class_docstr: class for two sample comparison
  
  The tests and the confidence interval work for multi-endpoint comparison:
  If d1 and d2 have the same number of rows, then each column of the data
  in d1 is compared with the corresponding column in d2.
  
  Parameters
  ----------
  d1, d2 : instances of DescrStatsW
  
  Notes
  -----
  The result for the statistical tests and the confidence interval are
  independent of the user specified ddof.
  
  TODO: Extend to any number of groups or write a version that works in that
  case, like in SAS and SPSS.
docstr: confidence interval for the difference in means
  
  Parameters
  ----------
  alpha : float
      significance level for the confidence interval, coverage is
      ``1-alpha``
  alternative : str
      This specifies the alternative hypothesis for the test that
      corresponds to the confidence interval.
      The alternative hypothesis, H1, has to be one of the following :
  
      'two-sided': H1: difference in means not equal to value (default)
      'larger' :   H1: difference in means larger than value
      'smaller' :  H1: difference in means smaller than value
  
  usevar : str, 'pooled' or 'unequal'
      If ``pooled``, then the standard deviation of the samples is assumed to be
      the same. If ``unequal``, then Welch ttest with Satterthwait degrees
      of freedom is used
  
  Returns
  -------
  lower, upper : floats
      lower and upper limits of the confidence interval
  
  Notes
  -----
  The result is independent of the user specified ddof.
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code: def tconfint_diff(
      self, alpha=0.05, alternative="two-sided", usevar="pooled"
  ):
      """confidence interval for the difference in means
  
      Parameters
      ----------
      alpha : float
          significance level for the confidence interval, coverage is
          ``1-alpha``
      alternative : str
          This specifies the alternative hypothesis for the test that
          corresponds to the confidence interval.
          The alternative hypothesis, H1, has to be one of the following :
  
          'two-sided': H1: difference in means not equal to value (default)
          'larger' :   H1: difference in means larger than value
          'smaller' :  H1: difference in means smaller than value
  
      usevar : str, 'pooled' or 'unequal'
          If ``pooled``, then the standard deviation of the samples is assumed to be
          the same. If ``unequal``, then Welch ttest with Satterthwait degrees
          of freedom is used
  
      Returns
      -------
      lower, upper : floats
          lower and upper limits of the confidence interval
  
      Notes
      -----
      The result is independent of the user specified ddof.
  
      """
      d1 = self.d1
      d2 = self.d2
      diff = d1.mean - d2.mean
      if usevar == "pooled":
          std_diff = self.std_meandiff_pooledvar
          dof = d1.nobs - 1 + d2.nobs - 1
      elif usevar == "unequal":
          std_diff = self.std_meandiff_separatevar
          dof = self.dof_satt()
      else:
          raise ValueError('usevar can only be "pooled" or "unequal"')
  
      res = _tconfint_generic(
          diff, std_diff, dof, alpha=alpha, alternative=alternative
      )
      return res
  
imports: import numpy
  from scipy import stats
  from statsmodels.tools.decorators import cache_readonly
label_desc: Use image node_id 9 for calling the CompareMeans obj's underlying member method code with example usage: obj.tconfint_diff(alpha, alternative, usevar) and returns: res