI</th>\n <th>LAMBERTES</th>\n <th>MEDICI</th>\n <th>PAZZI</th>\n <th>PERUZZI</th>\n <th>PUCCI</th>\n <th>RIDOLFI</th>\n <th>SALVIATI</th>\n <th>STROZZI</th>\n <th>TORNABUON</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>ACCIAIUOL</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>ALBIZZI</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>BARBADORI</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>BISCHERI</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>CASTELLAN</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>GINORI</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>GUADAGNI</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>7</th>\n <td>LAMBERTES</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>8</th>\n <td>MEDICI</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>9</th>\n <td>PAZZI</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>10</th>\n <td>PERUZZI</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>11</th>\n <td>PUCCI</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>12</th>\n <td>RIDOLFI</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>13</th>\n <td>SALVIATI</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>14</th>\n <td>STROZZI</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>15</th>\n <td>TORNABUON</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>"
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The difference in infection rates between the placebo and vaccine groups, rounded to the nearest thousandth, is 0.643.
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[
"Volunteer patients were randomized into one of two experiment groups where they would receive an experimental vaccine or a placebo. They were subsequently exposed to a drug-sensitive strain of malaria and observed to see whether they came down with an infection. The vaccine trial data is described in the CSV file malaria.csv.\n\nmalaria.csv\n{\"treatment\":{\"0\":\"vaccine\",\"1\":\"vaccine\",\"2\":\"vaccine\",\"3\":\"vaccine\",\"4\":\"vaccine\",\"5\":\"vaccine\",\"6\":\"vaccine\",\"7\":\"vaccine\",\"8\":\"vaccine\",\"9\":\"vaccine\",\"10\":\"vaccine\",\"11\":\"vaccine\",\"12\":\"vaccine\",\"13\":\"vaccine\",\"14\":\"placebo\",\"15\":\"placebo\",\"16\":\"placebo\",\"17\":\"placebo\",\"18\":\"placebo\",\"19\":\"placebo\"},\"outcome\":{\"0\":\"infection\",\"1\":\"infection\",\"2\":\"infection\",\"3\":\"infection\",\"4\":\"infection\",\"5\":\"no infection\",\"6\":\"no infection\",\"7\":\"no infection\",\"8\":\"no infection\",\"9\":\"no infection\",\"10\":\"no infection\",\"11\":\"no infection\",\"12\":\"no infection\",\"13\":\"no infection\",\"14\":\"infection\",\"15\":\"infection\",\"16\":\"infection\",\"17\":\"infection\",\"18\":\"infection\",\"19\":\"infection\"}}"
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The degrees of freedom associated with the chi-square distribution used for X2, given the null hypothesis that the questions had no impact on the sellers in the experiment, is 2.
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[
"In this experiment, each individual was asked to be a seller of an iPod (a product commonly used to store music on before smart phones...). The participant received $10 + 5% of the sale price for participating. The iPod they were selling had frozen twice in the past inexplicably but otherwise worked fine. The prospective buyer starts off and then asks one of three final questions, depending on the seller's treatment group. The experiment data is in the CSV file ask.csv.\n\nThe three possible questions:\nGeneral: What can you tell me about it?\nPositive Assumption: It doesn't have any problems, does it?\nNegative Assumption: What problems does it have?\nThe outcome variable is whether or not the participant discloses or hides the problem with the iPod.\n\nThe hypothesis test for the iPod experiment is really about assessing whether there is statistically significant evidence that the success each question had on getting the participant to disclose the problem with the iPod. In other words, the goal is to check whether the buyer's question was independent of whether the seller disclosed a problem.\n\nask.csv\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>question_class</th>\n <th>question</th>\n <th>response</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>1</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>2</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>3</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>4</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>5</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>6</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>7</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>8</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>9</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>10</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>11</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>12</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>13</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>14</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>15</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>16</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>17</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>18</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>19</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>20</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>21</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>22</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>23</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>24</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>25</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>26</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>27</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>28</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>29</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>30</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>31</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>32</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>33</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>34</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>35</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>36</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>37</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>38</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>39</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>40</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>41</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>42</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>43</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>44</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>45</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>46</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>47</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>48</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>49</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>50</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>51</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>52</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>53</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>54</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>55</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>56</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>57</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>58</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>59</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>60</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>61</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>62</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>63</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>64</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>65</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>66</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>67</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>68</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>69</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>70</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>71</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>72</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>73</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>74</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>75</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>76</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>77</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>78</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>79</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>80</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>81</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>82</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>83</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>84</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>85</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>86</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>87</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>88</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>89</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>90</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>91</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>92</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>93</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>94</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>95</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>96</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>97</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>98</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>99</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>100</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>101</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>102</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>103</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>104</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>105</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>106</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>107</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>108</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>109</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>110</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>111</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>112</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>113</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>114</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>115</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>116</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>117</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>118</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>119</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>120</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>121</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>122</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>123</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>124</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>125</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>126</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>127</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>128</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>129</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>130</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>131</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>132</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>133</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>134</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>135</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>136</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>137</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>138</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>139</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>140</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>141</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>142</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>143</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>144</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>145</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>146</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>147</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>148</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>149</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>150</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>151</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>152</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>153</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>154</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>155</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>156</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>157</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>158</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>159</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>160</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>161</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>162</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>163</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>164</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>165</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>166</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>167</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>168</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>169</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>170</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>171</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>172</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>173</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>174</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>175</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>176</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>177</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>178</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>179</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>180</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>181</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>182</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>183</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>184</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>185</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>186</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>187</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>188</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>189</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>190</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>191</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>192</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>193</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>194</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>195</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>196</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>197</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>198</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>199</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>200</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>201</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>202</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>203</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>204</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>205</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>206</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>207</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>208</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>209</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>210</th>\n <td>neg_assumption</td>\n <td>What problems does it have?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>211</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>212</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>213</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>214</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>215</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>216</th>\n <td>pos_assumption</td>\n <td>It doesn't have any problems, does it?</td>\n <td>disclose</td>\n </tr>\n <tr>\n <th>217</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n <tr>\n <th>218</th>\n <td>general</td>\n <td>What can you tell me about it?</td>\n <td>hide</td>\n </tr>\n </tbody>\n</table>"
] |
S
|
QRData
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coverbench
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The normal approximation cannot be used to construct a confidence interval for the difference in survival rate between the control and treatment groups.
|
[
"The Stanford University Heart Transplant Study was conducted to determine whether an experimental heart transplant program increased lifespan. Each patient entering the program was designated an official heart transplant candidate, meaning that he was gravely ill and would most likely benefit from a new heart. Some patients got a transplant and some did not. The variable transplant indicates which group the patients were in; patients in the treatment group got a transplant and those in the control group did not. Of the 34 patients in the control group, 30 died. Of the 69 people in the treatment group, 45 died. Another variable called survived was used to indicate whether or not the patient was alive at the end of the study. The data is in the CSV file heart_transplant.csv.\n\nheart_transplant.csv\n| | id | acceptyear | age | survived | survtime | prior | transplant | wait |\n|----:|-----:|-------------:|------:|:-----------|-----------:|:--------|:-------------|-------:|\n| 0 | 15 | 68 | 53 | dead | 1 | no | control | nan |\n| 1 | 43 | 70 | 43 | dead | 2 | no | control | nan |\n| 2 | 61 | 71 | 52 | dead | 2 | no | control | nan |\n| 3 | 75 | 72 | 52 | dead | 2 | no | control | nan |\n| 4 | 6 | 68 | 54 | dead | 3 | no | control | nan |\n| 5 | 42 | 70 | 36 | dead | 3 | no | control | nan |\n| 6 | 54 | 71 | 47 | dead | 3 | no | control | nan |\n| 7 | 38 | 70 | 41 | dead | 5 | no | treatment | 5 |\n| 8 | 85 | 73 | 47 | dead | 5 | no | control | nan |\n| 9 | 2 | 68 | 51 | dead | 6 | no | control | nan |\n| 10 | 103 | 67 | 39 | dead | 6 | no | control | nan |\n| 11 | 12 | 68 | 53 | dead | 8 | no | control | nan |\n| 12 | 48 | 71 | 56 | dead | 9 | no | control | nan |\n| 13 | 102 | 74 | 40 | alive | 11 | no | control | nan |\n| 14 | 35 | 70 | 43 | dead | 12 | no | control | nan |\n| 15 | 95 | 73 | 40 | dead | 16 | no | treatment | 2 |\n| 16 | 31 | 69 | 54 | dead | 16 | no | control | nan |\n| 17 | 3 | 68 | 54 | dead | 16 | no | treatment | 1 |\n| 18 | 74 | 72 | 29 | dead | 17 | no | treatment | 5 |\n| 19 | 5 | 68 | 20 | dead | 18 | no | control | nan |\n| 20 | 77 | 72 | 41 | dead | 21 | no | control | nan |\n| 21 | 99 | 73 | 49 | dead | 21 | no | control | nan |\n| 22 | 20 | 69 | 55 | dead | 28 | no | treatment | 1 |\n| 23 | 70 | 72 | 52 | dead | 30 | no | treatment | 5 |\n| 24 | 101 | 74 | 49 | alive | 31 | no | control | nan |\n| 25 | 66 | 72 | 53 | dead | 32 | no | control | nan |\n| 26 | 29 | 69 | 50 | dead | 35 | no | control | nan |\n| 27 | 17 | 68 | 20 | dead | 36 | no | control | nan |\n| 28 | 19 | 68 | 59 | dead | 37 | no | control | nan |\n| 29 | 4 | 68 | 40 | dead | 39 | no | treatment | 36 |\n| 30 | 100 | 74 | 35 | alive | 39 | yes | treatment | 38 |\n| 31 | 8 | 68 | 45 | dead | 40 | no | control | nan |\n| 32 | 44 | 70 | 42 | dead | 40 | no | control | nan |\n| 33 | 16 | 68 | 56 | dead | 43 | no | treatment | 20 |\n| 34 | 45 | 71 | 36 | dead | 45 | no | treatment | 1 |\n| 35 | 1 | 67 | 30 | dead | 50 | no | control | nan |\n| 36 | 22 | 69 | 42 | dead | 51 | no | treatment | 12 |\n| 37 | 39 | 70 | 50 | dead | 53 | no | treatment | 2 |\n| 38 | 10 | 68 | 42 | dead | 58 | no | treatment | 12 |\n| 39 | 35 | 71 | 52 | dead | 61 | no | treatment | 10 |\n| 40 | 37 | 70 | 61 | dead | 66 | no | treatment | 19 |\n| 41 | 68 | 72 | 45 | dead | 68 | no | treatment | 3 |\n| 42 | 60 | 71 | 49 | dead | 68 | no | treatment | 3 |\n| 43 | 62 | 71 | 39 | dead | 69 | no | control | nan |\n| 44 | 28 | 69 | 53 | dead | 72 | no | treatment | 71 |\n| 45 | 47 | 71 | 47 | dead | 72 | no | treatment | 21 |\n| 46 | 32 | 69 | 64 | dead | 77 | no | treatment | 17 |\n| 47 | 65 | 72 | 51 | dead | 78 | no | treatment | 12 |\n| 48 | 83 | 73 | 53 | dead | 80 | no | treatment | 32 |\n| 49 | 13 | 68 | 54 | dead | 81 | no | treatment | 17 |\n| 50 | 9 | 68 | 47 | dead | 85 | no | control | nan |\n| 51 | 73 | 72 | 56 | dead | 90 | no | treatment | 27 |\n| 52 | 79 | 72 | 53 | dead | 96 | no | treatment | 67 |\n| 53 | 36 | 70 | 48 | dead | 100 | no | treatment | 46 |\n| 54 | 32 | 71 | 41 | dead | 102 | no | control | nan |\n| 55 | 98 | 73 | 28 | alive | 109 | no | treatment | 96 |\n| 56 | 87 | 73 | 46 | dead | 110 | no | treatment | 60 |\n| 57 | 97 | 73 | 23 | alive | 131 | no | treatment | 21 |\n| 58 | 37 | 71 | 41 | dead | 149 | no | control | nan |\n| 59 | 11 | 68 | 47 | dead | 153 | no | treatment | 26 |\n| 60 | 94 | 73 | 43 | dead | 165 | yes | treatment | 4 |\n| 61 | 96 | 73 | 26 | alive | 180 | no | treatment | 13 |\n| 62 | 90 | 73 | 52 | dead | 186 | yes | treatment | 160 |\n| 63 | 53 | 71 | 47 | dead | 188 | no | treatment | 41 |\n| 64 | 89 | 73 | 51 | dead | 207 | no | treatment | 139 |\n| 65 | 24 | 69 | 51 | dead | 219 | no | treatment | 83 |\n| 66 | 27 | 69 | 8 | dead | 263 | no | control | nan |\n| 67 | 93 | 73 | 47 | alive | 265 | no | treatment | 28 |\n| 68 | 51 | 71 | 48 | dead | 285 | no | treatment | 32 |\n| 69 | 67 | 73 | 19 | dead | 285 | no | treatment | 57 |\n| 70 | 16 | 68 | 49 | dead | 308 | no | treatment | 28 |\n| 71 | 84 | 73 | 42 | dead | 334 | no | treatment | 37 |\n| 72 | 91 | 73 | 47 | dead | 340 | no | control | nan |\n| 73 | 92 | 73 | 44 | alive | 340 | no | treatment | 310 |\n| 74 | 58 | 71 | 47 | dead | 342 | yes | treatment | 21 |\n| 75 | 88 | 73 | 54 | alive | 370 | no | treatment | 31 |\n| 76 | 86 | 73 | 48 | alive | 397 | no | treatment | 8 |\n| 77 | 82 | 71 | 29 | alive | 427 | no | control | nan |\n| 78 | 81 | 73 | 52 | alive | 445 | no | treatment | 6 |\n| 79 | 80 | 72 | 46 | alive | 482 | yes | treatment | 26 |\n| 80 | 78 | 72 | 48 | alive | 515 | no | treatment | 210 |\n| 81 | 76 | 72 | 52 | alive | 545 | yes | treatment | 46 |\n| 82 | 64 | 72 | 48 | dead | 583 | yes | treatment | 32 |\n| 83 | 72 | 72 | 26 | alive | 596 | no | treatment | 4 |\n| 84 | 71 | 72 | 47 | alive | 630 | no | treatment | 31 |\n| 85 | 69 | 72 | 47 | alive | 670 | no | treatment | 10 |\n| 86 | 7 | 68 | 50 | dead | 675 | no | treatment | 51 |\n| 87 | 23 | 69 | 58 | dead | 733 | no | treatment | 3 |\n| 88 | 63 | 71 | 32 | alive | 841 | no | treatment | 27 |\n| 89 | 30 | 69 | 44 | dead | 852 | no | treatment | 16 |\n| 90 | 59 | 71 | 41 | alive | 915 | no | treatment | 78 |\n| 91 | 56 | 71 | 38 | alive | 941 | no | treatment | 67 |\n| 92 | 50 | 71 | 45 | dead | 979 | yes | treatment | 83 |\n| 93 | 46 | 71 | 48 | dead | 995 | yes | treatment | 2 |\n| 94 | 21 | 69 | 43 | dead | 1032 | no | treatment | 8 |\n| 95 | 49 | 71 | 36 | alive | 1141 | yes | treatment | 36 |\n| 96 | 41 | 70 | 45 | alive | 1321 | yes | treatment | 58 |\n| 97 | 14 | 68 | 53 | dead | 1386 | no | treatment | 37 |\n| 98 | 26 | 69 | 30 | alive | 1400 | no | control | nan |\n| 99 | 40 | 70 | 48 | alive | 1407 | yes | treatment | 41 |\n| 100 | 34 | 69 | 40 | alive | 1571 | no | treatment | 23 |\n| 101 | 33 | 69 | 48 | alive | 1586 | no | treatment | 51 |\n| 102 | 25 | 69 | 33 | alive | 1799 | no | treatment | 25 |"
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coverbench
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The degrees of freedom associated with the chi-square distribution used for X2, given the null hypothesis that the questions had no impact on the sellers in the experiment, is 2.
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"In this experiment, each individual was asked to be a seller of an iPod (a product commonly used to store music on before smart phones...). The participant received $10 + 5% of the sale price for participating. The iPod they were selling had frozen twice in the past inexplicably but otherwise worked fine. The prospective buyer starts off and then asks one of three final questions, depending on the seller's treatment group. The experiment data is in the CSV file ask.csv.\n\nThe three possible questions:\nGeneral: What can you tell me about it?\nPositive Assumption: It doesn't have any problems, does it?\nNegative Assumption: What problems does it have?\nThe outcome variable is whether or not the participant discloses or hides the problem with the iPod.\n\nThe hypothesis test for the iPod experiment is really about assessing whether there is statistically significant evidence that the success each question had on getting the participant to disclose the problem with the iPod. In other words, the goal is to check whether the buyer's question was independent of whether the seller disclosed a problem.\n\nask.csv\n| | question_class | question | response |\n|----:|:-----------------|:---------------------------------------|:-----------|\n| 0 | general | What can you tell me about it? | hide |\n| 1 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 2 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 3 | neg_assumption | What problems does it have? | disclose |\n| 4 | general | What can you tell me about it? | hide |\n| 5 | neg_assumption | What problems does it have? | disclose |\n| 6 | neg_assumption | What problems does it have? | disclose |\n| 7 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 8 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 9 | general | What can you tell me about it? | hide |\n| 10 | neg_assumption | What problems does it have? | disclose |\n| 11 | general | What can you tell me about it? | hide |\n| 12 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 13 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 14 | neg_assumption | What problems does it have? | hide |\n| 15 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 16 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 17 | neg_assumption | What problems does it have? | disclose |\n| 18 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 19 | neg_assumption | What problems does it have? | hide |\n| 20 | neg_assumption | What problems does it have? | disclose |\n| 21 | general | What can you tell me about it? | hide |\n| 22 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 23 | general | What can you tell me about it? | hide |\n| 24 | general | What can you tell me about it? | hide |\n| 25 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 26 | general | What can you tell me about it? | hide |\n| 27 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 28 | neg_assumption | What problems does it have? | hide |\n| 29 | general | What can you tell me about it? | hide |\n| 30 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 31 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 32 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 33 | general | What can you tell me about it? | hide |\n| 34 | neg_assumption | What problems does it have? | hide |\n| 35 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 36 | neg_assumption | What problems does it have? | disclose |\n| 37 | general | What can you tell me about it? | hide |\n| 38 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 39 | neg_assumption | What problems does it have? | disclose |\n| 40 | neg_assumption | What problems does it have? | hide |\n| 41 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 42 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 43 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 44 | neg_assumption | What problems does it have? | disclose |\n| 45 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 46 | general | What can you tell me about it? | hide |\n| 47 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 48 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 49 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 50 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 51 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 52 | neg_assumption | What problems does it have? | disclose |\n| 53 | general | What can you tell me about it? | hide |\n| 54 | general | What can you tell me about it? | hide |\n| 55 | general | What can you tell me about it? | hide |\n| 56 | general | What can you tell me about it? | hide |\n| 57 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 58 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 59 | general | What can you tell me about it? | hide |\n| 60 | neg_assumption | What problems does it have? | hide |\n| 61 | general | What can you tell me about it? | hide |\n| 62 | general | What can you tell me about it? | disclose |\n| 63 | neg_assumption | What problems does it have? | hide |\n| 64 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 65 | general | What can you tell me about it? | hide |\n| 66 | neg_assumption | What problems does it have? | disclose |\n| 67 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 68 | neg_assumption | What problems does it have? | disclose |\n| 69 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 70 | general | What can you tell me about it? | hide |\n| 71 | neg_assumption | What problems does it have? | disclose |\n| 72 | neg_assumption | What problems does it have? | hide |\n| 73 | general | What can you tell me about it? | hide |\n| 74 | general | What can you tell me about it? | hide |\n| 75 | neg_assumption | What problems does it have? | disclose |\n| 76 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 77 | general | What can you tell me about it? | hide |\n| 78 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 79 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 80 | general | What can you tell me about it? | hide |\n| 81 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 82 | general | What can you tell me about it? | hide |\n| 83 | general | What can you tell me about it? | hide |\n| 84 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 85 | general | What can you tell me about it? | hide |\n| 86 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 87 | neg_assumption | What problems does it have? | hide |\n| 88 | general | What can you tell me about it? | hide |\n| 89 | general | What can you tell me about it? | hide |\n| 90 | general | What can you tell me about it? | hide |\n| 91 | general | What can you tell me about it? | hide |\n| 92 | neg_assumption | What problems does it have? | disclose |\n| 93 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 94 | neg_assumption | What problems does it have? | hide |\n| 95 | neg_assumption | What problems does it have? | hide |\n| 96 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 97 | neg_assumption | What problems does it have? | hide |\n| 98 | neg_assumption | What problems does it have? | disclose |\n| 99 | neg_assumption | What problems does it have? | hide |\n| 100 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 101 | general | What can you tell me about it? | hide |\n| 102 | general | What can you tell me about it? | hide |\n| 103 | neg_assumption | What problems does it have? | hide |\n| 104 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 105 | neg_assumption | What problems does it have? | disclose |\n| 106 | general | What can you tell me about it? | hide |\n| 107 | general | What can you tell me about it? | hide |\n| 108 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 109 | neg_assumption | What problems does it have? | hide |\n| 110 | neg_assumption | What problems does it have? | hide |\n| 111 | neg_assumption | What problems does it have? | disclose |\n| 112 | general | What can you tell me about it? | hide |\n| 113 | general | What can you tell me about it? | hide |\n| 114 | general | What can you tell me about it? | hide |\n| 115 | general | What can you tell me about it? | hide |\n| 116 | neg_assumption | What problems does it have? | hide |\n| 117 | general | What can you tell me about it? | hide |\n| 118 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 119 | neg_assumption | What problems does it have? | disclose |\n| 120 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 121 | neg_assumption | What problems does it have? | hide |\n| 122 | neg_assumption | What problems does it have? | disclose |\n| 123 | neg_assumption | What problems does it have? | hide |\n| 124 | general | What can you tell me about it? | disclose |\n| 125 | neg_assumption | What problems does it have? | disclose |\n| 126 | general | What can you tell me about it? | hide |\n| 127 | neg_assumption | What problems does it have? | hide |\n| 128 | general | What can you tell me about it? | hide |\n| 129 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 130 | neg_assumption | What problems does it have? | disclose |\n| 131 | general | What can you tell me about it? | hide |\n| 132 | general | What can you tell me about it? | hide |\n| 133 | neg_assumption | What problems does it have? | disclose |\n| 134 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 135 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 136 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 137 | neg_assumption | What problems does it have? | disclose |\n| 138 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 139 | neg_assumption | What problems does it have? | hide |\n| 140 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 141 | neg_assumption | What problems does it have? | hide |\n| 142 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 143 | neg_assumption | What problems does it have? | disclose |\n| 144 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 145 | general | What can you tell me about it? | hide |\n| 146 | neg_assumption | What problems does it have? | hide |\n| 147 | neg_assumption | What problems does it have? | hide |\n| 148 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 149 | neg_assumption | What problems does it have? | hide |\n| 150 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 151 | general | What can you tell me about it? | hide |\n| 152 | general | What can you tell me about it? | hide |\n| 153 | general | What can you tell me about it? | hide |\n| 154 | neg_assumption | What problems does it have? | hide |\n| 155 | neg_assumption | What problems does it have? | hide |\n| 156 | neg_assumption | What problems does it have? | hide |\n| 157 | neg_assumption | What problems does it have? | hide |\n| 158 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 159 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 160 | neg_assumption | What problems does it have? | hide |\n| 161 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 162 | general | What can you tell me about it? | hide |\n| 163 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 164 | neg_assumption | What problems does it have? | hide |\n| 165 | general | What can you tell me about it? | hide |\n| 166 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 167 | general | What can you tell me about it? | hide |\n| 168 | neg_assumption | What problems does it have? | disclose |\n| 169 | neg_assumption | What problems does it have? | disclose |\n| 170 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 171 | neg_assumption | What problems does it have? | hide |\n| 172 | neg_assumption | What problems does it have? | hide |\n| 173 | neg_assumption | What problems does it have? | disclose |\n| 174 | general | What can you tell me about it? | hide |\n| 175 | neg_assumption | What problems does it have? | disclose |\n| 176 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 177 | neg_assumption | What problems does it have? | hide |\n| 178 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 179 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 180 | general | What can you tell me about it? | hide |\n| 181 | general | What can you tell me about it? | hide |\n| 182 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 183 | neg_assumption | What problems does it have? | hide |\n| 184 | neg_assumption | What problems does it have? | hide |\n| 185 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 186 | general | What can you tell me about it? | hide |\n| 187 | general | What can you tell me about it? | hide |\n| 188 | neg_assumption | What problems does it have? | disclose |\n| 189 | general | What can you tell me about it? | hide |\n| 190 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 191 | neg_assumption | What problems does it have? | disclose |\n| 192 | neg_assumption | What problems does it have? | disclose |\n| 193 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 194 | general | What can you tell me about it? | hide |\n| 195 | neg_assumption | What problems does it have? | disclose |\n| 196 | general | What can you tell me about it? | hide |\n| 197 | general | What can you tell me about it? | hide |\n| 198 | general | What can you tell me about it? | hide |\n| 199 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 200 | general | What can you tell me about it? | hide |\n| 201 | neg_assumption | What problems does it have? | disclose |\n| 202 | general | What can you tell me about it? | hide |\n| 203 | general | What can you tell me about it? | hide |\n| 204 | neg_assumption | What problems does it have? | disclose |\n| 205 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 206 | general | What can you tell me about it? | hide |\n| 207 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 208 | neg_assumption | What problems does it have? | hide |\n| 209 | general | What can you tell me about it? | hide |\n| 210 | neg_assumption | What problems does it have? | disclose |\n| 211 | general | What can you tell me about it? | hide |\n| 212 | general | What can you tell me about it? | hide |\n| 213 | general | What can you tell me about it? | hide |\n| 214 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 215 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 216 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 217 | general | What can you tell me about it? | hide |\n| 218 | general | What can you tell me about it? | hide |"
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QRData
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coverbench
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In the bottom quartile of Obama's 2008 standardized vote share, the proportion of states where Obama received a greater share of standardized votes in 2012 than in 2008 is 0.770.
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[
"Regression towards the mean represents an empirical phenomenon where an observation with a value of the predictor further away from the distribution’s mean tends to have a value of an outcome variable closer to that mean. This tendency can be explained by chance alone.\n\nWe will examine whether or not the US presidential election data exhibit the regression towards the mean phenomenon. To do this, we use Obama’s vote share in the 2008 election to predict his vote share in his 2012 reelection.\n\nThe CSV data file pres08.csv contains the election results by state of the 2008 US presidential election.\nVariable Description\nstate: abbreviated name of the state\nstate.name: unabbreviated name of the state\nObama: Obama’s vote share (percentage)\nMcCain: McCain’s vote share (percentage)\nEV: number of Electoral College votes for the state\n\nIn addition, we have the CSV file pres12.csv, which contains the election results by state of the 2012 US presidential election.\nVariable Description\nstate: abbreviated name of the state\nObama: Obama’s vote share (percentage)\nRomney: Romney’s vote share (percentage)\nEV: number of Electoral College votes for the state\n\npres08.csv\n| | state.name | state | Obama | McCain | EV |\n|---:|:---------------|:--------|--------:|---------:|-----:|\n| 0 | Alabama | AL | 39 | 60 | 9 |\n| 1 | Alaska | AK | 38 | 59 | 3 |\n| 2 | Arizona | AZ | 45 | 54 | 10 |\n| 3 | Arkansas | AR | 39 | 59 | 6 |\n| 4 | California | CA | 61 | 37 | 55 |\n| 5 | Colorado | CO | 54 | 45 | 9 |\n| 6 | Connecticut | CT | 61 | 38 | 7 |\n| 7 | D.C. | DC | 92 | 7 | 3 |\n| 8 | Delaware | DE | 62 | 37 | 3 |\n| 9 | Florida | FL | 51 | 48 | 27 |\n| 10 | Georgia | GA | 47 | 52 | 15 |\n| 11 | Hawaii | HI | 72 | 27 | 4 |\n| 12 | Idaho | ID | 36 | 62 | 4 |\n| 13 | Illinois | IL | 62 | 37 | 21 |\n| 14 | Indiana | IN | 50 | 49 | 11 |\n| 15 | Iowa | IA | 54 | 44 | 7 |\n| 16 | Kansas | KS | 42 | 57 | 6 |\n| 17 | Kentucky | KY | 41 | 57 | 8 |\n| 18 | Louisiana | LA | 40 | 59 | 9 |\n| 19 | Maine | ME | 58 | 40 | 4 |\n| 20 | Maryland | MD | 62 | 36 | 10 |\n| 21 | Massachusetts | MA | 62 | 36 | 12 |\n| 22 | Michigan | MI | 57 | 41 | 17 |\n| 23 | Minnesota | MN | 54 | 44 | 10 |\n| 24 | Mississippi | MS | 43 | 56 | 6 |\n| 25 | Missouri | MO | 48 | 49 | 11 |\n| 26 | Montana | MT | 47 | 50 | 3 |\n| 27 | Nebraska | NE | 42 | 57 | 5 |\n| 28 | Nevada | NV | 55 | 43 | 5 |\n| 29 | New Hampshire | NH | 54 | 45 | 4 |\n| 30 | New Jersey | NJ | 57 | 42 | 15 |\n| 31 | New Mexico | NM | 57 | 42 | 5 |\n| 32 | New York | NY | 63 | 36 | 31 |\n| 33 | North Carolina | NC | 50 | 49 | 15 |\n| 34 | North Dakota | ND | 45 | 53 | 3 |\n| 35 | Ohio | OH | 51 | 47 | 20 |\n| 36 | Oklahoma | OK | 34 | 66 | 7 |\n| 37 | Oregon | OR | 57 | 40 | 7 |\n| 38 | Pennsylvania | PA | 55 | 44 | 21 |\n| 39 | Rhode Island | RI | 63 | 35 | 4 |\n| 40 | South Carolina | SC | 45 | 54 | 8 |\n| 41 | South Dakota | SD | 45 | 53 | 3 |\n| 42 | Tennessee | TN | 42 | 57 | 11 |\n| 43 | Texas | TX | 44 | 55 | 34 |\n| 44 | Utah | UT | 34 | 63 | 5 |\n| 45 | Vermont | VT | 67 | 30 | 3 |\n| 46 | Virginia | VA | 53 | 46 | 13 |\n| 47 | Washington | WA | 58 | 40 | 11 |\n| 48 | West Virginia | WV | 43 | 56 | 5 |\n| 49 | Wisconsin | WI | 56 | 42 | 10 |\n| 50 | Wyoming | WY | 33 | 65 | 3 |\n\npres12.csv\n| | state | Obama | Romney | EV |\n|---:|:--------|--------:|---------:|-----:|\n| 0 | AL | 38 | 61 | 9 |\n| 1 | AK | 41 | 55 | 3 |\n| 2 | AZ | 45 | 54 | 11 |\n| 3 | AR | 37 | 61 | 6 |\n| 4 | CA | 60 | 37 | 55 |\n| 5 | CO | 51 | 46 | 9 |\n| 6 | CT | 58 | 41 | 7 |\n| 7 | DE | 59 | 40 | 3 |\n| 8 | DC | 91 | 7 | 3 |\n| 9 | FL | 50 | 49 | 29 |\n| 10 | GA | 45 | 53 | 16 |\n| 11 | HI | 71 | 28 | 4 |\n| 12 | ID | 33 | 65 | 4 |\n| 13 | IL | 58 | 41 | 20 |\n| 14 | IN | 44 | 54 | 11 |\n| 15 | IA | 52 | 46 | 6 |\n| 16 | KS | 38 | 60 | 6 |\n| 17 | KY | 38 | 60 | 8 |\n| 18 | LA | 41 | 58 | 8 |\n| 19 | ME | 56 | 41 | 4 |\n| 20 | MD | 62 | 36 | 10 |\n| 21 | MA | 61 | 38 | 11 |\n| 22 | MI | 54 | 45 | 16 |\n| 23 | MN | 53 | 45 | 10 |\n| 24 | MS | 44 | 55 | 6 |\n| 25 | MO | 44 | 54 | 10 |\n| 26 | MT | 42 | 55 | 3 |\n| 27 | NE | 38 | 60 | 5 |\n| 28 | NV | 52 | 46 | 6 |\n| 29 | NH | 52 | 46 | 4 |\n| 30 | NJ | 58 | 41 | 14 |\n| 31 | NM | 53 | 43 | 5 |\n| 32 | NY | 63 | 35 | 29 |\n| 33 | NC | 48 | 50 | 15 |\n| 34 | ND | 39 | 58 | 3 |\n| 35 | OH | 51 | 48 | 18 |\n| 36 | OK | 33 | 67 | 7 |\n| 37 | OR | 54 | 42 | 7 |\n| 38 | PA | 52 | 47 | 20 |\n| 39 | RI | 63 | 35 | 4 |\n| 40 | SC | 44 | 55 | 9 |\n| 41 | SD | 40 | 58 | 3 |\n| 42 | TN | 39 | 59 | 11 |\n| 43 | TX | 41 | 57 | 38 |\n| 44 | UT | 25 | 73 | 6 |\n| 45 | VT | 67 | 31 | 3 |\n| 46 | VA | 51 | 47 | 13 |\n| 47 | WA | 56 | 41 | 12 |\n| 48 | WV | 36 | 62 | 5 |\n| 49 | WI | 53 | 46 | 10 |\n| 50 | WY | 28 | 69 | 3 |"
] |
NS
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QRData
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coverbench
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In the bottom quartile of Obama's 2008 standardized vote share, the proportion of states where Obama received a greater share of standardized votes in 2012 than in 2008 is 0.770.
|
[
"Regression towards the mean represents an empirical phenomenon where an observation with a value of the predictor further away from the distribution’s mean tends to have a value of an outcome variable closer to that mean. This tendency can be explained by chance alone.\n\nWe will examine whether or not the US presidential election data exhibit the regression towards the mean phenomenon. To do this, we use Obama’s vote share in the 2008 election to predict his vote share in his 2012 reelection.\n\nThe CSV data file pres08.csv contains the election results by state of the 2008 US presidential election.\nVariable Description\nstate: abbreviated name of the state\nstate.name: unabbreviated name of the state\nObama: Obama’s vote share (percentage)\nMcCain: McCain’s vote share (percentage)\nEV: number of Electoral College votes for the state\n\nIn addition, we have the CSV file pres12.csv, which contains the election results by state of the 2012 US presidential election.\nVariable Description\nstate: abbreviated name of the state\nObama: Obama’s vote share (percentage)\nRomney: Romney’s vote share (percentage)\nEV: number of Electoral College votes for the state\n\npres08.csv\n{\"state.name\":{\"0\":\"Alabama\",\"1\":\"Alaska\",\"2\":\"Arizona\",\"3\":\"Arkansas\",\"4\":\"California\",\"5\":\"Colorado\",\"6\":\"Connecticut\",\"7\":\"D.C.\",\"8\":\"Delaware\",\"9\":\"Florida\",\"10\":\"Georgia\",\"11\":\"Hawaii\",\"12\":\"Idaho\",\"13\":\"Illinois\",\"14\":\"Indiana\",\"15\":\"Iowa\",\"16\":\"Kansas\",\"17\":\"Kentucky\",\"18\":\"Louisiana\",\"19\":\"Maine\",\"20\":\"Maryland\",\"21\":\"Massachusetts\",\"22\":\"Michigan\",\"23\":\"Minnesota\",\"24\":\"Mississippi\",\"25\":\"Missouri\",\"26\":\"Montana\",\"27\":\"Nebraska\",\"28\":\"Nevada\",\"29\":\"New Hampshire\",\"30\":\"New Jersey\",\"31\":\"New Mexico\",\"32\":\"New York\",\"33\":\"North Carolina\",\"34\":\"North Dakota\",\"35\":\"Ohio\",\"36\":\"Oklahoma\",\"37\":\"Oregon\",\"38\":\"Pennsylvania\",\"39\":\"Rhode Island\",\"40\":\"South Carolina\",\"41\":\"South Dakota\",\"42\":\"Tennessee\",\"43\":\"Texas\",\"44\":\"Utah\",\"45\":\"Vermont\",\"46\":\"Virginia\",\"47\":\"Washington\",\"48\":\"West Virginia\",\"49\":\"Wisconsin\",\"50\":\"Wyoming\"},\"state\":{\"0\":\"AL\",\"1\":\"AK\",\"2\":\"AZ\",\"3\":\"AR\",\"4\":\"CA\",\"5\":\"CO\",\"6\":\"CT\",\"7\":\"DC\",\"8\":\"DE\",\"9\":\"FL\",\"10\":\"GA\",\"11\":\"HI\",\"12\":\"ID\",\"13\":\"IL\",\"14\":\"IN\",\"15\":\"IA\",\"16\":\"KS\",\"17\":\"KY\",\"18\":\"LA\",\"19\":\"ME\",\"20\":\"MD\",\"21\":\"MA\",\"22\":\"MI\",\"23\":\"MN\",\"24\":\"MS\",\"25\":\"MO\",\"26\":\"MT\",\"27\":\"NE\",\"28\":\"NV\",\"29\":\"NH\",\"30\":\"NJ\",\"31\":\"NM\",\"32\":\"NY\",\"33\":\"NC\",\"34\":\"ND\",\"35\":\"OH\",\"36\":\"OK\",\"37\":\"OR\",\"38\":\"PA\",\"39\":\"RI\",\"40\":\"SC\",\"41\":\"SD\",\"42\":\"TN\",\"43\":\"TX\",\"44\":\"UT\",\"45\":\"VT\",\"46\":\"VA\",\"47\":\"WA\",\"48\":\"WV\",\"49\":\"WI\",\"50\":\"WY\"},\"Obama\":{\"0\":39,\"1\":38,\"2\":45,\"3\":39,\"4\":61,\"5\":54,\"6\":61,\"7\":92,\"8\":62,\"9\":51,\"10\":47,\"11\":72,\"12\":36,\"13\":62,\"14\":50,\"15\":54,\"16\":42,\"17\":41,\"18\":40,\"19\":58,\"20\":62,\"21\":62,\"22\":57,\"23\":54,\"24\":43,\"25\":48,\"26\":47,\"27\":42,\"28\":55,\"29\":54,\"30\":57,\"31\":57,\"32\":63,\"33\":50,\"34\":45,\"35\":51,\"36\":34,\"37\":57,\"38\":55,\"39\":63,\"40\":45,\"41\":45,\"42\":42,\"43\":44,\"44\":34,\"45\":67,\"46\":53,\"47\":58,\"48\":43,\"49\":56,\"50\":33},\"McCain\":{\"0\":60,\"1\":59,\"2\":54,\"3\":59,\"4\":37,\"5\":45,\"6\":38,\"7\":7,\"8\":37,\"9\":48,\"10\":52,\"11\":27,\"12\":62,\"13\":37,\"14\":49,\"15\":44,\"16\":57,\"17\":57,\"18\":59,\"19\":40,\"20\":36,\"21\":36,\"22\":41,\"23\":44,\"24\":56,\"25\":49,\"26\":50,\"27\":57,\"28\":43,\"29\":45,\"30\":42,\"31\":42,\"32\":36,\"33\":49,\"34\":53,\"35\":47,\"36\":66,\"37\":40,\"38\":44,\"39\":35,\"40\":54,\"41\":53,\"42\":57,\"43\":55,\"44\":63,\"45\":30,\"46\":46,\"47\":40,\"48\":56,\"49\":42,\"50\":65},\"EV\":{\"0\":9,\"1\":3,\"2\":10,\"3\":6,\"4\":55,\"5\":9,\"6\":7,\"7\":3,\"8\":3,\"9\":27,\"10\":15,\"11\":4,\"12\":4,\"13\":21,\"14\":11,\"15\":7,\"16\":6,\"17\":8,\"18\":9,\"19\":4,\"20\":10,\"21\":12,\"22\":17,\"23\":10,\"24\":6,\"25\":11,\"26\":3,\"27\":5,\"28\":5,\"29\":4,\"30\":15,\"31\":5,\"32\":31,\"33\":15,\"34\":3,\"35\":20,\"36\":7,\"37\":7,\"38\":21,\"39\":4,\"40\":8,\"41\":3,\"42\":11,\"43\":34,\"44\":5,\"45\":3,\"46\":13,\"47\":11,\"48\":5,\"49\":10,\"50\":3}}\n\npres12.csv\n{\"state\":{\"0\":\"AL\",\"1\":\"AK\",\"2\":\"AZ\",\"3\":\"AR\",\"4\":\"CA\",\"5\":\"CO\",\"6\":\"CT\",\"7\":\"DE\",\"8\":\"DC\",\"9\":\"FL\",\"10\":\"GA\",\"11\":\"HI\",\"12\":\"ID\",\"13\":\"IL\",\"14\":\"IN\",\"15\":\"IA\",\"16\":\"KS\",\"17\":\"KY\",\"18\":\"LA\",\"19\":\"ME\",\"20\":\"MD\",\"21\":\"MA\",\"22\":\"MI\",\"23\":\"MN\",\"24\":\"MS\",\"25\":\"MO\",\"26\":\"MT\",\"27\":\"NE\",\"28\":\"NV\",\"29\":\"NH\",\"30\":\"NJ\",\"31\":\"NM\",\"32\":\"NY\",\"33\":\"NC\",\"34\":\"ND\",\"35\":\"OH\",\"36\":\"OK\",\"37\":\"OR\",\"38\":\"PA\",\"39\":\"RI\",\"40\":\"SC\",\"41\":\"SD\",\"42\":\"TN\",\"43\":\"TX\",\"44\":\"UT\",\"45\":\"VT\",\"46\":\"VA\",\"47\":\"WA\",\"48\":\"WV\",\"49\":\"WI\",\"50\":\"WY\"},\"Obama\":{\"0\":38,\"1\":41,\"2\":45,\"3\":37,\"4\":60,\"5\":51,\"6\":58,\"7\":59,\"8\":91,\"9\":50,\"10\":45,\"11\":71,\"12\":33,\"13\":58,\"14\":44,\"15\":52,\"16\":38,\"17\":38,\"18\":41,\"19\":56,\"20\":62,\"21\":61,\"22\":54,\"23\":53,\"24\":44,\"25\":44,\"26\":42,\"27\":38,\"28\":52,\"29\":52,\"30\":58,\"31\":53,\"32\":63,\"33\":48,\"34\":39,\"35\":51,\"36\":33,\"37\":54,\"38\":52,\"39\":63,\"40\":44,\"41\":40,\"42\":39,\"43\":41,\"44\":25,\"45\":67,\"46\":51,\"47\":56,\"48\":36,\"49\":53,\"50\":28},\"Romney\":{\"0\":61,\"1\":55,\"2\":54,\"3\":61,\"4\":37,\"5\":46,\"6\":41,\"7\":40,\"8\":7,\"9\":49,\"10\":53,\"11\":28,\"12\":65,\"13\":41,\"14\":54,\"15\":46,\"16\":60,\"17\":60,\"18\":58,\"19\":41,\"20\":36,\"21\":38,\"22\":45,\"23\":45,\"24\":55,\"25\":54,\"26\":55,\"27\":60,\"28\":46,\"29\":46,\"30\":41,\"31\":43,\"32\":35,\"33\":50,\"34\":58,\"35\":48,\"36\":67,\"37\":42,\"38\":47,\"39\":35,\"40\":55,\"41\":58,\"42\":59,\"43\":57,\"44\":73,\"45\":31,\"46\":47,\"47\":41,\"48\":62,\"49\":46,\"50\":69},\"EV\":{\"0\":9,\"1\":3,\"2\":11,\"3\":6,\"4\":55,\"5\":9,\"6\":7,\"7\":3,\"8\":3,\"9\":29,\"10\":16,\"11\":4,\"12\":4,\"13\":20,\"14\":11,\"15\":6,\"16\":6,\"17\":8,\"18\":8,\"19\":4,\"20\":10,\"21\":11,\"22\":16,\"23\":10,\"24\":6,\"25\":10,\"26\":3,\"27\":5,\"28\":6,\"29\":4,\"30\":14,\"31\":5,\"32\":29,\"33\":15,\"34\":3,\"35\":18,\"36\":7,\"37\":7,\"38\":20,\"39\":4,\"40\":9,\"41\":3,\"42\":11,\"43\":38,\"44\":6,\"45\":3,\"46\":13,\"47\":12,\"48\":5,\"49\":10,\"50\":3}}"
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The estimated slope of the linear regression model using the Democratic margin in the two-party vote share as the response variable and the perceived competence for Democratic candidates as the predictor is 0.022.
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[
"Several psychologists have reported the intriguing result of an experiment showing that facial appearance predicts election outcomes better than chance. In their experiment, the researchers briefly showed student subjects the black-and-white head shots of two candidates from a US congressional election (winner and runner-up). The exposure of subjects to facial pictures lasted less than a second, and the subjects were then asked to evaluate the two candidates in terms of their perceived competence.\nThe researchers used these competence measures to predict election outcomes. The key hypothesis is whether or not a within-a-second evaluation of facial appearance can predict election outcomes. The CSV data set, face.csv, contains the data from the experiment. Note that we include data only from subjects who did not know the candidates’ political parties, their policies, or even which candidate was the incumbent or challenger. They were simply making snap judgments about which candidate appeared more competent based on their facial expression alone.\n\nVariable Description\ncongress: session of Congress\nyear: year of the election\nstate: state of the election\nwinner: name of the winner\nloser: name of the runner-up\nw.party: party of the winner\nl.party: party of the loser\nd.votes: number of votes for the Democratic candidate\nr.votes: number of votes for the Republican candidate\nd.comp: competence measure for the Democratic candidate\nr.comp: competence measure for the Republican candidate\n\nface.csv\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>year</th>\n <th>state</th>\n <th>winner</th>\n <th>loser</th>\n <th>w.party</th>\n <th>l.party</th>\n <th>d.comp</th>\n <th>r.comp</th>\n <th>d.votes</th>\n <th>r.votes</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2000</td>\n <td>CA</td>\n <td>Feinstein</td>\n <td>Campbell</td>\n <td>D</td>\n <td>R</td>\n <td>0.564568</td>\n <td>0.435432</td>\n <td>5790154</td>\n <td>3779325</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2000</td>\n <td>DE</td>\n <td>Carper</td>\n <td>Roth</td>\n <td>D</td>\n <td>R</td>\n <td>0.341912</td>\n <td>0.658088</td>\n <td>181387</td>\n <td>142683</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2000</td>\n <td>FL</td>\n <td>Nelson</td>\n <td>McCollum</td>\n <td>D</td>\n <td>R</td>\n <td>0.612368</td>\n <td>0.387632</td>\n <td>2987644</td>\n <td>2703608</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2000</td>\n <td>GA</td>\n <td>Miller</td>\n <td>Mattingly</td>\n <td>D</td>\n <td>R</td>\n <td>0.541533</td>\n <td>0.458467</td>\n <td>1390428</td>\n <td>933698</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2000</td>\n <td>HI</td>\n <td>Akaka</td>\n <td>Carroll</td>\n <td>D</td>\n <td>R</td>\n <td>0.680232</td>\n <td>0.319768</td>\n <td>251130</td>\n <td>84657</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2000</td>\n <td>IN</td>\n <td>Lugar</td>\n <td>Johnson</td>\n <td>R</td>\n <td>D</td>\n <td>0.320502</td>\n <td>0.679498</td>\n <td>684242</td>\n <td>1419629</td>\n </tr>\n <tr>\n <th>6</th>\n <td>2000</td>\n <td>MA</td>\n <td>Kennedy</td>\n <td>Robinson</td>\n <td>D</td>\n <td>R</td>\n <td>0.403756</td>\n <td>0.596244</td>\n <td>1877439</td>\n <td>334721</td>\n </tr>\n <tr>\n <th>7</th>\n <td>2000</td>\n <td>MD</td>\n <td>Sarbanes</td>\n <td>Rappaport</td>\n <td>D</td>\n <td>R</td>\n <td>0.603015</td>\n <td>0.396985</td>\n <td>1171151</td>\n <td>678376</td>\n </tr>\n <tr>\n <th>8</th>\n <td>2000</td>\n <td>ME</td>\n <td>Snowe</td>\n <td>Lawrence</td>\n <td>R</td>\n <td>D</td>\n <td>0.538573</td>\n <td>0.461427</td>\n <td>197742</td>\n <td>431727</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2000</td>\n <td>MI</td>\n <td>Stabenow</td>\n <td>Abraham</td>\n <td>D</td>\n <td>R</td>\n <td>0.869215</td>\n <td>0.130785</td>\n <td>2034342</td>\n <td>1991507</td>\n </tr>\n <tr>\n <th>10</th>\n <td>2000</td>\n <td>MN</td>\n <td>Dayton</td>\n <td>Grams</td>\n <td>D</td>\n <td>R</td>\n <td>0.565335</td>\n <td>0.434665</td>\n <td>1180335</td>\n <td>1048224</td>\n </tr>\n <tr>\n <th>11</th>\n <td>2000</td>\n <td>MO</td>\n <td>Lott</td>\n <td>Brown</td>\n <td>R</td>\n <td>D</td>\n <td>0.594791</td>\n <td>0.405209</td>\n <td>296149</td>\n <td>621500</td>\n </tr>\n <tr>\n <th>12</th>\n <td>2000</td>\n <td>MT</td>\n <td>Burns</td>\n <td>Schweitzer</td>\n <td>R</td>\n <td>D</td>\n <td>0.248399</td>\n <td>0.751601</td>\n <td>194567</td>\n <td>208026</td>\n </tr>\n <tr>\n <th>13</th>\n <td>2000</td>\n <td>ND</td>\n <td>Conrad</td>\n <td>Sand</td>\n <td>D</td>\n <td>R</td>\n <td>0.649669</td>\n <td>0.350331</td>\n <td>177661</td>\n <td>111376</td>\n </tr>\n <tr>\n <th>14</th>\n <td>2000</td>\n <td>NE</td>\n <td>Nelson</td>\n <td>Stenberg</td>\n <td>D</td>\n <td>R</td>\n <td>0.245012</td>\n <td>0.754988</td>\n <td>330366</td>\n <td>318368</td>\n </tr>\n <tr>\n <th>15</th>\n <td>2000</td>\n <td>NM</td>\n <td>Bingaman</td>\n <td>Redmond</td>\n <td>D</td>\n <td>R</td>\n <td>0.754391</td>\n <td>0.245609</td>\n <td>363279</td>\n <td>225040</td>\n </tr>\n <tr>\n <th>16</th>\n <td>2000</td>\n <td>NV</td>\n <td>Ensign</td>\n <td>Bernstein</td>\n <td>R</td>\n <td>D</td>\n <td>0.390400</td>\n <td>0.609600</td>\n <td>238243</td>\n <td>330663</td>\n </tr>\n <tr>\n <th>17</th>\n <td>2000</td>\n <td>OH</td>\n <td>DeWine</td>\n <td>Celeste</td>\n <td>R</td>\n <td>D</td>\n <td>0.325593</td>\n <td>0.674407</td>\n <td>1539001</td>\n <td>2590952</td>\n </tr>\n <tr>\n <th>18</th>\n <td>2000</td>\n <td>PA</td>\n <td>Santorum</td>\n <td>Klink</td>\n <td>R</td>\n <td>D</td>\n <td>0.578942</td>\n <td>0.421058</td>\n <td>2134734</td>\n <td>2473118</td>\n </tr>\n <tr>\n <th>19</th>\n <td>2000</td>\n <td>RI</td>\n <td>Chafee</td>\n <td>Weygand</td>\n <td>R</td>\n <td>D</td>\n <td>0.437047</td>\n <td>0.562953</td>\n <td>165367</td>\n <td>226592</td>\n </tr>\n <tr>\n <th>20</th>\n <td>2000</td>\n <td>TN</td>\n <td>Frist</td>\n <td>Clark</td>\n <td>R</td>\n <td>D</td>\n <td>0.114507</td>\n <td>0.885493</td>\n <td>617684</td>\n <td>1247436</td>\n </tr>\n <tr>\n <th>21</th>\n <td>2000</td>\n <td>TX</td>\n <td>Hutchison</td>\n <td>Kelly</td>\n <td>R</td>\n <td>D</td>\n <td>0.204603</td>\n <td>0.795397</td>\n <td>2026184</td>\n <td>4080582</td>\n </tr>\n <tr>\n <th>22</th>\n <td>2000</td>\n <td>UT</td>\n <td>Hatch</td>\n <td>Howell</td>\n <td>R</td>\n <td>D</td>\n <td>0.442465</td>\n <td>0.557535</td>\n <td>241129</td>\n <td>501925</td>\n </tr>\n <tr>\n <th>23</th>\n <td>2000</td>\n <td>VA</td>\n <td>Allen</td>\n <td>Robb</td>\n <td>R</td>\n <td>D</td>\n <td>0.741696</td>\n <td>0.258304</td>\n <td>1289087</td>\n <td>1414577</td>\n </tr>\n <tr>\n <th>24</th>\n <td>2000</td>\n <td>VT</td>\n <td>Jeffords</td>\n <td>Flanagan</td>\n <td>R</td>\n <td>D</td>\n <td>0.457816</td>\n <td>0.542184</td>\n <td>72909</td>\n <td>188070</td>\n </tr>\n <tr>\n <th>25</th>\n <td>2000</td>\n <td>WA</td>\n <td>Cantwell</td>\n <td>Gorton</td>\n <td>D</td>\n <td>R</td>\n <td>0.614515</td>\n <td>0.385485</td>\n <td>1199437</td>\n <td>1197208</td>\n </tr>\n <tr>\n <th>26</th>\n <td>2000</td>\n <td>WI</td>\n <td>Kohl</td>\n <td>Gillespie</td>\n <td>D</td>\n <td>R</td>\n <td>0.614357</td>\n <td>0.385643</td>\n <td>1563565</td>\n <td>941132</td>\n </tr>\n <tr>\n <th>27</th>\n <td>2000</td>\n <td>WV</td>\n <td>Byrd</td>\n <td>Gallaher</td>\n <td>D</td>\n <td>R</td>\n <td>0.683657</td>\n <td>0.316343</td>\n <td>462566</td>\n <td>119958</td>\n </tr>\n <tr>\n <th>28</th>\n <td>2000</td>\n <td>WY</td>\n <td>Thomas</td>\n <td>Logan</td>\n <td>R</td>\n <td>D</td>\n <td>0.224063</td>\n <td>0.775937</td>\n <td>47039</td>\n <td>157316</td>\n </tr>\n <tr>\n <th>29</th>\n <td>2002</td>\n <td>AK</td>\n <td>Stevens</td>\n <td>Vondersaar</td>\n <td>R</td>\n <td>D</td>\n <td>0.333592</td>\n <td>0.666408</td>\n <td>20466</td>\n <td>155054</td>\n </tr>\n <tr>\n <th>30</th>\n <td>2002</td>\n <td>AL</td>\n <td>Sessions</td>\n <td>Parker</td>\n <td>R</td>\n <td>D</td>\n <td>0.551095</td>\n <td>0.448905</td>\n <td>537882</td>\n <td>790757</td>\n </tr>\n <tr>\n <th>31</th>\n <td>2002</td>\n <td>AR</td>\n <td>Pryor</td>\n <td>Hutchinson</td>\n <td>D</td>\n <td>R</td>\n <td>0.273883</td>\n <td>0.726117</td>\n <td>435346</td>\n <td>372909</td>\n </tr>\n <tr>\n <th>32</th>\n <td>2002</td>\n <td>CO</td>\n <td>Allard</td>\n <td>Strickland</td>\n <td>R</td>\n <td>D</td>\n <td>0.401537</td>\n <td>0.598463</td>\n <td>634227</td>\n <td>707349</td>\n </tr>\n <tr>\n <th>33</th>\n <td>2002</td>\n <td>DE</td>\n <td>Biden</td>\n <td>Clatworthy</td>\n <td>D</td>\n <td>R</td>\n <td>0.639578</td>\n <td>0.360422</td>\n <td>135170</td>\n <td>94716</td>\n </tr>\n <tr>\n <th>34</th>\n <td>2002</td>\n <td>GA</td>\n <td>Chambliss</td>\n <td>Cleland</td>\n <td>R</td>\n <td>D</td>\n <td>0.246164</td>\n <td>0.753836</td>\n <td>928905</td>\n <td>1068902</td>\n </tr>\n <tr>\n <th>35</th>\n <td>2002</td>\n <td>IA</td>\n <td>Harkin</td>\n <td>Ganske</td>\n <td>D</td>\n <td>R</td>\n <td>0.710124</td>\n <td>0.289876</td>\n <td>550156</td>\n <td>446209</td>\n </tr>\n <tr>\n <th>36</th>\n <td>2002</td>\n <td>ID</td>\n <td>Craig</td>\n <td>Blinken</td>\n <td>R</td>\n <td>D</td>\n <td>0.457524</td>\n <td>0.542476</td>\n <td>132845</td>\n <td>265849</td>\n </tr>\n <tr>\n <th>37</th>\n <td>2002</td>\n <td>IL</td>\n <td>Durbin</td>\n <td>Durkin</td>\n <td>D</td>\n <td>R</td>\n <td>0.428035</td>\n <td>0.571965</td>\n <td>2080411</td>\n <td>1320621</td>\n </tr>\n <tr>\n <th>38</th>\n <td>2002</td>\n <td>KY</td>\n <td>McConnell</td>\n <td>Weinberg</td>\n <td>R</td>\n <td>D</td>\n <td>0.562735</td>\n <td>0.437265</td>\n <td>400818</td>\n <td>726396</td>\n </tr>\n <tr>\n <th>39</th>\n <td>2002</td>\n <td>LA</td>\n <td>Landrieu</td>\n <td>Terrell</td>\n <td>D</td>\n <td>R</td>\n <td>0.766916</td>\n <td>0.233084</td>\n <td>563400</td>\n <td>327975</td>\n </tr>\n <tr>\n <th>40</th>\n <td>2002</td>\n <td>ME</td>\n <td>Collins</td>\n <td>Pingree</td>\n <td>R</td>\n <td>D</td>\n <td>0.327268</td>\n <td>0.672732</td>\n <td>205901</td>\n <td>290266</td>\n </tr>\n <tr>\n <th>41</th>\n <td>2002</td>\n <td>MI</td>\n <td>Levin</td>\n <td>Raczkowski</td>\n <td>D</td>\n <td>R</td>\n <td>0.703998</td>\n <td>0.296002</td>\n <td>1893788</td>\n <td>1184548</td>\n </tr>\n <tr>\n <th>42</th>\n <td>2002</td>\n <td>MN</td>\n <td>Coleman</td>\n <td>Mondale</td>\n <td>R</td>\n <td>D</td>\n <td>0.665846</td>\n <td>0.334154</td>\n <td>1029982</td>\n <td>1091253</td>\n </tr>\n <tr>\n <th>43</th>\n <td>2002</td>\n <td>MO</td>\n <td>Talent</td>\n <td>Carnahan</td>\n <td>R</td>\n <td>D</td>\n <td>0.396744</td>\n <td>0.603256</td>\n <td>911507</td>\n <td>934093</td>\n </tr>\n <tr>\n <th>44</th>\n <td>2002</td>\n <td>MT</td>\n <td>Baucus</td>\n <td>Taylor</td>\n <td>D</td>\n <td>R</td>\n <td>0.897228</td>\n <td>0.102772</td>\n <td>202908</td>\n <td>102766</td>\n </tr>\n <tr>\n <th>45</th>\n <td>2002</td>\n <td>NC</td>\n <td>Dole</td>\n <td>Bowles</td>\n <td>R</td>\n <td>D</td>\n <td>0.313285</td>\n <td>0.686715</td>\n <td>1034941</td>\n <td>1238203</td>\n </tr>\n <tr>\n <th>46</th>\n <td>2002</td>\n <td>NE</td>\n <td>Hagel</td>\n <td>Matulka</td>\n <td>R</td>\n <td>D</td>\n <td>0.213806</td>\n <td>0.786194</td>\n <td>68657</td>\n <td>391648</td>\n </tr>\n <tr>\n <th>47</th>\n <td>2002</td>\n <td>NH</td>\n <td>Sununu</td>\n <td>Shaheen</td>\n <td>R</td>\n <td>D</td>\n <td>0.526114</td>\n <td>0.473886</td>\n <td>206689</td>\n <td>225506</td>\n </tr>\n <tr>\n <th>48</th>\n <td>2002</td>\n <td>NJ</td>\n <td>Lautenber</td>\n <td>Forrester</td>\n <td>D</td>\n <td>R</td>\n <td>0.636229</td>\n <td>0.363771</td>\n <td>1112499</td>\n <td>909383</td>\n </tr>\n <tr>\n <th>49</th>\n <td>2002</td>\n <td>NM</td>\n <td>Domenici</td>\n <td>Tristani</td>\n <td>R</td>\n <td>D</td>\n <td>0.346114</td>\n <td>0.653886</td>\n <td>161409</td>\n <td>296935</td>\n </tr>\n <tr>\n <th>50</th>\n <td>2002</td>\n <td>OK</td>\n <td>Inhofe</td>\n <td>Walters</td>\n <td>R</td>\n <td>D</td>\n <td>0.449049</td>\n <td>0.550951</td>\n <td>369789</td>\n <td>578579</td>\n </tr>\n <tr>\n <th>51</th>\n <td>2002</td>\n <td>OR</td>\n <td>Smith</td>\n <td>Bradbury</td>\n <td>R</td>\n <td>D</td>\n <td>0.402232</td>\n <td>0.597768</td>\n <td>487995</td>\n <td>695345</td>\n </tr>\n <tr>\n <th>52</th>\n <td>2002</td>\n <td>RI</td>\n <td>Reed</td>\n <td>Tingle</td>\n <td>D</td>\n <td>R</td>\n <td>0.711679</td>\n <td>0.288321</td>\n <td>241315</td>\n <td>66613</td>\n </tr>\n <tr>\n <th>53</th>\n <td>2002</td>\n <td>SC</td>\n <td>Graham</td>\n <td>Sanders</td>\n <td>R</td>\n <td>D</td>\n <td>0.573504</td>\n <td>0.426496</td>\n <td>484798</td>\n <td>597789</td>\n </tr>\n <tr>\n <th>54</th>\n <td>2002</td>\n <td>SD</td>\n <td>Johnson</td>\n <td>Thune</td>\n <td>D</td>\n <td>R</td>\n <td>0.318235</td>\n <td>0.681765</td>\n <td>167481</td>\n <td>166954</td>\n </tr>\n <tr>\n <th>55</th>\n <td>2002</td>\n <td>TN</td>\n <td>Alexander</td>\n <td>Clement</td>\n <td>R</td>\n <td>D</td>\n <td>0.522373</td>\n <td>0.477627</td>\n <td>726510</td>\n <td>888223</td>\n </tr>\n <tr>\n <th>56</th>\n <td>2002</td>\n <td>TX</td>\n <td>Cornyn</td>\n <td>Kirk</td>\n <td>R</td>\n <td>D</td>\n <td>0.430373</td>\n <td>0.569627</td>\n <td>1946681</td>\n <td>2480991</td>\n </tr>\n <tr>\n <th>57</th>\n <td>2002</td>\n <td>WV</td>\n <td>Rockefell</td>\n <td>Wolfe</td>\n <td>D</td>\n <td>R</td>\n <td>0.638702</td>\n <td>0.361298</td>\n <td>271314</td>\n <td>158211</td>\n </tr>\n <tr>\n <th>58</th>\n <td>2002</td>\n <td>WY</td>\n <td>Enzi</td>\n <td>Corcoran</td>\n <td>R</td>\n <td>D</td>\n <td>0.175816</td>\n <td>0.824184</td>\n <td>49587</td>\n <td>133615</td>\n </tr>\n <tr>\n <th>59</th>\n <td>2004</td>\n <td>AL</td>\n <td>Shelby</td>\n <td>Sowell</td>\n <td>R</td>\n <td>D</td>\n <td>0.322314</td>\n <td>0.677686</td>\n <td>593302</td>\n <td>1240061</td>\n </tr>\n <tr>\n <th>60</th>\n <td>2004</td>\n <td>AK</td>\n <td>Murkowski</td>\n <td>Knowles</td>\n <td>R</td>\n <td>D</td>\n <td>0.419355</td>\n <td>0.580645</td>\n <td>110699</td>\n <td>121027</td>\n </tr>\n <tr>\n <th>61</th>\n <td>2004</td>\n <td>AR</td>\n <td>Lincoln</td>\n <td>Holt</td>\n <td>D</td>\n <td>R</td>\n <td>0.736000</td>\n <td>0.264000</td>\n <td>573793</td>\n <td>454132</td>\n </tr>\n <tr>\n <th>62</th>\n <td>2004</td>\n <td>CA</td>\n <td>Boxer</td>\n <td>Jones</td>\n <td>D</td>\n <td>R</td>\n <td>0.598361</td>\n <td>0.401639</td>\n <td>5599219</td>\n <td>3642281</td>\n </tr>\n <tr>\n <th>63</th>\n <td>2004</td>\n <td>CO</td>\n <td>Salazar</td>\n <td>Coors</td>\n <td>D</td>\n <td>R</td>\n <td>0.512605</td>\n <td>0.487395</td>\n <td>1023803</td>\n <td>944520</td>\n </tr>\n <tr>\n <th>64</th>\n <td>2004</td>\n <td>CT</td>\n <td>Dodd</td>\n <td>Orchulli</td>\n <td>D</td>\n <td>R</td>\n <td>0.618644</td>\n <td>0.381356</td>\n <td>923836</td>\n <td>452874</td>\n </tr>\n <tr>\n <th>65</th>\n <td>2004</td>\n <td>FL</td>\n <td>Martinez</td>\n <td>Castor</td>\n <td>R</td>\n <td>D</td>\n <td>0.441667</td>\n <td>0.558333</td>\n <td>3544602</td>\n <td>3622823</td>\n </tr>\n <tr>\n <th>66</th>\n <td>2004</td>\n <td>GA</td>\n <td>Isakson</td>\n <td>Majette</td>\n <td>R</td>\n <td>D</td>\n <td>0.617886</td>\n <td>0.382114</td>\n <td>1268529</td>\n <td>1839069</td>\n </tr>\n <tr>\n <th>67</th>\n <td>2004</td>\n <td>HI</td>\n <td>Inouye</td>\n <td>Cavasso</td>\n <td>D</td>\n <td>R</td>\n <td>0.731707</td>\n <td>0.268293</td>\n <td>313269</td>\n <td>87119</td>\n </tr>\n <tr>\n <th>68</th>\n <td>2004</td>\n <td>IL</td>\n <td>Obama</td>\n <td>Keyes</td>\n <td>D</td>\n <td>R</td>\n <td>0.164835</td>\n <td>0.835165</td>\n <td>3524702</td>\n <td>1371882</td>\n </tr>\n <tr>\n <th>69</th>\n <td>2004</td>\n <td>IN</td>\n <td>Bayh</td>\n <td>Scott</td>\n <td>D</td>\n <td>R</td>\n <td>0.550000</td>\n <td>0.450000</td>\n <td>1488782</td>\n <td>902108</td>\n </tr>\n <tr>\n <th>70</th>\n <td>2004</td>\n <td>IA</td>\n <td>Grassley</td>\n <td>Small</td>\n <td>R</td>\n <td>D</td>\n <td>0.516949</td>\n <td>0.483051</td>\n <td>403434</td>\n <td>1025566</td>\n </tr>\n <tr>\n <th>71</th>\n <td>2004</td>\n <td>KY</td>\n <td>Brownback</td>\n <td>Jones</td>\n <td>R</td>\n <td>D</td>\n <td>0.357143</td>\n <td>0.642857</td>\n <td>307968</td>\n <td>777198</td>\n </tr>\n <tr>\n <th>72</th>\n <td>2004</td>\n <td>KZ</td>\n <td>Bunning</td>\n <td>Mongiardo</td>\n <td>R</td>\n <td>D</td>\n <td>0.696721</td>\n <td>0.303279</td>\n <td>850756</td>\n <td>873596</td>\n </tr>\n <tr>\n <th>73</th>\n <td>2004</td>\n <td>LA</td>\n <td>Vitter</td>\n <td>JohnKenne</td>\n <td>R</td>\n <td>D</td>\n <td>0.352000</td>\n <td>0.648000</td>\n <td>275494</td>\n <td>942755</td>\n </tr>\n <tr>\n <th>74</th>\n <td>2004</td>\n <td>MD</td>\n <td>Mikulski</td>\n <td>Pipkin</td>\n <td>D</td>\n <td>R</td>\n <td>0.508621</td>\n <td>0.491379</td>\n <td>1385009</td>\n <td>725898</td>\n </tr>\n <tr>\n <th>75</th>\n <td>2004</td>\n <td>MS</td>\n <td>Bond</td>\n <td>Farmer</td>\n <td>R</td>\n <td>D</td>\n <td>0.621849</td>\n <td>0.378151</td>\n <td>1153422</td>\n <td>1514793</td>\n </tr>\n <tr>\n <th>76</th>\n <td>2004</td>\n <td>NC</td>\n <td>Burr</td>\n <td>Bowles</td>\n <td>R</td>\n <td>D</td>\n <td>0.270000</td>\n <td>0.730000</td>\n <td>1586968</td>\n <td>1742182</td>\n </tr>\n <tr>\n <th>77</th>\n <td>2004</td>\n <td>ND</td>\n <td>Dorgan</td>\n <td>Liffrig</td>\n <td>D</td>\n <td>R</td>\n <td>0.758621</td>\n <td>0.241379</td>\n <td>211503</td>\n <td>98244</td>\n </tr>\n <tr>\n <th>78</th>\n <td>2004</td>\n <td>NV</td>\n <td>Reid</td>\n <td>Zizer</td>\n <td>D</td>\n <td>R</td>\n <td>0.747967</td>\n <td>0.252033</td>\n <td>490232</td>\n <td>282255</td>\n </tr>\n <tr>\n <th>79</th>\n <td>2004</td>\n <td>NH</td>\n <td>Gregg</td>\n <td>Haddock</td>\n <td>R</td>\n <td>D</td>\n <td>0.064000</td>\n <td>0.936000</td>\n <td>221011</td>\n <td>434292</td>\n </tr>\n <tr>\n <th>80</th>\n <td>2004</td>\n <td>NY</td>\n <td>Schumer</td>\n <td>Mills</td>\n <td>D</td>\n <td>R</td>\n <td>0.318182</td>\n <td>0.681818</td>\n <td>4409162</td>\n <td>1535871</td>\n </tr>\n <tr>\n <th>81</th>\n <td>2004</td>\n <td>OH</td>\n <td>Voinovich</td>\n <td>Fingerhut</td>\n <td>R</td>\n <td>D</td>\n <td>0.581967</td>\n <td>0.418033</td>\n <td>1907852</td>\n <td>3380364</td>\n </tr>\n <tr>\n <th>82</th>\n <td>2004</td>\n <td>OK</td>\n <td>Coburn</td>\n <td>Carson</td>\n <td>R</td>\n <td>D</td>\n <td>0.292683</td>\n <td>0.707317</td>\n <td>596672</td>\n <td>763332</td>\n </tr>\n <tr>\n <th>83</th>\n <td>2004</td>\n <td>OR</td>\n <td>Wyden</td>\n <td>King</td>\n <td>D</td>\n <td>R</td>\n <td>0.600000</td>\n <td>0.400000</td>\n <td>1072079</td>\n <td>536506</td>\n </tr>\n <tr>\n <th>84</th>\n <td>2004</td>\n <td>PA</td>\n <td>Spekter</td>\n <td>Hoeffel</td>\n <td>R</td>\n <td>D</td>\n <td>0.712000</td>\n <td>0.288000</td>\n <td>2295305</td>\n <td>2890818</td>\n </tr>\n <tr>\n <th>85</th>\n <td>2004</td>\n <td>SC</td>\n <td>Demint</td>\n <td>Tenenbaum</td>\n <td>R</td>\n <td>D</td>\n <td>0.464000</td>\n <td>0.536000</td>\n <td>691918</td>\n <td>843884</td>\n </tr>\n <tr>\n <th>86</th>\n <td>2004</td>\n <td>SD</td>\n <td>Thune</td>\n <td>Daschle</td>\n <td>R</td>\n <td>D</td>\n <td>0.367925</td>\n <td>0.632075</td>\n <td>193279</td>\n <td>197814</td>\n </tr>\n <tr>\n <th>87</th>\n <td>2004</td>\n <td>UT</td>\n <td>Bennett</td>\n <td>VanDam</td>\n <td>R</td>\n <td>D</td>\n <td>0.761905</td>\n <td>0.238095</td>\n <td>237415</td>\n <td>564260</td>\n </tr>\n <tr>\n <th>88</th>\n <td>2004</td>\n <td>VT</td>\n <td>Leahy</td>\n <td>McMullen</td>\n <td>D</td>\n <td>R</td>\n <td>0.660714</td>\n <td>0.339286</td>\n <td>212850</td>\n <td>74704</td>\n </tr>\n <tr>\n <th>89</th>\n <td>2004</td>\n <td>WA</td>\n <td>Murray</td>\n <td>Nethercutt</td>\n <td>D</td>\n <td>R</td>\n <td>0.264463</td>\n <td>0.735537</td>\n <td>1215647</td>\n <td>935992</td>\n </tr>\n <tr>\n <th>90</th>\n <td>2004</td>\n <td>WI</td>\n <td>Feingold</td>\n <td>Michels</td>\n <td>D</td>\n <td>R</td>\n <td>0.549180</td>\n <td>0.450820</td>\n <td>1632562</td>\n <td>1301305</td>\n </tr>\n <tr>\n <th>91</th>\n <td>2006</td>\n <td>AZ</td>\n <td>Kyl,Jon</td>\n <td>Pederson,</td>\n <td>R</td>\n <td>D</td>\n <td>0.206349</td>\n <td>0.793651</td>\n <td>505136</td>\n <td>605266</td>\n </tr>\n <tr>\n <th>92</th>\n <td>2006</td>\n <td>CA</td>\n <td>Feinstein</td>\n <td>Mountjoy,</td>\n <td>D</td>\n <td>R</td>\n <td>0.719298</td>\n <td>0.280702</td>\n <td>3889327</td>\n <td>2275304</td>\n </tr>\n <tr>\n <th>93</th>\n <td>2006</td>\n <td>DE</td>\n <td>Carper,T</td>\n <td>Ting,Jan</td>\n <td>D</td>\n <td>R</td>\n <td>0.838710</td>\n <td>0.161290</td>\n <td>170544</td>\n <td>69732</td>\n </tr>\n <tr>\n <th>94</th>\n <td>2006</td>\n <td>FL</td>\n <td>Nelson,B</td>\n <td>Harris,Ka</td>\n <td>D</td>\n <td>R</td>\n <td>0.548387</td>\n <td>0.451613</td>\n <td>2844459</td>\n <td>1797229</td>\n </tr>\n <tr>\n <th>95</th>\n <td>2006</td>\n <td>ME</td>\n <td>Snowe,Ol</td>\n <td>Bright,Je</td>\n <td>R</td>\n <td>D</td>\n <td>0.442623</td>\n <td>0.557377</td>\n <td>108796</td>\n <td>393230</td>\n </tr>\n <tr>\n <th>96</th>\n <td>2006</td>\n <td>MD</td>\n <td>Cardin,B</td>\n <td>Steele,Mi</td>\n <td>D</td>\n <td>R</td>\n <td>0.278689</td>\n <td>0.721311</td>\n <td>846709</td>\n <td>682641</td>\n </tr>\n <tr>\n <th>97</th>\n <td>2006</td>\n <td>MA</td>\n <td>Kennedy,</td>\n <td>Chase,Ken</td>\n <td>D</td>\n <td>R</td>\n <td>0.673469</td>\n <td>0.326531</td>\n <td>1497304</td>\n <td>658374</td>\n </tr>\n <tr>\n <th>98</th>\n <td>2006</td>\n <td>MI</td>\n <td>Stabenow,</td>\n <td>Vouchard,</td>\n <td>D</td>\n <td>R</td>\n <td>0.523810</td>\n <td>0.476190</td>\n <td>2146538</td>\n <td>1558483</td>\n </tr>\n <tr>\n <th>99</th>\n <td>2006</td>\n <td>MN</td>\n <td>Klobuchar</td>\n <td>Kennedy,M</td>\n <td>D</td>\n <td>R</td>\n <td>0.290323</td>\n <td>0.709677</td>\n <td>1279515</td>\n <td>839173</td>\n </tr>\n <tr>\n <th>100</th>\n <td>2006</td>\n <td>MS</td>\n <td>Lott,Tre</td>\n <td>Fleming,E</td>\n <td>R</td>\n <td>D</td>\n <td>0.436364</td>\n <td>0.563636</td>\n <td>205518</td>\n <td>375307</td>\n </tr>\n <tr>\n <th>101</th>\n <td>2006</td>\n <td>MO</td>\n <td>McCaskill</td>\n <td>Talent,Ji</td>\n <td>D</td>\n <td>R</td>\n <td>0.525424</td>\n <td>0.474576</td>\n <td>1028215</td>\n <td>987077</td>\n </tr>\n <tr>\n <th>102</th>\n <td>2006</td>\n <td>MT</td>\n <td>Tester,J</td>\n <td>Burns,Con</td>\n <td>D</td>\n <td>R</td>\n <td>0.163934</td>\n <td>0.836066</td>\n <td>198302</td>\n <td>195455</td>\n </tr>\n <tr>\n <th>103</th>\n <td>2006</td>\n <td>NE</td>\n <td>Nelson,B</td>\n <td>Ricketts,</td>\n <td>D</td>\n <td>R</td>\n <td>0.612903</td>\n <td>0.387097</td>\n <td>371777</td>\n <td>211111</td>\n </tr>\n <tr>\n <th>104</th>\n <td>2006</td>\n <td>NV</td>\n <td>Ensign,J</td>\n <td>Carter,Ja</td>\n <td>R</td>\n <td>D</td>\n <td>0.174603</td>\n <td>0.825397</td>\n <td>237875</td>\n <td>321186</td>\n </tr>\n <tr>\n <th>105</th>\n <td>2006</td>\n <td>NJ</td>\n <td>Menendez,</td>\n <td>Kean,Tom</td>\n <td>D</td>\n <td>R</td>\n <td>0.683333</td>\n <td>0.316667</td>\n <td>1159642</td>\n <td>973895</td>\n </tr>\n <tr>\n <th>106</th>\n <td>2006</td>\n <td>NM</td>\n <td>Bingaman,</td>\n <td>McCulloch,</td>\n <td>D</td>\n <td>R</td>\n <td>0.616667</td>\n <td>0.383333</td>\n <td>371068</td>\n <td>156314</td>\n </tr>\n <tr>\n <th>107</th>\n <td>2006</td>\n <td>ND</td>\n <td>Conrad,K</td>\n <td>Grotberg,</td>\n <td>D</td>\n <td>R</td>\n <td>0.539683</td>\n <td>0.460317</td>\n <td>149317</td>\n <td>64133</td>\n </tr>\n <tr>\n <th>108</th>\n <td>2006</td>\n <td>OH</td>\n <td>Brown,Sh</td>\n <td>DeWine,Mi</td>\n <td>D</td>\n <td>R</td>\n <td>0.590164</td>\n <td>0.409836</td>\n <td>2131741</td>\n <td>1680177</td>\n </tr>\n <tr>\n <th>109</th>\n <td>2006</td>\n <td>PA</td>\n <td>Casey,Bo</td>\n <td>Santorum,</td>\n <td>D</td>\n <td>R</td>\n <td>0.084746</td>\n <td>0.915254</td>\n <td>2341170</td>\n <td>1650139</td>\n </tr>\n <tr>\n <th>110</th>\n <td>2006</td>\n <td>RI</td>\n <td>Whitehous</td>\n <td>Chafee,Li</td>\n <td>D</td>\n <td>R</td>\n <td>0.786885</td>\n <td>0.213115</td>\n <td>205274</td>\n <td>178548</td>\n </tr>\n <tr>\n <th>111</th>\n <td>2006</td>\n <td>TN</td>\n <td>Corker,B</td>\n <td>Ford,Haro</td>\n <td>R</td>\n <td>D</td>\n <td>0.264151</td>\n <td>0.735849</td>\n <td>877716</td>\n <td>927343</td>\n </tr>\n <tr>\n <th>112</th>\n <td>2006</td>\n <td>TX</td>\n <td>Hutchison</td>\n <td>Radnofsky,</td>\n <td>R</td>\n <td>D</td>\n <td>0.416667</td>\n <td>0.583333</td>\n <td>1550950</td>\n <td>2654004</td>\n </tr>\n <tr>\n <th>113</th>\n <td>2006</td>\n <td>UT</td>\n <td>Hatch,Or</td>\n <td>Ashdown,P</td>\n <td>R</td>\n <td>D</td>\n <td>0.089286</td>\n <td>0.910714</td>\n <td>168551</td>\n <td>342901</td>\n </tr>\n <tr>\n <th>114</th>\n <td>2006</td>\n <td>VA</td>\n <td>Webb,Jam</td>\n <td>Allen,Geo</td>\n <td>D</td>\n <td>R</td>\n <td>0.114754</td>\n <td>0.885246</td>\n <td>1172671</td>\n <td>1165440</td>\n </tr>\n <tr>\n <th>115</th>\n <td>2006</td>\n <td>WA</td>\n <td>Cantwell,</td>\n <td>McGavick,</td>\n <td>D</td>\n <td>R</td>\n <td>0.396825</td>\n <td>0.603175</td>\n <td>652515</td>\n <td>445395</td>\n </tr>\n <tr>\n <th>116</th>\n <td>2006</td>\n <td>WV</td>\n <td>Byrd,Rob</td>\n <td>JohnRaese</td>\n <td>D</td>\n <td>R</td>\n <td>0.327869</td>\n <td>0.672131</td>\n <td>291058</td>\n <td>152315</td>\n </tr>\n <tr>\n <th>117</th>\n <td>2006</td>\n <td>WI</td>\n <td>Kohl,Her</td>\n <td>Lorge,Rob</td>\n <td>D</td>\n <td>R</td>\n <td>0.573770</td>\n <td>0.426230</td>\n <td>1436157</td>\n <td>628879</td>\n </tr>\n <tr>\n <th>118</th>\n <td>2006</td>\n <td>WY</td>\n <td>Thomas,C</td>\n <td>Groutage,</td>\n <td>R</td>\n <td>D</td>\n <td>0.250000</td>\n <td>0.750000</td>\n <td>57640</td>\n <td>134942</td>\n </tr>\n </tbody>\n</table>"
] |
NS
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QRData
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coverbench
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The proportion of patients in the treatment group who experienced improvement in symptoms is 0.94.
|
[
"Researchers studying the effect of antibiotic treatment for acute sinusitis compared to symptomatic treatments randomly assigned 166 adults diagnosed with acute sinusitis to one of two groups: treatment or control. Study participants received either a 10-day course of amoxicillin (an antibiotic) or a placebo similar in appearance and taste. The placebo consisted of\nsymptomatic treatments such as acetaminophen, nasal decongestants, etc. At the end of the 10-day period, patients were asked if they experienced improvement in symptoms. The research data is in the CSV file sinusitis.csv.\n\nsinusitis.csv\n{\"group\":{\"0\":\"treatment\",\"1\":\"treatment\",\"2\":\"treatment\",\"3\":\"treatment\",\"4\":\"treatment\",\"5\":\"treatment\",\"6\":\"treatment\",\"7\":\"treatment\",\"8\":\"treatment\",\"9\":\"treatment\",\"10\":\"treatment\",\"11\":\"treatment\",\"12\":\"treatment\",\"13\":\"treatment\",\"14\":\"treatment\",\"15\":\"treatment\",\"16\":\"treatment\",\"17\":\"treatment\",\"18\":\"treatment\",\"19\":\"treatment\",\"20\":\"treatment\",\"21\":\"treatment\",\"22\":\"treatment\",\"23\":\"treatment\",\"24\":\"treatment\",\"25\":\"treatment\",\"26\":\"treatment\",\"27\":\"treatment\",\"28\":\"treatment\",\"29\":\"treatment\",\"30\":\"treatment\",\"31\":\"treatment\",\"32\":\"treatment\",\"33\":\"treatment\",\"34\":\"treatment\",\"35\":\"treatment\",\"36\":\"treatment\",\"37\":\"treatment\",\"38\":\"treatment\",\"39\":\"treatment\",\"40\":\"treatment\",\"41\":\"treatment\",\"42\":\"treatment\",\"43\":\"treatment\",\"44\":\"treatment\",\"45\":\"treatment\",\"46\":\"treatment\",\"47\":\"treatment\",\"48\":\"treatment\",\"49\":\"treatment\",\"50\":\"treatment\",\"51\":\"treatment\",\"52\":\"treatment\",\"53\":\"treatment\",\"54\":\"treatment\",\"55\":\"treatment\",\"56\":\"treatment\",\"57\":\"treatment\",\"58\":\"treatment\",\"59\":\"treatment\",\"60\":\"treatment\",\"61\":\"treatment\",\"62\":\"treatment\",\"63\":\"treatment\",\"64\":\"treatment\",\"65\":\"treatment\",\"66\":\"treatment\",\"67\":\"treatment\",\"68\":\"treatment\",\"69\":\"treatment\",\"70\":\"treatment\",\"71\":\"treatment\",\"72\":\"treatment\",\"73\":\"treatment\",\"74\":\"treatment\",\"75\":\"treatment\",\"76\":\"treatment\",\"77\":\"treatment\",\"78\":\"treatment\",\"79\":\"treatment\",\"80\":\"treatment\",\"81\":\"treatment\",\"82\":\"treatment\",\"83\":\"treatment\",\"84\":\"treatment\",\"85\":\"control\",\"86\":\"control\",\"87\":\"control\",\"88\":\"control\",\"89\":\"control\",\"90\":\"control\",\"91\":\"control\",\"92\":\"control\",\"93\":\"control\",\"94\":\"control\",\"95\":\"control\",\"96\":\"control\",\"97\":\"control\",\"98\":\"control\",\"99\":\"control\",\"100\":\"control\",\"101\":\"control\",\"102\":\"control\",\"103\":\"control\",\"104\":\"control\",\"105\":\"control\",\"106\":\"control\",\"107\":\"control\",\"108\":\"control\",\"109\":\"control\",\"110\":\"control\",\"111\":\"control\",\"112\":\"control\",\"113\":\"control\",\"114\":\"control\",\"115\":\"control\",\"116\":\"control\",\"117\":\"control\",\"118\":\"control\",\"119\":\"control\",\"120\":\"control\",\"121\":\"control\",\"122\":\"control\",\"123\":\"control\",\"124\":\"control\",\"125\":\"control\",\"126\":\"control\",\"127\":\"control\",\"128\":\"control\",\"129\":\"control\",\"130\":\"control\",\"131\":\"control\",\"132\":\"control\",\"133\":\"control\",\"134\":\"control\",\"135\":\"control\",\"136\":\"control\",\"137\":\"control\",\"138\":\"control\",\"139\":\"control\",\"140\":\"control\",\"141\":\"control\",\"142\":\"control\",\"143\":\"control\",\"144\":\"control\",\"145\":\"control\",\"146\":\"control\",\"147\":\"control\",\"148\":\"control\",\"149\":\"control\",\"150\":\"control\",\"151\":\"control\",\"152\":\"control\",\"153\":\"control\",\"154\":\"control\",\"155\":\"control\",\"156\":\"control\",\"157\":\"control\",\"158\":\"control\",\"159\":\"control\",\"160\":\"control\",\"161\":\"control\",\"162\":\"control\",\"163\":\"control\",\"164\":\"control\",\"165\":\"control\"},\"self_reported_improvement\":{\"0\":\"yes\",\"1\":\"yes\",\"2\":\"yes\",\"3\":\"yes\",\"4\":\"yes\",\"5\":\"yes\",\"6\":\"yes\",\"7\":\"yes\",\"8\":\"yes\",\"9\":\"yes\",\"10\":\"yes\",\"11\":\"yes\",\"12\":\"yes\",\"13\":\"yes\",\"14\":\"yes\",\"15\":\"yes\",\"16\":\"yes\",\"17\":\"yes\",\"18\":\"yes\",\"19\":\"yes\",\"20\":\"yes\",\"21\":\"yes\",\"22\":\"yes\",\"23\":\"yes\",\"24\":\"yes\",\"25\":\"yes\",\"26\":\"yes\",\"27\":\"yes\",\"28\":\"yes\",\"29\":\"yes\",\"30\":\"yes\",\"31\":\"yes\",\"32\":\"yes\",\"33\":\"yes\",\"34\":\"yes\",\"35\":\"yes\",\"36\":\"yes\",\"37\":\"yes\",\"38\":\"yes\",\"39\":\"yes\",\"40\":\"yes\",\"41\":\"yes\",\"42\":\"yes\",\"43\":\"yes\",\"44\":\"yes\",\"45\":\"yes\",\"46\":\"yes\",\"47\":\"yes\",\"48\":\"yes\",\"49\":\"yes\",\"50\":\"yes\",\"51\":\"yes\",\"52\":\"yes\",\"53\":\"yes\",\"54\":\"yes\",\"55\":\"yes\",\"56\":\"yes\",\"57\":\"yes\",\"58\":\"yes\",\"59\":\"yes\",\"60\":\"yes\",\"61\":\"yes\",\"62\":\"yes\",\"63\":\"yes\",\"64\":\"yes\",\"65\":\"yes\",\"66\":\"no\",\"67\":\"no\",\"68\":\"no\",\"69\":\"no\",\"70\":\"no\",\"71\":\"no\",\"72\":\"no\",\"73\":\"no\",\"74\":\"no\",\"75\":\"no\",\"76\":\"no\",\"77\":\"no\",\"78\":\"no\",\"79\":\"no\",\"80\":\"no\",\"81\":\"no\",\"82\":\"no\",\"83\":\"no\",\"84\":\"no\",\"85\":\"yes\",\"86\":\"yes\",\"87\":\"yes\",\"88\":\"yes\",\"89\":\"yes\",\"90\":\"yes\",\"91\":\"yes\",\"92\":\"yes\",\"93\":\"yes\",\"94\":\"yes\",\"95\":\"yes\",\"96\":\"yes\",\"97\":\"yes\",\"98\":\"yes\",\"99\":\"yes\",\"100\":\"yes\",\"101\":\"yes\",\"102\":\"yes\",\"103\":\"yes\",\"104\":\"yes\",\"105\":\"yes\",\"106\":\"yes\",\"107\":\"yes\",\"108\":\"yes\",\"109\":\"yes\",\"110\":\"yes\",\"111\":\"yes\",\"112\":\"yes\",\"113\":\"yes\",\"114\":\"yes\",\"115\":\"yes\",\"116\":\"yes\",\"117\":\"yes\",\"118\":\"yes\",\"119\":\"yes\",\"120\":\"yes\",\"121\":\"yes\",\"122\":\"yes\",\"123\":\"yes\",\"124\":\"yes\",\"125\":\"yes\",\"126\":\"yes\",\"127\":\"yes\",\"128\":\"yes\",\"129\":\"yes\",\"130\":\"yes\",\"131\":\"yes\",\"132\":\"yes\",\"133\":\"yes\",\"134\":\"yes\",\"135\":\"yes\",\"136\":\"yes\",\"137\":\"yes\",\"138\":\"yes\",\"139\":\"yes\",\"140\":\"yes\",\"141\":\"yes\",\"142\":\"yes\",\"143\":\"yes\",\"144\":\"yes\",\"145\":\"yes\",\"146\":\"yes\",\"147\":\"yes\",\"148\":\"yes\",\"149\":\"yes\",\"150\":\"no\",\"151\":\"no\",\"152\":\"no\",\"153\":\"no\",\"154\":\"no\",\"155\":\"no\",\"156\":\"no\",\"157\":\"no\",\"158\":\"no\",\"159\":\"no\",\"160\":\"no\",\"161\":\"no\",\"162\":\"no\",\"163\":\"no\",\"164\":\"no\",\"165\":\"no\"}}"
] |
NS
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QRData
| |
coverbench
|
The lower bound of the 95% confidence interval for the effect of fish oils on heart attacks for patients well-represented by those in the study is 0.024.
|
[
"The results in fish_oil_18.csv summarize each of the health outcomes for an experiment where 12,933 subjects received a 1g fish oil supplement daily and 12,938 received a placebo daily. The experiment's duration was 5-years. The first row represents the treatment group and the second row represents the placebo group.\n\nVariables\nmajor_cardio_event - Major cardiovascular event. (Primary end point.)\ncardio_event_expanded - Cardiovascular event in expanded composite endpoint.\nmyocardioal_infarction - Total myocardial infarction. (Heart attack.)\nstroke - Total stroke.\ncardio_death - Death from cardiovascular causes.\nPCI - Percutaneous coronary intervention.\nCABG - Coronary artery bypass graft.\ntotal_coronary_heart_disease - Total coronary heart disease.\nischemic_stroke - Ischemic stroke.\nhemorrhagic_stroke - Hemorrhagic stroke.\nchd_death - Death from coronary heart disease.\nmyocardial_infarction_death - Death from myocardial infarction.\nstroke_death - Death from stroke.\ninvasive_cancer - Invasive cancer of any type. (Primary end point.)\nbreast_cancer - Breast cancer.\nprostate_cancer - Prostate cancer.\ncolorectal_cancer - Colorectal cancer.\ncancer_death - Death from cancer.\ndeath - Death from any cause.\nmajor_cardio_event_after_2y - Major cardiovascular event, excluding the first 2 years of follow-up.\nmyocardial_infarction_after_2y - Total myocardial infarction, excluding the first 2 years of follow-up.\ninvasive_cancer_after_2y - Invasive cancer of any type, excluding the first 2 years of follow-up.\ncancer_death_after_2y - Death from cancer, excluding the first 2 years of follow-up.\ndeath_after_2y - Death from any cause, excluding the first 2 years of follow-up.\n\nfish_oil_18.csv\n{\"major_cardio_event.major_cardio_event\":{\"0\":386,\"1\":419},\"major_cardio_event.no_event\":{\"0\":12547,\"1\":12519},\"cardio_event_expanded.cardio_event_expanded\":{\"0\":527,\"1\":567},\"cardio_event_expanded.no_event\":{\"0\":12406,\"1\":12371},\"myocardioal_infarction.myocardioal_infarction\":{\"0\":145,\"1\":200},\"myocardioal_infarction.no_event\":{\"0\":12788,\"1\":12738},\"stroke.stroke\":{\"0\":148,\"1\":142},\"stroke.no_event\":{\"0\":12785,\"1\":12796},\"cardio_death.cardio_death\":{\"0\":142,\"1\":148},\"cardio_death.no_event\":{\"0\":12791,\"1\":12790},\"PCI.PCI\":{\"0\":162,\"1\":208},\"PCI.not_performed\":{\"0\":12771,\"1\":12730},\"CABG.CABG\":{\"0\":85,\"1\":86},\"CABG.not_performed\":{\"0\":12848,\"1\":12852},\"total_coronary_heart_disease.total_coronary_heart_disease\":{\"0\":308,\"1\":370},\"total_coronary_heart_disease.not_present\":{\"0\":12625,\"1\":12568},\"ischemic_stroke.ischemic_stroke\":{\"0\":111,\"1\":116},\"ischemic_stroke.not_present\":{\"0\":12822,\"1\":12822},\"hemorrhagic_stroke.hemorrhagic_stroke\":{\"0\":25,\"1\":19},\"hemorrhagic_stroke.not_present\":{\"0\":12908,\"1\":12919},\"chd_death.chd_death\":{\"0\":37,\"1\":49},\"chd_death.not_present\":{\"0\":12896,\"1\":12889},\"myocardial_infarction_death.myocardial_infarction_death\":{\"0\":13,\"1\":26},\"myocardial_infarction_death.not_present\":{\"0\":12920,\"1\":12912},\"stroke_death.stroke_death\":{\"0\":22,\"1\":20},\"stroke_death.not_present\":{\"0\":12911,\"1\":12918},\"invasive_cancer.invasive_cancer\":{\"0\":820,\"1\":797},\"invasive_cancer.not_present\":{\"0\":12113,\"1\":12141},\"breast_cancer.breast_cancer\":{\"0\":117,\"1\":129},\"breast_cancer.not_present\":{\"0\":12816,\"1\":12809},\"prostate_cancer.prostate_cancer\":{\"0\":219,\"1\":192},\"prostate_cancer.not_present\":{\"0\":12714,\"1\":12746},\"colorectal_cancer.colorectal_cancer\":{\"0\":54,\"1\":44},\"colorectal_cancer.not_present\":{\"0\":12879,\"1\":12894},\"cancer_death.cancer_death\":{\"0\":168,\"1\":173},\"cancer_death.no_cancer_death\":{\"0\":12765,\"1\":12765},\"death.death\":{\"0\":493,\"1\":485},\"death.alive\":{\"0\":12440,\"1\":12453},\"major_cardio_event_after_2y.major_cardio_event_after_2y\":{\"0\":269,\"1\":301},\"major_cardio_event_after_2y.no_event\":{\"0\":12664,\"1\":12637},\"myocardial_infarction_after_2y.myocardial_infarction_after_2y\":{\"0\":94,\"1\":131},\"myocardial_infarction_after_2y.no_event\":{\"0\":12839,\"1\":12807},\"invasive_cancer_after_2y.invasive_cancer_after_2y\":{\"0\":536,\"1\":476},\"invasive_cancer_after_2y.no_event\":{\"0\":12397,\"1\":12462},\"cancer_death_after_2y.cancer_death_after_2y\":{\"0\":126,\"1\":135},\"cancer_death_after_2y.no_event\":{\"0\":12807,\"1\":12803},\"death_after_2y.death_after_2y\":{\"0\":371,\"1\":381},\"death_after_2y.remained_alive\":{\"0\":12562,\"1\":12557}}"
] |
NS
|
QRData
| |
coverbench
|
The first quartile of the infant death rate is approximately 4.08.
|
[
"This CSV file infmortrate.csv gives the number of deaths of infants under one year old in 2012 per 1,000 live births in the same year. This rate is often used as an indicator of the level of health in a country.\n\ninfmortrate.csv\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>country</th>\n <th>inf_mort_rate</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Afghanistan</td>\n <td>121.63</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Niger</td>\n <td>109.98</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Mali</td>\n <td>109.08</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Somalia</td>\n <td>103.72</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Central African Republic</td>\n <td>97.17</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Guinea-Bissau</td>\n <td>94.40</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Chad</td>\n <td>93.61</td>\n </tr>\n <tr>\n <th>7</th>\n <td>Angola</td>\n <td>83.53</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Burkina Faso</td>\n <td>79.84</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Malawi</td>\n <td>79.02</td>\n </tr>\n <tr>\n <th>10</th>\n <td>Mozambique</td>\n <td>76.85</td>\n </tr>\n <tr>\n <th>11</th>\n <td>Sierra Leone</td>\n <td>76.64</td>\n </tr>\n <tr>\n <th>12</th>\n <td>Congo, Democratic Republic of the</td>\n <td>76.63</td>\n </tr>\n <tr>\n <th>13</th>\n <td>Ethiopia</td>\n <td>75.29</td>\n </tr>\n <tr>\n <th>14</th>\n <td>Equatorial Guinea</td>\n <td>75.18</td>\n </tr>\n <tr>\n <th>15</th>\n <td>Nigeria</td>\n <td>74.36</td>\n </tr>\n <tr>\n <th>16</th>\n <td>Congo, Republic of the</td>\n <td>74.22</td>\n </tr>\n <tr>\n <th>17</th>\n <td>Liberia</td>\n <td>72.71</td>\n </tr>\n <tr>\n <th>18</th>\n <td>South Sudan</td>\n <td>71.83</td>\n </tr>\n <tr>\n <th>19</th>\n <td>Gambia, The</td>\n <td>69.58</td>\n </tr>\n <tr>\n <th>20</th>\n <td>Comoros</td>\n <td>68.97</td>\n </tr>\n <tr>\n <th>21</th>\n <td>Tanzania</td>\n <td>65.74</td>\n </tr>\n <tr>\n <th>22</th>\n <td>Zambia</td>\n <td>64.61</td>\n </tr>\n <tr>\n <th>23</th>\n <td>Cote d'Ivoire</td>\n <td>63.20</td>\n </tr>\n <tr>\n <th>24</th>\n <td>Rwanda</td>\n <td>62.51</td>\n </tr>\n <tr>\n <th>25</th>\n <td>Pakistan</td>\n <td>61.27</td>\n </tr>\n <tr>\n <th>26</th>\n <td>Uganda</td>\n <td>61.22</td>\n </tr>\n <tr>\n <th>27</th>\n <td>Burundi</td>\n <td>60.32</td>\n </tr>\n <tr>\n <th>28</th>\n <td>Benin</td>\n <td>60.03</td>\n </tr>\n <tr>\n <th>29</th>\n <td>Cameroon</td>\n <td>59.70</td>\n </tr>\n <tr>\n <th>30</th>\n <td>Swaziland</td>\n <td>59.57</td>\n </tr>\n <tr>\n <th>31</th>\n <td>Guinea</td>\n <td>59.04</td>\n </tr>\n <tr>\n <th>32</th>\n <td>Western Sahara</td>\n <td>58.96</td>\n </tr>\n <tr>\n <th>33</th>\n <td>Mauritania</td>\n <td>58.93</td>\n </tr>\n <tr>\n <th>34</th>\n <td>Laos</td>\n <td>57.77</td>\n </tr>\n <tr>\n <th>35</th>\n <td>Sudan</td>\n <td>55.63</td>\n </tr>\n <tr>\n <th>36</th>\n <td>Senegal</td>\n <td>55.16</td>\n </tr>\n <tr>\n <th>37</th>\n <td>Cambodia</td>\n <td>54.08</td>\n </tr>\n <tr>\n <th>38</th>\n <td>Yemen</td>\n <td>53.50</td>\n </tr>\n <tr>\n <th>39</th>\n <td>Lesotho</td>\n <td>53.44</td>\n </tr>\n <tr>\n <th>40</th>\n <td>Djibouti</td>\n <td>53.31</td>\n </tr>\n <tr>\n <th>41</th>\n <td>Haiti</td>\n <td>52.44</td>\n </tr>\n <tr>\n <th>42</th>\n <td>Sao Tome and Principe</td>\n <td>51.83</td>\n </tr>\n <tr>\n <th>43</th>\n <td>Madagascar</td>\n <td>50.09</td>\n </tr>\n <tr>\n <th>44</th>\n <td>Togo</td>\n <td>49.87</td>\n </tr>\n <tr>\n <th>45</th>\n <td>Gabon</td>\n <td>49.00</td>\n </tr>\n <tr>\n <th>46</th>\n <td>Bangladesh</td>\n <td>48.99</td>\n </tr>\n <tr>\n <th>47</th>\n <td>Burma</td>\n <td>47.74</td>\n </tr>\n <tr>\n <th>48</th>\n <td>Ghana</td>\n <td>47.26</td>\n </tr>\n <tr>\n <th>49</th>\n <td>India</td>\n <td>46.07</td>\n </tr>\n <tr>\n <th>50</th>\n <td>Namibia</td>\n <td>45.61</td>\n </tr>\n <tr>\n <th>51</th>\n <td>Vanuatu</td>\n <td>45.57</td>\n </tr>\n <tr>\n <th>52</th>\n <td>Kenya</td>\n <td>43.61</td>\n </tr>\n <tr>\n <th>53</th>\n <td>Nepal</td>\n <td>43.13</td>\n </tr>\n <tr>\n <th>54</th>\n <td>South Africa</td>\n <td>42.67</td>\n </tr>\n <tr>\n <th>55</th>\n <td>Bhutan</td>\n <td>42.17</td>\n </tr>\n <tr>\n <th>56</th>\n <td>Papua New Guinea</td>\n <td>42.05</td>\n </tr>\n <tr>\n <th>57</th>\n <td>Iran</td>\n <td>41.11</td>\n </tr>\n <tr>\n <th>58</th>\n <td>Bolivia</td>\n <td>40.94</td>\n </tr>\n <tr>\n <th>59</th>\n <td>Turkmenistan</td>\n <td>40.89</td>\n </tr>\n <tr>\n <th>60</th>\n <td>Eritrea</td>\n <td>40.34</td>\n </tr>\n <tr>\n <th>61</th>\n <td>Iraq</td>\n <td>40.25</td>\n </tr>\n <tr>\n <th>62</th>\n <td>Kiribati</td>\n <td>37.68</td>\n </tr>\n <tr>\n <th>63</th>\n <td>Tajikistan</td>\n <td>37.33</td>\n </tr>\n <tr>\n <th>64</th>\n <td>Timor-Leste</td>\n <td>36.78</td>\n </tr>\n <tr>\n <th>65</th>\n <td>Mongolia</td>\n <td>36.00</td>\n </tr>\n <tr>\n <th>66</th>\n <td>Guyana</td>\n <td>35.59</td>\n </tr>\n <tr>\n <th>67</th>\n <td>Tuvalu</td>\n <td>33.55</td>\n </tr>\n <tr>\n <th>68</th>\n <td>Kyrgyzstan</td>\n <td>30.78</td>\n </tr>\n <tr>\n <th>69</th>\n <td>Suriname</td>\n <td>28.94</td>\n </tr>\n <tr>\n <th>70</th>\n <td>Azerbaijan</td>\n <td>28.76</td>\n </tr>\n <tr>\n <th>71</th>\n <td>Zimbabwe</td>\n <td>28.23</td>\n </tr>\n <tr>\n <th>72</th>\n <td>Indonesia</td>\n <td>26.99</td>\n </tr>\n <tr>\n <th>73</th>\n <td>Trinidad and Tobago</td>\n <td>26.73</td>\n </tr>\n <tr>\n <th>74</th>\n <td>Morocco</td>\n <td>26.49</td>\n </tr>\n <tr>\n <th>75</th>\n <td>Maldives</td>\n <td>26.46</td>\n </tr>\n <tr>\n <th>76</th>\n <td>Korea, North</td>\n <td>26.21</td>\n </tr>\n <tr>\n <th>77</th>\n <td>Cape Verde</td>\n <td>26.02</td>\n </tr>\n <tr>\n <th>78</th>\n <td>Guatemala</td>\n <td>25.16</td>\n </tr>\n <tr>\n <th>79</th>\n <td>Tunisia</td>\n <td>24.98</td>\n </tr>\n <tr>\n <th>80</th>\n <td>Algeria</td>\n <td>24.90</td>\n </tr>\n <tr>\n <th>81</th>\n <td>Egypt</td>\n <td>24.23</td>\n </tr>\n <tr>\n <th>82</th>\n <td>Micronesia, Federated States of</td>\n <td>23.51</td>\n </tr>\n <tr>\n <th>83</th>\n <td>Turkey</td>\n <td>23.07</td>\n </tr>\n <tr>\n <th>84</th>\n <td>Kazakhstan</td>\n <td>23.06</td>\n </tr>\n <tr>\n <th>85</th>\n <td>Marshall Islands</td>\n <td>22.93</td>\n </tr>\n <tr>\n <th>86</th>\n <td>Paraguay</td>\n <td>22.24</td>\n </tr>\n <tr>\n <th>87</th>\n <td>Nicaragua</td>\n <td>21.86</td>\n </tr>\n <tr>\n <th>88</th>\n <td>Samoa</td>\n <td>21.85</td>\n </tr>\n <tr>\n <th>89</th>\n <td>Peru</td>\n <td>21.50</td>\n </tr>\n <tr>\n <th>90</th>\n <td>Belize</td>\n <td>21.37</td>\n </tr>\n <tr>\n <th>91</th>\n <td>Dominican Republic</td>\n <td>21.30</td>\n </tr>\n <tr>\n <th>92</th>\n <td>Uzbekistan</td>\n <td>21.20</td>\n </tr>\n <tr>\n <th>93</th>\n <td>Brazil</td>\n <td>20.50</td>\n </tr>\n <tr>\n <th>94</th>\n <td>Vietnam</td>\n <td>20.24</td>\n </tr>\n <tr>\n <th>95</th>\n <td>Venezuela</td>\n <td>20.18</td>\n </tr>\n <tr>\n <th>96</th>\n <td>Honduras</td>\n <td>19.85</td>\n </tr>\n <tr>\n <th>97</th>\n <td>El Salvador</td>\n <td>19.66</td>\n </tr>\n <tr>\n <th>98</th>\n <td>Libya</td>\n <td>19.34</td>\n </tr>\n <tr>\n <th>99</th>\n <td>Ecuador</td>\n <td>19.06</td>\n </tr>\n <tr>\n <th>100</th>\n <td>Philippines</td>\n <td>18.75</td>\n </tr>\n <tr>\n <th>101</th>\n <td>Armenia</td>\n <td>18.21</td>\n </tr>\n <tr>\n <th>102</th>\n <td>Solomon Islands</td>\n <td>17.25</td>\n </tr>\n <tr>\n <th>103</th>\n <td>Mexico</td>\n <td>16.77</td>\n </tr>\n <tr>\n <th>104</th>\n <td>Gaza Strip</td>\n <td>16.55</td>\n </tr>\n <tr>\n <th>105</th>\n <td>Bulgaria</td>\n <td>16.13</td>\n </tr>\n <tr>\n <th>106</th>\n <td>Colombia</td>\n <td>15.92</td>\n </tr>\n <tr>\n <th>107</th>\n <td>Thailand</td>\n <td>15.90</td>\n </tr>\n <tr>\n <th>108</th>\n <td>Jordan</td>\n <td>15.83</td>\n </tr>\n <tr>\n <th>109</th>\n <td>Saint Helena, Ascension, and Tristan da Cunha</td>\n <td>15.80</td>\n </tr>\n <tr>\n <th>110</th>\n <td>China</td>\n <td>15.62</td>\n </tr>\n <tr>\n <th>111</th>\n <td>Saudi Arabia</td>\n <td>15.61</td>\n </tr>\n <tr>\n <th>112</th>\n <td>Lebanon</td>\n <td>15.32</td>\n </tr>\n <tr>\n <th>113</th>\n <td>Cook Islands</td>\n <td>15.30</td>\n </tr>\n <tr>\n <th>114</th>\n <td>Syria</td>\n <td>15.12</td>\n </tr>\n <tr>\n <th>115</th>\n <td>Oman</td>\n <td>14.95</td>\n </tr>\n <tr>\n <th>116</th>\n <td>Montserrat</td>\n <td>14.69</td>\n </tr>\n <tr>\n <th>117</th>\n <td>Georgia</td>\n <td>14.68</td>\n </tr>\n <tr>\n <th>118</th>\n <td>Malaysia</td>\n <td>14.57</td>\n </tr>\n <tr>\n <th>119</th>\n <td>West Bank</td>\n <td>14.47</td>\n </tr>\n <tr>\n <th>120</th>\n <td>British Virgin Islands</td>\n <td>14.43</td>\n </tr>\n <tr>\n <th>121</th>\n <td>Jamaica</td>\n <td>14.30</td>\n </tr>\n <tr>\n <th>122</th>\n <td>Antigua and Barbuda</td>\n <td>14.17</td>\n </tr>\n <tr>\n <th>123</th>\n <td>Albania</td>\n <td>14.12</td>\n </tr>\n <tr>\n <th>124</th>\n <td>Saint Vincent and the Grenadines</td>\n <td>13.86</td>\n </tr>\n <tr>\n <th>125</th>\n <td>Moldova</td>\n <td>13.65</td>\n </tr>\n <tr>\n <th>126</th>\n <td>Tonga</td>\n <td>13.21</td>\n </tr>\n <tr>\n <th>127</th>\n <td>Bahamas, The</td>\n <td>13.09</td>\n </tr>\n <tr>\n <th>128</th>\n <td>Aruba</td>\n <td>12.51</td>\n </tr>\n <tr>\n <th>129</th>\n <td>Saint Lucia</td>\n <td>12.39</td>\n </tr>\n <tr>\n <th>130</th>\n <td>Dominica</td>\n <td>12.38</td>\n </tr>\n <tr>\n <th>131</th>\n <td>Palau</td>\n <td>12.10</td>\n </tr>\n <tr>\n <th>132</th>\n <td>Barbados</td>\n <td>11.63</td>\n </tr>\n <tr>\n <th>133</th>\n <td>Turks and Caicos Islands</td>\n <td>11.63</td>\n </tr>\n <tr>\n <th>134</th>\n <td>United Arab Emirates</td>\n <td>11.59</td>\n </tr>\n <tr>\n <th>135</th>\n <td>Seychelles</td>\n <td>11.35</td>\n </tr>\n <tr>\n <th>136</th>\n <td>Panama</td>\n <td>11.32</td>\n </tr>\n <tr>\n <th>137</th>\n <td>Mauritius</td>\n <td>11.20</td>\n </tr>\n <tr>\n <th>138</th>\n <td>Brunei</td>\n <td>11.15</td>\n </tr>\n <tr>\n <th>139</th>\n <td>Grenada</td>\n <td>11.12</td>\n </tr>\n <tr>\n <th>140</th>\n <td>Fiji</td>\n <td>10.73</td>\n </tr>\n <tr>\n <th>141</th>\n <td>Romania</td>\n <td>10.73</td>\n </tr>\n <tr>\n <th>142</th>\n <td>Argentina</td>\n <td>10.52</td>\n </tr>\n <tr>\n <th>143</th>\n <td>Botswana</td>\n <td>10.49</td>\n </tr>\n <tr>\n <th>144</th>\n <td>Bahrain</td>\n <td>10.20</td>\n </tr>\n <tr>\n <th>145</th>\n <td>Russia</td>\n <td>9.88</td>\n </tr>\n <tr>\n <th>146</th>\n <td>Greenland</td>\n <td>9.83</td>\n </tr>\n <tr>\n <th>147</th>\n <td>Sri Lanka</td>\n <td>9.47</td>\n </tr>\n <tr>\n <th>148</th>\n <td>Uruguay</td>\n <td>9.44</td>\n </tr>\n <tr>\n <th>149</th>\n <td>Saint Kitts and Nevis</td>\n <td>9.43</td>\n </tr>\n <tr>\n <th>150</th>\n <td>American Samoa</td>\n <td>9.42</td>\n </tr>\n <tr>\n <th>151</th>\n <td>Costa Rica</td>\n <td>9.20</td>\n </tr>\n <tr>\n <th>152</th>\n <td>Cyprus</td>\n <td>9.05</td>\n </tr>\n <tr>\n <th>153</th>\n <td>Nauru</td>\n <td>8.51</td>\n </tr>\n <tr>\n <th>154</th>\n <td>Bosnia and Herzegovina</td>\n <td>8.47</td>\n </tr>\n <tr>\n <th>155</th>\n <td>Ukraine</td>\n <td>8.38</td>\n </tr>\n <tr>\n <th>156</th>\n <td>Macedonia</td>\n <td>8.32</td>\n </tr>\n <tr>\n <th>157</th>\n <td>Latvia</td>\n <td>8.24</td>\n </tr>\n <tr>\n <th>158</th>\n <td>Puerto Rico</td>\n <td>7.90</td>\n </tr>\n <tr>\n <th>159</th>\n <td>Kuwait</td>\n <td>7.87</td>\n </tr>\n <tr>\n <th>160</th>\n <td>Chile</td>\n <td>7.36</td>\n </tr>\n <tr>\n <th>161</th>\n <td>Saint Pierre and Miquelon</td>\n <td>7.29</td>\n </tr>\n <tr>\n <th>162</th>\n <td>Virgin Islands</td>\n <td>7.09</td>\n </tr>\n <tr>\n <th>163</th>\n <td>Estonia</td>\n <td>6.94</td>\n </tr>\n <tr>\n <th>164</th>\n <td>Qatar</td>\n <td>6.81</td>\n </tr>\n <tr>\n <th>165</th>\n <td>Gibraltar</td>\n <td>6.55</td>\n </tr>\n <tr>\n <th>166</th>\n <td>Cayman Islands</td>\n <td>6.49</td>\n </tr>\n <tr>\n <th>167</th>\n <td>Slovakia</td>\n <td>6.47</td>\n </tr>\n <tr>\n <th>168</th>\n <td>Poland</td>\n <td>6.42</td>\n </tr>\n <tr>\n <th>169</th>\n <td>Serbia</td>\n <td>6.40</td>\n </tr>\n <tr>\n <th>170</th>\n <td>Lithuania</td>\n <td>6.18</td>\n </tr>\n <tr>\n <th>171</th>\n <td>Belarus</td>\n <td>6.16</td>\n </tr>\n <tr>\n <th>172</th>\n <td>Croatia</td>\n <td>6.06</td>\n </tr>\n <tr>\n <th>173</th>\n <td>United States</td>\n <td>5.98</td>\n </tr>\n <tr>\n <th>174</th>\n <td>FaroeIslands</td>\n <td>5.94</td>\n </tr>\n <tr>\n <th>175</th>\n <td>NorthernMariana Islands</td>\n <td>5.69</td>\n </tr>\n <tr>\n <th>176</th>\n <td>New Caledonia</td>\n <td>5.62</td>\n </tr>\n <tr>\n <th>177</th>\n <td>Hungary</td>\n <td>5.24</td>\n </tr>\n <tr>\n <th>178</th>\n <td>Taiwan</td>\n <td>5.10</td>\n </tr>\n <tr>\n <th>179</th>\n <td>Greece</td>\n <td>4.92</td>\n </tr>\n <tr>\n <th>180</th>\n <td>French Polynesia</td>\n <td>4.88</td>\n </tr>\n <tr>\n <th>181</th>\n <td>Canada</td>\n <td>4.85</td>\n </tr>\n <tr>\n <th>182</th>\n <td>Cuba</td>\n <td>4.83</td>\n </tr>\n <tr>\n <th>183</th>\n <td>New Zealand</td>\n <td>4.72</td>\n </tr>\n <tr>\n <th>184</th>\n <td>San Marino</td>\n <td>4.65</td>\n </tr>\n <tr>\n <th>185</th>\n <td>Wallis and Futuna</td>\n <td>4.61</td>\n </tr>\n <tr>\n <th>186</th>\n <td>Portugal</td>\n <td>4.60</td>\n </tr>\n <tr>\n <th>187</th>\n <td>United Kingdom</td>\n <td>4.56</td>\n </tr>\n <tr>\n <th>188</th>\n <td>Australia</td>\n <td>4.55</td>\n </tr>\n <tr>\n <th>189</th>\n <td>European Union</td>\n <td>4.49</td>\n </tr>\n <tr>\n <th>190</th>\n <td>Liechtenstein</td>\n <td>4.39</td>\n </tr>\n <tr>\n <th>191</th>\n <td>Luxembourg</td>\n <td>4.39</td>\n </tr>\n <tr>\n <th>192</th>\n <td>Belgium</td>\n <td>4.28</td>\n </tr>\n <tr>\n <th>193</th>\n <td>Isle of Man</td>\n <td>4.27</td>\n </tr>\n <tr>\n <th>194</th>\n <td>Austria</td>\n <td>4.26</td>\n </tr>\n <tr>\n <th>195</th>\n <td>Denmark</td>\n <td>4.19</td>\n </tr>\n <tr>\n <th>196</th>\n <td>Slovenia</td>\n <td>4.12</td>\n </tr>\n <tr>\n <th>197</th>\n <td>Korea, South</td>\n <td>4.08</td>\n </tr>\n <tr>\n <th>198</th>\n <td>Israel</td>\n <td>4.07</td>\n </tr>\n <tr>\n <th>199</th>\n <td>Switzerland</td>\n <td>4.03</td>\n </tr>\n <tr>\n <th>200</th>\n <td>Jersey</td>\n <td>3.94</td>\n </tr>\n <tr>\n <th>201</th>\n <td>Ireland</td>\n <td>3.81</td>\n </tr>\n <tr>\n <th>202</th>\n <td>Andorra</td>\n <td>3.76</td>\n </tr>\n <tr>\n <th>203</th>\n <td>Netherlands</td>\n <td>3.73</td>\n </tr>\n <tr>\n <th>204</th>\n <td>Czech Republic</td>\n <td>3.70</td>\n </tr>\n <tr>\n <th>205</th>\n <td>Malta</td>\n <td>3.65</td>\n </tr>\n <tr>\n <th>206</th>\n <td>Guernsey</td>\n <td>3.52</td>\n </tr>\n <tr>\n <th>207</th>\n <td>Germany</td>\n <td>3.51</td>\n </tr>\n <tr>\n <th>208</th>\n <td>Norway</td>\n <td>3.50</td>\n </tr>\n <tr>\n <th>209</th>\n <td>Anguilla</td>\n <td>3.44</td>\n </tr>\n <tr>\n <th>210</th>\n <td>Finland</td>\n <td>3.40</td>\n </tr>\n <tr>\n <th>211</th>\n <td>France</td>\n <td>3.37</td>\n </tr>\n <tr>\n <th>212</th>\n <td>Spain</td>\n <td>3.37</td>\n </tr>\n <tr>\n <th>213</th>\n <td>Italy</td>\n <td>3.36</td>\n </tr>\n <tr>\n <th>214</th>\n <td>Iceland</td>\n <td>3.18</td>\n </tr>\n <tr>\n <th>215</th>\n <td>Macau</td>\n <td>3.17</td>\n </tr>\n <tr>\n <th>216</th>\n <td>Hong Kong</td>\n <td>2.90</td>\n </tr>\n <tr>\n <th>217</th>\n <td>Sweden</td>\n <td>2.74</td>\n </tr>\n <tr>\n <th>218</th>\n <td>Singapore</td>\n <td>2.65</td>\n </tr>\n <tr>\n <th>219</th>\n <td>Bermuda</td>\n <td>2.47</td>\n </tr>\n <tr>\n <th>220</th>\n <td>Japan</td>\n <td>2.21</td>\n </tr>\n <tr>\n <th>221</th>\n <td>Monaco</td>\n <td>1.80</td>\n </tr>\n </tbody>\n</table>"
] |
NS
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QRData
| |
coverbench
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The lower bound of the 95% confidence interval for the effect of fish oils on heart attacks for patients well-represented by those in the study is -0.004.
|
[
"The results in fish_oil_18.csv summarize each of the health outcomes for an experiment where 12,933 subjects received a 1g fish oil supplement daily and 12,938 received a placebo daily. The experiment's duration was 5-years. The first row represents the treatment group and the second row represents the placebo group.\n\nVariables\nmajor_cardio_event - Major cardiovascular event. (Primary end point.)\ncardio_event_expanded - Cardiovascular event in expanded composite endpoint.\nmyocardioal_infarction - Total myocardial infarction. (Heart attack.)\nstroke - Total stroke.\ncardio_death - Death from cardiovascular causes.\nPCI - Percutaneous coronary intervention.\nCABG - Coronary artery bypass graft.\ntotal_coronary_heart_disease - Total coronary heart disease.\nischemic_stroke - Ischemic stroke.\nhemorrhagic_stroke - Hemorrhagic stroke.\nchd_death - Death from coronary heart disease.\nmyocardial_infarction_death - Death from myocardial infarction.\nstroke_death - Death from stroke.\ninvasive_cancer - Invasive cancer of any type. (Primary end point.)\nbreast_cancer - Breast cancer.\nprostate_cancer - Prostate cancer.\ncolorectal_cancer - Colorectal cancer.\ncancer_death - Death from cancer.\ndeath - Death from any cause.\nmajor_cardio_event_after_2y - Major cardiovascular event, excluding the first 2 years of follow-up.\nmyocardial_infarction_after_2y - Total myocardial infarction, excluding the first 2 years of follow-up.\ninvasive_cancer_after_2y - Invasive cancer of any type, excluding the first 2 years of follow-up.\ncancer_death_after_2y - Death from cancer, excluding the first 2 years of follow-up.\ndeath_after_2y - Death from any cause, excluding the first 2 years of follow-up.\n\nfish_oil_18.csv\n{\"major_cardio_event.major_cardio_event\":{\"0\":386,\"1\":419},\"major_cardio_event.no_event\":{\"0\":12547,\"1\":12519},\"cardio_event_expanded.cardio_event_expanded\":{\"0\":527,\"1\":567},\"cardio_event_expanded.no_event\":{\"0\":12406,\"1\":12371},\"myocardioal_infarction.myocardioal_infarction\":{\"0\":145,\"1\":200},\"myocardioal_infarction.no_event\":{\"0\":12788,\"1\":12738},\"stroke.stroke\":{\"0\":148,\"1\":142},\"stroke.no_event\":{\"0\":12785,\"1\":12796},\"cardio_death.cardio_death\":{\"0\":142,\"1\":148},\"cardio_death.no_event\":{\"0\":12791,\"1\":12790},\"PCI.PCI\":{\"0\":162,\"1\":208},\"PCI.not_performed\":{\"0\":12771,\"1\":12730},\"CABG.CABG\":{\"0\":85,\"1\":86},\"CABG.not_performed\":{\"0\":12848,\"1\":12852},\"total_coronary_heart_disease.total_coronary_heart_disease\":{\"0\":308,\"1\":370},\"total_coronary_heart_disease.not_present\":{\"0\":12625,\"1\":12568},\"ischemic_stroke.ischemic_stroke\":{\"0\":111,\"1\":116},\"ischemic_stroke.not_present\":{\"0\":12822,\"1\":12822},\"hemorrhagic_stroke.hemorrhagic_stroke\":{\"0\":25,\"1\":19},\"hemorrhagic_stroke.not_present\":{\"0\":12908,\"1\":12919},\"chd_death.chd_death\":{\"0\":37,\"1\":49},\"chd_death.not_present\":{\"0\":12896,\"1\":12889},\"myocardial_infarction_death.myocardial_infarction_death\":{\"0\":13,\"1\":26},\"myocardial_infarction_death.not_present\":{\"0\":12920,\"1\":12912},\"stroke_death.stroke_death\":{\"0\":22,\"1\":20},\"stroke_death.not_present\":{\"0\":12911,\"1\":12918},\"invasive_cancer.invasive_cancer\":{\"0\":820,\"1\":797},\"invasive_cancer.not_present\":{\"0\":12113,\"1\":12141},\"breast_cancer.breast_cancer\":{\"0\":117,\"1\":129},\"breast_cancer.not_present\":{\"0\":12816,\"1\":12809},\"prostate_cancer.prostate_cancer\":{\"0\":219,\"1\":192},\"prostate_cancer.not_present\":{\"0\":12714,\"1\":12746},\"colorectal_cancer.colorectal_cancer\":{\"0\":54,\"1\":44},\"colorectal_cancer.not_present\":{\"0\":12879,\"1\":12894},\"cancer_death.cancer_death\":{\"0\":168,\"1\":173},\"cancer_death.no_cancer_death\":{\"0\":12765,\"1\":12765},\"death.death\":{\"0\":493,\"1\":485},\"death.alive\":{\"0\":12440,\"1\":12453},\"major_cardio_event_after_2y.major_cardio_event_after_2y\":{\"0\":269,\"1\":301},\"major_cardio_event_after_2y.no_event\":{\"0\":12664,\"1\":12637},\"myocardial_infarction_after_2y.myocardial_infarction_after_2y\":{\"0\":94,\"1\":131},\"myocardial_infarction_after_2y.no_event\":{\"0\":12839,\"1\":12807},\"invasive_cancer_after_2y.invasive_cancer_after_2y\":{\"0\":536,\"1\":476},\"invasive_cancer_after_2y.no_event\":{\"0\":12397,\"1\":12462},\"cancer_death_after_2y.cancer_death_after_2y\":{\"0\":126,\"1\":135},\"cancer_death_after_2y.no_event\":{\"0\":12807,\"1\":12803},\"death_after_2y.death_after_2y\":{\"0\":371,\"1\":381},\"death_after_2y.remained_alive\":{\"0\":12562,\"1\":12557}}"
] |
NS
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QRData
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coverbench
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The correlation between the perceived competence of the Democratic candidate and the vote share differential of the Democratic candidate minus the Republican candidate is 0.721.
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[
"Several psychologists have reported the intriguing result of an experiment showing that facial appearance predicts election outcomes better than chance. In their experiment, the researchers briefly showed student subjects the black-and-white head shots of two candidates from a US congressional election (winner and runner-up). The exposure of subjects to facial pictures lasted less than a second, and the subjects were then asked to evaluate the two candidates in terms of their perceived competence.\nThe researchers used these competence measures to predict election outcomes. The key hypothesis is whether or not a within-a-second evaluation of facial appearance can predict election outcomes. The CSV data set, face.csv, contains the data from the experiment. Note that we include data only from subjects who did not know the candidates’ political parties, their policies, or even which candidate was the incumbent or challenger. They were simply making snap judgments about which candidate appeared more competent based on their facial expression alone.\n\nVariable Description\ncongress: session of Congress\nyear: year of the election\nstate: state of the election\nwinner: name of the winner\nloser: name of the runner-up\nw.party: party of the winner\nl.party: party of the loser\nd.votes: number of votes for the Democratic candidate\nr.votes: number of votes for the Republican candidate\nd.comp: competence measure for the Democratic candidate\nr.comp: competence measure for the Republican candidate\n\nface.csv\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>year</th>\n <th>state</th>\n <th>winner</th>\n <th>loser</th>\n <th>w.party</th>\n <th>l.party</th>\n <th>d.comp</th>\n <th>r.comp</th>\n <th>d.votes</th>\n <th>r.votes</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2000</td>\n <td>CA</td>\n <td>Feinstein</td>\n <td>Campbell</td>\n <td>D</td>\n <td>R</td>\n <td>0.564568</td>\n <td>0.435432</td>\n <td>5790154</td>\n <td>3779325</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2000</td>\n <td>DE</td>\n <td>Carper</td>\n <td>Roth</td>\n <td>D</td>\n <td>R</td>\n <td>0.341912</td>\n <td>0.658088</td>\n <td>181387</td>\n <td>142683</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2000</td>\n <td>FL</td>\n <td>Nelson</td>\n <td>McCollum</td>\n <td>D</td>\n <td>R</td>\n <td>0.612368</td>\n <td>0.387632</td>\n <td>2987644</td>\n <td>2703608</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2000</td>\n <td>GA</td>\n <td>Miller</td>\n <td>Mattingly</td>\n <td>D</td>\n <td>R</td>\n <td>0.541533</td>\n <td>0.458467</td>\n <td>1390428</td>\n <td>933698</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2000</td>\n <td>HI</td>\n <td>Akaka</td>\n <td>Carroll</td>\n <td>D</td>\n <td>R</td>\n <td>0.680232</td>\n <td>0.319768</td>\n <td>251130</td>\n <td>84657</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2000</td>\n <td>IN</td>\n <td>Lugar</td>\n <td>Johnson</td>\n <td>R</td>\n <td>D</td>\n <td>0.320502</td>\n <td>0.679498</td>\n <td>684242</td>\n <td>1419629</td>\n </tr>\n <tr>\n <th>6</th>\n <td>2000</td>\n <td>MA</td>\n <td>Kennedy</td>\n <td>Robinson</td>\n <td>D</td>\n <td>R</td>\n <td>0.403756</td>\n <td>0.596244</td>\n <td>1877439</td>\n <td>334721</td>\n </tr>\n <tr>\n <th>7</th>\n <td>2000</td>\n <td>MD</td>\n <td>Sarbanes</td>\n <td>Rappaport</td>\n <td>D</td>\n <td>R</td>\n <td>0.603015</td>\n <td>0.396985</td>\n <td>1171151</td>\n <td>678376</td>\n </tr>\n <tr>\n <th>8</th>\n <td>2000</td>\n <td>ME</td>\n <td>Snowe</td>\n <td>Lawrence</td>\n <td>R</td>\n <td>D</td>\n <td>0.538573</td>\n <td>0.461427</td>\n <td>197742</td>\n <td>431727</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2000</td>\n <td>MI</td>\n <td>Stabenow</td>\n <td>Abraham</td>\n <td>D</td>\n <td>R</td>\n <td>0.869215</td>\n <td>0.130785</td>\n <td>2034342</td>\n <td>1991507</td>\n </tr>\n <tr>\n <th>10</th>\n <td>2000</td>\n <td>MN</td>\n <td>Dayton</td>\n <td>Grams</td>\n <td>D</td>\n <td>R</td>\n <td>0.565335</td>\n <td>0.434665</td>\n <td>1180335</td>\n <td>1048224</td>\n </tr>\n <tr>\n <th>11</th>\n <td>2000</td>\n <td>MO</td>\n <td>Lott</td>\n <td>Brown</td>\n <td>R</td>\n <td>D</td>\n <td>0.594791</td>\n <td>0.405209</td>\n <td>296149</td>\n <td>621500</td>\n </tr>\n <tr>\n <th>12</th>\n <td>2000</td>\n <td>MT</td>\n <td>Burns</td>\n <td>Schweitzer</td>\n <td>R</td>\n <td>D</td>\n <td>0.248399</td>\n <td>0.751601</td>\n <td>194567</td>\n <td>208026</td>\n </tr>\n <tr>\n <th>13</th>\n <td>2000</td>\n <td>ND</td>\n <td>Conrad</td>\n <td>Sand</td>\n <td>D</td>\n <td>R</td>\n <td>0.649669</td>\n <td>0.350331</td>\n <td>177661</td>\n <td>111376</td>\n </tr>\n <tr>\n <th>14</th>\n <td>2000</td>\n <td>NE</td>\n <td>Nelson</td>\n <td>Stenberg</td>\n <td>D</td>\n <td>R</td>\n <td>0.245012</td>\n <td>0.754988</td>\n <td>330366</td>\n <td>318368</td>\n </tr>\n <tr>\n <th>15</th>\n <td>2000</td>\n <td>NM</td>\n <td>Bingaman</td>\n <td>Redmond</td>\n <td>D</td>\n <td>R</td>\n <td>0.754391</td>\n <td>0.245609</td>\n <td>363279</td>\n <td>225040</td>\n </tr>\n <tr>\n <th>16</th>\n <td>2000</td>\n <td>NV</td>\n <td>Ensign</td>\n <td>Bernstein</td>\n <td>R</td>\n <td>D</td>\n <td>0.390400</td>\n <td>0.609600</td>\n <td>238243</td>\n <td>330663</td>\n </tr>\n <tr>\n <th>17</th>\n <td>2000</td>\n <td>OH</td>\n <td>DeWine</td>\n <td>Celeste</td>\n <td>R</td>\n <td>D</td>\n <td>0.325593</td>\n <td>0.674407</td>\n <td>1539001</td>\n <td>2590952</td>\n </tr>\n <tr>\n <th>18</th>\n <td>2000</td>\n <td>PA</td>\n <td>Santorum</td>\n <td>Klink</td>\n <td>R</td>\n <td>D</td>\n <td>0.578942</td>\n <td>0.421058</td>\n <td>2134734</td>\n <td>2473118</td>\n </tr>\n <tr>\n <th>19</th>\n <td>2000</td>\n <td>RI</td>\n <td>Chafee</td>\n <td>Weygand</td>\n <td>R</td>\n <td>D</td>\n <td>0.437047</td>\n <td>0.562953</td>\n <td>165367</td>\n <td>226592</td>\n </tr>\n <tr>\n <th>20</th>\n <td>2000</td>\n <td>TN</td>\n <td>Frist</td>\n <td>Clark</td>\n <td>R</td>\n <td>D</td>\n <td>0.114507</td>\n <td>0.885493</td>\n <td>617684</td>\n <td>1247436</td>\n </tr>\n <tr>\n <th>21</th>\n <td>2000</td>\n <td>TX</td>\n <td>Hutchison</td>\n <td>Kelly</td>\n <td>R</td>\n <td>D</td>\n <td>0.204603</td>\n <td>0.795397</td>\n <td>2026184</td>\n <td>4080582</td>\n </tr>\n <tr>\n <th>22</th>\n <td>2000</td>\n <td>UT</td>\n <td>Hatch</td>\n <td>Howell</td>\n <td>R</td>\n <td>D</td>\n <td>0.442465</td>\n <td>0.557535</td>\n <td>241129</td>\n <td>501925</td>\n </tr>\n <tr>\n <th>23</th>\n <td>2000</td>\n <td>VA</td>\n <td>Allen</td>\n <td>Robb</td>\n <td>R</td>\n <td>D</td>\n <td>0.741696</td>\n <td>0.258304</td>\n <td>1289087</td>\n <td>1414577</td>\n </tr>\n <tr>\n <th>24</th>\n <td>2000</td>\n <td>VT</td>\n <td>Jeffords</td>\n <td>Flanagan</td>\n <td>R</td>\n <td>D</td>\n <td>0.457816</td>\n <td>0.542184</td>\n <td>72909</td>\n <td>188070</td>\n </tr>\n <tr>\n <th>25</th>\n <td>2000</td>\n <td>WA</td>\n <td>Cantwell</td>\n <td>Gorton</td>\n <td>D</td>\n <td>R</td>\n <td>0.614515</td>\n <td>0.385485</td>\n <td>1199437</td>\n <td>1197208</td>\n </tr>\n <tr>\n <th>26</th>\n <td>2000</td>\n <td>WI</td>\n <td>Kohl</td>\n <td>Gillespie</td>\n <td>D</td>\n <td>R</td>\n <td>0.614357</td>\n <td>0.385643</td>\n <td>1563565</td>\n <td>941132</td>\n </tr>\n <tr>\n <th>27</th>\n <td>2000</td>\n <td>WV</td>\n <td>Byrd</td>\n <td>Gallaher</td>\n <td>D</td>\n <td>R</td>\n <td>0.683657</td>\n <td>0.316343</td>\n <td>462566</td>\n <td>119958</td>\n </tr>\n <tr>\n <th>28</th>\n <td>2000</td>\n <td>WY</td>\n <td>Thomas</td>\n <td>Logan</td>\n <td>R</td>\n <td>D</td>\n <td>0.224063</td>\n <td>0.775937</td>\n <td>47039</td>\n <td>157316</td>\n </tr>\n <tr>\n <th>29</th>\n <td>2002</td>\n <td>AK</td>\n <td>Stevens</td>\n <td>Vondersaar</td>\n <td>R</td>\n <td>D</td>\n <td>0.333592</td>\n <td>0.666408</td>\n <td>20466</td>\n <td>155054</td>\n </tr>\n <tr>\n <th>30</th>\n <td>2002</td>\n <td>AL</td>\n <td>Sessions</td>\n <td>Parker</td>\n <td>R</td>\n <td>D</td>\n <td>0.551095</td>\n <td>0.448905</td>\n <td>537882</td>\n <td>790757</td>\n </tr>\n <tr>\n <th>31</th>\n <td>2002</td>\n <td>AR</td>\n <td>Pryor</td>\n <td>Hutchinson</td>\n <td>D</td>\n <td>R</td>\n <td>0.273883</td>\n <td>0.726117</td>\n <td>435346</td>\n <td>372909</td>\n </tr>\n <tr>\n <th>32</th>\n <td>2002</td>\n <td>CO</td>\n <td>Allard</td>\n <td>Strickland</td>\n <td>R</td>\n <td>D</td>\n <td>0.401537</td>\n <td>0.598463</td>\n <td>634227</td>\n <td>707349</td>\n </tr>\n <tr>\n <th>33</th>\n <td>2002</td>\n <td>DE</td>\n <td>Biden</td>\n <td>Clatworthy</td>\n <td>D</td>\n <td>R</td>\n <td>0.639578</td>\n <td>0.360422</td>\n <td>135170</td>\n <td>94716</td>\n </tr>\n <tr>\n <th>34</th>\n <td>2002</td>\n <td>GA</td>\n <td>Chambliss</td>\n <td>Cleland</td>\n <td>R</td>\n <td>D</td>\n <td>0.246164</td>\n <td>0.753836</td>\n <td>928905</td>\n <td>1068902</td>\n </tr>\n <tr>\n <th>35</th>\n <td>2002</td>\n <td>IA</td>\n <td>Harkin</td>\n <td>Ganske</td>\n <td>D</td>\n <td>R</td>\n <td>0.710124</td>\n <td>0.289876</td>\n <td>550156</td>\n <td>446209</td>\n </tr>\n <tr>\n <th>36</th>\n <td>2002</td>\n <td>ID</td>\n <td>Craig</td>\n <td>Blinken</td>\n <td>R</td>\n <td>D</td>\n <td>0.457524</td>\n <td>0.542476</td>\n <td>132845</td>\n <td>265849</td>\n </tr>\n <tr>\n <th>37</th>\n <td>2002</td>\n <td>IL</td>\n <td>Durbin</td>\n <td>Durkin</td>\n <td>D</td>\n <td>R</td>\n <td>0.428035</td>\n <td>0.571965</td>\n <td>2080411</td>\n <td>1320621</td>\n </tr>\n <tr>\n <th>38</th>\n <td>2002</td>\n <td>KY</td>\n <td>McConnell</td>\n <td>Weinberg</td>\n <td>R</td>\n <td>D</td>\n <td>0.562735</td>\n <td>0.437265</td>\n <td>400818</td>\n <td>726396</td>\n </tr>\n <tr>\n <th>39</th>\n <td>2002</td>\n <td>LA</td>\n <td>Landrieu</td>\n <td>Terrell</td>\n <td>D</td>\n <td>R</td>\n <td>0.766916</td>\n <td>0.233084</td>\n <td>563400</td>\n <td>327975</td>\n </tr>\n <tr>\n <th>40</th>\n <td>2002</td>\n <td>ME</td>\n <td>Collins</td>\n <td>Pingree</td>\n <td>R</td>\n <td>D</td>\n <td>0.327268</td>\n <td>0.672732</td>\n <td>205901</td>\n <td>290266</td>\n </tr>\n <tr>\n <th>41</th>\n <td>2002</td>\n <td>MI</td>\n <td>Levin</td>\n <td>Raczkowski</td>\n <td>D</td>\n <td>R</td>\n <td>0.703998</td>\n <td>0.296002</td>\n <td>1893788</td>\n <td>1184548</td>\n </tr>\n <tr>\n <th>42</th>\n <td>2002</td>\n <td>MN</td>\n <td>Coleman</td>\n <td>Mondale</td>\n <td>R</td>\n <td>D</td>\n <td>0.665846</td>\n <td>0.334154</td>\n <td>1029982</td>\n <td>1091253</td>\n </tr>\n <tr>\n <th>43</th>\n <td>2002</td>\n <td>MO</td>\n <td>Talent</td>\n <td>Carnahan</td>\n <td>R</td>\n <td>D</td>\n <td>0.396744</td>\n <td>0.603256</td>\n <td>911507</td>\n <td>934093</td>\n </tr>\n <tr>\n <th>44</th>\n <td>2002</td>\n <td>MT</td>\n <td>Baucus</td>\n <td>Taylor</td>\n <td>D</td>\n <td>R</td>\n <td>0.897228</td>\n <td>0.102772</td>\n <td>202908</td>\n <td>102766</td>\n </tr>\n <tr>\n <th>45</th>\n <td>2002</td>\n <td>NC</td>\n <td>Dole</td>\n <td>Bowles</td>\n <td>R</td>\n <td>D</td>\n <td>0.313285</td>\n <td>0.686715</td>\n <td>1034941</td>\n <td>1238203</td>\n </tr>\n <tr>\n <th>46</th>\n <td>2002</td>\n <td>NE</td>\n <td>Hagel</td>\n <td>Matulka</td>\n <td>R</td>\n <td>D</td>\n <td>0.213806</td>\n <td>0.786194</td>\n <td>68657</td>\n <td>391648</td>\n </tr>\n <tr>\n <th>47</th>\n <td>2002</td>\n <td>NH</td>\n <td>Sununu</td>\n <td>Shaheen</td>\n <td>R</td>\n <td>D</td>\n <td>0.526114</td>\n <td>0.473886</td>\n <td>206689</td>\n <td>225506</td>\n </tr>\n <tr>\n <th>48</th>\n <td>2002</td>\n <td>NJ</td>\n <td>Lautenber</td>\n <td>Forrester</td>\n <td>D</td>\n <td>R</td>\n <td>0.636229</td>\n <td>0.363771</td>\n <td>1112499</td>\n <td>909383</td>\n </tr>\n <tr>\n <th>49</th>\n <td>2002</td>\n <td>NM</td>\n <td>Domenici</td>\n <td>Tristani</td>\n <td>R</td>\n <td>D</td>\n <td>0.346114</td>\n <td>0.653886</td>\n <td>161409</td>\n <td>296935</td>\n </tr>\n <tr>\n <th>50</th>\n <td>2002</td>\n <td>OK</td>\n <td>Inhofe</td>\n <td>Walters</td>\n <td>R</td>\n <td>D</td>\n <td>0.449049</td>\n <td>0.550951</td>\n <td>369789</td>\n <td>578579</td>\n </tr>\n <tr>\n <th>51</th>\n <td>2002</td>\n <td>OR</td>\n <td>Smith</td>\n <td>Bradbury</td>\n <td>R</td>\n <td>D</td>\n <td>0.402232</td>\n <td>0.597768</td>\n <td>487995</td>\n <td>695345</td>\n </tr>\n <tr>\n <th>52</th>\n <td>2002</td>\n <td>RI</td>\n <td>Reed</td>\n <td>Tingle</td>\n <td>D</td>\n <td>R</td>\n <td>0.711679</td>\n <td>0.288321</td>\n <td>241315</td>\n <td>66613</td>\n </tr>\n <tr>\n <th>53</th>\n <td>2002</td>\n <td>SC</td>\n <td>Graham</td>\n <td>Sanders</td>\n <td>R</td>\n <td>D</td>\n <td>0.573504</td>\n <td>0.426496</td>\n <td>484798</td>\n <td>597789</td>\n </tr>\n <tr>\n <th>54</th>\n <td>2002</td>\n <td>SD</td>\n <td>Johnson</td>\n <td>Thune</td>\n <td>D</td>\n <td>R</td>\n <td>0.318235</td>\n <td>0.681765</td>\n <td>167481</td>\n <td>166954</td>\n </tr>\n <tr>\n <th>55</th>\n <td>2002</td>\n <td>TN</td>\n <td>Alexander</td>\n <td>Clement</td>\n <td>R</td>\n <td>D</td>\n <td>0.522373</td>\n <td>0.477627</td>\n <td>726510</td>\n <td>888223</td>\n </tr>\n <tr>\n <th>56</th>\n <td>2002</td>\n <td>TX</td>\n <td>Cornyn</td>\n <td>Kirk</td>\n <td>R</td>\n <td>D</td>\n <td>0.430373</td>\n <td>0.569627</td>\n <td>1946681</td>\n <td>2480991</td>\n </tr>\n <tr>\n <th>57</th>\n <td>2002</td>\n <td>WV</td>\n <td>Rockefell</td>\n <td>Wolfe</td>\n <td>D</td>\n <td>R</td>\n <td>0.638702</td>\n <td>0.361298</td>\n <td>271314</td>\n <td>158211</td>\n </tr>\n <tr>\n <th>58</th>\n <td>2002</td>\n <td>WY</td>\n <td>Enzi</td>\n <td>Corcoran</td>\n <td>R</td>\n <td>D</td>\n <td>0.175816</td>\n <td>0.824184</td>\n <td>49587</td>\n <td>133615</td>\n </tr>\n <tr>\n <th>59</th>\n <td>2004</td>\n <td>AL</td>\n <td>Shelby</td>\n <td>Sowell</td>\n <td>R</td>\n <td>D</td>\n <td>0.322314</td>\n <td>0.677686</td>\n <td>593302</td>\n <td>1240061</td>\n </tr>\n <tr>\n <th>60</th>\n <td>2004</td>\n <td>AK</td>\n <td>Murkowski</td>\n <td>Knowles</td>\n <td>R</td>\n <td>D</td>\n <td>0.419355</td>\n <td>0.580645</td>\n <td>110699</td>\n <td>121027</td>\n </tr>\n <tr>\n <th>61</th>\n <td>2004</td>\n <td>AR</td>\n <td>Lincoln</td>\n <td>Holt</td>\n <td>D</td>\n <td>R</td>\n <td>0.736000</td>\n <td>0.264000</td>\n <td>573793</td>\n <td>454132</td>\n </tr>\n <tr>\n <th>62</th>\n <td>2004</td>\n <td>CA</td>\n <td>Boxer</td>\n <td>Jones</td>\n <td>D</td>\n <td>R</td>\n <td>0.598361</td>\n <td>0.401639</td>\n <td>5599219</td>\n <td>3642281</td>\n </tr>\n <tr>\n <th>63</th>\n <td>2004</td>\n <td>CO</td>\n <td>Salazar</td>\n <td>Coors</td>\n <td>D</td>\n <td>R</td>\n <td>0.512605</td>\n <td>0.487395</td>\n <td>1023803</td>\n <td>944520</td>\n </tr>\n <tr>\n <th>64</th>\n <td>2004</td>\n <td>CT</td>\n <td>Dodd</td>\n <td>Orchulli</td>\n <td>D</td>\n <td>R</td>\n <td>0.618644</td>\n <td>0.381356</td>\n <td>923836</td>\n <td>452874</td>\n </tr>\n <tr>\n <th>65</th>\n <td>2004</td>\n <td>FL</td>\n <td>Martinez</td>\n <td>Castor</td>\n <td>R</td>\n <td>D</td>\n <td>0.441667</td>\n <td>0.558333</td>\n <td>3544602</td>\n <td>3622823</td>\n </tr>\n <tr>\n <th>66</th>\n <td>2004</td>\n <td>GA</td>\n <td>Isakson</td>\n <td>Majette</td>\n <td>R</td>\n <td>D</td>\n <td>0.617886</td>\n <td>0.382114</td>\n <td>1268529</td>\n <td>1839069</td>\n </tr>\n <tr>\n <th>67</th>\n <td>2004</td>\n <td>HI</td>\n <td>Inouye</td>\n <td>Cavasso</td>\n <td>D</td>\n <td>R</td>\n <td>0.731707</td>\n <td>0.268293</td>\n <td>313269</td>\n <td>87119</td>\n </tr>\n <tr>\n <th>68</th>\n <td>2004</td>\n <td>IL</td>\n <td>Obama</td>\n <td>Keyes</td>\n <td>D</td>\n <td>R</td>\n <td>0.164835</td>\n <td>0.835165</td>\n <td>3524702</td>\n <td>1371882</td>\n </tr>\n <tr>\n <th>69</th>\n <td>2004</td>\n <td>IN</td>\n <td>Bayh</td>\n <td>Scott</td>\n <td>D</td>\n <td>R</td>\n <td>0.550000</td>\n <td>0.450000</td>\n <td>1488782</td>\n <td>902108</td>\n </tr>\n <tr>\n <th>70</th>\n <td>2004</td>\n <td>IA</td>\n <td>Grassley</td>\n <td>Small</td>\n <td>R</td>\n <td>D</td>\n <td>0.516949</td>\n <td>0.483051</td>\n <td>403434</td>\n <td>1025566</td>\n </tr>\n <tr>\n <th>71</th>\n <td>2004</td>\n <td>KY</td>\n <td>Brownback</td>\n <td>Jones</td>\n <td>R</td>\n <td>D</td>\n <td>0.357143</td>\n <td>0.642857</td>\n <td>307968</td>\n <td>777198</td>\n </tr>\n <tr>\n <th>72</th>\n <td>2004</td>\n <td>KZ</td>\n <td>Bunning</td>\n <td>Mongiardo</td>\n <td>R</td>\n <td>D</td>\n <td>0.696721</td>\n <td>0.303279</td>\n <td>850756</td>\n <td>873596</td>\n </tr>\n <tr>\n <th>73</th>\n <td>2004</td>\n <td>LA</td>\n <td>Vitter</td>\n <td>JohnKenne</td>\n <td>R</td>\n <td>D</td>\n <td>0.352000</td>\n <td>0.648000</td>\n <td>275494</td>\n <td>942755</td>\n </tr>\n <tr>\n <th>74</th>\n <td>2004</td>\n <td>MD</td>\n <td>Mikulski</td>\n <td>Pipkin</td>\n <td>D</td>\n <td>R</td>\n <td>0.508621</td>\n <td>0.491379</td>\n <td>1385009</td>\n <td>725898</td>\n </tr>\n <tr>\n <th>75</th>\n <td>2004</td>\n <td>MS</td>\n <td>Bond</td>\n <td>Farmer</td>\n <td>R</td>\n <td>D</td>\n <td>0.621849</td>\n <td>0.378151</td>\n <td>1153422</td>\n <td>1514793</td>\n </tr>\n <tr>\n <th>76</th>\n <td>2004</td>\n <td>NC</td>\n <td>Burr</td>\n <td>Bowles</td>\n <td>R</td>\n <td>D</td>\n <td>0.270000</td>\n <td>0.730000</td>\n <td>1586968</td>\n <td>1742182</td>\n </tr>\n <tr>\n <th>77</th>\n <td>2004</td>\n <td>ND</td>\n <td>Dorgan</td>\n <td>Liffrig</td>\n <td>D</td>\n <td>R</td>\n <td>0.758621</td>\n <td>0.241379</td>\n <td>211503</td>\n <td>98244</td>\n </tr>\n <tr>\n <th>78</th>\n <td>2004</td>\n <td>NV</td>\n <td>Reid</td>\n <td>Zizer</td>\n <td>D</td>\n <td>R</td>\n <td>0.747967</td>\n <td>0.252033</td>\n <td>490232</td>\n <td>282255</td>\n </tr>\n <tr>\n <th>79</th>\n <td>2004</td>\n <td>NH</td>\n <td>Gregg</td>\n <td>Haddock</td>\n <td>R</td>\n <td>D</td>\n <td>0.064000</td>\n <td>0.936000</td>\n <td>221011</td>\n <td>434292</td>\n </tr>\n <tr>\n <th>80</th>\n <td>2004</td>\n <td>NY</td>\n <td>Schumer</td>\n <td>Mills</td>\n <td>D</td>\n <td>R</td>\n <td>0.318182</td>\n <td>0.681818</td>\n <td>4409162</td>\n <td>1535871</td>\n </tr>\n <tr>\n <th>81</th>\n <td>2004</td>\n <td>OH</td>\n <td>Voinovich</td>\n <td>Fingerhut</td>\n <td>R</td>\n <td>D</td>\n <td>0.581967</td>\n <td>0.418033</td>\n <td>1907852</td>\n <td>3380364</td>\n </tr>\n <tr>\n <th>82</th>\n <td>2004</td>\n <td>OK</td>\n <td>Coburn</td>\n <td>Carson</td>\n <td>R</td>\n <td>D</td>\n <td>0.292683</td>\n <td>0.707317</td>\n <td>596672</td>\n <td>763332</td>\n </tr>\n <tr>\n <th>83</th>\n <td>2004</td>\n <td>OR</td>\n <td>Wyden</td>\n <td>King</td>\n <td>D</td>\n <td>R</td>\n <td>0.600000</td>\n <td>0.400000</td>\n <td>1072079</td>\n <td>536506</td>\n </tr>\n <tr>\n <th>84</th>\n <td>2004</td>\n <td>PA</td>\n <td>Spekter</td>\n <td>Hoeffel</td>\n <td>R</td>\n <td>D</td>\n <td>0.712000</td>\n <td>0.288000</td>\n <td>2295305</td>\n <td>2890818</td>\n </tr>\n <tr>\n <th>85</th>\n <td>2004</td>\n <td>SC</td>\n <td>Demint</td>\n <td>Tenenbaum</td>\n <td>R</td>\n <td>D</td>\n <td>0.464000</td>\n <td>0.536000</td>\n <td>691918</td>\n <td>843884</td>\n </tr>\n <tr>\n <th>86</th>\n <td>2004</td>\n <td>SD</td>\n <td>Thune</td>\n <td>Daschle</td>\n <td>R</td>\n <td>D</td>\n <td>0.367925</td>\n <td>0.632075</td>\n <td>193279</td>\n <td>197814</td>\n </tr>\n <tr>\n <th>87</th>\n <td>2004</td>\n <td>UT</td>\n <td>Bennett</td>\n <td>VanDam</td>\n <td>R</td>\n <td>D</td>\n <td>0.761905</td>\n <td>0.238095</td>\n <td>237415</td>\n <td>564260</td>\n </tr>\n <tr>\n <th>88</th>\n <td>2004</td>\n <td>VT</td>\n <td>Leahy</td>\n <td>McMullen</td>\n <td>D</td>\n <td>R</td>\n <td>0.660714</td>\n <td>0.339286</td>\n <td>212850</td>\n <td>74704</td>\n </tr>\n <tr>\n <th>89</th>\n <td>2004</td>\n <td>WA</td>\n <td>Murray</td>\n <td>Nethercutt</td>\n <td>D</td>\n <td>R</td>\n <td>0.264463</td>\n <td>0.735537</td>\n <td>1215647</td>\n <td>935992</td>\n </tr>\n <tr>\n <th>90</th>\n <td>2004</td>\n <td>WI</td>\n <td>Feingold</td>\n <td>Michels</td>\n <td>D</td>\n <td>R</td>\n <td>0.549180</td>\n <td>0.450820</td>\n <td>1632562</td>\n <td>1301305</td>\n </tr>\n <tr>\n <th>91</th>\n <td>2006</td>\n <td>AZ</td>\n <td>Kyl,Jon</td>\n <td>Pederson,</td>\n <td>R</td>\n <td>D</td>\n <td>0.206349</td>\n <td>0.793651</td>\n <td>505136</td>\n <td>605266</td>\n </tr>\n <tr>\n <th>92</th>\n <td>2006</td>\n <td>CA</td>\n <td>Feinstein</td>\n <td>Mountjoy,</td>\n <td>D</td>\n <td>R</td>\n <td>0.719298</td>\n <td>0.280702</td>\n <td>3889327</td>\n <td>2275304</td>\n </tr>\n <tr>\n <th>93</th>\n <td>2006</td>\n <td>DE</td>\n <td>Carper,T</td>\n <td>Ting,Jan</td>\n <td>D</td>\n <td>R</td>\n <td>0.838710</td>\n <td>0.161290</td>\n <td>170544</td>\n <td>69732</td>\n </tr>\n <tr>\n <th>94</th>\n <td>2006</td>\n <td>FL</td>\n <td>Nelson,B</td>\n <td>Harris,Ka</td>\n <td>D</td>\n <td>R</td>\n <td>0.548387</td>\n <td>0.451613</td>\n <td>2844459</td>\n <td>1797229</td>\n </tr>\n <tr>\n <th>95</th>\n <td>2006</td>\n <td>ME</td>\n <td>Snowe,Ol</td>\n <td>Bright,Je</td>\n <td>R</td>\n <td>D</td>\n <td>0.442623</td>\n <td>0.557377</td>\n <td>108796</td>\n <td>393230</td>\n </tr>\n <tr>\n <th>96</th>\n <td>2006</td>\n <td>MD</td>\n <td>Cardin,B</td>\n <td>Steele,Mi</td>\n <td>D</td>\n <td>R</td>\n <td>0.278689</td>\n <td>0.721311</td>\n <td>846709</td>\n <td>682641</td>\n </tr>\n <tr>\n <th>97</th>\n <td>2006</td>\n <td>MA</td>\n <td>Kennedy,</td>\n <td>Chase,Ken</td>\n <td>D</td>\n <td>R</td>\n <td>0.673469</td>\n <td>0.326531</td>\n <td>1497304</td>\n <td>658374</td>\n </tr>\n <tr>\n <th>98</th>\n <td>2006</td>\n <td>MI</td>\n <td>Stabenow,</td>\n <td>Vouchard,</td>\n <td>D</td>\n <td>R</td>\n <td>0.523810</td>\n <td>0.476190</td>\n <td>2146538</td>\n <td>1558483</td>\n </tr>\n <tr>\n <th>99</th>\n <td>2006</td>\n <td>MN</td>\n <td>Klobuchar</td>\n <td>Kennedy,M</td>\n <td>D</td>\n <td>R</td>\n <td>0.290323</td>\n <td>0.709677</td>\n <td>1279515</td>\n <td>839173</td>\n </tr>\n <tr>\n <th>100</th>\n <td>2006</td>\n <td>MS</td>\n <td>Lott,Tre</td>\n <td>Fleming,E</td>\n <td>R</td>\n <td>D</td>\n <td>0.436364</td>\n <td>0.563636</td>\n <td>205518</td>\n <td>375307</td>\n </tr>\n <tr>\n <th>101</th>\n <td>2006</td>\n <td>MO</td>\n <td>McCaskill</td>\n <td>Talent,Ji</td>\n <td>D</td>\n <td>R</td>\n <td>0.525424</td>\n <td>0.474576</td>\n <td>1028215</td>\n <td>987077</td>\n </tr>\n <tr>\n <th>102</th>\n <td>2006</td>\n <td>MT</td>\n <td>Tester,J</td>\n <td>Burns,Con</td>\n <td>D</td>\n <td>R</td>\n <td>0.163934</td>\n <td>0.836066</td>\n <td>198302</td>\n <td>195455</td>\n </tr>\n <tr>\n <th>103</th>\n <td>2006</td>\n <td>NE</td>\n <td>Nelson,B</td>\n <td>Ricketts,</td>\n <td>D</td>\n <td>R</td>\n <td>0.612903</td>\n <td>0.387097</td>\n <td>371777</td>\n <td>211111</td>\n </tr>\n <tr>\n <th>104</th>\n <td>2006</td>\n <td>NV</td>\n <td>Ensign,J</td>\n <td>Carter,Ja</td>\n <td>R</td>\n <td>D</td>\n <td>0.174603</td>\n <td>0.825397</td>\n <td>237875</td>\n <td>321186</td>\n </tr>\n <tr>\n <th>105</th>\n <td>2006</td>\n <td>NJ</td>\n <td>Menendez,</td>\n <td>Kean,Tom</td>\n <td>D</td>\n <td>R</td>\n <td>0.683333</td>\n <td>0.316667</td>\n <td>1159642</td>\n <td>973895</td>\n </tr>\n <tr>\n <th>106</th>\n <td>2006</td>\n <td>NM</td>\n <td>Bingaman,</td>\n <td>McCulloch,</td>\n <td>D</td>\n <td>R</td>\n <td>0.616667</td>\n <td>0.383333</td>\n <td>371068</td>\n <td>156314</td>\n </tr>\n <tr>\n <th>107</th>\n <td>2006</td>\n <td>ND</td>\n <td>Conrad,K</td>\n <td>Grotberg,</td>\n <td>D</td>\n <td>R</td>\n <td>0.539683</td>\n <td>0.460317</td>\n <td>149317</td>\n <td>64133</td>\n </tr>\n <tr>\n <th>108</th>\n <td>2006</td>\n <td>OH</td>\n <td>Brown,Sh</td>\n <td>DeWine,Mi</td>\n <td>D</td>\n <td>R</td>\n <td>0.590164</td>\n <td>0.409836</td>\n <td>2131741</td>\n <td>1680177</td>\n </tr>\n <tr>\n <th>109</th>\n <td>2006</td>\n <td>PA</td>\n <td>Casey,Bo</td>\n <td>Santorum,</td>\n <td>D</td>\n <td>R</td>\n <td>0.084746</td>\n <td>0.915254</td>\n <td>2341170</td>\n <td>1650139</td>\n </tr>\n <tr>\n <th>110</th>\n <td>2006</td>\n <td>RI</td>\n <td>Whitehous</td>\n <td>Chafee,Li</td>\n <td>D</td>\n <td>R</td>\n <td>0.786885</td>\n <td>0.213115</td>\n <td>205274</td>\n <td>178548</td>\n </tr>\n <tr>\n <th>111</th>\n <td>2006</td>\n <td>TN</td>\n <td>Corker,B</td>\n <td>Ford,Haro</td>\n <td>R</td>\n <td>D</td>\n <td>0.264151</td>\n <td>0.735849</td>\n <td>877716</td>\n <td>927343</td>\n </tr>\n <tr>\n <th>112</th>\n <td>2006</td>\n <td>TX</td>\n <td>Hutchison</td>\n <td>Radnofsky,</td>\n <td>R</td>\n <td>D</td>\n <td>0.416667</td>\n <td>0.583333</td>\n <td>1550950</td>\n <td>2654004</td>\n </tr>\n <tr>\n <th>113</th>\n <td>2006</td>\n <td>UT</td>\n <td>Hatch,Or</td>\n <td>Ashdown,P</td>\n <td>R</td>\n <td>D</td>\n <td>0.089286</td>\n <td>0.910714</td>\n <td>168551</td>\n <td>342901</td>\n </tr>\n <tr>\n <th>114</th>\n <td>2006</td>\n <td>VA</td>\n <td>Webb,Jam</td>\n <td>Allen,Geo</td>\n <td>D</td>\n <td>R</td>\n <td>0.114754</td>\n <td>0.885246</td>\n <td>1172671</td>\n <td>1165440</td>\n </tr>\n <tr>\n <th>115</th>\n <td>2006</td>\n <td>WA</td>\n <td>Cantwell,</td>\n <td>McGavick,</td>\n <td>D</td>\n <td>R</td>\n <td>0.396825</td>\n <td>0.603175</td>\n <td>652515</td>\n <td>445395</td>\n </tr>\n <tr>\n <th>116</th>\n <td>2006</td>\n <td>WV</td>\n <td>Byrd,Rob</td>\n <td>JohnRaese</td>\n <td>D</td>\n <td>R</td>\n <td>0.327869</td>\n <td>0.672131</td>\n <td>291058</td>\n <td>152315</td>\n </tr>\n <tr>\n <th>117</th>\n <td>2006</td>\n <td>WI</td>\n <td>Kohl,Her</td>\n <td>Lorge,Rob</td>\n <td>D</td>\n <td>R</td>\n <td>0.573770</td>\n <td>0.426230</td>\n <td>1436157</td>\n <td>628879</td>\n </tr>\n <tr>\n <th>118</th>\n <td>2006</td>\n <td>WY</td>\n <td>Thomas,C</td>\n <td>Groutage,</td>\n <td>R</td>\n <td>D</td>\n <td>0.250000</td>\n <td>0.750000</td>\n <td>57640</td>\n <td>134942</td>\n </tr>\n </tbody>\n</table>"
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NS
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QRData
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coverbench
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The estimated slope of the linear regression model using the Democratic margin in the two-party vote share as the response variable and the perceived competence for Democratic candidates as the predictor is -0.077.
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[
"Several psychologists have reported the intriguing result of an experiment showing that facial appearance predicts election outcomes better than chance. In their experiment, the researchers briefly showed student subjects the black-and-white head shots of two candidates from a US congressional election (winner and runner-up). The exposure of subjects to facial pictures lasted less than a second, and the subjects were then asked to evaluate the two candidates in terms of their perceived competence.\nThe researchers used these competence measures to predict election outcomes. The key hypothesis is whether or not a within-a-second evaluation of facial appearance can predict election outcomes. The CSV data set, face.csv, contains the data from the experiment. Note that we include data only from subjects who did not know the candidates’ political parties, their policies, or even which candidate was the incumbent or challenger. They were simply making snap judgments about which candidate appeared more competent based on their facial expression alone.\n\nVariable Description\ncongress: session of Congress\nyear: year of the election\nstate: state of the election\nwinner: name of the winner\nloser: name of the runner-up\nw.party: party of the winner\nl.party: party of the loser\nd.votes: number of votes for the Democratic candidate\nr.votes: number of votes for the Republican candidate\nd.comp: competence measure for the Democratic candidate\nr.comp: competence measure for the Republican candidate\n\nface.csv\n| | year | state | winner | loser | w.party | l.party | d.comp | r.comp | d.votes | r.votes |\n|----:|-------:|:--------|:----------|:-----------|:----------|:----------|----------:|---------:|----------:|----------:|\n| 0 | 2000 | CA | Feinstein | Campbell | D | R | 0.564568 | 0.435432 | 5790154 | 3779325 |\n| 1 | 2000 | DE | Carper | Roth | D | R | 0.341912 | 0.658088 | 181387 | 142683 |\n| 2 | 2000 | FL | Nelson | McCollum | D | R | 0.612368 | 0.387632 | 2987644 | 2703608 |\n| 3 | 2000 | GA | Miller | Mattingly | D | R | 0.541533 | 0.458467 | 1390428 | 933698 |\n| 4 | 2000 | HI | Akaka | Carroll | D | R | 0.680232 | 0.319768 | 251130 | 84657 |\n| 5 | 2000 | IN | Lugar | Johnson | R | D | 0.320502 | 0.679498 | 684242 | 1419629 |\n| 6 | 2000 | MA | Kennedy | Robinson | D | R | 0.403756 | 0.596244 | 1877439 | 334721 |\n| 7 | 2000 | MD | Sarbanes | Rappaport | D | R | 0.603015 | 0.396985 | 1171151 | 678376 |\n| 8 | 2000 | ME | Snowe | Lawrence | R | D | 0.538573 | 0.461427 | 197742 | 431727 |\n| 9 | 2000 | MI | Stabenow | Abraham | D | R | 0.869215 | 0.130785 | 2034342 | 1991507 |\n| 10 | 2000 | MN | Dayton | Grams | D | R | 0.565335 | 0.434665 | 1180335 | 1048224 |\n| 11 | 2000 | MO | Lott | Brown | R | D | 0.594791 | 0.405209 | 296149 | 621500 |\n| 12 | 2000 | MT | Burns | Schweitzer | R | D | 0.248399 | 0.751601 | 194567 | 208026 |\n| 13 | 2000 | ND | Conrad | Sand | D | R | 0.649669 | 0.350331 | 177661 | 111376 |\n| 14 | 2000 | NE | Nelson | Stenberg | D | R | 0.245012 | 0.754988 | 330366 | 318368 |\n| 15 | 2000 | NM | Bingaman | Redmond | D | R | 0.754391 | 0.245609 | 363279 | 225040 |\n| 16 | 2000 | NV | Ensign | Bernstein | R | D | 0.3904 | 0.6096 | 238243 | 330663 |\n| 17 | 2000 | OH | DeWine | Celeste | R | D | 0.325593 | 0.674407 | 1539001 | 2590952 |\n| 18 | 2000 | PA | Santorum | Klink | R | D | 0.578942 | 0.421058 | 2134734 | 2473118 |\n| 19 | 2000 | RI | Chafee | Weygand | R | D | 0.437047 | 0.562953 | 165367 | 226592 |\n| 20 | 2000 | TN | Frist | Clark | R | D | 0.114507 | 0.885493 | 617684 | 1247436 |\n| 21 | 2000 | TX | Hutchison | Kelly | R | D | 0.204603 | 0.795397 | 2026184 | 4080582 |\n| 22 | 2000 | UT | Hatch | Howell | R | D | 0.442465 | 0.557535 | 241129 | 501925 |\n| 23 | 2000 | VA | Allen | Robb | R | D | 0.741696 | 0.258304 | 1289087 | 1414577 |\n| 24 | 2000 | VT | Jeffords | Flanagan | R | D | 0.457816 | 0.542184 | 72909 | 188070 |\n| 25 | 2000 | WA | Cantwell | Gorton | D | R | 0.614515 | 0.385485 | 1199437 | 1197208 |\n| 26 | 2000 | WI | Kohl | Gillespie | D | R | 0.614357 | 0.385643 | 1563565 | 941132 |\n| 27 | 2000 | WV | Byrd | Gallaher | D | R | 0.683657 | 0.316343 | 462566 | 119958 |\n| 28 | 2000 | WY | Thomas | Logan | R | D | 0.224063 | 0.775937 | 47039 | 157316 |\n| 29 | 2002 | AK | Stevens | Vondersaar | R | D | 0.333592 | 0.666408 | 20466 | 155054 |\n| 30 | 2002 | AL | Sessions | Parker | R | D | 0.551095 | 0.448905 | 537882 | 790757 |\n| 31 | 2002 | AR | Pryor | Hutchinson | D | R | 0.273883 | 0.726117 | 435346 | 372909 |\n| 32 | 2002 | CO | Allard | Strickland | R | D | 0.401537 | 0.598463 | 634227 | 707349 |\n| 33 | 2002 | DE | Biden | Clatworthy | D | R | 0.639578 | 0.360422 | 135170 | 94716 |\n| 34 | 2002 | GA | Chambliss | Cleland | R | D | 0.246164 | 0.753836 | 928905 | 1068902 |\n| 35 | 2002 | IA | Harkin | Ganske | D | R | 0.710124 | 0.289876 | 550156 | 446209 |\n| 36 | 2002 | ID | Craig | Blinken | R | D | 0.457524 | 0.542476 | 132845 | 265849 |\n| 37 | 2002 | IL | Durbin | Durkin | D | R | 0.428035 | 0.571965 | 2080411 | 1320621 |\n| 38 | 2002 | KY | McConnell | Weinberg | R | D | 0.562735 | 0.437265 | 400818 | 726396 |\n| 39 | 2002 | LA | Landrieu | Terrell | D | R | 0.766916 | 0.233084 | 563400 | 327975 |\n| 40 | 2002 | ME | Collins | Pingree | R | D | 0.327268 | 0.672732 | 205901 | 290266 |\n| 41 | 2002 | MI | Levin | Raczkowski | D | R | 0.703998 | 0.296002 | 1893788 | 1184548 |\n| 42 | 2002 | MN | Coleman | Mondale | R | D | 0.665846 | 0.334154 | 1029982 | 1091253 |\n| 43 | 2002 | MO | Talent | Carnahan | R | D | 0.396744 | 0.603256 | 911507 | 934093 |\n| 44 | 2002 | MT | Baucus | Taylor | D | R | 0.897228 | 0.102772 | 202908 | 102766 |\n| 45 | 2002 | NC | Dole | Bowles | R | D | 0.313285 | 0.686715 | 1034941 | 1238203 |\n| 46 | 2002 | NE | Hagel | Matulka | R | D | 0.213806 | 0.786194 | 68657 | 391648 |\n| 47 | 2002 | NH | Sununu | Shaheen | R | D | 0.526114 | 0.473886 | 206689 | 225506 |\n| 48 | 2002 | NJ | Lautenber | Forrester | D | R | 0.636229 | 0.363771 | 1112499 | 909383 |\n| 49 | 2002 | NM | Domenici | Tristani | R | D | 0.346114 | 0.653886 | 161409 | 296935 |\n| 50 | 2002 | OK | Inhofe | Walters | R | D | 0.449049 | 0.550951 | 369789 | 578579 |\n| 51 | 2002 | OR | Smith | Bradbury | R | D | 0.402232 | 0.597768 | 487995 | 695345 |\n| 52 | 2002 | RI | Reed | Tingle | D | R | 0.711679 | 0.288321 | 241315 | 66613 |\n| 53 | 2002 | SC | Graham | Sanders | R | D | 0.573504 | 0.426496 | 484798 | 597789 |\n| 54 | 2002 | SD | Johnson | Thune | D | R | 0.318235 | 0.681765 | 167481 | 166954 |\n| 55 | 2002 | TN | Alexander | Clement | R | D | 0.522373 | 0.477627 | 726510 | 888223 |\n| 56 | 2002 | TX | Cornyn | Kirk | R | D | 0.430373 | 0.569627 | 1946681 | 2480991 |\n| 57 | 2002 | WV | Rockefell | Wolfe | D | R | 0.638702 | 0.361298 | 271314 | 158211 |\n| 58 | 2002 | WY | Enzi | Corcoran | R | D | 0.175816 | 0.824184 | 49587 | 133615 |\n| 59 | 2004 | AL | Shelby | Sowell | R | D | 0.322314 | 0.677686 | 593302 | 1240061 |\n| 60 | 2004 | AK | Murkowski | Knowles | R | D | 0.419355 | 0.580645 | 110699 | 121027 |\n| 61 | 2004 | AR | Lincoln | Holt | D | R | 0.736 | 0.264 | 573793 | 454132 |\n| 62 | 2004 | CA | Boxer | Jones | D | R | 0.598361 | 0.401639 | 5599219 | 3642281 |\n| 63 | 2004 | CO | Salazar | Coors | D | R | 0.512605 | 0.487395 | 1023803 | 944520 |\n| 64 | 2004 | CT | Dodd | Orchulli | D | R | 0.618644 | 0.381356 | 923836 | 452874 |\n| 65 | 2004 | FL | Martinez | Castor | R | D | 0.441667 | 0.558333 | 3544602 | 3622823 |\n| 66 | 2004 | GA | Isakson | Majette | R | D | 0.617886 | 0.382114 | 1268529 | 1839069 |\n| 67 | 2004 | HI | Inouye | Cavasso | D | R | 0.731707 | 0.268293 | 313269 | 87119 |\n| 68 | 2004 | IL | Obama | Keyes | D | R | 0.164835 | 0.835165 | 3524702 | 1371882 |\n| 69 | 2004 | IN | Bayh | Scott | D | R | 0.55 | 0.45 | 1488782 | 902108 |\n| 70 | 2004 | IA | Grassley | Small | R | D | 0.516949 | 0.483051 | 403434 | 1025566 |\n| 71 | 2004 | KY | Brownback | Jones | R | D | 0.357143 | 0.642857 | 307968 | 777198 |\n| 72 | 2004 | KZ | Bunning | Mongiardo | R | D | 0.696721 | 0.303279 | 850756 | 873596 |\n| 73 | 2004 | LA | Vitter | JohnKenne | R | D | 0.352 | 0.648 | 275494 | 942755 |\n| 74 | 2004 | MD | Mikulski | Pipkin | D | R | 0.508621 | 0.491379 | 1385009 | 725898 |\n| 75 | 2004 | MS | Bond | Farmer | R | D | 0.621849 | 0.378151 | 1153422 | 1514793 |\n| 76 | 2004 | NC | Burr | Bowles | R | D | 0.27 | 0.73 | 1586968 | 1742182 |\n| 77 | 2004 | ND | Dorgan | Liffrig | D | R | 0.758621 | 0.241379 | 211503 | 98244 |\n| 78 | 2004 | NV | Reid | Zizer | D | R | 0.747967 | 0.252033 | 490232 | 282255 |\n| 79 | 2004 | NH | Gregg | Haddock | R | D | 0.064 | 0.936 | 221011 | 434292 |\n| 80 | 2004 | NY | Schumer | Mills | D | R | 0.318182 | 0.681818 | 4409162 | 1535871 |\n| 81 | 2004 | OH | Voinovich | Fingerhut | R | D | 0.581967 | 0.418033 | 1907852 | 3380364 |\n| 82 | 2004 | OK | Coburn | Carson | R | D | 0.292683 | 0.707317 | 596672 | 763332 |\n| 83 | 2004 | OR | Wyden | King | D | R | 0.6 | 0.4 | 1072079 | 536506 |\n| 84 | 2004 | PA | Spekter | Hoeffel | R | D | 0.712 | 0.288 | 2295305 | 2890818 |\n| 85 | 2004 | SC | Demint | Tenenbaum | R | D | 0.464 | 0.536 | 691918 | 843884 |\n| 86 | 2004 | SD | Thune | Daschle | R | D | 0.367925 | 0.632075 | 193279 | 197814 |\n| 87 | 2004 | UT | Bennett | VanDam | R | D | 0.761905 | 0.238095 | 237415 | 564260 |\n| 88 | 2004 | VT | Leahy | McMullen | D | R | 0.660714 | 0.339286 | 212850 | 74704 |\n| 89 | 2004 | WA | Murray | Nethercutt | D | R | 0.264463 | 0.735537 | 1215647 | 935992 |\n| 90 | 2004 | WI | Feingold | Michels | D | R | 0.54918 | 0.45082 | 1632562 | 1301305 |\n| 91 | 2006 | AZ | Kyl,Jon | Pederson, | R | D | 0.206349 | 0.793651 | 505136 | 605266 |\n| 92 | 2006 | CA | Feinstein | Mountjoy, | D | R | 0.719298 | 0.280702 | 3889327 | 2275304 |\n| 93 | 2006 | DE | Carper,T | Ting,Jan | D | R | 0.83871 | 0.16129 | 170544 | 69732 |\n| 94 | 2006 | FL | Nelson,B | Harris,Ka | D | R | 0.548387 | 0.451613 | 2844459 | 1797229 |\n| 95 | 2006 | ME | Snowe,Ol | Bright,Je | R | D | 0.442623 | 0.557377 | 108796 | 393230 |\n| 96 | 2006 | MD | Cardin,B | Steele,Mi | D | R | 0.278689 | 0.721311 | 846709 | 682641 |\n| 97 | 2006 | MA | Kennedy, | Chase,Ken | D | R | 0.673469 | 0.326531 | 1497304 | 658374 |\n| 98 | 2006 | MI | Stabenow, | Vouchard, | D | R | 0.52381 | 0.47619 | 2146538 | 1558483 |\n| 99 | 2006 | MN | Klobuchar | Kennedy,M | D | R | 0.290323 | 0.709677 | 1279515 | 839173 |\n| 100 | 2006 | MS | Lott,Tre | Fleming,E | R | D | 0.436364 | 0.563636 | 205518 | 375307 |\n| 101 | 2006 | MO | McCaskill | Talent,Ji | D | R | 0.525424 | 0.474576 | 1028215 | 987077 |\n| 102 | 2006 | MT | Tester,J | Burns,Con | D | R | 0.163934 | 0.836066 | 198302 | 195455 |\n| 103 | 2006 | NE | Nelson,B | Ricketts, | D | R | 0.612903 | 0.387097 | 371777 | 211111 |\n| 104 | 2006 | NV | Ensign,J | Carter,Ja | R | D | 0.174603 | 0.825397 | 237875 | 321186 |\n| 105 | 2006 | NJ | Menendez, | Kean,Tom | D | R | 0.683333 | 0.316667 | 1159642 | 973895 |\n| 106 | 2006 | NM | Bingaman, | McCulloch, | D | R | 0.616667 | 0.383333 | 371068 | 156314 |\n| 107 | 2006 | ND | Conrad,K | Grotberg, | D | R | 0.539683 | 0.460317 | 149317 | 64133 |\n| 108 | 2006 | OH | Brown,Sh | DeWine,Mi | D | R | 0.590164 | 0.409836 | 2131741 | 1680177 |\n| 109 | 2006 | PA | Casey,Bo | Santorum, | D | R | 0.0847458 | 0.915254 | 2341170 | 1650139 |\n| 110 | 2006 | RI | Whitehous | Chafee,Li | D | R | 0.786885 | 0.213115 | 205274 | 178548 |\n| 111 | 2006 | TN | Corker,B | Ford,Haro | R | D | 0.264151 | 0.735849 | 877716 | 927343 |\n| 112 | 2006 | TX | Hutchison | Radnofsky, | R | D | 0.416667 | 0.583333 | 1550950 | 2654004 |\n| 113 | 2006 | UT | Hatch,Or | Ashdown,P | R | D | 0.0892857 | 0.910714 | 168551 | 342901 |\n| 114 | 2006 | VA | Webb,Jam | Allen,Geo | D | R | 0.114754 | 0.885246 | 1172671 | 1165440 |\n| 115 | 2006 | WA | Cantwell, | McGavick, | D | R | 0.396825 | 0.603175 | 652515 | 445395 |\n| 116 | 2006 | WV | Byrd,Rob | JohnRaese | D | R | 0.327869 | 0.672131 | 291058 | 152315 |\n| 117 | 2006 | WI | Kohl,Her | Lorge,Rob | D | R | 0.57377 | 0.42623 | 1436157 | 628879 |\n| 118 | 2006 | WY | Thomas,C | Groutage, | R | D | 0.25 | 0.75 | 57640 | 134942 |"
] |
NS
|
QRData
| |
coverbench
|
The proportion of patients in the treatment group who experienced improvement in symptoms is 0.79.
|
[
"Researchers studying the effect of antibiotic treatment for acute sinusitis compared to symptomatic treatments randomly assigned 166 adults diagnosed with acute sinusitis to one of two groups: treatment or control. Study participants received either a 10-day course of amoxicillin (an antibiotic) or a placebo similar in appearance and taste. The placebo consisted of\nsymptomatic treatments such as acetaminophen, nasal decongestants, etc. At the end of the 10-day period, patients were asked if they experienced improvement in symptoms. The research data is in the CSV file sinusitis.csv.\n\nsinusitis.csv\n| | group | self_reported_improvement |\n|----:|:----------|:----------------------------|\n| 0 | treatment | yes |\n| 1 | treatment | yes |\n| 2 | treatment | yes |\n| 3 | treatment | yes |\n| 4 | treatment | yes |\n| 5 | treatment | yes |\n| 6 | treatment | yes |\n| 7 | treatment | yes |\n| 8 | treatment | yes |\n| 9 | treatment | yes |\n| 10 | treatment | yes |\n| 11 | treatment | yes |\n| 12 | treatment | yes |\n| 13 | treatment | yes |\n| 14 | treatment | yes |\n| 15 | treatment | yes |\n| 16 | treatment | yes |\n| 17 | treatment | yes |\n| 18 | treatment | yes |\n| 19 | treatment | yes |\n| 20 | treatment | yes |\n| 21 | treatment | yes |\n| 22 | treatment | yes |\n| 23 | treatment | yes |\n| 24 | treatment | yes |\n| 25 | treatment | yes |\n| 26 | treatment | yes |\n| 27 | treatment | yes |\n| 28 | treatment | yes |\n| 29 | treatment | yes |\n| 30 | treatment | yes |\n| 31 | treatment | yes |\n| 32 | treatment | yes |\n| 33 | treatment | yes |\n| 34 | treatment | yes |\n| 35 | treatment | yes |\n| 36 | treatment | yes |\n| 37 | treatment | yes |\n| 38 | treatment | yes |\n| 39 | treatment | yes |\n| 40 | treatment | yes |\n| 41 | treatment | yes |\n| 42 | treatment | yes |\n| 43 | treatment | yes |\n| 44 | treatment | yes |\n| 45 | treatment | yes |\n| 46 | treatment | yes |\n| 47 | treatment | yes |\n| 48 | treatment | yes |\n| 49 | treatment | yes |\n| 50 | treatment | yes |\n| 51 | treatment | yes |\n| 52 | treatment | yes |\n| 53 | treatment | yes |\n| 54 | treatment | yes |\n| 55 | treatment | yes |\n| 56 | treatment | yes |\n| 57 | treatment | yes |\n| 58 | treatment | yes |\n| 59 | treatment | yes |\n| 60 | treatment | yes |\n| 61 | treatment | yes |\n| 62 | treatment | yes |\n| 63 | treatment | yes |\n| 64 | treatment | yes |\n| 65 | treatment | yes |\n| 66 | treatment | no |\n| 67 | treatment | no |\n| 68 | treatment | no |\n| 69 | treatment | no |\n| 70 | treatment | no |\n| 71 | treatment | no |\n| 72 | treatment | no |\n| 73 | treatment | no |\n| 74 | treatment | no |\n| 75 | treatment | no |\n| 76 | treatment | no |\n| 77 | treatment | no |\n| 78 | treatment | no |\n| 79 | treatment | no |\n| 80 | treatment | no |\n| 81 | treatment | no |\n| 82 | treatment | no |\n| 83 | treatment | no |\n| 84 | treatment | no |\n| 85 | control | yes |\n| 86 | control | yes |\n| 87 | control | yes |\n| 88 | control | yes |\n| 89 | control | yes |\n| 90 | control | yes |\n| 91 | control | yes |\n| 92 | control | yes |\n| 93 | control | yes |\n| 94 | control | yes |\n| 95 | control | yes |\n| 96 | control | yes |\n| 97 | control | yes |\n| 98 | control | yes |\n| 99 | control | yes |\n| 100 | control | yes |\n| 101 | control | yes |\n| 102 | control | yes |\n| 103 | control | yes |\n| 104 | control | yes |\n| 105 | control | yes |\n| 106 | control | yes |\n| 107 | control | yes |\n| 108 | control | yes |\n| 109 | control | yes |\n| 110 | control | yes |\n| 111 | control | yes |\n| 112 | control | yes |\n| 113 | control | yes |\n| 114 | control | yes |\n| 115 | control | yes |\n| 116 | control | yes |\n| 117 | control | yes |\n| 118 | control | yes |\n| 119 | control | yes |\n| 120 | control | yes |\n| 121 | control | yes |\n| 122 | control | yes |\n| 123 | control | yes |\n| 124 | control | yes |\n| 125 | control | yes |\n| 126 | control | yes |\n| 127 | control | yes |\n| 128 | control | yes |\n| 129 | control | yes |\n| 130 | control | yes |\n| 131 | control | yes |\n| 132 | control | yes |\n| 133 | control | yes |\n| 134 | control | yes |\n| 135 | control | yes |\n| 136 | control | yes |\n| 137 | control | yes |\n| 138 | control | yes |\n| 139 | control | yes |\n| 140 | control | yes |\n| 141 | control | yes |\n| 142 | control | yes |\n| 143 | control | yes |\n| 144 | control | yes |\n| 145 | control | yes |\n| 146 | control | yes |\n| 147 | control | yes |\n| 148 | control | yes |\n| 149 | control | yes |\n| 150 | control | no |\n| 151 | control | no |\n| 152 | control | no |\n| 153 | control | no |\n| 154 | control | no |\n| 155 | control | no |\n| 156 | control | no |\n| 157 | control | no |\n| 158 | control | no |\n| 159 | control | no |\n| 160 | control | no |\n| 161 | control | no |\n| 162 | control | no |\n| 163 | control | no |\n| 164 | control | no |\n| 165 | control | no |"
] |
NS
|
QRData
| |
coverbench
|
The null hypothesis that the questions had no impact on the sellers in the experiment is accepted at the 5% significance level.
|
[
"In this experiment, each individual was asked to be a seller of an iPod (a product commonly used to store music on before smart phones...). The participant received $10 + 5% of the sale price for participating. The iPod they were selling had frozen twice in the past inexplicably but otherwise worked fine. The prospective buyer starts off and then asks one of three final questions, depending on the seller's treatment group. The experiment data is in the CSV file ask.csv.\n\nThe three possible questions:\nGeneral: What can you tell me about it?\nPositive Assumption: It doesn't have any problems, does it?\nNegative Assumption: What problems does it have?\nThe outcome variable is whether or not the participant discloses or hides the problem with the iPod.\n\nThe hypothesis test for the iPod experiment is really about assessing whether there is statistically significant evidence that the success each question had on getting the participant to disclose the problem with the iPod. In other words, the goal is to check whether the buyer's question was independent of whether the seller disclosed a problem.\n\nask.csv\n| | question_class | question | response |\n|----:|:-----------------|:---------------------------------------|:-----------|\n| 0 | general | What can you tell me about it? | hide |\n| 1 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 2 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 3 | neg_assumption | What problems does it have? | disclose |\n| 4 | general | What can you tell me about it? | hide |\n| 5 | neg_assumption | What problems does it have? | disclose |\n| 6 | neg_assumption | What problems does it have? | disclose |\n| 7 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 8 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 9 | general | What can you tell me about it? | hide |\n| 10 | neg_assumption | What problems does it have? | disclose |\n| 11 | general | What can you tell me about it? | hide |\n| 12 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 13 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 14 | neg_assumption | What problems does it have? | hide |\n| 15 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 16 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 17 | neg_assumption | What problems does it have? | disclose |\n| 18 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 19 | neg_assumption | What problems does it have? | hide |\n| 20 | neg_assumption | What problems does it have? | disclose |\n| 21 | general | What can you tell me about it? | hide |\n| 22 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 23 | general | What can you tell me about it? | hide |\n| 24 | general | What can you tell me about it? | hide |\n| 25 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 26 | general | What can you tell me about it? | hide |\n| 27 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 28 | neg_assumption | What problems does it have? | hide |\n| 29 | general | What can you tell me about it? | hide |\n| 30 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 31 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 32 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 33 | general | What can you tell me about it? | hide |\n| 34 | neg_assumption | What problems does it have? | hide |\n| 35 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 36 | neg_assumption | What problems does it have? | disclose |\n| 37 | general | What can you tell me about it? | hide |\n| 38 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 39 | neg_assumption | What problems does it have? | disclose |\n| 40 | neg_assumption | What problems does it have? | hide |\n| 41 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 42 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 43 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 44 | neg_assumption | What problems does it have? | disclose |\n| 45 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 46 | general | What can you tell me about it? | hide |\n| 47 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 48 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 49 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 50 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 51 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 52 | neg_assumption | What problems does it have? | disclose |\n| 53 | general | What can you tell me about it? | hide |\n| 54 | general | What can you tell me about it? | hide |\n| 55 | general | What can you tell me about it? | hide |\n| 56 | general | What can you tell me about it? | hide |\n| 57 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 58 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 59 | general | What can you tell me about it? | hide |\n| 60 | neg_assumption | What problems does it have? | hide |\n| 61 | general | What can you tell me about it? | hide |\n| 62 | general | What can you tell me about it? | disclose |\n| 63 | neg_assumption | What problems does it have? | hide |\n| 64 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 65 | general | What can you tell me about it? | hide |\n| 66 | neg_assumption | What problems does it have? | disclose |\n| 67 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 68 | neg_assumption | What problems does it have? | disclose |\n| 69 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 70 | general | What can you tell me about it? | hide |\n| 71 | neg_assumption | What problems does it have? | disclose |\n| 72 | neg_assumption | What problems does it have? | hide |\n| 73 | general | What can you tell me about it? | hide |\n| 74 | general | What can you tell me about it? | hide |\n| 75 | neg_assumption | What problems does it have? | disclose |\n| 76 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 77 | general | What can you tell me about it? | hide |\n| 78 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 79 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 80 | general | What can you tell me about it? | hide |\n| 81 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 82 | general | What can you tell me about it? | hide |\n| 83 | general | What can you tell me about it? | hide |\n| 84 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 85 | general | What can you tell me about it? | hide |\n| 86 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 87 | neg_assumption | What problems does it have? | hide |\n| 88 | general | What can you tell me about it? | hide |\n| 89 | general | What can you tell me about it? | hide |\n| 90 | general | What can you tell me about it? | hide |\n| 91 | general | What can you tell me about it? | hide |\n| 92 | neg_assumption | What problems does it have? | disclose |\n| 93 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 94 | neg_assumption | What problems does it have? | hide |\n| 95 | neg_assumption | What problems does it have? | hide |\n| 96 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 97 | neg_assumption | What problems does it have? | hide |\n| 98 | neg_assumption | What problems does it have? | disclose |\n| 99 | neg_assumption | What problems does it have? | hide |\n| 100 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 101 | general | What can you tell me about it? | hide |\n| 102 | general | What can you tell me about it? | hide |\n| 103 | neg_assumption | What problems does it have? | hide |\n| 104 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 105 | neg_assumption | What problems does it have? | disclose |\n| 106 | general | What can you tell me about it? | hide |\n| 107 | general | What can you tell me about it? | hide |\n| 108 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 109 | neg_assumption | What problems does it have? | hide |\n| 110 | neg_assumption | What problems does it have? | hide |\n| 111 | neg_assumption | What problems does it have? | disclose |\n| 112 | general | What can you tell me about it? | hide |\n| 113 | general | What can you tell me about it? | hide |\n| 114 | general | What can you tell me about it? | hide |\n| 115 | general | What can you tell me about it? | hide |\n| 116 | neg_assumption | What problems does it have? | hide |\n| 117 | general | What can you tell me about it? | hide |\n| 118 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 119 | neg_assumption | What problems does it have? | disclose |\n| 120 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 121 | neg_assumption | What problems does it have? | hide |\n| 122 | neg_assumption | What problems does it have? | disclose |\n| 123 | neg_assumption | What problems does it have? | hide |\n| 124 | general | What can you tell me about it? | disclose |\n| 125 | neg_assumption | What problems does it have? | disclose |\n| 126 | general | What can you tell me about it? | hide |\n| 127 | neg_assumption | What problems does it have? | hide |\n| 128 | general | What can you tell me about it? | hide |\n| 129 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 130 | neg_assumption | What problems does it have? | disclose |\n| 131 | general | What can you tell me about it? | hide |\n| 132 | general | What can you tell me about it? | hide |\n| 133 | neg_assumption | What problems does it have? | disclose |\n| 134 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 135 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 136 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 137 | neg_assumption | What problems does it have? | disclose |\n| 138 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 139 | neg_assumption | What problems does it have? | hide |\n| 140 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 141 | neg_assumption | What problems does it have? | hide |\n| 142 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 143 | neg_assumption | What problems does it have? | disclose |\n| 144 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 145 | general | What can you tell me about it? | hide |\n| 146 | neg_assumption | What problems does it have? | hide |\n| 147 | neg_assumption | What problems does it have? | hide |\n| 148 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 149 | neg_assumption | What problems does it have? | hide |\n| 150 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 151 | general | What can you tell me about it? | hide |\n| 152 | general | What can you tell me about it? | hide |\n| 153 | general | What can you tell me about it? | hide |\n| 154 | neg_assumption | What problems does it have? | hide |\n| 155 | neg_assumption | What problems does it have? | hide |\n| 156 | neg_assumption | What problems does it have? | hide |\n| 157 | neg_assumption | What problems does it have? | hide |\n| 158 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 159 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 160 | neg_assumption | What problems does it have? | hide |\n| 161 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 162 | general | What can you tell me about it? | hide |\n| 163 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 164 | neg_assumption | What problems does it have? | hide |\n| 165 | general | What can you tell me about it? | hide |\n| 166 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 167 | general | What can you tell me about it? | hide |\n| 168 | neg_assumption | What problems does it have? | disclose |\n| 169 | neg_assumption | What problems does it have? | disclose |\n| 170 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 171 | neg_assumption | What problems does it have? | hide |\n| 172 | neg_assumption | What problems does it have? | hide |\n| 173 | neg_assumption | What problems does it have? | disclose |\n| 174 | general | What can you tell me about it? | hide |\n| 175 | neg_assumption | What problems does it have? | disclose |\n| 176 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 177 | neg_assumption | What problems does it have? | hide |\n| 178 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 179 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 180 | general | What can you tell me about it? | hide |\n| 181 | general | What can you tell me about it? | hide |\n| 182 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 183 | neg_assumption | What problems does it have? | hide |\n| 184 | neg_assumption | What problems does it have? | hide |\n| 185 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 186 | general | What can you tell me about it? | hide |\n| 187 | general | What can you tell me about it? | hide |\n| 188 | neg_assumption | What problems does it have? | disclose |\n| 189 | general | What can you tell me about it? | hide |\n| 190 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 191 | neg_assumption | What problems does it have? | disclose |\n| 192 | neg_assumption | What problems does it have? | disclose |\n| 193 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 194 | general | What can you tell me about it? | hide |\n| 195 | neg_assumption | What problems does it have? | disclose |\n| 196 | general | What can you tell me about it? | hide |\n| 197 | general | What can you tell me about it? | hide |\n| 198 | general | What can you tell me about it? | hide |\n| 199 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 200 | general | What can you tell me about it? | hide |\n| 201 | neg_assumption | What problems does it have? | disclose |\n| 202 | general | What can you tell me about it? | hide |\n| 203 | general | What can you tell me about it? | hide |\n| 204 | neg_assumption | What problems does it have? | disclose |\n| 205 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 206 | general | What can you tell me about it? | hide |\n| 207 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 208 | neg_assumption | What problems does it have? | hide |\n| 209 | general | What can you tell me about it? | hide |\n| 210 | neg_assumption | What problems does it have? | disclose |\n| 211 | general | What can you tell me about it? | hide |\n| 212 | general | What can you tell me about it? | hide |\n| 213 | general | What can you tell me about it? | hide |\n| 214 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 215 | pos_assumption | It doesn't have any problems, does it? | hide |\n| 216 | pos_assumption | It doesn't have any problems, does it? | disclose |\n| 217 | general | What can you tell me about it? | hide |\n| 218 | general | What can you tell me about it? | hide |"
] |
NS
|
QRData
| |
coverbench
|
The event rate of the Poisson distribution used to approximate the data is 4.7.
|
[
"The CSV file ami_occurrences.csv contains occurrences of acute myocardial infarction (AMI) on 365 days in New York City.\n\nami_occurrences.csv\n| | ami |\n|----:|------:|\n| 0 | 3 |\n| 1 | 4 |\n| 2 | 5 |\n| 3 | 7 |\n| 4 | 3 |\n| 5 | 7 |\n| 6 | 8 |\n| 7 | 5 |\n| 8 | 5 |\n| 9 | 1 |\n| 10 | 3 |\n| 11 | 2 |\n| 12 | 5 |\n| 13 | 4 |\n| 14 | 6 |\n| 15 | 4 |\n| 16 | 5 |\n| 17 | 10 |\n| 18 | 4 |\n| 19 | 6 |\n| 20 | 8 |\n| 21 | 3 |\n| 22 | 5 |\n| 23 | 2 |\n| 24 | 3 |\n| 25 | 4 |\n| 26 | 1 |\n| 27 | 4 |\n| 28 | 7 |\n| 29 | 3 |\n| 30 | 4 |\n| 31 | 5 |\n| 32 | 4 |\n| 33 | 2 |\n| 34 | 6 |\n| 35 | 5 |\n| 36 | 6 |\n| 37 | 2 |\n| 38 | 6 |\n| 39 | 4 |\n| 40 | 6 |\n| 41 | 5 |\n| 42 | 6 |\n| 43 | 4 |\n| 44 | 4 |\n| 45 | 6 |\n| 46 | 1 |\n| 47 | 4 |\n| 48 | 6 |\n| 49 | 5 |\n| 50 | 4 |\n| 51 | 7 |\n| 52 | 4 |\n| 53 | 3 |\n| 54 | 2 |\n| 55 | 2 |\n| 56 | 3 |\n| 57 | 4 |\n| 58 | 5 |\n| 59 | 4 |\n| 60 | 7 |\n| 61 | 3 |\n| 62 | 4 |\n| 63 | 3 |\n| 64 | 5 |\n| 65 | 3 |\n| 66 | 4 |\n| 67 | 6 |\n| 68 | 2 |\n| 69 | 7 |\n| 70 | 3 |\n| 71 | 6 |\n| 72 | 3 |\n| 73 | 3 |\n| 74 | 4 |\n| 75 | 7 |\n| 76 | 7 |\n| 77 | 4 |\n| 78 | 6 |\n| 79 | 8 |\n| 80 | 4 |\n| 81 | 5 |\n| 82 | 4 |\n| 83 | 3 |\n| 84 | 6 |\n| 85 | 3 |\n| 86 | 5 |\n| 87 | 2 |\n| 88 | 3 |\n| 89 | 2 |\n| 90 | 3 |\n| 91 | 1 |\n| 92 | 5 |\n| 93 | 7 |\n| 94 | 6 |\n| 95 | 6 |\n| 96 | 4 |\n| 97 | 4 |\n| 98 | 6 |\n| 99 | 5 |\n| 100 | 5 |\n| 101 | 3 |\n| 102 | 3 |\n| 103 | 10 |\n| 104 | 5 |\n| 105 | 3 |\n| 106 | 2 |\n| 107 | 4 |\n| 108 | 8 |\n| 109 | 5 |\n| 110 | 9 |\n| 111 | 6 |\n| 112 | 3 |\n| 113 | 4 |\n| 114 | 2 |\n| 115 | 1 |\n| 116 | 5 |\n| 117 | 2 |\n| 118 | 4 |\n| 119 | 5 |\n| 120 | 10 |\n| 121 | 4 |\n| 122 | 4 |\n| 123 | 2 |\n| 124 | 6 |\n| 125 | 4 |\n| 126 | 4 |\n| 127 | 3 |\n| 128 | 3 |\n| 129 | 5 |\n| 130 | 5 |\n| 131 | 2 |\n| 132 | 1 |\n| 133 | 5 |\n| 134 | 8 |\n| 135 | 5 |\n| 136 | 5 |\n| 137 | 4 |\n| 138 | 9 |\n| 139 | 4 |\n| 140 | 5 |\n| 141 | 5 |\n| 142 | 3 |\n| 143 | 3 |\n| 144 | 6 |\n| 145 | 4 |\n| 146 | 2 |\n| 147 | 6 |\n| 148 | 2 |\n| 149 | 7 |\n| 150 | 5 |\n| 151 | 5 |\n| 152 | 3 |\n| 153 | 4 |\n| 154 | 4 |\n| 155 | 2 |\n| 156 | 4 |\n| 157 | 2 |\n| 158 | 3 |\n| 159 | 3 |\n| 160 | 3 |\n| 161 | 7 |\n| 162 | 4 |\n| 163 | 6 |\n| 164 | 7 |\n| 165 | 4 |\n| 166 | 1 |\n| 167 | 3 |\n| 168 | 6 |\n| 169 | 3 |\n| 170 | 5 |\n| 171 | 6 |\n| 172 | 7 |\n| 173 | 4 |\n| 174 | 4 |\n| 175 | 7 |\n| 176 | 5 |\n| 177 | 6 |\n| 178 | 5 |\n| 179 | 7 |\n| 180 | 3 |\n| 181 | 3 |\n| 182 | 7 |\n| 183 | 4 |\n| 184 | 7 |\n| 185 | 3 |\n| 186 | 6 |\n| 187 | 6 |\n| 188 | 8 |\n| 189 | 4 |\n| 190 | 5 |\n| 191 | 4 |\n| 192 | 2 |\n| 193 | 8 |\n| 194 | 3 |\n| 195 | 5 |\n| 196 | 2 |\n| 197 | 6 |\n| 198 | 3 |\n| 199 | 6 |\n| 200 | 3 |\n| 201 | 3 |\n| 202 | 4 |\n| 203 | 3 |\n| 204 | 2 |\n| 205 | 4 |\n| 206 | 5 |\n| 207 | 2 |\n| 208 | 3 |\n| 209 | 5 |\n| 210 | 8 |\n| 211 | 2 |\n| 212 | 6 |\n| 213 | 8 |\n| 214 | 6 |\n| 215 | 3 |\n| 216 | 5 |\n| 217 | 8 |\n| 218 | 8 |\n| 219 | 3 |\n| 220 | 3 |\n| 221 | 2 |\n| 222 | 3 |\n| 223 | 4 |\n| 224 | 8 |\n| 225 | 4 |\n| 226 | 3 |\n| 227 | 1 |\n| 228 | 4 |\n| 229 | 7 |\n| 230 | 3 |\n| 231 | 2 |\n| 232 | 4 |\n| 233 | 5 |\n| 234 | 4 |\n| 235 | 5 |\n| 236 | 5 |\n| 237 | 5 |\n| 238 | 4 |\n| 239 | 4 |\n| 240 | 3 |\n| 241 | 5 |\n| 242 | 7 |\n| 243 | 2 |\n| 244 | 4 |\n| 245 | 3 |\n| 246 | 4 |\n| 247 | 2 |\n| 248 | 4 |\n| 249 | 8 |\n| 250 | 6 |\n| 251 | 8 |\n| 252 | 4 |\n| 253 | 5 |\n| 254 | 4 |\n| 255 | 2 |\n| 256 | 3 |\n| 257 | 4 |\n| 258 | 4 |\n| 259 | 8 |\n| 260 | 4 |\n| 261 | 3 |\n| 262 | 3 |\n| 263 | 6 |\n| 264 | 5 |\n| 265 | 2 |\n| 266 | 1 |\n| 267 | 6 |\n| 268 | 5 |\n| 269 | 2 |\n| 270 | 1 |\n| 271 | 2 |\n| 272 | 4 |\n| 273 | 2 |\n| 274 | 3 |\n| 275 | 3 |\n| 276 | 3 |\n| 277 | 2 |\n| 278 | 4 |\n| 279 | 6 |\n| 280 | 1 |\n| 281 | 4 |\n| 282 | 7 |\n| 283 | 4 |\n| 284 | 1 |\n| 285 | 2 |\n| 286 | 3 |\n| 287 | 2 |\n| 288 | 2 |\n| 289 | 3 |\n| 290 | 3 |\n| 291 | 2 |\n| 292 | 9 |\n| 293 | 3 |\n| 294 | 4 |\n| 295 | 5 |\n| 296 | 2 |\n| 297 | 2 |\n| 298 | 1 |\n| 299 | 8 |\n| 300 | 5 |\n| 301 | 2 |\n| 302 | 4 |\n| 303 | 4 |\n| 304 | 4 |\n| 305 | 10 |\n| 306 | 2 |\n| 307 | 6 |\n| 308 | 2 |\n| 309 | 4 |\n| 310 | 2 |\n| 311 | 3 |\n| 312 | 6 |\n| 313 | 5 |\n| 314 | 3 |\n| 315 | 4 |\n| 316 | 2 |\n| 317 | 3 |\n| 318 | 9 |\n| 319 | 5 |\n| 320 | 5 |\n| 321 | 3 |\n| 322 | 4 |\n| 323 | 11 |\n| 324 | 7 |\n| 325 | 8 |\n| 326 | 6 |\n| 327 | 6 |\n| 328 | 3 |\n| 329 | 6 |\n| 330 | 10 |\n| 331 | 3 |\n| 332 | 4 |\n| 333 | 6 |\n| 334 | 2 |\n| 335 | 4 |\n| 336 | 4 |\n| 337 | 2 |\n| 338 | 5 |\n| 339 | 9 |\n| 340 | 10 |\n| 341 | 2 |\n| 342 | 4 |\n| 343 | 4 |\n| 344 | 5 |\n| 345 | 3 |\n| 346 | 4 |\n| 347 | 2 |\n| 348 | 4 |\n| 349 | 6 |\n| 350 | 4 |\n| 351 | 1 |\n| 352 | 6 |\n| 353 | 4 |\n| 354 | 9 |\n| 355 | 3 |\n| 356 | 7 |\n| 357 | 2 |\n| 358 | 7 |\n| 359 | 4 |\n| 360 | 2 |\n| 361 | 3 |\n| 362 | 6 |\n| 363 | 3 |\n| 364 | 5 |"
] |
NS
|
QRData
| |
coverbench
|
The lower bound of the 95% confidence interval for the effect of fish oils on heart attacks for patients well-represented by those in the study is -0.004.
|
[
"The results in fish_oil_18.csv summarize each of the health outcomes for an experiment where 12,933 subjects received a 1g fish oil supplement daily and 12,938 received a placebo daily. The experiment's duration was 5-years. The first row represents the treatment group and the second row represents the placebo group.\n\nVariables\nmajor_cardio_event - Major cardiovascular event. (Primary end point.)\ncardio_event_expanded - Cardiovascular event in expanded composite endpoint.\nmyocardioal_infarction - Total myocardial infarction. (Heart attack.)\nstroke - Total stroke.\ncardio_death - Death from cardiovascular causes.\nPCI - Percutaneous coronary intervention.\nCABG - Coronary artery bypass graft.\ntotal_coronary_heart_disease - Total coronary heart disease.\nischemic_stroke - Ischemic stroke.\nhemorrhagic_stroke - Hemorrhagic stroke.\nchd_death - Death from coronary heart disease.\nmyocardial_infarction_death - Death from myocardial infarction.\nstroke_death - Death from stroke.\ninvasive_cancer - Invasive cancer of any type. (Primary end point.)\nbreast_cancer - Breast cancer.\nprostate_cancer - Prostate cancer.\ncolorectal_cancer - Colorectal cancer.\ncancer_death - Death from cancer.\ndeath - Death from any cause.\nmajor_cardio_event_after_2y - Major cardiovascular event, excluding the first 2 years of follow-up.\nmyocardial_infarction_after_2y - Total myocardial infarction, excluding the first 2 years of follow-up.\ninvasive_cancer_after_2y - Invasive cancer of any type, excluding the first 2 years of follow-up.\ncancer_death_after_2y - Death from cancer, excluding the first 2 years of follow-up.\ndeath_after_2y - Death from any cause, excluding the first 2 years of follow-up.\n\nfish_oil_18.csv\n| | major_cardio_event.major_cardio_event | major_cardio_event.no_event | cardio_event_expanded.cardio_event_expanded | cardio_event_expanded.no_event | myocardioal_infarction.myocardioal_infarction | myocardioal_infarction.no_event | stroke.stroke | stroke.no_event | cardio_death.cardio_death | cardio_death.no_event | PCI.PCI | PCI.not_performed | CABG.CABG | CABG.not_performed | total_coronary_heart_disease.total_coronary_heart_disease | total_coronary_heart_disease.not_present | ischemic_stroke.ischemic_stroke | ischemic_stroke.not_present | hemorrhagic_stroke.hemorrhagic_stroke | hemorrhagic_stroke.not_present | chd_death.chd_death | chd_death.not_present | myocardial_infarction_death.myocardial_infarction_death | myocardial_infarction_death.not_present | stroke_death.stroke_death | stroke_death.not_present | invasive_cancer.invasive_cancer | invasive_cancer.not_present | breast_cancer.breast_cancer | breast_cancer.not_present | prostate_cancer.prostate_cancer | prostate_cancer.not_present | colorectal_cancer.colorectal_cancer | colorectal_cancer.not_present | cancer_death.cancer_death | cancer_death.no_cancer_death | death.death | death.alive | major_cardio_event_after_2y.major_cardio_event_after_2y | major_cardio_event_after_2y.no_event | myocardial_infarction_after_2y.myocardial_infarction_after_2y | myocardial_infarction_after_2y.no_event | invasive_cancer_after_2y.invasive_cancer_after_2y | invasive_cancer_after_2y.no_event | cancer_death_after_2y.cancer_death_after_2y | cancer_death_after_2y.no_event | death_after_2y.death_after_2y | death_after_2y.remained_alive |\n|---:|----------------------------------------:|------------------------------:|----------------------------------------------:|---------------------------------:|------------------------------------------------:|----------------------------------:|----------------:|------------------:|----------------------------:|------------------------:|----------:|--------------------:|------------:|---------------------:|------------------------------------------------------------:|-------------------------------------------:|----------------------------------:|------------------------------:|----------------------------------------:|---------------------------------:|----------------------:|------------------------:|----------------------------------------------------------:|------------------------------------------:|----------------------------:|---------------------------:|----------------------------------:|------------------------------:|------------------------------:|----------------------------:|----------------------------------:|------------------------------:|--------------------------------------:|--------------------------------:|----------------------------:|-------------------------------:|--------------:|--------------:|----------------------------------------------------------:|---------------------------------------:|----------------------------------------------------------------:|------------------------------------------:|----------------------------------------------------:|------------------------------------:|----------------------------------------------:|---------------------------------:|--------------------------------:|--------------------------------:|\n| 0 | 386 | 12547 | 527 | 12406 | 145 | 12788 | 148 | 12785 | 142 | 12791 | 162 | 12771 | 85 | 12848 | 308 | 12625 | 111 | 12822 | 25 | 12908 | 37 | 12896 | 13 | 12920 | 22 | 12911 | 820 | 12113 | 117 | 12816 | 219 | 12714 | 54 | 12879 | 168 | 12765 | 493 | 12440 | 269 | 12664 | 94 | 12839 | 536 | 12397 | 126 | 12807 | 371 | 12562 |\n| 1 | 419 | 12519 | 567 | 12371 | 200 | 12738 | 142 | 12796 | 148 | 12790 | 208 | 12730 | 86 | 12852 | 370 | 12568 | 116 | 12822 | 19 | 12919 | 49 | 12889 | 26 | 12912 | 20 | 12918 | 797 | 12141 | 129 | 12809 | 192 | 12746 | 44 | 12894 | 173 | 12765 | 485 | 12453 | 301 | 12637 | 131 | 12807 | 476 | 12462 | 135 | 12803 | 381 | 12557 |"
] |
NS
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QRData
| |
coverbench
|
The correlation between the perceived competence of the Democratic candidate and the vote share differential of the Democratic candidate minus the Republican candidate is 0.770.
|
[
"Several psychologists have reported the intriguing result of an experiment showing that facial appearance predicts election outcomes better than chance. In their experiment, the researchers briefly showed student subjects the black-and-white head shots of two candidates from a US congressional election (winner and runner-up). The exposure of subjects to facial pictures lasted less than a second, and the subjects were then asked to evaluate the two candidates in terms of their perceived competence.\nThe researchers used these competence measures to predict election outcomes. The key hypothesis is whether or not a within-a-second evaluation of facial appearance can predict election outcomes. The CSV data set, face.csv, contains the data from the experiment. Note that we include data only from subjects who did not know the candidates’ political parties, their policies, or even which candidate was the incumbent or challenger. They were simply making snap judgments about which candidate appeared more competent based on their facial expression alone.\n\nVariable Description\ncongress: session of Congress\nyear: year of the election\nstate: state of the election\nwinner: name of the winner\nloser: name of the runner-up\nw.party: party of the winner\nl.party: party of the loser\nd.votes: number of votes for the Democratic candidate\nr.votes: number of votes for the Republican candidate\nd.comp: competence measure for the Democratic candidate\nr.comp: competence measure for the Republican candidate\n\nface.csv\n{\"year\":{\"0\":2000,\"1\":2000,\"2\":2000,\"3\":2000,\"4\":2000,\"5\":2000,\"6\":2000,\"7\":2000,\"8\":2000,\"9\":2000,\"10\":2000,\"11\":2000,\"12\":2000,\"13\":2000,\"14\":2000,\"15\":2000,\"16\":2000,\"17\":2000,\"18\":2000,\"19\":2000,\"20\":2000,\"21\":2000,\"22\":2000,\"23\":2000,\"24\":2000,\"25\":2000,\"26\":2000,\"27\":2000,\"28\":2000,\"29\":2002,\"30\":2002,\"31\":2002,\"32\":2002,\"33\":2002,\"34\":2002,\"35\":2002,\"36\":2002,\"37\":2002,\"38\":2002,\"39\":2002,\"40\":2002,\"41\":2002,\"42\":2002,\"43\":2002,\"44\":2002,\"45\":2002,\"46\":2002,\"47\":2002,\"48\":2002,\"49\":2002,\"50\":2002,\"51\":2002,\"52\":2002,\"53\":2002,\"54\":2002,\"55\":2002,\"56\":2002,\"57\":2002,\"58\":2002,\"59\":2004,\"60\":2004,\"61\":2004,\"62\":2004,\"63\":2004,\"64\":2004,\"65\":2004,\"66\":2004,\"67\":2004,\"68\":2004,\"69\":2004,\"70\":2004,\"71\":2004,\"72\":2004,\"73\":2004,\"74\":2004,\"75\":2004,\"76\":2004,\"77\":2004,\"78\":2004,\"79\":2004,\"80\":2004,\"81\":2004,\"82\":2004,\"83\":2004,\"84\":2004,\"85\":2004,\"86\":2004,\"87\":2004,\"88\":2004,\"89\":2004,\"90\":2004,\"91\":2006,\"92\":2006,\"93\":2006,\"94\":2006,\"95\":2006,\"96\":2006,\"97\":2006,\"98\":2006,\"99\":2006,\"100\":2006,\"101\":2006,\"102\":2006,\"103\":2006,\"104\":2006,\"105\":2006,\"106\":2006,\"107\":2006,\"108\":2006,\"109\":2006,\"110\":2006,\"111\":2006,\"112\":2006,\"113\":2006,\"114\":2006,\"115\":2006,\"116\":2006,\"117\":2006,\"118\":2006},\"state\":{\"0\":\"CA\",\"1\":\"DE\",\"2\":\"FL\",\"3\":\"GA\",\"4\":\"HI\",\"5\":\"IN\",\"6\":\"MA\",\"7\":\"MD\",\"8\":\"ME\",\"9\":\"MI\",\"10\":\"MN\",\"11\":\"MO\",\"12\":\"MT\",\"13\":\"ND\",\"14\":\"NE\",\"15\":\"NM\",\"16\":\"NV\",\"17\":\"OH\",\"18\":\"PA\",\"19\":\"RI\",\"20\":\"TN\",\"21\":\"TX\",\"22\":\"UT\",\"23\":\"VA\",\"24\":\"VT\",\"25\":\"WA\",\"26\":\"WI\",\"27\":\"WV\",\"28\":\"WY\",\"29\":\"AK\",\"30\":\"AL\",\"31\":\"AR\",\"32\":\"CO\",\"33\":\"DE\",\"34\":\"GA\",\"35\":\"IA\",\"36\":\"ID\",\"37\":\"IL\",\"38\":\"KY\",\"39\":\"LA\",\"40\":\"ME\",\"41\":\"MI\",\"42\":\"MN\",\"43\":\"MO\",\"44\":\"MT\",\"45\":\"NC\",\"46\":\"NE\",\"47\":\"NH\",\"48\":\"NJ\",\"49\":\"NM\",\"50\":\"OK\",\"51\":\"OR\",\"52\":\"RI\",\"53\":\"SC\",\"54\":\"SD\",\"55\":\"TN\",\"56\":\"TX\",\"57\":\"WV\",\"58\":\"WY\",\"59\":\"AL\",\"60\":\"AK\",\"61\":\"AR\",\"62\":\"CA\",\"63\":\"CO\",\"64\":\"CT\",\"65\":\"FL\",\"66\":\"GA\",\"67\":\"HI\",\"68\":\"IL\",\"69\":\"IN\",\"70\":\"IA\",\"71\":\"KY\",\"72\":\"KZ\",\"73\":\"LA\",\"74\":\"MD\",\"75\":\"MS\",\"76\":\"NC\",\"77\":\"ND\",\"78\":\"NV\",\"79\":\"NH\",\"80\":\"NY\",\"81\":\"OH\",\"82\":\"OK\",\"83\":\"OR\",\"84\":\"PA\",\"85\":\"SC\",\"86\":\"SD\",\"87\":\"UT\",\"88\":\"VT\",\"89\":\"WA\",\"90\":\"WI\",\"91\":\"AZ\",\"92\":\"CA\",\"93\":\"DE\",\"94\":\"FL\",\"95\":\"ME\",\"96\":\"MD\",\"97\":\"MA\",\"98\":\"MI\",\"99\":\"MN\",\"100\":\"MS\",\"101\":\"MO\",\"102\":\"MT\",\"103\":\"NE\",\"104\":\"NV\",\"105\":\"NJ\",\"106\":\"NM\",\"107\":\"ND\",\"108\":\"OH\",\"109\":\"PA\",\"110\":\"RI\",\"111\":\"TN\",\"112\":\"TX\",\"113\":\"UT\",\"114\":\"VA\",\"115\":\"WA\",\"116\":\"WV\",\"117\":\"WI\",\"118\":\"WY\"},\"winner\":{\"0\":\"Feinstein\",\"1\":\"Carper\",\"2\":\"Nelson\",\"3\":\"Miller\",\"4\":\"Akaka\",\"5\":\"Lugar\",\"6\":\"Kennedy\",\"7\":\"Sarbanes\",\"8\":\"Snowe\",\"9\":\"Stabenow\",\"10\":\"Dayton\",\"11\":\"Lott\",\"12\":\"Burns\",\"13\":\"Conrad\",\"14\":\"Nelson\",\"15\":\"Bingaman\",\"16\":\"Ensign\",\"17\":\"DeWine\",\"18\":\"Santorum\",\"19\":\"Chafee\",\"20\":\"Frist\",\"21\":\"Hutchison\",\"22\":\"Hatch\",\"23\":\"Allen\",\"24\":\"Jeffords\",\"25\":\"Cantwell\",\"26\":\"Kohl\",\"27\":\"Byrd\",\"28\":\"Thomas\",\"29\":\"Stevens\",\"30\":\"Sessions\",\"31\":\"Pryor\",\"32\":\"Allard\",\"33\":\"Biden\",\"34\":\"Chambliss\",\"35\":\"Harkin\",\"36\":\"Craig\",\"37\":\"Durbin\",\"38\":\"McConnell\",\"39\":\"Landrieu\",\"40\":\"Collins\",\"41\":\"Levin\",\"42\":\"Coleman\",\"43\":\"Talent\",\"44\":\"Baucus\",\"45\":\"Dole\",\"46\":\"Hagel\",\"47\":\"Sununu\",\"48\":\"Lautenber\",\"49\":\"Domenici\",\"50\":\"Inhofe\",\"51\":\"Smith\",\"52\":\"Reed\",\"53\":\"Graham\",\"54\":\"Johnson\",\"55\":\"Alexander\",\"56\":\"Cornyn\",\"57\":\"Rockefell\",\"58\":\"Enzi\",\"59\":\"Shelby\",\"60\":\"Murkowski\",\"61\":\"Lincoln\",\"62\":\"Boxer\",\"63\":\"Salazar\",\"64\":\"Dodd\",\"65\":\"Martinez\",\"66\":\"Isakson\",\"67\":\"Inouye\",\"68\":\"Obama\",\"69\":\"Bayh\",\"70\":\"Grassley\",\"71\":\"Brownback\",\"72\":\"Bunning\",\"73\":\"Vitter\",\"74\":\"Mikulski\",\"75\":\"Bond\",\"76\":\"Burr\",\"77\":\"Dorgan\",\"78\":\"Reid\",\"79\":\"Gregg\",\"80\":\"Schumer\",\"81\":\"Voinovich\",\"82\":\"Coburn\",\"83\":\"Wyden\",\"84\":\"Spekter\",\"85\":\"Demint\",\"86\":\"Thune\",\"87\":\"Bennett\",\"88\":\"Leahy\",\"89\":\"Murray\",\"90\":\"Feingold\",\"91\":\"Kyl,Jon\",\"92\":\"Feinstein\",\"93\":\"Carper,T\",\"94\":\"Nelson,B\",\"95\":\"Snowe,Ol\",\"96\":\"Cardin,B\",\"97\":\"Kennedy,\",\"98\":\"Stabenow,\",\"99\":\"Klobuchar\",\"100\":\"Lott,Tre\",\"101\":\"McCaskill\",\"102\":\"Tester,J\",\"103\":\"Nelson,B\",\"104\":\"Ensign,J\",\"105\":\"Menendez,\",\"106\":\"Bingaman,\",\"107\":\"Conrad,K\",\"108\":\"Brown,Sh\",\"109\":\"Casey,Bo\",\"110\":\"Whitehous\",\"111\":\"Corker,B\",\"112\":\"Hutchison\",\"113\":\"Hatch,Or\",\"114\":\"Webb,Jam\",\"115\":\"Cantwell,\",\"116\":\"Byrd,Rob\",\"117\":\"Kohl,Her\",\"118\":\"Thomas,C\"},\"loser\":{\"0\":\"Campbell\",\"1\":\"Roth\",\"2\":\"McCollum\",\"3\":\"Mattingly\",\"4\":\"Carroll\",\"5\":\"Johnson\",\"6\":\"Robinson\",\"7\":\"Rappaport\",\"8\":\"Lawrence\",\"9\":\"Abraham\",\"10\":\"Grams\",\"11\":\"Brown\",\"12\":\"Schweitzer\",\"13\":\"Sand\",\"14\":\"Stenberg\",\"15\":\"Redmond\",\"16\":\"Bernstein\",\"17\":\"Celeste\",\"18\":\"Klink\",\"19\":\"Weygand\",\"20\":\"Clark\",\"21\":\"Kelly\",\"22\":\"Howell\",\"23\":\"Robb\",\"24\":\"Flanagan\",\"25\":\"Gorton\",\"26\":\"Gillespie\",\"27\":\"Gallaher\",\"28\":\"Logan\",\"29\":\"Vondersaar\",\"30\":\"Parker\",\"31\":\"Hutchinson\",\"32\":\"Strickland\",\"33\":\"Clatworthy\",\"34\":\"Cleland\",\"35\":\"Ganske\",\"36\":\"Blinken\",\"37\":\"Durkin\",\"38\":\"Weinberg\",\"39\":\"Terrell\",\"40\":\"Pingree\",\"41\":\"Raczkowski\",\"42\":\"Mondale\",\"43\":\"Carnahan\",\"44\":\"Taylor\",\"45\":\"Bowles\",\"46\":\"Matulka\",\"47\":\"Shaheen\",\"48\":\"Forrester\",\"49\":\"Tristani\",\"50\":\"Walters\",\"51\":\"Bradbury\",\"52\":\"Tingle\",\"53\":\"Sanders\",\"54\":\"Thune\",\"55\":\"Clement\",\"56\":\"Kirk\",\"57\":\"Wolfe\",\"58\":\"Corcoran\",\"59\":\"Sowell\",\"60\":\"Knowles\",\"61\":\"Holt\",\"62\":\"Jones\",\"63\":\"Coors\",\"64\":\"Orchulli\",\"65\":\"Castor\",\"66\":\"Majette\",\"67\":\"Cavasso\",\"68\":\"Keyes\",\"69\":\"Scott\",\"70\":\"Small\",\"71\":\"Jones\",\"72\":\"Mongiardo\",\"73\":\"JohnKenne\",\"74\":\"Pipkin\",\"75\":\"Farmer\",\"76\":\"Bowles\",\"77\":\"Liffrig\",\"78\":\"Zizer\",\"79\":\"Haddock\",\"80\":\"Mills\",\"81\":\"Fingerhut\",\"82\":\"Carson\",\"83\":\"King\",\"84\":\"Hoeffel\",\"85\":\"Tenenbaum\",\"86\":\"Daschle\",\"87\":\"VanDam\",\"88\":\"McMullen\",\"89\":\"Nethercutt\",\"90\":\"Michels\",\"91\":\"Pederson,\",\"92\":\"Mountjoy,\",\"93\":\"Ting,Jan\",\"94\":\"Harris,Ka\",\"95\":\"Bright,Je\",\"96\":\"Steele,Mi\",\"97\":\"Chase,Ken\",\"98\":\"Vouchard,\",\"99\":\"Kennedy,M\",\"100\":\"Fleming,E\",\"101\":\"Talent,Ji\",\"102\":\"Burns,Con\",\"103\":\"Ricketts,\",\"104\":\"Carter,Ja\",\"105\":\"Kean,Tom\",\"106\":\"McCulloch,\",\"107\":\"Grotberg,\",\"108\":\"DeWine,Mi\",\"109\":\"Santorum,\",\"110\":\"Chafee,Li\",\"111\":\"Ford,Haro\",\"112\":\"Radnofsky,\",\"113\":\"Ashdown,P\",\"114\":\"Allen,Geo\",\"115\":\"McGavick,\",\"116\":\"JohnRaese\",\"117\":\"Lorge,Rob\",\"118\":\"Groutage,\"},\"w.party\":{\"0\":\"D\",\"1\":\"D\",\"2\":\"D\",\"3\":\"D\",\"4\":\"D\",\"5\":\"R\",\"6\":\"D\",\"7\":\"D\",\"8\":\"R\",\"9\":\"D\",\"10\":\"D\",\"11\":\"R\",\"12\":\"R\",\"13\":\"D\",\"14\":\"D\",\"15\":\"D\",\"16\":\"R\",\"17\":\"R\",\"18\":\"R\",\"19\":\"R\",\"20\":\"R\",\"21\":\"R\",\"22\":\"R\",\"23\":\"R\",\"24\":\"R\",\"25\":\"D\",\"26\":\"D\",\"27\":\"D\",\"28\":\"R\",\"29\":\"R\",\"30\":\"R\",\"31\":\"D\",\"32\":\"R\",\"33\":\"D\",\"34\":\"R\",\"35\":\"D\",\"36\":\"R\",\"37\":\"D\",\"38\":\"R\",\"39\":\"D\",\"40\":\"R\",\"41\":\"D\",\"42\":\"R\",\"43\":\"R\",\"44\":\"D\",\"45\":\"R\",\"46\":\"R\",\"47\":\"R\",\"48\":\"D\",\"49\":\"R\",\"50\":\"R\",\"51\":\"R\",\"52\":\"D\",\"53\":\"R\",\"54\":\"D\",\"55\":\"R\",\"56\":\"R\",\"57\":\"D\",\"58\":\"R\",\"59\":\"R\",\"60\":\"R\",\"61\":\"D\",\"62\":\"D\",\"63\":\"D\",\"64\":\"D\",\"65\":\"R\",\"66\":\"R\",\"67\":\"D\",\"68\":\"D\",\"69\":\"D\",\"70\":\"R\",\"71\":\"R\",\"72\":\"R\",\"73\":\"R\",\"74\":\"D\",\"75\":\"R\",\"76\":\"R\",\"77\":\"D\",\"78\":\"D\",\"79\":\"R\",\"80\":\"D\",\"81\":\"R\",\"82\":\"R\",\"83\":\"D\",\"84\":\"R\",\"85\":\"R\",\"86\":\"R\",\"87\":\"R\",\"88\":\"D\",\"89\":\"D\",\"90\":\"D\",\"91\":\"R\",\"92\":\"D\",\"93\":\"D\",\"94\":\"D\",\"95\":\"R\",\"96\":\"D\",\"97\":\"D\",\"98\":\"D\",\"99\":\"D\",\"100\":\"R\",\"101\":\"D\",\"102\":\"D\",\"103\":\"D\",\"104\":\"R\",\"105\":\"D\",\"106\":\"D\",\"107\":\"D\",\"108\":\"D\",\"109\":\"D\",\"110\":\"D\",\"111\":\"R\",\"112\":\"R\",\"113\":\"R\",\"114\":\"D\",\"115\":\"D\",\"116\":\"D\",\"117\":\"D\",\"118\":\"R\"},\"l.party\":{\"0\":\"R\",\"1\":\"R\",\"2\":\"R\",\"3\":\"R\",\"4\":\"R\",\"5\":\"D\",\"6\":\"R\",\"7\":\"R\",\"8\":\"D\",\"9\":\"R\",\"10\":\"R\",\"11\":\"D\",\"12\":\"D\",\"13\":\"R\",\"14\":\"R\",\"15\":\"R\",\"16\":\"D\",\"17\":\"D\",\"18\":\"D\",\"19\":\"D\",\"20\":\"D\",\"21\":\"D\",\"22\":\"D\",\"23\":\"D\",\"24\":\"D\",\"25\":\"R\",\"26\":\"R\",\"27\":\"R\",\"28\":\"D\",\"29\":\"D\",\"30\":\"D\",\"31\":\"R\",\"32\":\"D\",\"33\":\"R\",\"34\":\"D\",\"35\":\"R\",\"36\":\"D\",\"37\":\"R\",\"38\":\"D\",\"39\":\"R\",\"40\":\"D\",\"41\":\"R\",\"42\":\"D\",\"43\":\"D\",\"44\":\"R\",\"45\":\"D\",\"46\":\"D\",\"47\":\"D\",\"48\":\"R\",\"49\":\"D\",\"50\":\"D\",\"51\":\"D\",\"52\":\"R\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] |
NS
|
QRData
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coverbench
|
Shaving the incision site can increase the infection rate after spinal surgery.
|
[
"A prospective randomized clinical study.\nTo determine whether shaving the incision site before spinal surgery causes postsurgical infection.\nSpine surgeons usually shave the skin of the incision site immediately before surgery is performed. However, evidence from some surgical series suggests that presurgical shaving may increase the postsurgical infection rate. To our knowledge, no previously published studies have addressed this issue.\nA total of 789 patients scheduled to undergo spinal surgery were randomly allocated into 2 groups: those in whom the site of operation was shaved immediately before surgery (shaved group; 371 patients) and the patients in whom presurgical shaving was not performed (unshaved group; 418 patients). The mean duration of anesthesia and the infection rates in both groups were recorded and compared.\nThe duration of anesthesia did not differ in the 2 groups (P>0.05). A postoperative infection developed in 4 patients in the shaved group and in 1 patient in the nonshaved group (P<0.01)."
] |
S
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PubMedQA
| |
coverbench
|
Octogenarians are not at high risk for carotid endarterectomy.
|
[
"Several prospective randomized trials have proved carotid endarterectomy to be safe and effective for both symptomatic and asymptomatic patients younger than 80 years of age. Recently, carotid artery stenting (CAS) has been approved for use in selected high-risk patients. It has been proposed that being an octogenarian places patients in this high-risk category.\nAll patients between the ages of 80 to 89 years undergoing carotid endarterectomy during a 12-year period were included in the study. Information included indications for carotid endarterectomy, associated risk factors, length of stay, and hospital course. Perioperative morbidity and mortality, including neurologic events and myocardial infarction, were recorded.\nA total of 103 carotid endarterectomies were performed in 95 octogenarians. Procedures were performed on 59 men and 36 women. Indications for operation included symptomatic carotid stenosis in 44 patients (43%) and asymptomatic carotid stenosis in 59 (57%). Associated risk factors included diabetes mellitus (17%), hypertension (76%), coronary artery disease (28%), hyperlipidemia (39%), and history of smoking (42%). There were 4 perioperative neurologic complications, which included 1 transient ischemic attack (0.97%), 2 minor strokes (1.94%), and 1 major stroke (0.97%). There were no deaths."
] |
S
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PubMedQA
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coverbench
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A SPECT study with I-123-Ioflupane (DaTSCAN) in patients with essential tremor is uncorrelated with Parkinson's disease.
|
[
"The differential diagnosis between essential tremor (ET) and Parkinson's disease (PD) may be, in some cases, very difficult on clinical grounds alone. In addition, it is accepted that a small percentage of ET patients presenting symptoms and signs of possible PD may progress finally to a typical pattern of parkinsonism. Ioflupane, N-u-fluoropropyl-2a-carbomethoxy-3a-(4-iodophenyl) nortropane, also called FP-CIT, labelled with (123)I (commercially known as DaTSCAN) has been proven to be useful in the differential diagnosis between PD and ET and to confirm dopaminergic degeneration in patients with parkinsonism. The aim of this study is to identify dopaminergic degeneration in patients with PD and distinguish them from others with ET using semi-quantitative SPECT (123)I-Ioflupane (DaTSCAN) data in comparison with normal volunteers (NV), in addition with the respective ones of patients referred as suffering from ET, as well as, of patients with a PD diagnosis at an initial stage with a unilateral presentation of motor signs.\nTwenty-eight patients suffering from ET (10 males plus 18 females) and 28 NV (12 males and 16 females) were enroled in this study. In addition, 33 patients (11 males and 22 females) with an established diagnosis of PD with unilateral limb involvement (12 left hemi-body and 21 right hemi-body) were included for comparison with ET. We used DaTSCAN to obtain SPECT images and measure the radiopharmaceutical uptake in the striatum (S), as well as the caudate nucleus (CN) and putamen (P) in all individuals.\nQualitative (Visual) interpretation of the SPECT data did not find any difference in the uptake of the radiopharmaceutical at the level of the S, CN and P between NV and ET patients. Reduced accumulation of the radiopharmaceutical uptake was found in the P of all PD patients. Semiquantitative analysis revealed significant differences between NV and ET patients in the striatum, reduced in the latter. There was also a significant reduction in the tracer accumulation in the left putamen of patients with right hemi-parkinsonism compared to ET and NV. Patients with left hemi-parkinsonism, demonstrated reduced radioligand uptake in the right putamen in comparison with ET and NV. Clinical follow-up of 20 patients with ET at (so many months afterwards) revealed no significant change in clinical presentation, particularly no signs of PD. Follow-up DaTSCAN performed in 10 of them (so many months afterwards) was negative in all but one. This one had an equivocal baseline study which deteriorated 12 months later."
] |
S
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PubMedQA
| |
coverbench
|
Bezafibrate, a lipid-lowering peroxisome proliferator-activated receptors ligand, prevents colon cancer in patients with coronary artery disease.
|
[
"Epidemiologic studies have suggested that hypertriglyceridemia and insulin resistance are related to the development of colon cancer. Nuclear peroxisome proliferator-activated receptors (PPAR), which play a central role in lipid and glucose metabolism, had been hypothesized as being involved in colon cancerogenesis. In animal studies the lipid-lowering PPAR ligand bezafibrate suppressed colonic tumors. However, the effect of bezafibrate on colon cancer development in humans is unknown. Therefore, we proposed to investigate a possible preventive effect of bezafibrate on the development of colon cancer in patients with coronary artery disease during a 6-year follow-up.\nOur population included 3011 patients without any cancer diagnosis who were enrolled in the randomized, double blind Bezafibrate Infarction Prevention (BIP) Study. The patients received either 400 mg of bezafibrate retard (1506 patients) or placebo (1505 patients) once a day. Cancer incidence data were obtained by matching a subject's identification numbers with the National Cancer Registry. Each matched record was checked for correct identification.\nDevelopment of new cancer (all types) was recorded in 177 patients: in 79 (5.25%) patients from the bezafibrate group vs. 98 (6.51%) from the placebo group. Development of colon cancer was recorded in 25 patients: in 8 (0.53%) patients from the bezafibrate group vs. 17 (1.13%) from the placebo group, (Fisher's exact test: one side p = 0.05; two side p = 0.07). A difference in the incidence of cancer was only detectable after a 4 year lag and progressively increased with continued follow-up. On multivariable analysis the colon cancer risk in patients who received bezafibrate tended to be lower with a hazard ratio of 0.47 and 95% confidence interval 0.2-1.1."
] |
S
|
PubMedQA
| |
coverbench
|
Severe macrosomia is manifested at 11-14 weeks of gestation.
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[
"To determine the association between fetal biometry in the first or early second trimester and severe macrosomia at delivery.\nThis case-control study included 30 term severely macrosomic neonates; 90 appropriate-for-gestational age (AGA) neonates served as controls. All pregnancies underwent nuchal translucency (NT) screening at 11-14 weeks' gestation. Pregnancies were dated by accurate last menstrual period consistent with crown-rump length (CRL) measurements at the time of screening, early pregnancy CRL or date of fertilization. The association between birth weight and the difference between the measured and the expected CRL at the time of NT screening was analyzed.\nThe difference between measured and expected CRL, expressed both in mm and in days of gestation, was statistically greater in the severely macrosomic neonates compared with controls (mean, 6.66 +/- 4.78 mm vs. 1.17 +/- 4.6 mm, P<0.0001 and 3 +/- 2.2 days vs. 0.5 +/- 2.3 days, P<0.0001, respectively). Furthermore, there were significant correlations between the extent of macrosomia and the discrepancy between expected and measured fetal size at the time of NT screening (r = 0.47, P<0.01 and r = 0.48, P<0.01, respectively)."
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Transcranial direct current stimulation can be useful in differentiating unresponsive wakefulness syndrome from minimally conscious state patients.
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"Disorders of consciousness (DOC) diagnosis relies on the presence or absence of purposeful motor responsiveness, which characterizes the minimally conscious state (MCS) and the unresponsive wakefulness syndrome (UWS), respectively. Functional neuroimaging studies have raised the question of possible residual conscious awareness also in clinically-defined UWS patients. The aim of our study was to identify electrophysiological parameters, by means of a transcranial magnetic stimulation approach, which might potentially express the presence of residual networks sustaining fragmentary behavioral patterns, even when no conscious behavior can be observed.\nWe enrolled 25 severe DOC patients, following post-anoxic or traumatic brain injury and 20 healthy individuals (HC) as control group. Baseline electrophysiological evaluation evidenced, in comparison to HC, a partial preservation of cortical effective connectivity and excitability in clinically defined MCS, whereas these components were absent in clinically defined UWS. Then, we applied an anodal transcranial direct current stimulation (a-tDCS) protocol over the orbitofrontal cortex.\na-tDCS was able to boost cortical connectivity and excitability in all HC, MCS, and to unmask such excitability/connectivity in some UWS patients."
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A 9-month treatment is sufficient in tuberculous enterocolitis.
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"Tuberculosis has increased in parallel with the acquired immunodeficiency syndrome epidemic and the use of immunosuppressive therapy, and the growing incidence of extra-pulmonary tuberculosis, especially with intestinal involvement, reflects this trend. However, the duration of anti-tuberculous therapy has not been clarified in intestinal tuberculosis.AIM: To compare the efficacy of different treatment durations in tuberculous enterocolitis in terms of response and recurrence rates.\nForty patients with tuberculous enterocolitis were randomized prospectively: 22 patients into a 9-month and 18 into a 15-month group. Diagnosis was made either by colonoscopic findings of discrete ulcers and histopathological findings of caseating granuloma and/or acid-fast bacilli, or by clinical improvement after therapeutic trial. Patients were followed up with colonoscopy every other month until complete response or treatment completion, and then every 6 months for 1 year and annually. Complete response was defined as a resolution of symptoms and active tuberculosis by colonoscopy.\nComplete response was obtained in all patients in both groups. Two patients in the 9-month group and one in the 15-month group underwent operation due to intestinal obstruction and perianal fistula, respectively. No recurrence of active intestinal tuberculosis occurred during the follow-up period in either group."
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Magnetic resonance imaging can accurately predict concordant pain provocation during provocative disc injection.
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"To correlate magnetic resonance (MR) image findings with pain response by provocation discography in patients with discogenic low back pain, with an emphasis on the combination analysis of a high intensity zone (HIZ) and disc contour abnormalities.\nSixty-two patients (aged 17-68 years) with axial low back pain that was likely to be disc related underwent lumbar discography (178 discs tested). The MR images were evaluated for disc degeneration, disc contour abnormalities, HIZ, and endplate abnormalities. Based on the combination of an HIZ and disc contour abnormalities, four classes were determined: (1) normal or bulging disc without HIZ; (2) normal or bulging disc with HIZ; (3) disc protrusion without HIZ; (4) disc protrusion with HIZ. These MR image findings and a new combined MR classification were analyzed in the base of concordant pain determined by discography.\nDisc protrusion with HIZ [sensitivity 45.5%; specificity 97.8%; positive predictive value (PPV), 87.0%] correlated significantly with concordant pain provocation (P<0.01). A normal or bulging disc with HIZ was not associated with reproduction of pain. Disc degeneration (sensitivity 95.4%; specificity 38.8%; PPV 33.9%), disc protrusion (sensitivity 68.2%; specificity 80.6%; PPV 53.6%), and HIZ (sensitivity 56.8%; specificity 83.6%; PPV 53.2%) were not helpful in the identification of a disc with concordant pain."
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Magnetic resonance imaging can substitute for diagnostic arthroscopy in detecting occult posttraumatic lesions of the knee.
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"We investigated the actual role of MRI versus arthroscopy in the detection and characterization of occult bone and/or cartilage injuries in patients with previous musculoskeletal trauma of the knee, pain and severe functional impairment. Occult post-traumatic osteochondral injuries of the knee are trauma-related bone and/or cartilage damage missed at plain radiography.\nWe retrospectively selected 70 patients (men:women = 7:3; age range: 35 +/- 7 years) with a history of acute musculoskeletal trauma, negative conventional radiographs, pain and limited joint movements. All patients were submitted to conventional radiography, arthroscopy and MRI, the latter with 0.5 T units and T1-weighted SE. T2-weighted GE and FIR sequences with fat suppression.\nWe identified three types of occult post-traumatic injuries by morpho-topographic and signal intensity patterns: bone bruises (no. 25), subchondral (no. 33) and osteochondral (no. 35) injuries. Arthroscopy depicted 45 osteochondral and 19 chondral injuries. A bone bruise was defined as a typical subcortical area of signal loss, with various shapes, on T1-weighted images and of increased signal intensity on T2-weighted and FIR images. The cortical bone and articular cartilage were normal in all cases, while osteochondral injuries exhibited associated bone and cartilage damage with the same abnormal MR signal intensity. Sprain was the mechanism of injury in 52 cases, bruise in 12 and stress in 6. In 52 sprains (30 in valgus), the injury site was the lateral compartment in 92.3% of cases (100% in valgus), associated with meniscal damage in 73% of cases (90% in valgus) and with ligament injury in 90.4% (100% in valgus). In 12 bruises, the injury site was the lateral compartment in 58.3% of cases, the knee cap in 25% and the medial compartment in 16.7%; meniscal damage was associated in 25% of cases and ligament damage in 8.3%. In 6 stress injuries, the injury site was localized in the medial tibial condyle in 80% of cases, while meniscal and ligament tears were absent."
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T-cell deficiency can affect spatial learning ability following toluene exposure.
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"The present studywas designed to investigate the possible role of T cells in spatial learning ability in mouse after toluene exposure.\nEight-week-old male wild-type (WT) and nude mice of BALB/c strain were exposed to toluene (0, 9 and 90 ppm) in a nose-only exposure chamber for 30 min per day for 3 consecutive days and then once per week for 4 weeks. Twenty-four hours after the completion of exposure, we examined the spatial learning ability in each mouse using the Morris water maze apparatus.\nIn the acquisition phase, a longer escape latency was observed in nude mice exposed to 90 ppm toluene on days 3 and 4 when compared with corresponding WT mice. However, the effect of toluene on the escape latency was not significant in nude mice. In the probe trial, WT mice exposed to 90 ppm toluene showed poor retention memory compared with the control group. In the reversal phase, we did not find any significant difference between groups."
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Syncope during bathing in infants is a pediatric form of water-induced urticaria.
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"Apparent life-threatening events in infants are a difficult and frequent problem in pediatric practice. The prognosis is uncertain because of risk of sudden infant death syndrome.\nEight infants aged 2 to 15 months were admitted during a period of 6 years; they suffered from similar maladies in the bath: on immersion, they became pale, hypotonic, still and unreactive; recovery took a few seconds after withdrawal from the bath and stimulation. Two diagnoses were initially considered: seizure or gastroesophageal reflux but this was doubtful. The hypothesis of an equivalent of aquagenic urticaria was then considered; as for patients with this disease, each infant's family contained members suffering from dermographism, maladies or eruption after exposure to water or sun. All six infants had dermographism. We found an increase in blood histamine levels after a trial bath in the two infants tested. The evolution of these \"aquagenic maladies\" was favourable after a few weeks without baths. After a 2-7 year follow-up, three out of seven infants continue to suffer from troubles associated with sun or water."
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Antral follicle assessment can be a better predictor of outcome in IVF than age and FSH.
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"The purpose of this study is to determine if baseline antral follicle assessment may serve as additional information in predicting in vitro fertilization outcome.\nProspective, descriptive preliminary study of in vitro fertilization outcome. From July 1998 to July 1999, 224 patients underwent antral follicle assessment (follicle 2-6 mm in diameter) on baseline of the planned, stimulated in vitro fertilization cycle. The outcomes were analyzed with respect to antral follicle assessment (<or = 6 or>6), basal cycle day 3 follicle stimulated hormone (<or = 10 or>10 IU/L) and maternal age (<or = 35 or>35 years).\nThe clinical pregnancy rate was significantly higher in the group with baseline antral follicle>6 compared to that in the group with antral follicle<or = 6 (51% vs. 19%, respectively). Controlling for patient age, and basal follicle stimulated hormone, the pregnancy rate was significantly higher in the group with antral follicle>6 compared to that in the group with antral follicle<or = 6. The cancellation rate was significantly increased with advancing maternal age, elevated basal follicle stimulated hormone levels, and baseline antral follicle<or = 6. The cancellation rate was significantly higher in the group with antral follicle<or = 6 compared to that in the group with antral follicle>or = 6 (33% vs. 1%, respectively)."
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Oral endotracheal intubation efficacy is impaired in the helicopter environment.
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"Patients transported by helicopter often require advanced airway management. The purpose of this study was to determine whether or not the in-flight environment of air medical transport in a BO-105 helicopter impairs the ability of flight nurses to perform oral endotracheal intubation.\nThe study was conducted in an MBB BO-105 helicopter.\nFlight nurses performed three manikin intubations in each of the two study environments: on an emergency department stretcher and in-flight in the BO-105 helicopter.\nThe mean time required for in-flight intubation (25.9 +/- 10.9 seconds) was significantly longer than the corresponding time (13.2 +/- 2.8 seconds) required for intubation in the control setting (ANOVA, F = 38.7, p<.001). All intubations performed in the control setting were placed correctly in the trachea; there were two (6.7%) esophageal intubations in the in-flight setting. The difference in appropriate endotracheal intubation between the two settings was not significant (chi 2 = 0.3; p>0.05)."
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Multi-modal cervical physical therapy improves tinnitus in patients with cervicogenic somatic tinnitus.
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"Tinnitus can be related to many different aetiologies such as hearing loss or a noise trauma, but it can also be related to the somatosensory system of the cervical spine, called cervicogenic somatic tinnitus (CST). Case studies suggest a positive effect of cervical spine treatment on tinnitus complaints in patients with CST, but no experimental studies are available.\nTo investigate the effect of a multimodal cervical physical therapy treatment on tinnitus complaints in patients with CST.\nRandomized controlled trial.\nPatients with a combination of severe subjective tinnitus (Tinnitus Functional Index (TFI): 25-90 points) and neck complaints (Neck Bournemouth Questionnaire (NBQ) > 14 points).\nAll patients received cervical physical therapy for 6 weeks (12 sessions). Patients were randomized in an immediate-start therapy group (n = 19) and a 6-week delayed-start therapy group (n = 19).\nTFI and NBQ-scores were documented at baseline, after the wait-and-see period in the delayed-start group, after treatment and after 6 weeks follow-up. The Global Perceived Effect (GPE) was documented at all measuring moments, except at baseline.\nIn all patients (n = 38) TFI and NBQ-scores decreased significantly after treatment (p = 0.04 and p < 0.001). NBQ-scores remained significantly lower after follow-up (p = 0.001). Immediately after treatment, 53% (n = 38) experienced substantial improvement of tinnitus. This effect was maintained in 24% of patients after follow-up at six weeks."
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Gingival crevicular blood can be used to assess blood glucose levels.
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"Diabetes mellitus (DM) is undiagnosed in approximately half of the patients actually suffering from the disease. In addition, the prevalence of DM is more than twice as high as in patients with periodontitis when compared to periodontally healthy subjects. Thus, a high number of patients with periodontitis may have undiagnosed DM. The purpose of the present study was to evaluate whether blood oozing from a gingival crevice during routine periodontal examination can be used for determining glucose levels.\nObservational cross-sectional studies were carried out in 75 patients (43 males and 32 females) with chronic periodontitis who were divided into two groups: Group I and Group II, respectively. Blood oozing from the gingival crevices of anterior teeth following periodontal probing was collected with the stick of glucose self-monitoring device, and the blood glucose levels were measured. At the same time, finger-prick blood was taken for glucometric analysis and subsequent readings were recorded.\nThe patient's blood glucose values ranged from 74 to 256 mg/dl. The comparison between gingival crevicular blood and finger-prick blood showed a very strong correlation, with a t value of 3.97 (at P value = 0.001)."
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The long-term results of the transanal pull-through differ from those of the transabdominal pull-through.
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"The transanal endorectal pull-through (TERPT) is becoming the most popular procedure in the treatment of Hirschsprung disease (HD), but overstretching of the anal sphincters remains a critical issue that may impact the continence. This study examined the long-term outcome of TERPT versus conventional transabdominal (ABD) pull-through for HD.\nRecords of 41 patients more than 3 years old who underwent a pull-through for HD (TERPT, n = 20; ABD, n = 21) were reviewed, and their families were thoroughly interviewed and scored via a 15-item post-pull-through long-term outcome questionnaire. Patients were operated on between the years 1995 and 2003. During this time, our group transitioned from the ABD to the TERPT technique. Total scoring ranged from 0 to 40: 0 to 10, excellent; 11 to 20 good; 21 to 30 fair; 31 to 40 poor. A 2-tailed Student t test, analysis of covariance, as well as logistic and linear regression were used to analyze the collected data with confidence interval higher than 95%.\nOverall scores were similar. However, continence score was significantly better in the ABD group, and the stool pattern score was better in the TERPT group. A significant difference in age at interview between the 2 groups was noted; we therefore reanalyzed the data controlling for age, and this showed that age did not significantly affect the long-term scoring outcome between groups."
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The Retromandibular Transparotid Approach is a reliable option for the surgical treatment of Condylar Fractures.
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"This study evaluated the outcomes and complications of the surgical treatment of condylar fractures by the retromandibular transparotid approach. The authors hypothesized that such an approach would be safe and reliable for the treatment of most condylar fractures.\nA retrospective evaluation of patients who underwent surgical reduction of a condylar fracture from January 2012 to December 2014 at the Clinic of Dentistry and Maxillofacial Surgery of the University Hospital of Verona (Verona, Italy) was performed. Inclusion criteria were having undergone surgical treatment of condylar fractures with a retromandibular transparotid approach and the availability of computed tomograms of the preoperative and postoperative facial skeleton with a minimum follow-up of 1 year. Static and dynamic occlusal function, temporomandibular joint health status, presence of neurologic impairments, and esthetic outcomes were evaluated in all patients.\nThe sample was composed of 25 patients. Preinjury occlusion and temporomandibular joint health were restored in most patients. Esthetic outcomes were deemed satisfactory by clinicians and patients. Neither permanent neurologic impairments nor major postoperative complications were observed."
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Oxybutynin hydrochloride causes arrhythmia in children with bladder dysfunction.
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"This study represents a subset of a complete data set, considering only those children aged admitted to the Pediatric Surgery and Pediatric Nephrology Clinics during the period January 2011 to July 2012.\nIn this study, we have determined that the QT interval changes significantly depending on the use of oxybutynin. The QT changes increased cardiac arrhythmia in children."
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Adjuvant aromatase inhibitors increase the cardiovascular risk in postmenopausal women with early breast cancer.
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"Despite the advantages from using aromatase inhibitors (AIs) compared with tamoxifen for early breast cancer, an unexpectedly greater number of grade 3 and 4 cardiovascular events (CVAE) (as defined by National Cancer Institute of Canada-Common Toxicity Criteria [version 2.0] was demonstrated.\nPhase 3 randomized clinical trials (RCTs) comparing AI with tamoxifen in early breast cancer were considered eligible for this review. The event-based risk ratios (RRs) with 95% confidence intervals (95% CIs) were derived, and a test of heterogeneity was applied. Finally, absolute differences (ADs) in event rates and the number of patients needed to harm 1 patient (NNH) were determined.\nSeven eligible RCTs (19,818 patients) reported CVAE results. When considering all RCTs, the AD of the primary endpoint (CVAE) between the 2 arms (0.52%), tamoxifen versus AI, was statistically significant (RR, 1.31; 95% CI, 1.07-1.60; P= .007). This translated into an NNH value of 189 patients; when only third-generation AIs were considered, the difference (0.57%) remained significant (RR, 1.34; 95% CI, 1.09-1.63; P= .0038). Thromboembolic events were significantly more frequent in the tamoxifen arm, regardless of the strategy adopted (RR, 0.53; 95% CI, 0.42-0.65; P<.0001), without significant heterogeneity (P= .21). An AD of 1.17% and an NNH value of 85 patients were observed."
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The cell death in mesial temporal sclerosis is apoptotic.
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"Mesial temporal sclerosis (MTS) is characterized by neuronal loss in the hippocampus. Studies on experimental models and patients with intractable epilepsy suggest that apoptosis may be involved in neuronal death induced by recurrent seizures.\nWe searched evidence for apoptotic cell death in temporal lobes resected from drug-resistant epilepsy patients with MTS by using the terminal deoxynucleotidyl transferase (TdT) and digoxigenin-11-dUTP (TUNEL) method and immunohistochemistry for Bcl-2, Bax, and caspase-cleaved actin fragment, fractin. The temporal lobe specimens were obtained from 15 patients (six women and nine men; mean age, 29 +/- 8 years).\nUnlike that in normal adult brain, we observed Bcl-2 immunoreactivity in some of the remaining neurons dispersed throughout the hippocampus proper as well as in most of the reactive astroglia. Bax immunopositivity was increased in almost all neurons. Fractin immunostaining, an indicator of caspase activity, was detected in approximately 10% of these neurons. Despite increased Bax expression and activation of caspases, we could not find evidence for DNA fragmentation by TUNEL staining. We also could not detect typical apoptotic changes in nuclear morphology by Hoechst-33258 or hematoxylin counterstaining."
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Vaginal intraepithelial neoplasia has the same evolution as cervical intraepithelial neoplasia.
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"Vaginal intraepithelial neoplasia is a little known disease which could be related to risk factors different from simple HPV infections.\nTo ascertain wheter vaginal lesions have a natural history similar to cervical lesions.MATERIALS &\nA retrospective study to identify patients with vaginal lesions and synchronous cervical lesions through biopsy. The rate of mild cervical lesions (koilocytosis, warts, CIN I with and without koilocytosis) was compared with the rate of severe cervical lesions (CIN II and III, cervical carcinoma) in patients with mild vaginal lesions (warts and koilocytosis, and low-grade VAIN) and in patients with severe vaginal lesions (high-grade VAIN). Using koilocytosis as a marker, the rate of \"active\" cervical lesions was compared with the rate of \"non active\" cervical lesions in patients with \"active\" versus \"non active\" vaginal lesions. Finally, the rates of mild and severe cervical lesions were compared among each group of VAIN (low-grade, high-grade, with or without koilocytosis).\nIn patients with mild vaginal lesions, mild cervical lesions were significantly more frequent than severe cervical lesions. In patients with \"active\" vaginal lesions the rate of \"active\" cervical lesions was significantly higher than \"non active\" cervical lesions. The differences in rates of mild cervical lesions and severe cervical lesions among patients with high-grade VAIN and low-grade VAIN (with and without koilocytosis) were not significant."
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Technology alone is not sufficient to improve glycaemic control in patients with type 1 diabetes using telemedicine.
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"Each patient received a smartphone with an insulin dose advisor (IDA) and with (G3 group) or without (G2 group) the telemonitoring/teleconsultation function. Patients were classified as \"high users\" if the proportion of \"informed\" meals using the IDA exceeded 67% (median) and as \"low users\" if not. Also analyzed was the respective impact of the IDA function and teleconsultations on the final HbA1c levels.\nAmong the high users, the proportion of informed meals remained stable from baseline to the end of the study 6months later (from 78.1±21.5% to 73.8±25.1%; P=0.107), but decreased in the low users (from 36.6±29.4% to 26.7±28.4%; P=0.005). As expected, HbA1c improved in high users from 8.7% [range: 8.3-9.2%] to 8.2% [range: 7.8-8.7%]in patients with (n=26) vs without (n=30) the benefit of telemonitoring/teleconsultation (-0.49±0.60% vs -0.52±0.73%, respectively; P=0.879). However, although HbA1c also improved in low users from 9.0% [8.5-10.1] to 8.5% [7.9-9.6], those receiving support via teleconsultation tended to show greater improvement than the others (-0.93±0.97 vs -0.46±1.05, respectively; P=0.084)."
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Continuous intravenous infusion of low-concentration epinephrine impairs uterine blood flow in pregnant ewes.
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"Bolus intravenous injection of epinephrine can decrease uterine blood flow. This study examined the effects of intravenous infusion of epinephrine on uterine blood flow in the gravid ewe.\nMaternal and fetal vascular catheters and a maternal electromagnetic uterine artery flow probe were implanted in 10 near-term gravid ewes. After recovery, saline, 0.125% bupivacaine, 0.125% bupivacaine with 1:200,000 epinephrine, 0.125% bupivacaine with 1:400,000 epinephrine, and 0.125% bupivacaine with 1:800,000 epinephrine were infused into the maternal superior vena cava. Drugs were infused at 10 mL/h for 30 minutes and then at 20 mL/h for an additional 30 minutes. Animals also received an intravenous bolus of epinephrine 15 micrograms. Throughout all infusions, maternal heart rate, systemic and pulmonary blood pressures, uterine blood flow, cardiac output, and acid-base balance were measured, as well as fetal heart rate, blood pressure, and acid-base balance.\nEpinephrine 15 micrograms decreased uterine blood flow to 68 +/- 14% of baseline (mean +/- SD). Infusion of all solutions had no effect on any measured hemodynamic variable."
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Oral endotracheal intubation efficacy is not impaired in the helicopter environment.
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"Patients transported by helicopter often require advanced airway management. The purpose of this study was to determine whether or not the in-flight environment of air medical transport in a BO-105 helicopter impairs the ability of flight nurses to perform oral endotracheal intubation.\nThe study was conducted in an MBB BO-105 helicopter.\nFlight nurses performed three manikin intubations in each of the two study environments: on an emergency department stretcher and in-flight in the BO-105 helicopter.\nThe mean time required for in-flight intubation (25.9 +/- 10.9 seconds) was significantly longer than the corresponding time (13.2 +/- 2.8 seconds) required for intubation in the control setting (ANOVA, F = 38.7, p<.001). All intubations performed in the control setting were placed correctly in the trachea; there were two (6.7%) esophageal intubations in the in-flight setting. The difference in appropriate endotracheal intubation between the two settings was not significant (chi 2 = 0.3; p>0.05)."
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Lifetime prevalence of impetigo, molluscum, and herpes infection are increased in children with atopic dermatitis.
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"Cutaneous infections such as impetigo contagiosum (IC), molluscum contagiosum (MC) and herpes virus infection (HI) appear to be associated with atopic dermatitis (AD), but there are no reports of concrete epidemiological evidence.\nWe evaluated the association of childhood AD with these infections by conducting a population-based cross-sectional study.\nEnrolled in this study were 1117 children aged 0-6 years old attending nursery schools in Ishigaki City, Okinawa Prefecture, Japan. Physical examination was performed by dermatologists, and a questionnaire was completed on each child's history of allergic diseases including AD, asthma, allergic rhinitis and egg allergy, and that of skin infections including IC, MC and HI, as well as familial history of AD.\nIn 913 children (AD; 132), a history of IC, MC or HI was observed in 45.1%, 19.7%, and 2.5%, respectively. Multiple logistic regression analysis revealed that the odds of having a history of IC were 1.8 times higher in AD children than in non-AD children. Meanwhile, a history of MC was significantly correlated to the male gender, but not to a personal history of AD. As for HI, we found no correlated factors in this study."
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The severity of obstructive sleep apnea predicts patients requiring high continuous positive airway pressure.
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"To investigate polysomnographic and anthropomorphic factors predicting need of high optimal continuous positive airway pressure (CPAP).\nRetrospective data analysis.\nThree hundred fifty-three consecutive obstructive sleep apnea (OSA) patients who had a successful manual CPAP titration in our sleep disorders unit.\nThe mean optimal CPAP was 9.5 +/- 2.4 cm H2O. The optimal CPAP pressure increases with an increase in OSA severity from 7.79 +/- 2.2 in the mild, to 8.7 +/- 1.8 in the moderate, and to 10.1 +/- 2.3 cm H2O in the severe OSA group. A high CPAP was defined as the mean + 1 standard deviation (SD;>or =12 cm H2O). The predictor variables included apnea-hypopnea index (AHI), age, sex, body mass index (BMI), Epworth Sleepiness Scale (ESS), and the Multiple Sleep Latency Test (MSLT). High CPAP was required in 2 (6.9%), 6 (5.8%), and 63 (28.6%) patients with mild, moderate, and severe OSA, respectively. On univariate analysis, AHI, BMI, ESS score, and the proportion of males were significantly higher in those needing high CPAP. They also have a lower MSLT mean. On logistic regression, the use of high CPAP was 5.90 times more frequent (95% confidence interval 2.67-13.1) in severe OSA patients after adjustment for the other variables. The area under the receiver operator curve was 72.4%, showing that the model was adequate."
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Sternal fracture in growing children is a rare and often overlooked fracture.
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"Sternal fractures in childhood are rare. The aim of the study was to investigate the accident mechanism, the detection of radiological and sonographical criteria and consideration of associated injuries.\nIn the period from January 2010 to December 2012 all inpatients and outpatients with sternal fractures were recorded according to the documentation.\nA total of 4 children aged 5-14 years with a sternal fracture were treated in 2 years, 2 children were hospitalized for pain management and 2 remained in outpatient care."
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Cardiopulmonary bypass temperature does not affect postoperative euthyroid sick syndrome.
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"To determine if temperature during cardiopulmonary bypass (CPB) has an effect on perioperative and postoperative thyroid function.\nProspective study comparing thyroid function during and after hypothermic and normothermic CPB.\nCardiac surgical unit at a university-affiliated hospital.\nTwelve patients scheduled to undergo cardiac operations with normothermic (n = 6) or hypothermic (n = 6) CPB.\nBlood was analyzed for serum concentration of total thyroxine (TT4), total triiodothyronine (TT3), free T3 (fT3), reverse T3 (rT3), and thyroid stimulating hormone (TSH) preoperatively, 60 min after CPB was initiated, 30 min after discontinuing CPB, and on postoperative days (POD) 1, 3, and 5.\nPatients who underwent either cold (26 degrees +/- 5 degrees C) or warm (35 degrees +/- 1 degree C) CPB were comparable with regard to age, body weight, duration of CPB, cross-clamp time, use of inotropes, total heparin dose, and length of hospital stay. Incidence of postoperative myocardial infarction, congestive heart failure, and death were similar. In both groups, TT4 and TT3 were reduced below baseline values beginning with CPB and persisting for up to 5 days after CPB (p<0.05), free T3 was reduced for up to 3 days after CPB (p<0.05), mean serum rT3 was elevated on POD 1 and POD 3 (p<0.05), and TSH remained unchanged."
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The use of hydrophilic guidewires significantly improves technical success rates of peripheral PTA.
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"To determine whether the use of hydrophilic guidewires has increased the technical success rate of peripheral percutaneous transluminal angioplasty (PTA).MATERIAL/\nWe performed 125 procedures and analyzed the technical success rates of PTA using the conventional guidewire first and then if needed, the hydrophilic guidewire for iliac and SFA stenoses or occlusions. Angioplasty was performed on 25 stenosed, 25 occluded iliac arteries and 25 stenosed, 50 occluded femoral arteries. The result was defined as technical success when the lesion was crossed by a guidewire and balloon, then it was dilated with restoration of vessel lumen and less than 30% residual stenosis and the rise in ABI values was at least 0.15 after 24 hours.\nThe technical success rate after PTA of stenosed iliac arteries was achieved in 96% (24/25) using conventional wires and 100% using hydrophilic guidewire; in iliac occlusions, the rates were 60% (15/25) and 96%, respectively; in femoral stenosis - 84% (21/25) and 100%; in occlusions in the first group: lesions<10 cm -64% (16/25) and 96%, in the second group: lesions>10 cm -48% (12/25) and 88%. In the iliac group, there was no significant difference in the success of iliac stenosis PTA. However, there were significant changes in the success rates of PTA performed for SFA stenosis and iliac and SFA occlusions."
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PubMedQA
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coverbench
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Chest wall irradiation is informative after mastectomy and negative node breast cancer.
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"This study aims to evaluate local failure patterns in node negative breast cancer patients treated with post-mastectomy radiotherapy including internal mammary chain only.\nRetrospective analysis of 92 internal or central-breast node-negative tumours with mastectomy and external irradiation of the internal mammary chain at the dose of 50 Gy, from 1994 to 1998.\nLocal recurrence rate was 5 % (five cases). Recurrence sites were the operative scare and chest wall. Factors associated with increased risk of local failure were age<or = 40 years and tumour size greater than 20mm, without statistical significance."
] |
NS
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PubMedQA
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coverbench
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The use of the contralateral knee to assess joint line positions is inadequate for planning knee revision surgery.
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"Reconstructing the natural joint line in knee revision surgery improves clinical and functional outcome but may be challenging when both cartilage and bone were removed during previous operations. Assessing joint lines (JLs) by means of bony landmarks is inadvisable because of large variations in human anatomy. Because of the inherent symmetry of the human body, we hypothesised that JLs may be directly assessed by measuring the distances from the bony landmarks to the JL of the contralateral knee by means of radiographic images.\nUsing scaled weight-bearing radiographs in anteroposterior view of both knees, two independent observers measured the distances from the fibular head, the medial and lateral epicondyle, and the adductor tubercle to the JL. A two-sided p value of ≤0.05 was considered statistically significant.\nTwo hundred knees of 100 patients (50 men and 50 women) were examined. For the fibular head, the mean difference between the treated and the control knee was 0.0 mm with narrow confidence limits ranging from -1.1 to 1.1."
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NS
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PubMedQA
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coverbench
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Amoxapine as an antipsychotic is typical.
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"All currently available atypical antipsychotics have, at clinically relevant doses: i) high serotonin (5-HT)2 occupancy; ii) greater 5-HT2 than dopamine (D)2 occupancy; and iii) a higher incidence of extrapyramidal side effects when their D2 occupancy exceeds 80%. A review of pharmacologic and behavioral data suggested that amoxapine should also conform to this profile; therefore, we undertook a positron-emission tomography (PET) study of its 5-HT2 and D2 occupancy.\nSeven healthy volunteers received 50-250 mg/day of amoxapine for 5 days and then had [11C]-raclopride and [18F]-setoperone PET scans.\n5-HT2 receptors showed near saturation at doses of 100 mg/day and above. The D2 receptor occupancies showed a dose-dependent increase, never exceeding 80%; at all doses 5-HT2 occupancy exceeded D2 occupancy."
] |
NS
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PubMedQA
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coverbench
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Ultrasound imaging before puncture facilitates internal jugular vein cannulation.
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"To investigate whether prepuncture ultrasound evaluation of vascular anatomy facilitates internal jugular vein cannulation compared with landmark-guided puncture.\nProspective randomized study.\nSingle community hospital.\nAdult patients undergoing general anesthesia (n = 240).\nThe right internal jugular vein was cannulated using either anatomic landmarks or prepuncture ultrasound (3.75/7.5 MHz) guidance. In the landmark group, respiratory jugular venodilation was used as the primary landmark for locating the vein. Results of cannulation and the incidence of complications were compared.\nPatients were randomly assigned to the ultrasound or landmark group. Respiratory jugular venodilation was identified in 188 patients (78.3%), in whom results of cannulation did not differ between the 2 techniques with respect to the venous access rate (cannulated at the first attempt: 83.5% in the landmark v 85.7% in the ultrasound group), the success rate (cannulated within 3 attempts: 96.9% v 95.6%), and the incidence of arterial puncture (1.0% v 3.3%). In the remaining 52 respiratory jugular venodilation-unidentified patients, the access rate (30.4% v 86.2%, p<0.001) and the success rate (78.3 v 100%, p<0.05) were significantly better in the ultrasound group, and no arterial puncture was recorded in the ultrasound group, whereas the incidence was 13.0% in the landmark group. The results were similar regardless of the ultrasound frequency used."
] |
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PubMedQA
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coverbench
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Surgical management is unnecessary for the testicular remnant associated with the vanishing testes syndrome, as determined by histologic evaluation.
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[
"There is controversy surrounding the optimal management of the testicular remnant associated with the vanishing testes syndrome. Some urologists advocate the need for surgical exploration, whereas others believe this is unnecessary. These differing opinions are based on the variable reports of viable germ cell elements found within the testicular remnants. To better understand the pathology associated with this syndrome and the need for surgical management, we reviewed our experience regarding the incidence of viable germ cell elements within the testicular remnant.\nAn institutional review board-approved, retrospective review was performed of all consecutive patients undergoing exploration for a nonpalpable testis at Eastern Virginia Medical School and Geisinger Medical Center between 1994 and 2006. Patients who were found to have spermatic vessels and a vas deferens exiting a closed internal inguinal ring were included in this analysis.\nFifty-six patients underwent removal of the testicular remnant. Patient age ranged from 11 to 216 months. In 8 of the specimens (14%), we identified viable germ cell elements. In an additional 4 patients (7%), we identified seminiferous tubules without germ cell elements."
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NS
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PubMedQA
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coverbench
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Kenneth Ferrie and Geoff Ogilvy of Australia were the only players to score one over par.
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"Table Title: 2006 u.s. open (golf)\n{\"place\": {\"0\": \"1\", \"1\": \"2\", \"2\": \"t3\", \"3\": \"t3\", \"4\": \"t5\", \"5\": \"t5\", \"6\": \"t7\", \"7\": \"t7\", \"8\": \"t7\", \"9\": \"t7\"}, \"player\": {\"0\": \"steve stricker\", \"1\": \"colin montgomerie\", \"2\": \"kenneth ferrie\", \"3\": \"geoff ogilvy\", \"4\": \"jim furyk\", \"5\": \"p\\u00e1draig harrington\", \"6\": \"jason dufner\", \"7\": \"graeme mcdowell\", \"8\": \"phil mickelson\", \"9\": \"arron oberholser\"}, \"country\": {\"0\": \"united states\", \"1\": \"scotland\", \"2\": \"england\", \"3\": \"australia\", \"4\": \"united states\", \"5\": \"ireland\", \"6\": \"united states\", \"7\": \"northern ireland\", \"8\": \"united states\", \"9\": \"united states\"}, \"score\": {\"0\": \"70 + 69 = 139\", \"1\": \"69 + 71 = 140\", \"2\": \"71 + 70 = 141\", \"3\": \"71 + 70 = 141\", \"4\": \"70 + 72 = 142\", \"5\": \"70 + 72 = 142\", \"6\": \"72 + 71 = 143\", \"7\": \"71 + 72 = 143\", \"8\": \"70 + 73 = 143\", \"9\": \"75 + 68 = 143\"}, \"to par\": {\"0\": \"- 1\", \"1\": \"e\", \"2\": \"+ 1\", \"3\": \"+ 1\", \"4\": \"+ 2\", \"5\": \"+ 2\", \"6\": \"+ 3\", \"7\": \"+ 3\", \"8\": \"+ 3\", \"9\": \"+ 3\"}}"
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The Tampa Bay Buccaneers won five games during the 1988 season.
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"Table Title: 1988 tampa bay buccaneers season\n| | week | date | opponent | result | kickoff | game site | attendance | record |\n|---:|:-------|:--------------------|:---------------------|:------------|:----------|:--------------------------------|:-------------|:---------|\n| 0 | week | date | opponent | result | kickoff | game site | attendance | record |\n| 1 | 1 | september 4 , 1988 | philadelphia eagles | l 41 - 14 | 1:00 | tampa stadium | 43502 | 0 - 1 |\n| 2 | 2 | september 11 , 1988 | green bay packers | w 13 - 10 | 1:00 | lambeau field | 52584 | 1 - 1 |\n| 3 | 3 | september 18 , 1988 | phoenix cardinals | l 30 - 24 | 1:00 | tampa stadium | 35034 | 1 - 2 |\n| 4 | 4 | september 25 , 1988 | new orleans saints | l 13 - 9 | 1:00 | louisiana superdome | 66714 | 1 - 3 |\n| 5 | 5 | october 2 , 1988 | green bay packers | w 27 - 24 | 1:00 | tampa stadium | 40003 | 2 - 3 |\n| 6 | 6 | october 9 , 1988 | minnesota vikings | l 14 - 13 | 1:00 | hubert h humphrey metrodome | 55274 | 2 - 4 |\n| 7 | 7 | october 16 , 1988 | indianapolis colts | l 35 - 31 | 1:00 | hoosier dome | 53135 | 2 - 5 |\n| 8 | 8 | october 23 , 1988 | minnesota vikings | l 49 - 20 | 1:00 | tampa stadium | 48020 | 2 - 6 |\n| 9 | 9 | october 30 , 1988 | miami dolphins | l 17 - 14 | 1:00 | tampa stadium | 67352 | 2 - 7 |\n| 10 | 10 | november 6 , 1988 | chicago bears | l 28 - 10 | 1:00 | soldier field | 56892 | 2 - 8 |\n| 11 | 11 | november 13 , 1988 | detroit lions | w 23 - 20 | 1:00 | pontiac silverdome | 25956 | 3 - 8 |\n| 12 | 12 | november 20 , 1988 | chicago bears | l 27 - 15 | 1:00 | tampa stadium | 67070 | 3 - 9 |\n| 13 | 13 | november 27 , 1988 | atlanta falcons | l 17 - 10 | 1:00 | atlanta - fulton county stadium | 14020 | 3 - 10 |\n| 14 | 14 | december 4 , 1988 | buffalo bills | w 10 - 5 | 1:00 | tampa stadium | 49498 | 4 - 10 |\n| 15 | 15 | december 11 , 1988 | new england patriots | l 10 - 7 ot | 1:00 | sullivan stadium | 39889 | 4 - 11 |\n| 16 | 16 | december 18 , 1988 | detroit lions | w 21 - 10 | 1:00 | tampa stadium | 37778 | 5 - 11 |"
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Three golfers scored a +8 at the 1955 US Open.
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"Table Title: 1955 u.s. open (golf)\n| | place | player | country | score | to par |\n|---:|:--------|:------------------|:--------------|:--------------|:---------|\n| 0 | t1 | harvie ward (a) | united states | 74 + 70 = 144 | + 4 |\n| 1 | t1 | tommy bolt | united states | 67 + 77 = 144 | + 4 |\n| 2 | t3 | julius boros | united states | 76 + 69 = 145 | + 5 |\n| 3 | t3 | jack fleck | united states | 76 + 69 = 145 | + 5 |\n| 4 | t3 | ben hogan | united states | 72 + 73 = 145 | + 5 |\n| 5 | t3 | walker inman , jr | united states | 70 + 75 = 145 | + 5 |\n| 6 | t7 | sam snead | united states | 79 + 69 = 148 | + 8 |\n| 7 | t7 | bob harris | united states | 79 + 69 = 148 | + 8 |\n| 8 | t7 | jack burke , jr | united states | 71 + 77 = 148 | + 8 |\n| 9 | 10 | gene littler | united states | 76 + 73 = 149 | + 9 |"
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Chris Vermeulen retired after completing 16 laps in the 12th grid.
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"Table Title: 2008 japanese motorcycle grand prix\n{\"rider\": {\"0\": \"valentino rossi\", \"1\": \"casey stoner\", \"2\": \"dani pedrosa\", \"3\": \"jorge lorenzo\", \"4\": \"nicky hayden\", \"5\": \"loris capirossi\", \"6\": \"colin edwards\", \"7\": \"shinya nakano\", \"8\": \"andrea dovizioso\", \"9\": \"john hopkins\", \"10\": \"james toseland\", \"11\": \"randy de puniet\", \"12\": \"marco melandri\", \"13\": \"sylvain guintoli\", \"14\": \"anthony west\", \"15\": \"toni elias\", \"16\": \"alex de angelis\", \"17\": \"chris vermeulen\", \"18\": \"kousuke akiyoshi\"}, \"manufacturer\": {\"0\": \"yamaha\", \"1\": \"ducati\", \"2\": \"honda\", \"3\": \"yamaha\", \"4\": \"honda\", \"5\": \"suzuki\", \"6\": \"yamaha\", \"7\": \"honda\", \"8\": \"honda\", \"9\": \"kawasaki\", \"10\": \"yamaha\", \"11\": \"honda\", \"12\": \"ducati\", \"13\": \"ducati\", \"14\": \"kawasaki\", \"15\": \"ducati\", \"16\": \"honda\", \"17\": \"suzuki\", \"18\": \"suzuki\"}, \"laps\": {\"0\": 24, \"1\": 24, \"2\": 24, \"3\": 24, \"4\": 24, \"5\": 24, \"6\": 24, \"7\": 24, \"8\": 24, \"9\": 24, \"10\": 24, \"11\": 24, \"12\": 24, \"13\": 24, \"14\": 24, \"15\": 24, \"16\": 24, \"17\": 16, \"18\": 0}, \"time\": {\"0\": \"43:09.599\", \"1\": \"+ 1.943\", \"2\": \"+ 4.866\", \"3\": \"+ 6.165\", \"4\": \"+ 24.593\", \"5\": \"+ 25.685\", \"6\": \"+ 25.918\", \"7\": \"+ 26.003\", \"8\": \"+ 26.219\", \"9\": \"+ 37.131\", \"10\": \"+ 37.574\", \"11\": \"+ 38.020\", \"12\": \"+ 39.768\", \"13\": \"+ 45.846\", \"14\": \"+ 55.748\", \"15\": \"+ 59.320\", \"16\": \"+ 1:12.398\", \"17\": \"retirement\", \"18\": \"accident\"}, \"grid\": {\"0\": 4, \"1\": 2, \"2\": 5, \"3\": 1, \"4\": 3, \"5\": 6, \"6\": 7, \"7\": 9, \"8\": 13, \"9\": 11, \"10\": 10, \"11\": 8, \"12\": 16, \"13\": 15, \"14\": 17, \"15\": 14, \"16\": 18, \"17\": 12, \"18\": 19}}"
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The PRR Class GF25 locomotive classification resulted in the production of the largest number of locomotives.
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"Table Title: prr locomotive classification\n{\"prr class\": {\"0\": \"gs4\", \"1\": \"gf25\", \"2\": \"gf25a\", \"3\": \"gf28a\", \"4\": \"gf30a\"}, \"builders model\": {\"0\": \"44 ton\", \"1\": \"u25b\", \"2\": \"u25c\", \"3\": \"u28c\", \"4\": \"u30c\"}, \"build date\": {\"0\": \"1948 - 1950\", \"1\": \"1965\", \"2\": \"1965\", \"3\": \"1966\", \"4\": \"1967\"}, \"total produced\": {\"0\": 46, \"1\": 59, \"2\": 20, \"3\": 15, \"4\": 5}, \"wheel arrangement\": {\"0\": \"b - b\", \"1\": \"b - b\", \"2\": \"c - c\", \"3\": \"c - c\", \"4\": \"c - c\"}, \"service\": {\"0\": \"switcher\", \"1\": \"freight\", \"2\": \"freight\", \"3\": \"freight\", \"4\": \"freight\"}, \"power output\": {\"0\": NaN, \"1\": NaN, \"2\": NaN, \"3\": NaN, \"4\": NaN}}"
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coverbench
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John Rich directed the first seven episodes of The Brady Bunch.
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[
"Table Title: list of the brady bunch episodes\n| | episode | season | title | directed by | written by | original airdate | production code (order they were made) |\n|---:|----------:|---------:|:--------------------------------|:-----------------|:-----------------------------|:--------------------|-----------------------------------------:|\n| 0 | 1 | 101 | the honeymoon | john rich | sherwood schwartz | september 26 , 1969 | 0 |\n| 1 | 2 | 102 | dear libby | john rich | lois hire | october 3 , 1969 | 2 |\n| 2 | 3 | 103 | eenie , meenie , mommy , daddy | john rich | joanna lee | october 10 , 1969 | 5 |\n| 3 | 4 | 104 | alice doesn't live here anymore | john rich | paul west | october 17 , 1969 | 6 |\n| 4 | 5 | 105 | katchoo | john rich | william cowley | october 24 , 1969 | 4 |\n| 5 | 6 | 106 | a clubhouse is not a home | john rich | skip webster | october 31 , 1969 | 1 |\n| 6 | 7 | 107 | kitty karry - all is missing | john rich | al schwartz & bill freedman | november 7 , 1969 | 3 |\n| 7 | 8 | 108 | a - camping we will go | oscar rudolph | herbert finn & alan dinehart | november 14 , 1969 | 12 |\n| 8 | 9 | 109 | sorry , right number | george cahan | ruth brooks flippen | november 21 , 1969 | 9 |\n| 9 | 10 | 110 | every boy does it once | oscar rudolph | lois peyser & arnold peyser | december 5 , 1969 | 14 |\n| 10 | 11 | 111 | vote for brady | david alexander | elroy schwartz | december 12 , 1969 | 13 |\n| 11 | 12 | 112 | the voice of christmas | oscar rudolph | john fenton murray | december 19 , 1969 | 15 |\n| 12 | 13 | 113 | is there a doctor in the house | oscar rudolph | ruth brooks flippen | december 26 , 1969 | 10 |\n| 13 | 14 | 114 | father of the year | george cahan | skip webster | january 2 , 1970 | 7 |\n| 14 | 15 | 115 | 54 - 40 and fight | oscar rudolph | burt styler | january 9 , 1970 | 11 |\n| 15 | 16 | 116 | mike 's horror - scope | david alexander | ruth brooks flippen | january 16 , 1970 | 16 |\n| 16 | 17 | 117 | the undergraduate | oscar rudolph | david p harmon | january 23 , 1970 | 18 |\n| 17 | 18 | 118 | tiger! tiger! | herb wallerstein | elroy schwartz | january 30 , 1970 | 20 |\n| 18 | 19 | 119 | the big sprain | russ mayberry | tam spiva | february 6 , 1970 | 21 |\n| 19 | 20 | 120 | brace yourself | oscar rudolph | brad radnitz | february 13 , 1970 | 20 |\n| 20 | 21 | 121 | the hero | oscar rudolph | elroy schwartz | february 20 , 1970 | 22 |\n| 21 | 22 | 122 | the possible dream | oscar rudolph | al schwartz & bill freedman | february 27 , 1970 | 24 |\n| 22 | 23 | 123 | to move or not to move | oscar rudolph | paul west | march 6 , 1970 | 18 |\n| 23 | 24 | 124 | the grass is always greener | george cahan | david p harmon | march 13 , 1970 | 8 |"
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coverbench
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Australia and New Zealand shared a release date of May 2008, but the releases were distributed under different labels.
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[
"Table Title: lessons to be learned\n| | region | date | label | format | catalogue |\n|---:|:---------------------------|:------------------|:-------------------|:----------------------|:--------------|\n| 0 | united kingdom | 31 march 2008 | island | cd , digital download | 1763307 |\n| 1 | australia | 10 may 2008 | mushroom | cd , digital download | 5144275002 |\n| 2 | new zealand | 12 may 2008 | warner bros | cd , digital download | 5144275002 |\n| 3 | europe | 20 june 2008 | island | cd , digital download | 060251773945 |\n| 4 | brazil | 10 september 2008 | universal | cd | 602517739468 |\n| 5 | australia (deluxe edition) | 11 october 2008 | mushroom | cd | 5186504315 |\n| 6 | poland | 28 october 2008 | universal | cd | 1785089 |\n| 7 | united states | 17 march 2009 | universal republic | cd | b0012720 - 02 |"
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No Premier League team has made fewer than 10 appearances in UEFA competitions or fewer than 4 appearances in the League Cup.
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[
"Table Title: 2006 - 07 tottenham hotspur f.c. season\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>player</th>\n <th>position</th>\n <th>premier league</th>\n <th>fa cup</th>\n <th>league cup</th>\n <th>uefa cup</th>\n <th>total</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>michael dawson</td>\n <td>defender</td>\n <td>37</td>\n <td>6</td>\n <td>4</td>\n <td>10</td>\n <td>57</td>\n </tr>\n <tr>\n <th>1</th>\n <td>paul robinson</td>\n <td>goalkeeper</td>\n <td>38</td>\n <td>4</td>\n <td>3</td>\n <td>9</td>\n <td>54</td>\n </tr>\n <tr>\n <th>2</th>\n <td>pascal chimbonda</td>\n <td>defender</td>\n <td>33</td>\n <td>4</td>\n <td>4</td>\n <td>10</td>\n <td>51</td>\n </tr>\n <tr>\n <th>3</th>\n <td>jermain defoe</td>\n <td>forward</td>\n <td>33</td>\n <td>5</td>\n <td>5</td>\n <td>5</td>\n <td>48</td>\n </tr>\n <tr>\n <th>4</th>\n <td>robbie keane</td>\n <td>forward</td>\n <td>27</td>\n <td>5</td>\n <td>3</td>\n <td>9</td>\n <td>44</td>\n </tr>\n </tbody>\n</table>"
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Five players participating in the Champion Tour are from the United States.
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[
"Table Title: 2008 champions tour\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>rank</th>\n <th>player</th>\n <th>country</th>\n <th>earnings</th>\n <th>wins</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>hale irwin</td>\n <td>united states</td>\n <td>24920665</td>\n <td>45</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>gil morgan</td>\n <td>united states</td>\n <td>18964040</td>\n <td>25</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>dana quigley</td>\n <td>united states</td>\n <td>14406269</td>\n <td>11</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>bruce fleisher</td>\n <td>united states</td>\n <td>13990356</td>\n <td>18</td>\n </tr>\n <tr>\n <th>4</th>\n <td>5</td>\n <td>larry nelson</td>\n <td>united states</td>\n <td>13262808</td>\n <td>19</td>\n </tr>\n </tbody>\n</table>"
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All Class 253 trains have nine cars per set.
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[
"Table Title: british rail classes 253 , 254 and 255\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>class</th>\n <th>operator</th>\n <th>number</th>\n <th>year built</th>\n <th>cars per set</th>\n <th>unit numbers</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>class 253</td>\n <td>br western region</td>\n <td>27</td>\n <td>1975 - 1977</td>\n <td>9</td>\n <td>253001 - 253027</td>\n </tr>\n <tr>\n <th>1</th>\n <td>class 253</td>\n <td>br western region</td>\n <td>13</td>\n <td>1978 - 1979</td>\n <td>9</td>\n <td>253028 - 253040</td>\n </tr>\n <tr>\n <th>2</th>\n <td>class 253</td>\n <td>br cross country</td>\n <td>18</td>\n <td>1981 - 1982</td>\n <td>9</td>\n <td>253041 - 253058</td>\n </tr>\n <tr>\n <th>3</th>\n <td>class 254</td>\n <td>br eastern region br scottish region</td>\n <td>32</td>\n <td>1977 - 1979</td>\n <td>10</td>\n <td>254001 - 254032</td>\n </tr>\n <tr>\n <th>4</th>\n <td>class 254</td>\n <td>br eastern region br scottish region</td>\n <td>4</td>\n <td>1982</td>\n <td>10</td>\n <td>254033 - 254037</td>\n </tr>\n </tbody>\n</table>"
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José María Olazabal is the only Spanish player to have scored 4 under par.
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[
"Table Title: 2005 open championship\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>place</th>\n <th>player</th>\n <th>country</th>\n <th>score</th>\n <th>to par</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>tiger woods</td>\n <td>united states</td>\n <td>66</td>\n <td>6</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>mark hensby</td>\n <td>australia</td>\n <td>67</td>\n <td>5</td>\n </tr>\n <tr>\n <th>2</th>\n <td>t3</td>\n <td>fred couples</td>\n <td>united states</td>\n <td>68</td>\n <td>4</td>\n </tr>\n <tr>\n <th>3</th>\n <td>t3</td>\n <td>luke donald</td>\n <td>england</td>\n <td>68</td>\n <td>4</td>\n </tr>\n <tr>\n <th>4</th>\n <td>t3</td>\n <td>retief goosen</td>\n <td>south africa</td>\n <td>68</td>\n <td>4</td>\n </tr>\n <tr>\n <th>5</th>\n <td>t3</td>\n <td>trevor immelman</td>\n <td>south africa</td>\n <td>68</td>\n <td>4</td>\n </tr>\n <tr>\n <th>6</th>\n <td>t3</td>\n <td>peter lonard</td>\n <td>australia</td>\n <td>68</td>\n <td>4</td>\n </tr>\n <tr>\n <th>7</th>\n <td>t3</td>\n <td>josé maría olazábal</td>\n <td>spain</td>\n <td>68</td>\n <td>4</td>\n </tr>\n <tr>\n <th>8</th>\n <td>t3</td>\n <td>eric ramsay (a)</td>\n <td>scotland</td>\n <td>68</td>\n <td>4</td>\n </tr>\n <tr>\n <th>9</th>\n <td>t3</td>\n <td>chris riley</td>\n <td>united states</td>\n <td>68</td>\n <td>4</td>\n </tr>\n <tr>\n <th>10</th>\n <td>t3</td>\n <td>tino schuster</td>\n <td>germany</td>\n <td>68</td>\n <td>4</td>\n </tr>\n <tr>\n <th>11</th>\n <td>t3</td>\n <td>scott verplank</td>\n <td>united states</td>\n <td>68</td>\n <td>4</td>\n </tr>\n </tbody>\n</table>"
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The majority of standardized names for Asymmetric Digital Subscriber Line (ADSL) begin with the prefix "ITU".
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[
"Table Title: asymmetric digital subscriber line\n{\"version\": {\"0\": \"adsl\", \"1\": \"adsl\", \"2\": \"adsl\", \"3\": \"adsl\", \"4\": \"adsl\", \"5\": \"adsl2\", \"6\": \"adsl2\", \"7\": \"adsl2\", \"8\": \"adsl2\", \"9\": \"adsl2 +\", \"10\": \"adsl2 +\", \"11\": \"adsl2 + +\"}, \"standard name\": {\"0\": \"ansi t1.413 - 1998 issue 2\", \"1\": \"itu g992.1\", \"2\": \"itu g992.1 annex a\", \"3\": \"itu g992.1 annex b\", \"4\": \"itu g992.2\", \"5\": \"itu g992.3\", \"6\": \"itu g992.3 annex j\", \"7\": \"itu g992.3 annex l\", \"8\": \"itu g992.4\", \"9\": \"itu g992.5\", \"10\": \"itu g992.5 annex m\", \"11\": \"(up to 3.75 mhz)\"}, \"common name\": {\"0\": \"adsl\", \"1\": \"adsl ( gdmt )\", \"2\": \"adsl over pots\", \"3\": \"adsl over isdn\", \"4\": \"adsl lite ( glite )\", \"5\": \"adsl2\", \"6\": \"adsl2\", \"7\": \"re - adsl2\", \"8\": \"splitterless adsl2\", \"9\": \"adsl2 +\", \"10\": \"adsl2 + m\", \"11\": \"adsl4\"}, \"downstream rate\": {\"0\": \"08.0 8.0 mbit / s\", \"1\": \"8.0 mbit / s\", \"2\": \"12.0 mbit / s\", \"3\": \"12.0 mbit / s\", \"4\": \"01.5 1.5 mbit / s\", \"5\": \"12.0 mbit / s\", \"6\": \"12.0 mbit / s\", \"7\": \"05.0 5.0 mbit / s\", \"8\": \"01.5 1.5 mbit / s\", \"9\": \"20.0 mbit / s\", \"10\": \"24.0 mbit / s\", \"11\": \"52.0 mbit / s\"}, \"upstream rate\": {\"0\": \"1.0 mbit / s\", \"1\": \"1.3 mbit / s\", \"2\": \"1.3 mbit / s\", \"3\": \"1.8 mbit / s\", \"4\": \"0.5 mbit / s\", \"5\": \"1.3 mbit / s\", \"6\": \"3.5 mbit / s\", \"7\": \"0.8 mbit / s\", \"8\": \"0.5 mbit / s\", \"9\": \"1.1 mbit / s\", \"10\": \"3.3 mbit / s\", \"11\": \"5.0 mbit / s \"}}"
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José David de Gea is the only rider who did not complete a single lap.
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[
"Table Title: 1996 malaysian motorcycle grand prix\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>rider</th>\n <th>manufacturer</th>\n <th>laps</th>\n <th>time / retired</th>\n <th>grid</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>stefano perugini</td>\n <td>aprilia</td>\n <td>29</td>\n <td>44:46.542</td>\n <td>4</td>\n </tr>\n <tr>\n <th>1</th>\n <td>haruchika aoki</td>\n <td>honda</td>\n <td>29</td>\n <td>+ 0.405</td>\n <td>7</td>\n </tr>\n <tr>\n <th>2</th>\n <td>peter öttl</td>\n <td>aprilia</td>\n <td>29</td>\n <td>+ 0.758</td>\n <td>10</td>\n </tr>\n <tr>\n <th>3</th>\n <td>masaki tokudome</td>\n <td>aprilia</td>\n <td>29</td>\n <td>+ 0.785</td>\n <td>9</td>\n </tr>\n <tr>\n <th>4</th>\n <td>emilio alzamora</td>\n <td>honda</td>\n <td>29</td>\n <td>+ 1.267</td>\n <td>3</td>\n </tr>\n <tr>\n <th>5</th>\n <td>valentino rossi</td>\n <td>aprilia</td>\n <td>29</td>\n <td>+ 7.379</td>\n <td>13</td>\n </tr>\n <tr>\n <th>6</th>\n <td>tomomi manako</td>\n <td>honda</td>\n <td>29</td>\n <td>+ 7.406</td>\n <td>5</td>\n </tr>\n <tr>\n <th>7</th>\n <td>akira saito</td>\n <td>honda</td>\n <td>29</td>\n <td>+ 7.814</td>\n <td>11</td>\n </tr>\n <tr>\n <th>8</th>\n <td>noboru ueda</td>\n <td>honda</td>\n <td>29</td>\n <td>+ 18.034</td>\n <td>14</td>\n </tr>\n <tr>\n <th>9</th>\n <td>kazuto sakata</td>\n <td>aprilia</td>\n <td>29</td>\n <td>+ 35.742</td>\n <td>1</td>\n </tr>\n <tr>\n <th>10</th>\n <td>frédéric petit</td>\n <td>honda</td>\n <td>29</td>\n <td>+ 39.738</td>\n <td>28</td>\n </tr>\n <tr>\n <th>11</th>\n <td>garry mccoy</td>\n <td>aprilia</td>\n <td>29</td>\n <td>+ 39.812</td>\n <td>18</td>\n </tr>\n <tr>\n <th>12</th>\n <td>andrea ballerini</td>\n <td>aprilia</td>\n <td>29</td>\n <td>+ 57.770</td>\n <td>19</td>\n </tr>\n <tr>\n <th>13</th>\n <td>jaroslav huleš</td>\n <td>honda</td>\n <td>29</td>\n <td>+ 1:10.237</td>\n <td>17</td>\n </tr>\n <tr>\n <th>14</th>\n <td>paolo tessari</td>\n <td>honda</td>\n <td>29</td>\n <td>+ 1:10.772</td>\n <td>22</td>\n </tr>\n <tr>\n <th>15</th>\n <td>ivan goi</td>\n <td>honda</td>\n <td>29</td>\n <td>+ 1:12.419</td>\n <td>26</td>\n </tr>\n <tr>\n <th>16</th>\n <td>stefano cruciani</td>\n <td>aprilia</td>\n <td>29</td>\n <td>+ 1:34.951</td>\n <td>25</td>\n </tr>\n <tr>\n <th>17</th>\n <td>loek bodelier</td>\n <td>honda</td>\n <td>28</td>\n <td>+ 1 lap</td>\n <td>21</td>\n </tr>\n <tr>\n <th>18</th>\n <td>josep sardá</td>\n <td>honda</td>\n <td>28</td>\n <td>+ 1 lap</td>\n <td>27</td>\n </tr>\n <tr>\n <th>19</th>\n <td>yau chuen tang</td>\n <td>yamaha</td>\n <td>27</td>\n <td>+ 2 laps</td>\n <td>33</td>\n </tr>\n <tr>\n <th>20</th>\n <td>dirk raudies</td>\n <td>honda</td>\n <td>17</td>\n <td>retirement</td>\n <td>2</td>\n </tr>\n <tr>\n <th>21</th>\n <td>ángel nieto , jr</td>\n <td>aprilia</td>\n <td>16</td>\n <td>retirement</td>\n <td>29</td>\n </tr>\n <tr>\n <th>22</th>\n <td>yasir said</td>\n <td>yamaha</td>\n <td>14</td>\n <td>retirement</td>\n <td>31</td>\n </tr>\n <tr>\n <th>23</th>\n <td>manfred geissler</td>\n <td>aprilia</td>\n <td>12</td>\n <td>retirement</td>\n <td>12</td>\n </tr>\n <tr>\n <th>24</th>\n <td>yoshiaki katoh</td>\n <td>yamaha</td>\n <td>9</td>\n <td>retirement</td>\n <td>8</td>\n </tr>\n <tr>\n <th>25</th>\n <td>youichi ui</td>\n <td>yamaha</td>\n <td>8</td>\n <td>retirement</td>\n <td>23</td>\n </tr>\n <tr>\n <th>26</th>\n <td>chow yan kit</td>\n <td>yamaha</td>\n <td>8</td>\n <td>retirement</td>\n <td>32</td>\n </tr>\n <tr>\n <th>27</th>\n <td>lucio cecchinello</td>\n <td>honda</td>\n <td>7</td>\n <td>retirement</td>\n <td>16</td>\n </tr>\n <tr>\n <th>28</th>\n <td>chao chee hou</td>\n <td>yamaha</td>\n <td>6</td>\n <td>retirement</td>\n <td>30</td>\n </tr>\n <tr>\n <th>29</th>\n <td>gabriele debbia</td>\n <td>yamaha</td>\n <td>6</td>\n <td>retirement</td>\n <td>15</td>\n </tr>\n <tr>\n <th>30</th>\n <td>jorge martínez</td>\n <td>aprilia</td>\n <td>3</td>\n <td>retirement</td>\n <td>6</td>\n </tr>\n <tr>\n <th>31</th>\n <td>darren barton</td>\n <td>aprilia</td>\n <td>3</td>\n <td>retirement</td>\n <td>24</td>\n </tr>\n <tr>\n <th>32</th>\n <td>josé david de gea</td>\n <td>honda</td>\n <td>0</td>\n <td>retirement</td>\n <td>20</td>\n </tr>\n </tbody>\n</table>"
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The runner achieved third place twice: once in the 5000-meter event in 2007 and again in the 10000-meter event in 2009.
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[
"Table Title: moses ndiema masai\n{\"year\": {\"0\": 2004, \"1\": 2005, \"2\": 2005, \"3\": 2007, \"4\": 2008, \"5\": 2008, \"6\": 2009, \"7\": 2013}, \"competition\": {\"0\": \"world junior championships\", \"1\": \"african junior championships\", \"2\": \"african junior championships\", \"3\": \"world athletics final\", \"4\": \"world cross country championships\", \"5\": \"world cross country championships\", \"6\": \"world championships\", \"7\": \"okpekpe international road race\"}, \"venue\": {\"0\": \"grosseto , italy\", \"1\": \"rad\\u00e8s , tunisia\", \"2\": \"rad\\u00e8s , tunisia\", \"3\": \"stuttgart , germany\", \"4\": \"edinburgh , scotland\", \"5\": \"edinburgh , scotland\", \"6\": \"berlin , germany\", \"7\": \"okpekpe , nigeria\"}, \"position\": {\"0\": \"10th\", \"1\": \"1st\", \"2\": \"1st\", \"3\": \"3rd\", \"4\": \"5th\", \"5\": \"1st\", \"6\": \"3rd\", \"7\": \"1st\"}, \"event\": {\"0\": \"10000 m\", \"1\": \"5000 m\", \"2\": \"10000 m\", \"3\": \"5000 m\", \"4\": \"senior race\", \"5\": \"team competition\", \"6\": \"10000 m\", \"7\": \"10 kilometres\"}}"
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The Denver Broncos game held at San Diego Stadium in 1975 had the lowest attendance of any Broncos game that year.
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[
"Table Title: 1975 denver broncos season\n{\"week\": {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 6, \"6\": 7, \"7\": 8, \"8\": 9, \"9\": 10, \"10\": 11, \"11\": 12, \"12\": 13, \"13\": 14}, \"date\": {\"0\": \"september 21\", \"1\": \"september 29\", \"2\": \"october 5\", \"3\": \"october 12\", \"4\": \"october 19\", \"5\": \"october 26\", \"6\": \"november 26\", \"7\": \"november 9\", \"8\": \"november 16\", \"9\": \"november 23\", \"10\": \"november 30\", \"11\": \"december 8\", \"12\": \"december 14\", \"13\": \"december 20\"}, \"opponent\": {\"0\": \"kansas city chiefs\", \"1\": \"green bay packers\", \"2\": \"buffalo bills\", \"3\": \"pittsburgh steelers\", \"4\": \"cleveland browns\", \"5\": \"kansas city chiefs\", \"6\": \"oakland raiders\", \"7\": \"cincinnati bengals\", \"8\": \"san diego chargers\", \"9\": \"atlanta falcons\", \"10\": \"san diego chargers\", \"11\": \"oakland raiders\", \"12\": \"philadelphia eagles\", \"13\": \"miami dolphins\"}, \"result\": {\"0\": \"w 37 - 33\", \"1\": \"w 23 - 13\", \"2\": \"l 14 - 38\", \"3\": \"l 9 - 20\", \"4\": \"w 16 - 15\", \"5\": \"l 13 - 26\", \"6\": \"l 17 - 42\", \"7\": \"l 16 - 17\", \"8\": \"w 27 - 17\", \"9\": \"l 21 - 35\", \"10\": \"w 13 - 10 (ot)\", \"11\": \"l 10 - 17\", \"12\": \"w 25 - 10\", \"13\": \"l 13 - 14\"}, \"game site\": {\"0\": \"mile high stadium\", \"1\": \"mile high stadium\", \"2\": \"rich stadium\", \"3\": \"three rivers stadium\", \"4\": \"mile high stadium\", \"5\": \"arrowhead stadium\", \"6\": \"mile high stadium\", \"7\": \"mile high stadium\", \"8\": \"san diego stadium\", \"9\": \"atlanta - fulton county stadium\", \"10\": \"mile high stadium\", \"11\": \"oakland - alameda county coliseum\", \"12\": \"mile high stadium\", \"13\": \"miami orange bowl\"}, \"record\": {\"0\": \"1 - 0\", \"1\": \"2 - 0\", \"2\": \"2 - 1\", \"3\": \"2 - 2\", \"4\": \"3 - 2\", \"5\": \"3 - 3\", \"6\": \"3 - 4\", \"7\": \"3 - 5\", \"8\": \"4 - 5\", \"9\": \"4 - 6\", \"10\": \"5 - 6\", \"11\": \"5 - 7\", \"12\": \"6 - 7\", \"13\": \"6 - 8\"}, \"attendance\": {\"0\": 51858, \"1\": 52621, \"2\": 79864, \"3\": 49169, \"4\": 52590, \"5\": 70043, \"6\": 52505, \"7\": 49919, \"8\": 26048, \"9\": 28686, \"10\": 44982, \"11\": 51075, \"12\": 36860, \"13\": 43064}}"
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The third overall draft pick from Oklahoma is a colt.
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[
"Table Title: indianapolis colts draft history\n| | round | pick | overall | name | position | college |\n|---:|--------:|-------:|----------:|:------------------|:-----------------|:------------------|\n| 0 | 1 | 2 | 2 | billy vessels | halfback | oklahoma |\n| 1 | 2 | 1 | 14 | bernie flowers | end | purdue |\n| 2 | 3 | 1 | 26 | buck mcphail | fullback | oklahoma |\n| 3 | 4 | 1 | 38 | tom catlin | center | oklahoma |\n| 4 | 5 | 1 | 50 | jack little | offensive tackle | texas a&m |\n| 5 | 6 | 1 | 62 | jim sears | quarterback | usc |\n| 6 | 7 | 1 | 74 | bill athey | guard | baylor |\n| 7 | 8 | 1 | 86 | jim prewett | tackle | tulsa |\n| 8 | 9 | 1 | 98 | bob blair | tight end | tcu |\n| 9 | 10 | 1 | 110 | john cole | halfback | arkansas |\n| 10 | 11 | 1 | 122 | gene rossi | halfback | cincinnati |\n| 11 | 12 | 1 | 134 | kaye vaughn | guard | tulsa |\n| 12 | 13 | 1 | 146 | bobby moorhead | halfback | georgia tech |\n| 13 | 14 | 1 | 158 | frank continetti | guard | george washington |\n| 14 | 15 | 1 | 170 | buddy sutton | halfback | arkansas |\n| 15 | 16 | 1 | 182 | jim currin | end | dayton |\n| 16 | 17 | 1 | 194 | george rambour | tackle | dartmouth |\n| 17 | 18 | 1 | 206 | leroy labat | halfback | lsu |\n| 18 | 19 | 1 | 218 | bill powell | halfback | california |\n| 19 | 20 | 1 | 230 | pete russo | tackle | indiana |\n| 20 | 21 | 1 | 242 | frank kirby | tackle | bucknell |\n| 21 | 22 | 1 | 254 | merlin gish | center | kansas |\n| 22 | 23 | 1 | 266 | mike houseplan | guard | tulane |\n| 23 | 24 | 1 | 278 | monte brethauer | defensive back | oregon |\n| 24 | 25 | 1 | 290 | joe szombathy | end | syracuse |\n| 25 | 26 | 1 | 302 | scott prescott | center | minnesota |\n| 26 | 27 | 1 | 314 | ray graves | halfback | texas a&m |\n| 27 | 28 | 1 | 326 | joe sabol | halfback | ucla |\n| 28 | 29 | 1 | 338 | jack alessandrini | guard | notre dame |\n| 29 | 30 | 1 | 350 | tom roche | tackle | northwestern |"
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The BC Open is located in New York.
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[
"Table Title: 1979 pga tour\n| | date | tournament | location | winner | score | 1st prize |\n|---:|:-------|:---------------------------------------------|:---------------|:----------------------------------|:------------|:-------------|\n| 0 | jan 14 | bob hope desert classic | california | john mahaffey (4) | 343 ( - 17) | 50000 |\n| 1 | jan 22 | phoenix open | arizona | ben crenshaw (6) | 199 ( - 14) | 33750 |\n| 2 | jan 28 | andy williams - san diego open invitational | california | fuzzy zoeller (1) | 282 ( - 6) | 45000 |\n| 3 | feb 4 | bing crosby national pro - am | california | lon hinkle (2) | 284 ( - 4) | 54000 |\n| 4 | feb 11 | hawaiian open | hawaii | hubert green (15) | 267 ( - 21) | 54000 |\n| 5 | feb 18 | joe garagiola - tucson open | arizona | bruce lietzke (4) | 265 ( - 15) | 45000 |\n| 6 | feb 25 | glen campbell - los angeles open | california | lanny wadkins (6) | 276 ( - 8) | 45000 |\n| 7 | mar 4 | bay hill citrus classic | florida | bob byman (1) | 278 ( - 6) | 45000 |\n| 8 | mar 11 | jackie gleason - inverrary classic | florida | larry nelson (1) | 274 ( - 14) | 54000 |\n| 9 | mar 18 | doral - eastern open | florida | mark mccumber (1) | 279 ( - 9) | 45000 |\n| 10 | mar 25 | tournament players championship | florida | lanny wadkins (7) | 283 ( - 5) | 72000 |\n| 11 | apr 1 | sea pines heritage classic | south carolina | tom watson (14) | 270 ( - 14) | 54000 |\n| 12 | apr 8 | greater greensboro open | north carolina | raymond floyd (11) | 282 ( - 6) | 45000 |\n| 13 | apr 15 | masters tournament | georgia | fuzzy zoeller (2) | 280 ( - 8) | 50000 |\n| 14 | apr 22 | tallahassee open | florida | chi chi rodriguez (8) | 269 ( - 19) | 18000 |\n| 15 | apr 22 | mony tournament of champions | california | tom watson (15) | 275 ( - 13) | 54000 |\n| 16 | apr 29 | first nbc new orleans open | louisiana | hubert green (16) | 273 ( - 15) | 45000 |\n| 17 | may 6 | houston open | texas | wayne levi (2) | 268 ( - 16) | 54000 |\n| 18 | may 13 | byron nelson golf classic | texas | tom watson (16) | 275 ( - 5) | 54000 |\n| 19 | may 20 | colonial national invitation | texas | al geiberger (11) | 274 ( - 6) | 54000 |\n| 20 | may 27 | memorial tournament | ohio | tom watson (17) | 285 ( - 3) | 54000 |\n| 21 | jun 3 | kemper open | north carolina | jerry mcgee (3) | 272 ( - 16) | 63000 |\n| 22 | jun 10 | atlanta classic | georgia | andy bean (5) | 265 ( - 23) | 54000 |\n| 23 | jun 17 | us open | ohio | hale irwin (11) | 284 (e) | 50000 |\n| 24 | jun 24 | canadian open | canada | lee trevino (24) | 281 ( - 3) | 63000 |\n| 25 | jul 1 | danny thomas memphis classic | tennessee | gil morgan (4) | 278 ( - 10) | 54000 |\n| 26 | jul 8 | western open | illinois | larry nelson (2) | 286 ( - 2) | 54000 |\n| 27 | jul 15 | greater milwaukee open | wisconsin | calvin peete (1) | 269 ( - 19) | 36000 |\n| 28 | jul 21 | british open | england | seve ballesteros (2) | 283 ( - 1) | 31500 |\n| 29 | jul 22 | ed mcmahon - jaycees quad cities open | illinois | d a weibring (1) | 266 ( - 14) | 36000 |\n| 30 | jul 29 | ivb - philadelphia golf classic | pennsylvania | lou graham (4) | 273 ( - 11) | 45000 |\n| 31 | aug 5 | pga championship | michigan | david graham (4) | 272 ( - 8) | 60000 |\n| 32 | aug 12 | sammy davis jr - greater hartford open | connecticut | jerry mcgee (4) | 267 ( - 17) | 54000 |\n| 33 | aug 19 | manufacturers hanover westchester classic | new york | jack renner (1) | 277 ( - 7) | 72000 |\n| 34 | aug 26 | colgate hall of fame classic | north carolina | tom watson (18) | 272 ( - 12) | 45000 |\n| 35 | sep 2 | bc open | new york | howard twitty (1) | 270 ( - 14) | 49500 |\n| 36 | sep 9 | american optical classic | massachusetts | lou graham (5) | 275 ( - 9) | 45000 |\n| 37 | sep 16 | buick - goodwrench open | michigan | john fought (1) | 280 ( - 8) | 27000 |\n| 38 | sep 23 | anheuser - busch golf classic | california | john fought (2) | 277 ( - 11) | 54000 |\n| 39 | sep 30 | world series of golf | ohio | lon hinkle (3) | 272 ( - 8) | 100000 |\n| 40 | oct 7 | san antonio texas open | texas | lou graham (6) | 268 ( - 12) | 45000 |\n| 41 | oct 14 | southern open | georgia | ed fiori (1) | 274 ( - 6) | 36000 |\n| 42 | oct 21 | pensacola open | florida | curtis strange (1) | 271 ( - 17) | 36000 |\n| 43 | oct 28 | walt disney world national team championship | florida | george burns (1) ben crenshaw (7) | 255 ( - 33) | 22500 (each) |"
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Although Tom Kite had two more victories than Greg Norman, Norman earned approximately $300,000 more.
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[
"Table Title: 1995 pga tour\n| | rank | player | country | earnings | wins |\n|---:|-------:|:--------------|:--------------|-----------:|-------:|\n| 0 | 1 | greg norman | australia | 9592829 | 17 |\n| 1 | 2 | tom kite | united states | 9337998 | 19 |\n| 2 | 3 | payne stewart | united states | 7389479 | 9 |\n| 3 | 4 | nick price | zimbabwe | 7338119 | 15 |\n| 4 | 5 | fred couples | united states | 7188408 | 11 |"
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Garou: Mark of the Wolves was released in Japan approximately six months before The Last Blade 1 and 2.
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[
"Table Title: neo geo online collection\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>vol</th>\n <th>english title</th>\n <th>japanese title</th>\n <th>release date (japan)</th>\n <th>sony catalog no</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>garou : mark of the wolves</td>\n <td>餓狼 - mark of the wolves -</td>\n <td>june 30 , 2005</td>\n <td>slps - 25509</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>the last blade 1 and 2</td>\n <td>幕末浪漫 月華の剣士１・２</td>\n <td>january 12 , 2006</td>\n <td>slps - 25503</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>the king of fighters : orochi collection</td>\n <td>the king of fighters - オロチ編 -</td>\n <td>april 20 , 2006</td>\n <td>slps - 25605</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>art of fighting anthology</td>\n <td>龍虎の拳〜天・地・人〜</td>\n <td>may 11 , 2006</td>\n <td>slps - 25610</td>\n </tr>\n <tr>\n <th>4</th>\n <td>5</td>\n <td>fatal fury battle archives 1</td>\n <td>餓狼伝説バトルアーカイブズ１</td>\n <td>july 20 , 2006</td>\n <td>slps - 25664</td>\n </tr>\n <tr>\n <th>5</th>\n <td>6</td>\n <td>fatal fury battle archives 2</td>\n <td>餓狼伝説バトルアーカイブズ２</td>\n <td>february 22 , 2007</td>\n <td>slps - 25698</td>\n </tr>\n <tr>\n <th>6</th>\n <td>7</td>\n <td>the king of fighters : nests collection</td>\n <td>the king of fighters - ネスツ編 -</td>\n <td>april 19 , 2007</td>\n <td>slps - 25661</td>\n </tr>\n <tr>\n <th>7</th>\n <td>8</td>\n <td>fu'un super combo</td>\n <td>風雲スーパーコンボ</td>\n <td>june 21 , 2007</td>\n <td>slps - 25781</td>\n </tr>\n <tr>\n <th>8</th>\n <td>9</td>\n <td>world heroes anthology</td>\n <td>ワールドヒーローズ ゴージャス</td>\n <td>october 18 , 2007</td>\n <td>slps - 25782</td>\n </tr>\n <tr>\n <th>9</th>\n <td>10</td>\n <td>the king of fighters '98: ultimate match</td>\n <td>the king of fighters '98 ultimate match</td>\n <td>june 26 , 2008</td>\n <td>slps - 25783</td>\n </tr>\n <tr>\n <th>10</th>\n <td>11</td>\n <td>sunsoft collection</td>\n <td>サンソフトコレクション</td>\n <td>june 26 , 2008</td>\n <td>slps - 25849</td>\n </tr>\n <tr>\n <th>11</th>\n <td>12</td>\n <td>samurai shodown anthology</td>\n <td>サムライスピリッツ六番勝負</td>\n <td>july 24 , 2008</td>\n <td>slps - 25839</td>\n </tr>\n </tbody>\n</table>"
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Polona had the highest number of Grand Slam match victories in 2010 and 2011.
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[
"Table Title: polona hercog\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>tournament</th>\n <th>2006</th>\n <th>2007</th>\n <th>2008</th>\n <th>2009</th>\n <th>2010</th>\n <th>2011</th>\n <th>2012</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>grand slam tournaments</td>\n <td>grand slam tournaments</td>\n <td>grand slam tournaments</td>\n <td>grand slam tournaments</td>\n <td>grand slam tournaments</td>\n <td>grand slam tournaments</td>\n <td>grand slam tournaments</td>\n <td>grand slam tournaments</td>\n </tr>\n <tr>\n <th>1</th>\n <td>australian open</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>2r</td>\n <td>1r</td>\n <td>1r</td>\n </tr>\n <tr>\n <th>2</th>\n <td>french open</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>2r</td>\n <td>3r</td>\n <td>2r</td>\n <td>1r</td>\n </tr>\n <tr>\n <th>3</th>\n <td>wimbledon</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>lq</td>\n <td>1r</td>\n <td>2r</td>\n <td>1r</td>\n </tr>\n <tr>\n <th>4</th>\n <td>us open</td>\n <td>a</td>\n <td>a</td>\n <td>lq</td>\n <td>1r</td>\n <td>1r</td>\n <td>2r</td>\n <td>1r</td>\n </tr>\n <tr>\n <th>5</th>\n <td>win - loss</td>\n <td>0 - 0</td>\n <td>0 - 0</td>\n <td>0 - 0</td>\n <td>1 - 2</td>\n <td>3 - 4</td>\n <td>3 - 4</td>\n <td>0 - 4</td>\n </tr>\n <tr>\n <th>6</th>\n <td>olympic games</td>\n <td>olympic games</td>\n <td>olympic games</td>\n <td>olympic games</td>\n <td>olympic games</td>\n <td>olympic games</td>\n <td>olympic games</td>\n <td>olympic games</td>\n </tr>\n <tr>\n <th>7</th>\n <td>summer olympics</td>\n <td>not held</td>\n <td>not held</td>\n <td>a</td>\n <td>not held</td>\n <td>not held</td>\n <td>not held</td>\n <td>1r</td>\n </tr>\n <tr>\n <th>8</th>\n <td>win - loss</td>\n <td>0 - 0</td>\n <td>0 - 0</td>\n <td>0 - 0</td>\n <td>0 - 0</td>\n <td>0 - 0</td>\n <td>0 - 0</td>\n <td>0 - 1</td>\n </tr>\n <tr>\n <th>9</th>\n <td>premier mandatory</td>\n <td>premier mandatory</td>\n <td>premier mandatory</td>\n <td>premier mandatory</td>\n <td>premier mandatory</td>\n <td>premier mandatory</td>\n <td>premier mandatory</td>\n <td>premier mandatory</td>\n </tr>\n <tr>\n <th>10</th>\n <td>indian wells</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>2r</td>\n <td>1r</td>\n <td>1r</td>\n </tr>\n <tr>\n <th>11</th>\n <td>miami</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>3r</td>\n <td>1r</td>\n <td>2r</td>\n </tr>\n <tr>\n <th>12</th>\n <td>madrid</td>\n <td>not held</td>\n <td>not held</td>\n <td>not held</td>\n <td>a</td>\n <td>1r</td>\n <td>lq</td>\n <td>1r</td>\n </tr>\n <tr>\n <th>13</th>\n <td>beijing</td>\n <td>tier ii</td>\n <td>tier ii</td>\n <td>tier ii</td>\n <td>prem</td>\n <td>2r</td>\n <td>2r</td>\n <td>3r</td>\n </tr>\n <tr>\n <th>14</th>\n <td>win - loss</td>\n <td>0 - 0</td>\n <td>0 - 0</td>\n <td>0 - 0</td>\n <td>0 - 0</td>\n <td>4 - 4</td>\n <td>1 - 3</td>\n <td>3 - 4</td>\n </tr>\n <tr>\n <th>15</th>\n <td>premier 5</td>\n <td>premier 5</td>\n <td>premier 5</td>\n <td>premier 5</td>\n <td>premier 5</td>\n <td>premier 5</td>\n <td>premier 5</td>\n <td>premier 5</td>\n </tr>\n <tr>\n <th>16</th>\n <td>doha</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>not held</td>\n <td>not held</td>\n <td>a</td>\n <td>1r</td>\n </tr>\n <tr>\n <th>17</th>\n <td>rome</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>2r</td>\n <td>3r</td>\n <td>a</td>\n </tr>\n <tr>\n <th>18</th>\n <td>toronto</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>1r</td>\n <td>a</td>\n </tr>\n <tr>\n <th>19</th>\n <td>cincinnati</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>1r</td>\n <td>a</td>\n </tr>\n <tr>\n <th>20</th>\n <td>tokyo</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>a</td>\n <td>1r</td>\n <td>a</td>\n <td>a</td>\n </tr>\n <tr>\n <th>21</th>\n <td>win - loss</td>\n <td>0 - 0</td>\n <td>0 - 0</td>\n <td>0 - 0</td>\n <td>0 - 0</td>\n <td>1 - 2</td>\n <td>2 - 3</td>\n <td>0 - 1</td>\n </tr>\n <tr>\n <th>22</th>\n <td>statistics</td>\n <td>statistics</td>\n <td>statistics</td>\n <td>statistics</td>\n <td>statistics</td>\n <td>statistics</td>\n <td>statistics</td>\n <td>statistics</td>\n </tr>\n <tr>\n <th>23</th>\n <td>tour level win - loss</td>\n <td>0 - 0</td>\n <td>0 - 2</td>\n <td>1 - 2</td>\n <td>6 - 8</td>\n <td>27 - 26</td>\n <td>30 - 25</td>\n <td>14 - 23</td>\n </tr>\n <tr>\n <th>24</th>\n <td>tour level win %</td>\n <td>0%</td>\n <td>0%</td>\n <td>33%</td>\n <td>43%</td>\n <td>51%</td>\n <td>55%</td>\n <td>38%</td>\n </tr>\n <tr>\n <th>25</th>\n <td>overall win - loss</td>\n <td>14 - 7</td>\n <td>26 - 8</td>\n <td>37 - 16</td>\n <td>48 - 18</td>\n <td>32 - 26</td>\n <td>32 - 27</td>\n <td>20 - 26</td>\n </tr>\n <tr>\n <th>26</th>\n <td>overall win %</td>\n <td>67%</td>\n <td>76%</td>\n <td>70%</td>\n <td>73%</td>\n <td>55%</td>\n <td>54%</td>\n <td>43%</td>\n </tr>\n <tr>\n <th>27</th>\n <td>year end ranking</td>\n <td>717</td>\n <td>345</td>\n <td>243</td>\n <td>71</td>\n <td>48</td>\n <td>36</td>\n <td>80</td>\n </tr>\n </tbody>\n</table>"
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Ralph Sampson received an award in the 1982-83, 1983-84, and 1984-85 seasons.
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[
"Table Title: list of houston rockets seasons\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>season</th>\n <th>team</th>\n <th>conf</th>\n <th>conf finish</th>\n <th>div</th>\n <th>div finish</th>\n <th>awards</th>\n <th>head coach</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1967 - 68</td>\n <td>sdr</td>\n <td>-</td>\n <td>-</td>\n <td>western</td>\n <td>6th</td>\n <td>-</td>\n <td>jack mcmahon</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1968 - 69</td>\n <td>sdr</td>\n <td>-</td>\n <td>-</td>\n <td>western</td>\n <td>4th</td>\n <td>-</td>\n <td>jack mcmahon</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1969 - 70</td>\n <td>sdr</td>\n <td>-</td>\n <td>-</td>\n <td>western</td>\n <td>7th</td>\n <td>-</td>\n <td>jack mcmahon alex hannum</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1970 - 71</td>\n <td>sdr</td>\n <td>western</td>\n <td>7th</td>\n <td>pacific</td>\n <td>3rd</td>\n <td>-</td>\n <td>alex hannum</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1971 - 72</td>\n <td>hou</td>\n <td>western</td>\n <td>7th</td>\n <td>pacific</td>\n <td>4th</td>\n <td>-</td>\n <td>tex winter</td>\n </tr>\n <tr>\n <th>5</th>\n <td>1972 - 73</td>\n <td>hou</td>\n <td>eastern</td>\n <td>5th</td>\n <td>central</td>\n <td>3rd</td>\n <td>-</td>\n <td>tex winter johnny egan</td>\n </tr>\n <tr>\n <th>6</th>\n <td>1973 - 74</td>\n <td>hou</td>\n <td>eastern</td>\n <td>6th</td>\n <td>central</td>\n <td>3rd</td>\n <td>-</td>\n <td>johnny egan</td>\n </tr>\n <tr>\n <th>7</th>\n <td>1974 - 75</td>\n <td>hou</td>\n <td>eastern</td>\n <td>4th</td>\n <td>central</td>\n <td>2nd</td>\n <td>-</td>\n <td>johnny egan</td>\n </tr>\n <tr>\n <th>8</th>\n <td>1975 - 76</td>\n <td>hou</td>\n <td>eastern</td>\n <td>6th</td>\n <td>central</td>\n <td>3rd</td>\n <td>-</td>\n <td>johnny egan</td>\n </tr>\n <tr>\n <th>9</th>\n <td>1976 - 77</td>\n <td>hou</td>\n <td>eastern</td>\n <td>2nd</td>\n <td>central</td>\n <td>1st</td>\n <td>tom nissalke ( coy ) ray patterson ( eoy )</td>\n <td>tom nissalke</td>\n </tr>\n <tr>\n <th>10</th>\n <td>1977 - 78</td>\n <td>hou</td>\n <td>eastern</td>\n <td>9th</td>\n <td>central</td>\n <td>6th</td>\n <td>-</td>\n <td>tom nissalke</td>\n </tr>\n <tr>\n <th>11</th>\n <td>1978 - 79</td>\n <td>hou</td>\n <td>eastern</td>\n <td>4th</td>\n <td>central</td>\n <td>2nd</td>\n <td>calvin murphy ( jwkc ) moses malone ( mvp )</td>\n <td>tom nissalke</td>\n </tr>\n <tr>\n <th>12</th>\n <td>1979 - 80</td>\n <td>hou</td>\n <td>eastern</td>\n <td>4th</td>\n <td>central</td>\n <td>2nd</td>\n <td>-</td>\n <td>del harris</td>\n </tr>\n <tr>\n <th>13</th>\n <td>1980 - 81</td>\n <td>hou</td>\n <td>western</td>\n <td>6th</td>\n <td>midwest</td>\n <td>3rd</td>\n <td>-</td>\n <td>del harris</td>\n </tr>\n <tr>\n <th>14</th>\n <td>1981 - 82</td>\n <td>hou</td>\n <td>western</td>\n <td>6th</td>\n <td>midwest</td>\n <td>3rd</td>\n <td>moses malone ( mvp )</td>\n <td>del harris</td>\n </tr>\n <tr>\n <th>15</th>\n <td>1982 - 83</td>\n <td>hou</td>\n <td>western</td>\n <td>12th</td>\n <td>midwest</td>\n <td>6th</td>\n <td>-</td>\n <td>del harris</td>\n </tr>\n <tr>\n <th>16</th>\n <td>1983 - 84</td>\n <td>hou</td>\n <td>western</td>\n <td>12th</td>\n <td>midwest</td>\n <td>6th</td>\n <td>ralph sampson ( roy )</td>\n <td>bill fitch</td>\n </tr>\n <tr>\n <th>17</th>\n <td>1984 - 85</td>\n <td>hou</td>\n <td>western</td>\n <td>3rd</td>\n <td>midwest</td>\n <td>2nd</td>\n <td>ralph sampson ( amvp )</td>\n <td>bil fitch</td>\n </tr>\n <tr>\n <th>18</th>\n <td>1985 - 86</td>\n <td>hou</td>\n <td>western</td>\n <td>2nd</td>\n <td>midwest</td>\n <td>1st</td>\n <td>-</td>\n <td>bill fitch</td>\n </tr>\n <tr>\n <th>19</th>\n <td>1986 - 87</td>\n <td>hou</td>\n <td>western</td>\n <td>6th</td>\n <td>midwest</td>\n <td>3rd</td>\n <td>-</td>\n <td>bill fitch</td>\n </tr>\n <tr>\n <th>20</th>\n <td>1987 - 88</td>\n <td>hou</td>\n <td>western</td>\n <td>6th</td>\n <td>midwest</td>\n <td>4th</td>\n <td>-</td>\n <td>bill fitch</td>\n </tr>\n <tr>\n <th>21</th>\n <td>1988 - 89</td>\n <td>hou</td>\n <td>western</td>\n <td>5th</td>\n <td>midwest</td>\n <td>2nd</td>\n <td>-</td>\n <td>don chaney</td>\n </tr>\n <tr>\n <th>22</th>\n <td>1989 - 90</td>\n <td>hou</td>\n <td>western</td>\n <td>8th</td>\n <td>midwest</td>\n <td>5th</td>\n <td>-</td>\n <td>don chaney</td>\n </tr>\n <tr>\n <th>23</th>\n <td>1990 - 91</td>\n <td>hou</td>\n <td>western</td>\n <td>6th</td>\n <td>midwest</td>\n <td>3rd</td>\n <td>don chaney ( coy )</td>\n <td>don chaney</td>\n </tr>\n <tr>\n <th>24</th>\n <td>1991 - 92</td>\n <td>hou</td>\n <td>western</td>\n <td>9th</td>\n <td>midwest</td>\n <td>3rd</td>\n <td>-</td>\n <td>don chaney rudy tomjanovich</td>\n </tr>\n <tr>\n <th>25</th>\n <td>1992 - 93</td>\n <td>hou</td>\n <td>western</td>\n <td>2nd</td>\n <td>midwest</td>\n <td>1st</td>\n <td>hakeem olajuwon ( dpoy )</td>\n <td>rudy tomjanovich</td>\n </tr>\n <tr>\n <th>26</th>\n <td>1993 - 94 ¤</td>\n <td>hou</td>\n <td>western</td>\n <td>2nd</td>\n <td>midwest</td>\n <td>1st</td>\n <td>hakeem olajuwon ( dpoy , fmvp and mvp )</td>\n <td>rudy tomjanovich</td>\n </tr>\n <tr>\n <th>27</th>\n <td>1994 - 95 ¤</td>\n <td>hou</td>\n <td>western</td>\n <td>6th</td>\n <td>midwest</td>\n <td>3rd</td>\n <td>hakeem olajuwon ( fmvp )</td>\n <td>rudy tomjanovich</td>\n </tr>\n <tr>\n <th>28</th>\n <td>1995 - 96</td>\n <td>hou</td>\n <td>western</td>\n <td>5th</td>\n <td>midwest</td>\n <td>3rd</td>\n <td>-</td>\n <td>rudy tomjanovich</td>\n </tr>\n <tr>\n <th>29</th>\n <td>1996 - 97</td>\n <td>hou</td>\n <td>western</td>\n <td>3rd</td>\n <td>midwest</td>\n <td>2nd</td>\n <td>-</td>\n <td>rudy tomjanovich</td>\n </tr>\n <tr>\n <th>30</th>\n <td>1997 - 98</td>\n <td>hou</td>\n <td>western</td>\n <td>8th</td>\n <td>midwest</td>\n <td>4th</td>\n <td>-</td>\n <td>rudy tomjanovich</td>\n </tr>\n <tr>\n <th>31</th>\n <td>1998 - 99</td>\n <td>hou</td>\n <td>western</td>\n <td>5th</td>\n <td>midwest</td>\n <td>3rd</td>\n <td>-</td>\n <td>rudy tomjanovich</td>\n </tr>\n <tr>\n <th>32</th>\n <td>1999 - 00</td>\n <td>hou</td>\n <td>western</td>\n <td>11th</td>\n <td>midwest</td>\n <td>6th</td>\n <td>steve francis ( roy )</td>\n <td>rudy tomjanovich</td>\n </tr>\n <tr>\n <th>33</th>\n <td>2000 - 01</td>\n <td>hou</td>\n <td>western</td>\n <td>9th</td>\n <td>midwest</td>\n <td>5th</td>\n <td>-</td>\n <td>rudy tomjanovich</td>\n </tr>\n <tr>\n <th>34</th>\n <td>2001 - 02</td>\n <td>hou</td>\n <td>western</td>\n <td>11th</td>\n <td>midwest</td>\n <td>5th</td>\n <td>-</td>\n <td>rudy tomjanovich</td>\n </tr>\n <tr>\n <th>35</th>\n <td>2002 - 03</td>\n <td>hou</td>\n <td>western</td>\n <td>9th</td>\n <td>midwest</td>\n <td>5th</td>\n <td>-</td>\n <td>rudy tomjanovich</td>\n </tr>\n <tr>\n <th>36</th>\n <td>2003 - 04</td>\n <td>hou</td>\n <td>western</td>\n <td>7th</td>\n <td>midwest</td>\n <td>5th</td>\n <td>-</td>\n <td>jeff van gundy</td>\n </tr>\n <tr>\n <th>37</th>\n <td>2004 - 05</td>\n <td>hou</td>\n <td>western</td>\n <td>5th</td>\n <td>southwest</td>\n <td>3rd</td>\n <td>-</td>\n <td>jeff van gundy</td>\n </tr>\n <tr>\n <th>38</th>\n <td>2005 - 06</td>\n <td>hou</td>\n <td>western</td>\n <td>12th</td>\n <td>southwest</td>\n <td>5th</td>\n <td>-</td>\n <td>jeff van gundy</td>\n </tr>\n <tr>\n <th>39</th>\n <td>2006 - 07</td>\n <td>hou</td>\n <td>western</td>\n <td>5th</td>\n <td>southwest</td>\n <td>3rd</td>\n <td>-</td>\n <td>jeff van gundy</td>\n </tr>\n <tr>\n <th>40</th>\n <td>2007 - 08</td>\n <td>hou</td>\n <td>western</td>\n <td>5th</td>\n <td>southwest</td>\n <td>3rd</td>\n <td>-</td>\n <td>rick adelman</td>\n </tr>\n <tr>\n <th>41</th>\n <td>2008 - 09</td>\n <td>hou</td>\n <td>western</td>\n <td>5th</td>\n <td>southwest</td>\n <td>2nd</td>\n <td>dikembe mutombo ( jwkc )</td>\n <td>rick adelman</td>\n </tr>\n <tr>\n <th>42</th>\n <td>2009 - 10</td>\n <td>hou</td>\n <td>western</td>\n <td>9th</td>\n <td>southwest</td>\n <td>3rd</td>\n <td>aaron brooks ( mip )</td>\n <td>rick adelman</td>\n </tr>\n <tr>\n <th>43</th>\n <td>2010 - 11</td>\n <td>hou</td>\n <td>western</td>\n <td>9th</td>\n <td>southwest</td>\n <td>5th</td>\n <td>-</td>\n <td>rick adelman</td>\n </tr>\n <tr>\n <th>44</th>\n <td>2011 - 12</td>\n <td>hou</td>\n <td>western</td>\n <td>9th</td>\n <td>southwest</td>\n <td>4th</td>\n <td>-</td>\n <td>kevin mchale</td>\n </tr>\n <tr>\n <th>45</th>\n <td>2012 - 13</td>\n <td>hou</td>\n <td>western</td>\n <td>8th</td>\n <td>southwest</td>\n <td>3rd</td>\n <td>-</td>\n <td>kevin mchale</td>\n </tr>\n </tbody>\n</table>"
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The overall word count rank is 393 (4th), with 139 being the second most frequent word (m).
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[
"Table Title: 2007 - 08 fis ski jumping world cup\n{\"rank\": {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5}, \"name\": {\"0\": \"andreas k\\u00e3\\u00bcttel\", \"1\": \"gregor schlierenzauer\", \"2\": \"thomas morgenstern\", \"3\": \"wolfgang loitzl\", \"4\": \"andreas kofler\"}, \"nationality\": {\"0\": \"sui\", \"1\": \"aut\", \"2\": \"aut\", \"3\": \"aut\", \"4\": \"aut\"}, \"1st (m)\": {\"0\": 125.5, \"1\": 124.5, \"2\": 124.5, \"3\": 122.5, \"4\": 130.0}, \"2nd (m)\": {\"0\": 136.0, \"1\": 136.0, \"2\": 135.0, \"3\": 136.0, \"4\": 139.0}, \"points\": {\"0\": 252.7, \"1\": 248.9, \"2\": 246.6, \"3\": 246.3, \"4\": 243.2}, \"overall wc points (rank)\": {\"0\": \"227 (7)\", \"1\": \"429 (2)\", \"2\": \"660 (1)\", \"3\": \"300 (3)\", \"4\": \"293 (4)\"}}"
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The combined attendance for the four Sugar Bowl games was 491,424.
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[
"Table Title: list of virginia tech hokies bowl games\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>season</th>\n <th>bowl game</th>\n <th>result</th>\n <th>opponent</th>\n <th>stadium</th>\n <th>location</th>\n <th>attendance</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1946</td>\n <td>1947 sun bowl</td>\n <td>l 18 - 6</td>\n <td>cincinnati bearcats</td>\n <td>kidd field</td>\n <td>el paso , tx</td>\n <td>10000</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1966</td>\n <td>1966 liberty bowl</td>\n <td>l 14 - 7</td>\n <td>miami hurricanes</td>\n <td>memphis memorial stadium</td>\n <td>memphis , tn</td>\n <td>39101</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1968</td>\n <td>1968 liberty bowl</td>\n <td>l 34 - 17</td>\n <td>ole miss rebels</td>\n <td>memphis memorial stadium</td>\n <td>memphis , tn</td>\n <td>46206</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1980</td>\n <td>1981 peach bowl</td>\n <td>l 20 - 10</td>\n <td>miami hurricanes</td>\n <td>fulton county stadium</td>\n <td>atlanta , ga</td>\n <td>45384</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1984</td>\n <td>1984 independence bowl</td>\n <td>l 23 - 7</td>\n <td>air force falcons</td>\n <td>independence stadium</td>\n <td>shreveport , la</td>\n <td>41100</td>\n </tr>\n <tr>\n <th>5</th>\n <td>1986</td>\n <td>1986 peach bowl</td>\n <td>w 25 - 24</td>\n <td>north carolina state wolfpack</td>\n <td>fulton county stadium</td>\n <td>atlanta , ga</td>\n <td>53668</td>\n </tr>\n <tr>\n <th>6</th>\n <td>1993</td>\n <td>1993 independence bowl</td>\n <td>w 45 - 20</td>\n <td>indiana hoosiers</td>\n <td>independence stadium</td>\n <td>shreveport , la</td>\n <td>33819</td>\n </tr>\n <tr>\n <th>7</th>\n <td>1994</td>\n <td>1994 gator bowl</td>\n <td>l 45 - 23</td>\n <td>tennessee volunteers</td>\n <td>ben hill griffin stadium</td>\n <td>gainesville , fl</td>\n <td>62200</td>\n </tr>\n <tr>\n <th>8</th>\n <td>1995</td>\n <td>1995 sugar bowl</td>\n <td>w 28 - 10</td>\n <td>texas longhorns</td>\n <td>louisiana superdome</td>\n <td>new orleans , la</td>\n <td>70283</td>\n </tr>\n <tr>\n <th>9</th>\n <td>1996</td>\n <td>1996 orange bowl</td>\n <td>l 41 - 21</td>\n <td>nebraska cornhuskers</td>\n <td>pro player stadium</td>\n <td>miami gardens , fl</td>\n <td>51212</td>\n </tr>\n <tr>\n <th>10</th>\n <td>1997</td>\n <td>1998 gator bowl</td>\n <td>l 42 - 3</td>\n <td>north carolina tar heels</td>\n <td>alltel stadium</td>\n <td>jacksonville , fl</td>\n <td>54116</td>\n </tr>\n <tr>\n <th>11</th>\n <td>1998</td>\n <td>1998 music city bowl</td>\n <td>w 38 - 7</td>\n <td>alabama crimson tide</td>\n <td>vanderbilt stadium</td>\n <td>nashville , tn</td>\n <td>41600</td>\n </tr>\n <tr>\n <th>12</th>\n <td>1999</td>\n <td>2000 sugar bowl</td>\n <td>l 46 - 29</td>\n <td>florida state seminoles</td>\n <td>louisiana superdome</td>\n <td>new orleans , la</td>\n <td>79280</td>\n </tr>\n <tr>\n <th>13</th>\n <td>2000</td>\n <td>2001 gator bowl</td>\n <td>w 41 - 20</td>\n <td>clemson tigers</td>\n <td>alltel stadium</td>\n <td>jacksonville , fl</td>\n <td>68741</td>\n </tr>\n <tr>\n <th>14</th>\n <td>2001</td>\n <td>2002 gator bowl</td>\n <td>l 30 - 17</td>\n <td>florida state seminoles</td>\n <td>alltel stadium</td>\n <td>jacksonville , fl</td>\n <td>72202</td>\n </tr>\n <tr>\n <th>15</th>\n <td>2002</td>\n <td>2002 san francisco bowl</td>\n <td>w 20 - 13</td>\n <td>air force falcons</td>\n <td>pacific bell park</td>\n <td>san francisco , ca</td>\n <td>25966</td>\n </tr>\n <tr>\n <th>16</th>\n <td>2003</td>\n <td>2003 insight bowl</td>\n <td>l 52 - 49</td>\n <td>california golden bears</td>\n <td>bank one ballpark</td>\n <td>phoenix , az</td>\n <td>42364</td>\n </tr>\n <tr>\n <th>17</th>\n <td>2004</td>\n <td>2005 sugar bowl</td>\n <td>l 16 - 13</td>\n <td>auburn tigers</td>\n <td>louisiana superdome</td>\n <td>new orleans , la</td>\n <td>77349</td>\n </tr>\n <tr>\n <th>18</th>\n <td>2005</td>\n <td>2006 gator bowl</td>\n <td>w 35 - 24</td>\n <td>louisville cardinals</td>\n <td>alltel stadium</td>\n <td>jacksonville , fl</td>\n <td>63780</td>\n </tr>\n <tr>\n <th>19</th>\n <td>2006</td>\n <td>2006 chick - fil - a bowl</td>\n <td>l 31 - 24</td>\n <td>georgia bulldogs</td>\n <td>georgia dome</td>\n <td>atlanta , ga</td>\n <td>75406</td>\n </tr>\n <tr>\n <th>20</th>\n <td>2007</td>\n <td>2008 orange bowl</td>\n <td>l 24 - 21</td>\n <td>kansas jayhawks</td>\n <td>pro player stadium</td>\n <td>miami gardens , fl</td>\n <td>74111</td>\n </tr>\n <tr>\n <th>21</th>\n <td>2008</td>\n <td>2009 orange bowl</td>\n <td>w 20 - 7</td>\n <td>cincinnati bearcats</td>\n <td>pro player stadium</td>\n <td>miami gardens , fl</td>\n <td>57821</td>\n </tr>\n <tr>\n <th>22</th>\n <td>2009</td>\n <td>2009 chick - fil - a bowl</td>\n <td>w 37 - 14</td>\n <td>tennessee volunteers</td>\n <td>georgia dome</td>\n <td>atlanta , ga</td>\n <td>73777</td>\n </tr>\n <tr>\n <th>23</th>\n <td>2010</td>\n <td>2011 orange bowl</td>\n <td>l 40 - 12</td>\n <td>stanford cardinal</td>\n <td>sun life stadium</td>\n <td>miami gardens , fl</td>\n <td>65453</td>\n </tr>\n <tr>\n <th>24</th>\n <td>2011</td>\n <td>2012 sugar bowl</td>\n <td>l 23 - 20</td>\n <td>michigan wolverines</td>\n <td>mercedes - benz superdome</td>\n <td>new orleans , louisiana</td>\n <td>64512</td>\n </tr>\n <tr>\n <th>25</th>\n <td>2012</td>\n <td>2012 russell athletic bowl</td>\n <td>w 13 - 10</td>\n <td>rutgers scarlet knights</td>\n <td>florida citrus bowl stadium</td>\n <td>orlando , florida</td>\n <td>48127</td>\n </tr>\n </tbody>\n</table>"
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Between 1995 and 2008, the Grizzly team finished second in their final two conference appearances, but never achieved first place.
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"Table Title: list of memphis grizzlies seasons\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>season</th>\n <th>conference</th>\n <th>division</th>\n <th>finish</th>\n <th>wins</th>\n <th>losses</th>\n <th>win%</th>\n <th>awards</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>vancouver grizzlies</td>\n <td>vancouver grizzlies</td>\n <td>vancouver grizzlies</td>\n <td>vancouver grizzlies</td>\n <td>vancouver grizzlies</td>\n <td>vancouver grizzlies</td>\n <td>vancouver grizzlies</td>\n <td>vancouver grizzlies</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1995 - 96</td>\n <td>west</td>\n <td>midwest</td>\n <td>7th</td>\n <td>15</td>\n <td>67</td>\n <td>183</td>\n <td>-</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1996 - 97</td>\n <td>west</td>\n <td>midwest</td>\n <td>7th</td>\n <td>14</td>\n <td>68</td>\n <td>171</td>\n <td>-</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1997 - 98</td>\n <td>west</td>\n <td>midwest</td>\n <td>6th</td>\n <td>19</td>\n <td>63</td>\n <td>232</td>\n <td>-</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1998 - 99</td>\n <td>west</td>\n <td>midwest</td>\n <td>7th</td>\n <td>8</td>\n <td>42</td>\n <td>160</td>\n <td>-</td>\n </tr>\n <tr>\n <th>5</th>\n <td>1999 - 00</td>\n <td>west</td>\n <td>midwest</td>\n <td>7th</td>\n <td>22</td>\n <td>60</td>\n <td>268</td>\n <td>-</td>\n </tr>\n <tr>\n <th>6</th>\n <td>2000 - 01</td>\n <td>west</td>\n <td>midwest</td>\n <td>7th</td>\n <td>23</td>\n <td>59</td>\n <td>280</td>\n <td>-</td>\n </tr>\n <tr>\n <th>7</th>\n <td>memphis grizzlies</td>\n <td>memphis grizzlies</td>\n <td>memphis grizzlies</td>\n <td>memphis grizzlies</td>\n <td>memphis grizzlies</td>\n <td>memphis grizzlies</td>\n <td>memphis grizzlies</td>\n <td>memphis grizzlies</td>\n </tr>\n <tr>\n <th>8</th>\n <td>2001 - 02</td>\n <td>west</td>\n <td>midwest</td>\n <td>7th</td>\n <td>23</td>\n <td>59</td>\n <td>280</td>\n <td>pau gasol (roy)</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2002 - 03</td>\n <td>west</td>\n <td>midwest</td>\n <td>6th</td>\n <td>28</td>\n <td>54</td>\n <td>341</td>\n <td>-</td>\n </tr>\n <tr>\n <th>10</th>\n <td>2003 - 04</td>\n <td>west</td>\n <td>midwest</td>\n <td>4th ¤</td>\n <td>50</td>\n <td>32</td>\n <td>610</td>\n <td>hubie brown (coy) jerry west (eoy)</td>\n </tr>\n <tr>\n <th>11</th>\n <td>2004 - 05</td>\n <td>west</td>\n <td>southwest</td>\n <td>4th ¤</td>\n <td>45</td>\n <td>37</td>\n <td>549</td>\n <td>-</td>\n </tr>\n <tr>\n <th>12</th>\n <td>2005 - 06</td>\n <td>west</td>\n <td>southwest</td>\n <td>3rd ¤</td>\n <td>49</td>\n <td>33</td>\n <td>598</td>\n <td>mike miller (smoy)</td>\n </tr>\n <tr>\n <th>13</th>\n <td>2006 - 07</td>\n <td>west</td>\n <td>southwest</td>\n <td>5th</td>\n <td>22</td>\n <td>60</td>\n <td>268</td>\n <td>-</td>\n </tr>\n <tr>\n <th>14</th>\n <td>2007 - 08</td>\n <td>west</td>\n <td>southwest</td>\n <td>5th</td>\n <td>22</td>\n <td>60</td>\n <td>268</td>\n <td>-</td>\n </tr>\n <tr>\n <th>15</th>\n <td>2008 - 09</td>\n <td>west</td>\n <td>southwest</td>\n <td>5th</td>\n <td>24</td>\n <td>58</td>\n <td>293</td>\n <td>-</td>\n </tr>\n <tr>\n <th>16</th>\n <td>2009 - 10</td>\n <td>west</td>\n <td>southwest</td>\n <td>4th</td>\n <td>40</td>\n <td>42</td>\n <td>488</td>\n <td>-</td>\n </tr>\n <tr>\n <th>17</th>\n <td>2010 - 11</td>\n <td>west</td>\n <td>southwest</td>\n <td>3rd ¤</td>\n <td>46</td>\n <td>36</td>\n <td>561</td>\n <td>-</td>\n </tr>\n <tr>\n <th>18</th>\n <td>2011 - 12</td>\n <td>west</td>\n <td>southwest</td>\n <td>2nd ¤</td>\n <td>41</td>\n <td>25</td>\n <td>621</td>\n <td>-</td>\n </tr>\n <tr>\n <th>19</th>\n <td>2012 - 13</td>\n <td>west</td>\n <td>southwest</td>\n <td>2nd ¤</td>\n <td>56</td>\n <td>26</td>\n <td>683</td>\n <td>marc gasol (dpoy)</td>\n </tr>\n </tbody>\n</table>"
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The show took a 26-day break between episodes 9, 10, and 11 of the season.
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"Table Title: cougar town (season 2)\n{\"series episode\": {\"0\": 25, \"1\": 26, \"2\": 27, \"3\": 28, \"4\": 29, \"5\": 30, \"6\": 31, \"7\": 32, \"8\": 33, \"9\": 34, \"10\": 35, \"11\": 36, \"12\": 37, \"13\": 38, \"14\": 39, \"15\": 40, \"16\": 41, \"17\": 42, \"18\": 43, \"19\": 44, \"20\": 45}, \"season episode\": {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 6, \"6\": 7, \"7\": 8, \"8\": 9, \"9\": 10, \"10\": 11, \"11\": 12, \"12\": 13, \"13\": 14, \"14\": 15, \"15\": 16, \"16\": 17, \"17\": 18, \"18\": 19, \"19\": 20, \"20\": 21}, \"title\": {\"0\": \"all mixed up\", \"1\": \"let yourself go\", \"2\": \"makin' some noise\", \"3\": \"the damage you've done\", \"4\": \"keeping me alive\", \"5\": \"you don't know how it feels\", \"6\": \"fooled again (i don't like it)\", \"7\": \"little girl blues\", \"8\": \"when the time comes\", \"9\": \"the same old you\", \"10\": \"no reason to cry\", \"11\": \"a thing about you\", \"12\": \"lost children\", \"13\": \"cry to me\", \"14\": \"walls\", \"15\": \"baby 's a rock 'n' roller\", \"16\": \"you 're gonna get it\", \"17\": \"lonesome sundown\", \"18\": \"damaged by love\", \"19\": \"free fallin'\", \"20\": \"something good coming , part 1\"}, \"directed by\": {\"0\": \"bill lawrence\", \"1\": \"michael mcdonald\", \"2\": \"john putch\", \"3\": \"john putch\", \"4\": \"michael mcdonald\", \"5\": \"michael mcdonald\", \"6\": \"john putch\", \"7\": \"michael mcdonald\", \"8\": \"bruce leddy\", \"9\": \"michael mcdonald\", \"10\": \"gail mancuso\", \"11\": \"gail mancuso\", \"12\": \"michael mcdonald\", \"13\": \"bruce leddy\", \"14\": \"bill lawrence\", \"15\": \"michael mcdonald\", \"16\": \"michael mcdonald\", \"17\": \"bruce leddy\", \"18\": \"michael mcdonald\", \"19\": \"michael mcdonald\", \"20\": \"michael mcdonald\"}, \"written by\": {\"0\": \"kevin biegel & bill lawrence\", \"1\": \"kevin biegel\", \"2\": \"sam laybourne\", \"3\": \"chrissy pietrosh & jessica goldstein\", \"4\": \"sanjay shah\", \"5\": \"blake mccormick\", \"6\": \"peter saji & melody derloshon\", \"7\": \"kate purdy\", \"8\": \"mary fitzgerald\", \"9\": \"ryan koh\", \"10\": \"gregg mettler\", \"11\": \"mary fitzgerald & kate purdy\", \"12\": \"ryan koh & sam laybourne\", \"13\": \"melody deloshon\", \"14\": \"sean lavery\", \"15\": \"peter saji\", \"16\": \"michael mcdonald\", \"17\": \"sanjay shah & blake mccormick\", \"18\": \"aaron ho\", \"19\": \"gregg mettler\", \"20\": \"jessica goldstein & chrissy pietrosh\"}, \"original air date\": {\"0\": \"september 22 , 2010\", \"1\": \"september 29 , 2010\", \"2\": \"october 6 , 2010\", \"3\": \"october 13 , 2010\", \"4\": \"october 20 , 2010\", \"5\": \"october 27 , 2010\", \"6\": \"november 3 , 2010\", \"7\": \"november 17 , 2010\", \"8\": \"november 24 , 2010\", \"9\": \"december 8 , 2010\", \"10\": \"january 5 , 2011\", \"11\": \"january 19 , 2011\", \"12\": \"january 26 , 2011\", \"13\": \"february 2 , 2011\", \"14\": \"april 18 , 2011\", \"15\": \"april 20 , 2011\", \"16\": \"april 27 , 2011\", \"17\": \"may 4 , 2011\", \"18\": \"may 11 , 2011\", \"19\": \"may 18 , 2011\", \"20\": \"may 25 , 2011\"}, \"us viewers (in million)\": {\"0\": 8.32, \"1\": 6.97, \"2\": 7.1, \"3\": 7.23, \"4\": 7.38, \"5\": 8.16, \"6\": 7.43, \"7\": 7.05, \"8\": 6.62, \"9\": 6.44, \"10\": 6.57, \"11\": 5.68, \"12\": 5.03, \"13\": 6.51, \"14\": 7.88, \"15\": 6.18, \"16\": 5.62, \"17\": 5.49, \"18\": 6.02, \"19\": 5.23, \"20\": 5.01}}"
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The Philadelphia Flyers went into overtime in five games during the 1995-1996 season.
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"Table Title: 1995 - 96 philadelphia flyers season\n| | game | january | opponent | score | record | points |\n|---:|-------:|----------:|:------------------------|:---------|:-------------|---------:|\n| 0 | 40 | 3 | san jose sharks | 3 - 1 | 23 - 11 - 6 | 52 |\n| 1 | 41 | 4 | colorado avalanche | 2 - 2 ot | 23 - 11 - 7 | 53 |\n| 2 | 42 | 9 | mighty ducks of anaheim | 2 - 2 ot | 23 - 11 - 8 | 54 |\n| 3 | 43 | 11 | st louis blues | 4 - 4 ot | 23 - 12 - 9 | 55 |\n| 4 | 44 | 13 | new york rangers | 0 - 4 | 23 - 13 - 9 | 55 |\n| 5 | 45 | 15 | dallas stars | 6 - 1 | 24 - 13 - 9 | 57 |\n| 6 | 46 | 22 | florida panthers | 1 - 1 ot | 24 - 12 - 10 | 58 |\n| 7 | 47 | 24 | new york rangers | 4 - 4 ot | 24 - 12 - 11 | 59 |\n| 8 | 48 | 27 | pittsburgh penguins | 4 - 7 | 24 - 13 - 11 | 59 |\n| 9 | 49 | 28 | washington capitals | 2 - 3 ot | 24 - 14 - 11 | 59 |"
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King Island was established prior to 1999.
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"Table Title: drop tower : scream zone\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>park</th>\n <th>tower height</th>\n <th>drop height</th>\n <th>speed</th>\n <th>model</th>\n <th>opened</th>\n <th>height requirement</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>canada 's wonderland</td>\n <td>230feet</td>\n <td>200feet</td>\n <td>62 mph</td>\n <td>giant drop</td>\n <td>1997</td>\n <td>inches (cm)</td>\n </tr>\n <tr>\n <th>1</th>\n <td>carowinds</td>\n <td>174feet</td>\n <td>100feet</td>\n <td>56 mph</td>\n <td>giant drop</td>\n <td>march 1996</td>\n <td>inches (cm)</td>\n </tr>\n <tr>\n <th>2</th>\n <td>california 's great america</td>\n <td>224feet</td>\n <td>207feet</td>\n <td>62 mph</td>\n <td>giant drop</td>\n <td>march 1996</td>\n <td>inches (cm)</td>\n </tr>\n <tr>\n <th>3</th>\n <td>kings dominion</td>\n <td>305feet</td>\n <td>272feet</td>\n <td>72 mph</td>\n <td>gyro drop</td>\n <td>march 22 , 2003</td>\n <td>inches (cm)</td>\n </tr>\n <tr>\n <th>4</th>\n <td>kings island</td>\n <td>315feet</td>\n <td>264feet</td>\n <td>67 mph</td>\n <td>gyro drop</td>\n <td>1999</td>\n <td>inches (cm)</td>\n </tr>\n </tbody>\n</table>"
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Wang Liqin won the ITTF Pro Tour Men's Singles title three times between 1997 and 2012.
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"Table Title: list of ittf pro tour winners\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>year location</th>\n <th>mens singles</th>\n <th>womens singles</th>\n <th>mens doubles</th>\n <th>womens doubles</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2012 kobe</td>\n <td>jun mizutani</td>\n <td>shen yanfei</td>\n <td>kim min - seok seo hyun - deok</td>\n <td>hiroko fujii misako wakamiya</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2011 kobe</td>\n <td>seiya kishikawa</td>\n <td>feng tianwei</td>\n <td>lin gaoyuan wu jiaji</td>\n <td>hiroko fujii misako wakamiya</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2010 kobe</td>\n <td>timo boll</td>\n <td>wang yuegu</td>\n <td>kenta matsudaira koki niwa</td>\n <td>yuka ishigaki yuri yamanashi</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2009 wakayama</td>\n <td>oh sang - eun</td>\n <td>park mi - young</td>\n <td>seiya kishikawa jun mizutani</td>\n <td>sayaka hirano reiko hiura</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2008 yokohama</td>\n <td>ma lin</td>\n <td>zhang yining</td>\n <td>china</td>\n <td>china</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2007 chiba</td>\n <td>wang hao</td>\n <td>wang nan</td>\n <td>chen qi wang liqin</td>\n <td>guo yue li xiaoxia</td>\n </tr>\n <tr>\n <th>6</th>\n <td>2006 yokohama</td>\n <td>wang liqin</td>\n <td>wang yuegu</td>\n <td>ma lin wang hao</td>\n <td>tie yana zhang rui</td>\n </tr>\n <tr>\n <th>7</th>\n <td>2005 yokohama</td>\n <td>timo boll</td>\n <td>zhang yining</td>\n <td>timo boll christian süß</td>\n <td>bai yang cao zhen</td>\n </tr>\n <tr>\n <th>8</th>\n <td>2004 kobe</td>\n <td>chen qi</td>\n <td>zhang yining</td>\n <td>wang liqin yan sen</td>\n <td>guo yue niu jianfeng</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2003 kobe</td>\n <td>timo boll</td>\n <td>guo yue</td>\n <td>chen qi ma lin</td>\n <td>guo yue niu jianfeng</td>\n </tr>\n <tr>\n <th>10</th>\n <td>2002 kobe</td>\n <td>kalinikos kreanga</td>\n <td>kim kyung - ah</td>\n <td>akira kito toshio tasaki</td>\n <td>jing junhong li jiawei</td>\n </tr>\n <tr>\n <th>11</th>\n <td>2001 yokohama</td>\n <td>chiang peng - lung</td>\n <td>wang nan</td>\n <td>ma lin wang hao</td>\n <td>kim bok - rae kim kyung - ah</td>\n </tr>\n <tr>\n <th>12</th>\n <td>2000 kobe</td>\n <td>wang liqin</td>\n <td>wang nan</td>\n <td>kong linghui liu guoliang</td>\n <td>sun jin yang ying</td>\n </tr>\n <tr>\n <th>13</th>\n <td>1999 kobe</td>\n <td>vladimir samsonov</td>\n <td>wang nan</td>\n <td>ma lin qin zhijian</td>\n <td>sun jin yang ying</td>\n </tr>\n <tr>\n <th>14</th>\n <td>1998 wakayama</td>\n <td>kong linghui</td>\n <td>li ju</td>\n <td>ma lin wang tao</td>\n <td>li ju wang nan</td>\n </tr>\n <tr>\n <th>15</th>\n <td>1997 chiba</td>\n <td>jan - ove waldner</td>\n <td>wang chen</td>\n <td>wang liqin yan sen</td>\n <td>lee eun - sil ryu ji - hae</td>\n </tr>\n </tbody>\n</table>"
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The Ford Cosworth DFV engine is used in a Lotus chassis.
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[
"Table Title: dave charlton\n| | year | entrant | chassis | engine | points |\n|---:|-------:|:----------------------------|:-------------|:------------------|---------:|\n| 0 | 1965 | ecurie tomahawk | lotus 20 | ford straight - 4 | 0 |\n| 1 | 1967 | scuderia scribante | brabham bt11 | coventry climax | 0 |\n| 2 | 1968 | scuderia scribante | brabham bt11 | repco | 0 |\n| 3 | 1970 | scuderia scribante | lotus 49c | ford cosworth dfv | 0 |\n| 4 | 1971 | brabham racing organisation | brabham bt33 | ford cosworth dfv | 0 |\n| 5 | 1971 | team lotus | lotus 72d | ford cosworth dfv | 0 |\n| 6 | 1972 | scuderia scribante | lotus 72d | ford cosworth dfv | 0 |\n| 7 | 1973 | scuderia scribante | lotus 72d | ford cosworth dfv | 0 |\n| 8 | 1974 | scuderia scribante | mclaren m23 | ford cosworth dfv | 0 |\n| 9 | 1975 | lucky strike racing | mclaren m23 | ford cosworth dfv | 0 |"
] |
S
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TabFact
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coverbench
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During the 1993 New York Jets season, six games had an attendance exceeding 7,000 spectators.
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[
"Table Title: 1993 new york jets season\n{\"week\": {\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5, \"4\": 6, \"5\": 8, \"6\": 9, \"7\": 10, \"8\": 11, \"9\": 12, \"10\": 13, \"11\": 14, \"12\": 15, \"13\": 16, \"14\": 17, \"15\": 18}, \"date\": {\"0\": \"1993 - 09 - 05\", \"1\": \"1993 - 09 - 12\", \"2\": \"1993 - 09 - 26\", \"3\": \"1993 - 10 - 03\", \"4\": \"1993 - 10 - 10\", \"5\": \"1993 - 10 - 24\", \"6\": \"1993 - 10 - 31\", \"7\": \"1993 - 11 - 07\", \"8\": \"1993 - 11 - 14\", \"9\": \"1993 - 11 - 21\", \"10\": \"1993 - 11 - 28\", \"11\": \"1993 - 12 - 05\", \"12\": \"1993 - 12 - 11\", \"13\": \"1993 - 12 - 18\", \"14\": \"1993 - 12 - 26\", \"15\": \"1994 - 01 - 02\"}, \"opponent\": {\"0\": \"denver broncos\", \"1\": \"miami dolphins\", \"2\": \"new england patriots\", \"3\": \"philadelphia eagles\", \"4\": \"los angeles raiders\", \"5\": \"buffalo bills\", \"6\": \"new york giants\", \"7\": \"miami dolphins\", \"8\": \"indianapolis colts\", \"9\": \"cincinnati bengals\", \"10\": \"new england patriots\", \"11\": \"indianapolis colts\", \"12\": \"washington redskins\", \"13\": \"dallas cowboys\", \"14\": \"buffalo bills\", \"15\": \"houston oilers\"}, \"result\": {\"0\": \"l 26 - 20\", \"1\": \"w 24 - 14\", \"2\": \"w 45 - 7\", \"3\": \"l 35 - 30\", \"4\": \"l 24 - 20\", \"5\": \"l 19 - 10\", \"6\": \"w 10 - 6\", \"7\": \"w 27 - 10\", \"8\": \"w 31 - 17\", \"9\": \"w 17 - 12\", \"10\": \"w 6 - 0\", \"11\": \"l 9 - 6\", \"12\": \"w 3 - 0\", \"13\": \"l 28 - 7\", \"14\": \"l 16 - 14\", \"15\": \"l 24 - 0\"}, \"game site\": {\"0\": \"the meadowlands\", \"1\": \"joe robbie stadium\", \"2\": \"the meadowlands\", \"3\": \"the meadowlands\", \"4\": \"los angeles memorial coliseum\", \"5\": \"the meadowlands\", \"6\": \"giants stadium\", \"7\": \"the meadowlands\", \"8\": \"rca dome\", \"9\": \"the meadowlands\", \"10\": \"foxboro stadium\", \"11\": \"the meadowlands\", \"12\": \"robert f kennedy memorial stadium\", \"13\": \"the meadowlands\", \"14\": \"rich stadium\", \"15\": \"houston astrodome\"}, \"attendance\": {\"0\": 68130, \"1\": 70314, \"2\": 64836, \"3\": 72593, \"4\": 41627, \"5\": 71541, \"6\": 71659, \"7\": 71306, \"8\": 47351, \"9\": 64264, \"10\": 42810, \"11\": 45799, \"12\": 47970, \"13\": 73233, \"14\": 70817, \"15\": 61040}}"
] |
NS
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TabFact
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