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

    return _zconfint_generic(self.mean, self.std_mean, alpha, alternative)
