statsmodels.discrete.discrete_model.NegativeBinomial.cdf NegativeBinomial.cdf(X) The cumulative distribution function of the model.

statsmodels.discrete.discrete_model.LogitResults.llr static LogitResults.llr()

statsmodels.regression.mixed_linear_model.MixedLMResults.llf static MixedLMResults.llf()

statsmodels.tsa.vector_ar.var_model.VARResults.summary VARResults.summary() [source] Compute console output summary of estimates Returns: summary : VARSummary

statsmodels.discrete.discrete_model.LogitResults.wald_test LogitResults.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 tu

statsmodels.sandbox.regression.gmm.GMMResults.jval static GMMResults.jval() [source]

statsmodels.discrete.discrete_model.CountResults.fittedvalues static CountResults.fittedvalues()

statsmodels.tsa.arima_model.ARMAResults.maroots static ARMAResults.maroots() [source]

statsmodels.miscmodels.count.PoissonGMLE.predict_distribution PoissonGMLE.predict_distribution(exog) [source] return frozen scipy.stats distribution with mu at estimated prediction

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.std ExpTransf_gen.std(*args, **kwds) Standard deviation of the distribution. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: std : float standard deviation of the distribution