'summarize the results of the hypothesis test
Parameters
use_t : bool, optional
if use_t is True, then t test results are returned
if use_t is False, then z test results are returned
alpha : float
significance level for the confidence interval, coverage is
``1-alpha``
usevar : string, \'pooled\' or \'unequal\'
If ``pooled``, then the standard deviation of the samples is
assumed to be the same. If ``unequal``, then the variance of
Welsh ttest will be used, and the degrees of freedom are those
of Satterthwaite if ``use_t`` is True.
value : float
difference between the means under the Null hypothesis.
Returns
smry : SimpleTable'

def summary(self, use_t=True, alpha=0.05, usevar='pooled', value=0):

    d1 = self.d1
    d2 = self.d2
    confint_percents = (100 - (alpha * 100))
    if use_t:
        (tstat, pvalue, _) = self.ttest_ind(usevar=usevar, value=value)
        (lower, upper) = self.tconfint_diff(alpha=alpha, usevar=usevar)
    else:
        (tstat, pvalue) = self.ztest_ind(usevar=usevar, value=value)
        (lower, upper) = self.zconfint_diff(alpha=alpha, usevar=usevar)
    if (usevar == 'pooled'):
        std_err = self.std_meandiff_pooledvar
    else:
        std_err = self.std_meandiff_separatevar
    std_err = np.atleast_1d(std_err)
    tstat = np.atleast_1d(tstat)
    pvalue = np.atleast_1d(pvalue)
    lower = np.atleast_1d(lower)
    upper = np.atleast_1d(upper)
    conf_int = np.column_stack((lower, upper))
    params = np.atleast_1d(((d1.mean - d2.mean) - value))
    title = 'Test    for    equality    of    means'
    yname = 'y'
    xname = [('subset    #%d' % (ii + 1)) for ii in range(tstat.shape[0])]
    from statsmodels.iolib.summary import summary_params
    return summary_params((None, params, std_err, tstat, pvalue, conf_int), alpha=alpha, use_t=use_t, yname=yname, xname=xname, title=title)
