'Returns  -2 x log-likelihoog ratio and the p-value for the
hypothesized variance
Parameters
sig2_0 : float
Hypothesized variance to be tested
return_weights : bool
If True, returns the weights that maximize the
likelihood of observing sig2_0. Default is False
Returns
test_results : tuple
The  log-likelihood ratio and the p_value  of sig2_0
Examples
>>> import numpy as np
>>> import statsmodels.api as sm
>>> random_numbers = np.random.standard_normal(1000)*100
>>> el_analysis = sm.emplike.DescStat(random_numbers)
>>> hyp_test = el_analysis.test_var(9500)'

def test_var(self, sig2_0, return_weights=False):

    self.sig2_0 = sig2_0
    mu_max = max(self.endog)
    mu_min = min(self.endog)
    llr = optimize.fminbound(self._opt_var, mu_min, mu_max, full_output=1)[1]
    p_val = chi2.sf(llr, 1)
    if return_weights:
        return (llr, p_val, self.new_weights.T)
    else:
        return (llr, p_val)
