class_docstr: Results for multivariate linear regression
      
class_docstr_tok: ["Results","for","multivariate","linear","regression"]
docstr: Summarize the Regression Results.
  
  Parameters
  ----------
  yname : str, optional
      The name of the endog variable in the tables. The default is `y`.
  xname : list[str], optional
      The names for the exogenous variables, default is "var_xx".
      Must match the number of parameters in the model.
  title : str, optional
      Title for the top table. If not None, then this replaces the
      default title.
  alpha : float
      The significance level for the confidence intervals.
  
  Returns
  -------
  Summary
      Class that holds the summary tables and text, which can be printed
      or converted to various output formats.
  
  See Also
  --------
  statsmodels.iolib.summary.Summary : Class that hold summary results.
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code: def summary(
      self,
      yname=None,
      xname=None,
      title=None,
      alpha=0.05,
      yname_list=None,
  ):
      """
      Summarize the Regression Results.
  
      Parameters
      ----------
      yname : str, optional
          The name of the endog variable in the tables. The default is `y`.
      xname : list[str], optional
          The names for the exogenous variables, default is "var_xx".
          Must match the number of parameters in the model.
      title : str, optional
          Title for the top table. If not None, then this replaces the
          default title.
      alpha : float
          The significance level for the confidence intervals.
  
      Returns
      -------
      Summary
          Class that holds the summary tables and text, which can be printed
          or converted to various output formats.
  
      See Also
      --------
      statsmodels.iolib.summary.Summary : Class that hold summary results.
      """
      # used in generic part of io summary
      self.nobs = self.model.nobs
      self.df_model = self.model.k_endog * (self.model.k_exog - 1)
  
      top_left = [
          ("Dep. Variable:", None),
          ("Model:", [self.model.__class__.__name__]),
          ("Method:", [self.method]),
          ("Date:", None),
          ("Time:", None),
          # ('converged:', ["%s" % self.mle_retvals['converged']]),
      ]
  
      top_right = [
          ("No. Observations:", None),
          ("Df Residuals:", None),
          ("Df Model:", None),
          # ('Pseudo R-squ.:', ["%#6.4g" % self.prsquared]),
          # ('Log-Likelihood:', None),
          # ('LL-Null:', ["%#8.5g" % self.llnull]),
          # ('LLR p-value:', ["%#6.4g" % self.llr_pvalue])
      ]
  
      if hasattr(self, "cov_type"):
          top_left.append(("Covariance Type:", [self.cov_type]))
  
      if title is None:
          title = (
              self.model.__class__.__name__ + " " + "Regression Results"
          )
  
      # boiler plate
      from statsmodels.iolib.summary import Summary
  
      smry = Summary()
      yname, yname_list = self._get_endog_name(yname, yname_list)
  
      # for top of table
      smry.add_table_2cols(
          self,
          gleft=top_left,
          gright=top_right,
          yname=yname,
          xname=xname,
          title=title,
      )
  
      # for parameters, etc
      smry.add_table_params(
          self,
          yname=yname_list,
          xname=xname,
          alpha=alpha,
          use_t=self.use_t,
      )
  
      if hasattr(self, "constraints"):
          smry.add_extra_txt(
              [
                  "Model has been estimated subject to linear "
                  "equality constraints."
              ]
          )
  
      return smry
  
imports: import numpy
  from numpy.linalg import eigvals, inv, solve, matrix_rank, pinv, svd
  from scipy import stats
  import pandas
  from patsy import DesignInfo
  from statsmodels.compat.pandas import Substitution
  from statsmodels.base.model import Model, LikelihoodModelResults
  import statsmodels.base.wrapper
  from statsmodels.regression.linear_model import RegressionResultsWrapper
  from statsmodels.iolib import summary2
  from statsmodels.tools.decorators import cache_readonly
label_desc: Use image node_id 10 for calling the MultivariateLSResults obj's underlying member method code with example usage: obj.summary(yname, xname, title, alpha, yname_list) and returns: smry