'Summarize the Results of the hypothesis test
Parameters
xname : list of strings, optional
Default is `c_##` for ## in p the number of regressors
alpha : float
significance level for the confidence intervals. Default is
alpha = 0.05 which implies a confidence level of 95%.
title : string, optional
Title for the params table. If not None, then this replaces the
default title
Returns
smry : string or Summary instance
This contains a parameter results table in the case of t or z test
in the same form as the parameter results table in the model
results summary.
For F or Wald test, the return is a string.'

def summary(self, xname=None, alpha=0.05, title=None):

    if (self.effect is not None):
        if (title is None):
            title = 'Test    for    Constraints'
        elif (title == ''):
            title = None
        use_t = (self.distribution == 't')
        yname = 'constraints'
        if (xname is None):
            xname = [('c%d' % ii) for ii in range(len(self.effect))]
        from statsmodels.iolib.summary import summary_params
        pvalues = np.atleast_1d(self.pvalue)
        summ = summary_params((self, self.effect, self.sd, self.statistic, pvalues, self.conf_int(alpha)), yname=yname, xname=xname, use_t=use_t, title=title, alpha=alpha)
        return summ
    elif hasattr(self, 'fvalue'):
        return ('<F    test:    F=%s,    p=%s,    df_denom=%d,    df_num=%d>' % (repr(self.fvalue), self.pvalue, self.df_denom, self.df_num))
    else:
        return ('<Wald    test:    statistic=%s,    p-value=%s>' % (self.statistic, self.pvalue))
