statsmodels.discrete.discrete_model.BinaryResults.wald_test BinaryResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A

statsmodels.stats.weightstats.DescrStatsW.var static DescrStatsW.var() [source] variance with default degrees of freedom correction

statsmodels.tsa.vector_ar.irf.IRAnalysis.fevd_table IRAnalysis.fevd_table() [source]

statsmodels.discrete.discrete_model.DiscreteResults.get_margeff DiscreteResults.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) [source] Get marginal effects of the fitted model. Parameters: at : str, optional Options are: ?overall?, The average of the marginal effects at each observation. ?mean?, The marginal effects at the mean of each regressor. ?median?, The marginal effects at the median of each regressor. ?zero?, The marginal effects at zero for each r

statsmodels.tsa.vector_ar.irf.IRAnalysis.cov IRAnalysis.cov(orth=False) [source] Compute asymptotic standard errors for impulse response coefficients Notes Lutkepohl eq 3.7.5

statsmodels.duration.hazard_regression.PHReg.loglike PHReg.loglike(params) [source] Returns the log partial likelihood function evaluated at params.

statsmodels.regression.linear_model.OLSResults.ssr static OLSResults.ssr()

statsmodels.stats.gof.gof_binning_discrete statsmodels.stats.gof.gof_binning_discrete(rvs, distfn, arg, nsupp=20) [source] get bins for chisquare type gof tests for a discrete distribution Parameters: rvs : array sample data distname : string name of distribution function arg : sequence parameters of distribution nsupp : integer number of bins. The algorithm tries to find bins with equal weights. depending on the distribution, the actual number of bins can be smaller. Returns: fre

statsmodels.stats.power.NormalIndPower.solve_power NormalIndPower.solve_power(effect_size=None, nobs1=None, alpha=None, power=None, ratio=1.0, alternative='two-sided') [source] solve for any one parameter of the power of a two sample z-test for z-test the keywords are: effect_size, nobs1, alpha, power, ratio exactly one needs to be None, all others need numeric values Parameters: effect_size : float standardized effect size, difference between the two means divided by the standard deviatio

statsmodels.miscmodels.count.PoissonZiGMLE.hessian PoissonZiGMLE.hessian(params) Hessian of log-likelihood evaluated at params