class_docstr: Perform an ordinary least-squares regression predicting the returns of all
  other assets on the given asset.
  
  Parameters
  ----------
  target : catalyst.assets.Asset
      The asset to regress against all other assets.
  returns_length : int >= 2
      Length of the lookback window over which to compute returns. Daily
      returns require a window length of 2.
  regression_length : int >= 1
      Length of the lookback window over which to compute each regression.
  mask : catalyst.pipeline.Filter, optional
      A Filter describing which assets should be regressed against the target
      asset each day.
  
  Notes
  -----
  Computing this factor over many assets can be time consuming. It is
  recommended that a mask be used in order to limit the number of assets over
  which regressions are computed.
  
  This factor is designed to return five outputs:
  
  - alpha, a factor that computes the intercepts of each regression.
  - beta, a factor that computes the slopes of each regression.
  - r_value, a factor that computes the correlation coefficient of each
    regression.
  - p_value, a factor that computes, for each regression, the two-sided
    p-value for a hypothesis test whose null hypothesis is that the slope is
    zero.
  - stderr, a factor that computes the standard error of the estimate of each
    regression.
  
  For more help on factors with multiple outputs, see
  :class:`catalyst.pipeline.factors.CustomFactor`.
  
  Examples
  --------
  Let the following be example 10-day returns for three different assets::
  
                     SPY    MSFT     FB
      2017-03-13    -.03     .03    .04
      2017-03-14    -.02    -.03    .02
      2017-03-15    -.01     .02    .01
      2017-03-16       0    -.02    .01
      2017-03-17     .01     .04   -.01
      2017-03-20     .02    -.03   -.02
      2017-03-21     .03     .01   -.02
      2017-03-22     .04    -.02   -.02
  
  Suppose we are interested in predicting each stock's returns from SPY's
  over rolling 5-day look back windows. We can compute rolling regression
  coefficients (alpha and beta) from 2017-03-17 to 2017-03-22 by doing::
  
      regression_factor = RollingRegressionOfReturns(
          target=sid(8554),
          returns_length=10,
          regression_length=5,
      )
      alpha = regression_factor.alpha
      beta = regression_factor.beta
  
  The result of computing ``alpha`` from 2017-03-17 to 2017-03-22 gives::
  
                     SPY    MSFT     FB
      2017-03-17       0    .011   .003
      2017-03-20       0   -.004   .004
      2017-03-21       0    .007   .006
      2017-03-22       0    .002   .008
  
  And the result of computing ``beta`` from 2017-03-17 to 2017-03-22 gives::
  
                     SPY    MSFT     FB
      2017-03-17       1      .3   -1.1
      2017-03-20       1      .2     -1
      2017-03-21       1     -.3     -1
      2017-03-22       1     -.3    -.9
  
  Note that SPY's column for alpha is all 0's and for beta is all 1's, as the
  regression line of SPY with itself is simply the function y = x.
  
  To understand how each of the other values were calculated, take for
  example MSFT's ``alpha`` and ``beta`` values on 2017-03-17 (.011 and .3,
  respectively). These values are the result of running a linear regression
  predicting MSFT's returns from SPY's returns, using values starting at
  2017-03-17 and looking back 5 days. That is, the regression was run with
  x = [-.03, -.02, -.01, 0, .01] and y = [.03, -.03, .02, -.02, .04], and it
  produced a slope of .3 and an intercept of .011.
  
  See Also
  --------
  :class:`catalyst.pipeline.factors.RollingPearsonOfReturns`
  :class:`catalyst.pipeline.factors.RollingSpearmanOfReturns`
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code: def __new__(
      cls, target, returns_length, regression_length, mask=NotSpecified
  ):
      # Use the `SingleAsset` filter here because it protects against
      # inputting a non-existent target asset.
      returns = Returns(
          window_length=returns_length,
          mask=(AssetExists() | SingleAsset(asset=target)),
      )
      return super(RollingLinearRegressionOfReturns, cls).__new__(
          cls,
          dependent=returns,
          independent=returns[target],
          regression_length=regression_length,
          mask=mask,
      )
  
imports: from numpy import broadcast_arrays
  from scipy.stats import linregress, pearsonr, spearmanr
  from catalyst.errors import IncompatibleTerms
  from catalyst.pipeline.factors import CustomFactor
  from catalyst.pipeline.filters import SingleAsset
  from catalyst.pipeline.mixins import SingleInputMixin
  from catalyst.pipeline.sentinels import NotSpecified
  from catalyst.pipeline.term import AssetExists
  from catalyst.utils.input_validation import expect_bounded, expect_dtypes
  from catalyst.utils.numpy_utils import float64_dtype, int64_dtype
  from .technical import Returns
label_desc: Use image node_id 1 for calling the RollingLinearRegressionOfReturns obj's underlying member method code with example usage: obj.__new__(cls, target, returns_length, regression_length, mask) and returns: super