code: def summary_params(
      results,
      yname=None,
      xname=None,
      alpha=0.05,
      use_t=True,
      skip_header=False,
      float_format="%.4f",
  ):
      """create a summary table of parameters from results instance
  
      Parameters
      ----------
      res : results instance
          some required information is directly taken from the result
          instance
      yname : {str, None}
          optional name for the endogenous variable, default is "y"
      xname : {list[str], None}
          optional names for the exogenous variables, default is "var_xx"
      alpha : float
          significance level for the confidence intervals
      use_t : bool
          indicator whether the p-values are based on the Student-t
          distribution (if True) or on the normal distribution (if False)
      skip_header : bool
          If false (default), then the header row is added. If true, then no
          header row is added.
      float_format : str
          float formatting options (e.g. ".3g")
  
      Returns
      -------
      params_table : SimpleTable instance
      """
  
      if isinstance(results, tuple):
          results, params, bse, tvalues, pvalues, conf_int = results
      else:
          params = results.params
          bse = results.bse
          tvalues = results.tvalues
          pvalues = results.pvalues
          conf_int = results.conf_int(alpha)
  
      data = np.array([params, bse, tvalues, pvalues]).T
      data = np.hstack([data, conf_int])
      data = pd.DataFrame(data)
  
      if use_t:
          data.columns = [
              "Coef.",
              "Std.Err.",
              "t",
              "P>|t|",
              "[" + str(alpha / 2),
              str(1 - alpha / 2) + "]",
          ]
      else:
          data.columns = [
              "Coef.",
              "Std.Err.",
              "z",
              "P>|z|",
              "[" + str(alpha / 2),
              str(1 - alpha / 2) + "]",
          ]
  
      if not xname:
          try:
              data.index = results.model.data.param_names
          except AttributeError:
              data.index = results.model.exog_names
      else:
          data.index = xname
  
      return data
  
imports: from statsmodels.compat.pandas import FUTURE_STACK
  from statsmodels.compat.python import lzip
  import datetime
  from functools import reduce
  import re
  import textwrap
  import numpy
  import pandas
  from .table import SimpleTable
  from .tableformatting import fmt_latex, fmt_txt
label_desc: Use image node_id 3 for calling a global function with example usage: summary_params(results, yname, xname, alpha, use_t, skip_header, float_format) and returns: data