statsmodels.discrete.discrete_model.MultinomialResults.f_test MultinomialResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. 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 hypothese

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

statsmodels.sandbox.regression.gmm.IVRegressionResults.summary2 IVRegressionResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') Experimental summary function to summarize the regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replac

statsmodels.tsa.vector_ar.var_model.VARResults.pvalues static VARResults.pvalues() [source] Two-sided p-values for model coefficients from Student t-distribution

statsmodels.regression.linear_model.WLS.whiten WLS.whiten(X) [source] Whitener for WLS model, multiplies each column by sqrt(self.weights) Parameters: X : array-like Data to be whitened Returns: sqrt(weights)*X :

statsmodels.discrete.discrete_model.BinaryModel.fit_regularized BinaryModel.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, **kwargs) [source] Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters: start_params : array-like, optional Initial guess of

statsmodels.stats.diagnostic.het_goldfeldquandt statsmodels.stats.diagnostic.het_goldfeldquandt = see class docstring

statsmodels.regression.linear_model.RegressionResults.cov_HC3 static RegressionResults.cov_HC3() [source] See statsmodels.RegressionResults

statsmodels.sandbox.regression.gmm.IVRegressionResults.HC2_se static IVRegressionResults.HC2_se() See statsmodels.RegressionResults

statsmodels.regression.linear_model.OLSResults.HC0_se static OLSResults.HC0_se() See statsmodels.RegressionResults