statsmodels.regression.mixed_linear_model.MixedLMResults.t_test MixedLMResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. 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.sandbox.regression.gmm.IVRegressionResults.condition_number static IVRegressionResults.condition_number() Return condition number of exogenous matrix. Calculated as ratio of largest to smallest eigenvalue.

statsmodels.discrete.discrete_model.NegativeBinomialResults.llf static NegativeBinomialResults.llf()

statsmodels.tsa.ar_model.ARResults.fittedvalues static ARResults.fittedvalues() [source]

statsmodels.miscmodels.count.PoissonGMLE.reduceparams PoissonGMLE.reduceparams(params)

statsmodels.miscmodels.count.PoissonGMLE.score PoissonGMLE.score(params) Gradient of log-likelihood evaluated at params

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

statsmodels.discrete.discrete_model.NegativeBinomialResults.llnull static NegativeBinomialResults.llnull()

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

statsmodels.stats.proportion.binom_tost statsmodels.stats.proportion.binom_tost(count, nobs, low, upp) [source] exact TOST test for one proportion using binomial distribution Parameters: count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. low, upp : floats lower and upper limit of equivalence region Returns: pvalue : float p-value of equivalence test pval_low, pval_upp : floats p-values of lower and upper one-