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: variance with default degrees of freedom correction
          
docstr_tok: ["variance","with","default","degrees","of","freedom","correction"]
code: def var(self):
      """variance with default degrees of freedom correction"""
      return self.sumsquares / (self.sum_weights - self.ddof)
  
imports: import numpy
  from scipy import stats
  from statsmodels.tools.decorators import cache_readonly
label_desc: Use image node_id 10 for calling the DescrStatsW obj's underlying member method code with example usage: obj.var() and returns: unknown