class_docstr: Descriptive statistics and tests with weights for case weights
  
  Assumes that the data is 1d or 2d with (nobs, nvars) observations in rows,
  variables in columns, and that the same weight applies to each column.
  
  If degrees of freedom correction is used, then weights should add up to the
  number of observations. ttest also assumes that the sum of weights
  corresponds to the sample size.
  
  This is essentially the same as replicating each observations by its
  weight, if the weights are integers, often called case or frequency weights.
  
  Parameters
  ----------
  data : array_like, 1-D or 2-D
      dataset
  weights : None or 1-D ndarray
      weights for each observation, with same length as zero axis of data
  ddof : int
      default ddof=0, degrees of freedom correction used for second moments,
      var, std, cov, corrcoef.
      However, statistical tests are independent of `ddof`, based on the
      standard formulas.
  
  Examples
  --------
  
  >>> import numpy as np
  >>> np.random.seed(0)
  >>> x1_2d = 1.0 + np.random.randn(20, 3)
  >>> w1 = np.random.randint(1, 4, 20)
  >>> d1 = DescrStatsW(x1_2d, weights=w1)
  >>> d1.mean
  array([ 1.42739844,  1.23174284,  1.083753  ])
  >>> d1.var
  array([ 0.94855633,  0.52074626,  1.12309325])
  >>> d1.std_mean
  array([ 0.14682676,  0.10878944,  0.15976497])
  
  >>> tstat, pval, df = d1.ttest_mean(0)
  >>> tstat; pval; df
  array([  9.72165021,  11.32226471,   6.78342055])
  array([  1.58414212e-12,   1.26536887e-14,   2.37623126e-08])
  44.0
  
  >>> tstat, pval, df = d1.ttest_mean([0, 1, 1])
  >>> tstat; pval; df
  array([ 9.72165021,  2.13019609,  0.52422632])
  array([  1.58414212e-12,   3.87842808e-02,   6.02752170e-01])
  44.0
  
  # if weights are integers, then asrepeats can be used
  
  >>> x1r = d1.asrepeats()
  >>> x1r.shape
  ...
  >>> stats.ttest_1samp(x1r, [0, 1, 1])
  ...
docstr: two-sided confidence interval for weighted mean of data
  
  Confidence interval is based on normal distribution.
  If the data is 2d, then these are separate confidence intervals
  for each column.
  
  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: mean not equal to value (default)
        'larger' :   H1: mean larger than value
        'smaller' :  H1: mean smaller than value
  
  Returns
  -------
  lower, upper : floats or ndarrays
      lower and upper bound of confidence interval
  
  Notes
  -----
  In a previous version, statsmodels 0.4, alpha was the confidence
  level, e.g. 0.95
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code: def zconfint_mean(self, alpha=0.05, alternative="two-sided"):
      """two-sided confidence interval for weighted mean of data
  
      Confidence interval is based on normal distribution.
      If the data is 2d, then these are separate confidence intervals
      for each column.
  
      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: mean not equal to value (default)
            'larger' :   H1: mean larger than value
            'smaller' :  H1: mean smaller than value
  
      Returns
      -------
      lower, upper : floats or ndarrays
          lower and upper bound of confidence interval
  
      Notes
      -----
      In a previous version, statsmodels 0.4, alpha was the confidence
      level, e.g. 0.95
      """
  
      return _zconfint_generic(
          self.mean, self.std_mean, alpha, alternative
      )
  
imports: import numpy
  from scipy import stats
  from statsmodels.tools.decorators import cache_readonly
label_desc: Use image node_id 19 for calling the DescrStatsW obj's underlying member method code with example usage: obj.zconfint_mean(alpha, alternative) and returns: _zconfint_generic