'two-sided confidence interval for weighted mean of data
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 : string
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'

def tconfint_mean(self, alpha=0.05, alternative='two-sided'):

    dof = (self.sum_weights - 1)
    ci = _tconfint_generic(self.mean, self.std_mean, dof, alpha, alternative)
    return ci
