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

statsmodels.sandbox.regression.gmm.IVGMM.get_error IVGMM.get_error(params) [source]

statsmodels.regression.linear_model.OLSResults.compare_lr_test OLSResults.compare_lr_test(restricted, large_sample=False) Likelihood ratio test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. large_sample : bool Flag indicating whether to

statsmodels.regression.linear_model.RegressionResults.llf static RegressionResults.llf()

statsmodels.sandbox.regression.gmm.IVRegressionResults.rsquared static IVRegressionResults.rsquared()

statsmodels.discrete.discrete_model.MultinomialResults.tvalues static MultinomialResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.tsa.vector_ar.var_model.VARResults.get_eq_index VARResults.get_eq_index(name) Return integer position of requested equation name

statsmodels.stats.weightstats.CompareMeans.std_meandiff_pooledvar static CompareMeans.std_meandiff_pooledvar() [source] variance assuming equal variance in both data sets

statsmodels.sandbox.stats.multicomp.StepDown.stepdown StepDown.stepdown(indices) [source]

statsmodels.duration.hazard_regression.PHReg.breslow_hessian PHReg.breslow_hessian(params) [source] Returns the Hessian of the log partial likelihood evaluated at params, using the Breslow method to handle tied times.