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

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

statsmodels.discrete.discrete_model.BinaryResults.resid_response static BinaryResults.resid_response() [source] The response residuals Notes Response residuals are defined to be where .

statsmodels.discrete.discrete_model.BinaryResults.resid_pearson static BinaryResults.resid_pearson() [source] Pearson residuals Notes Pearson residuals are defined to be where and is the total number of observations sharing the covariate pattern . For now is always set to 1.

statsmodels.discrete.discrete_model.BinaryResults.resid_dev static BinaryResults.resid_dev() [source] Deviance residuals Notes Deviance residuals are defined where and is the total number of observations sharing the covariate pattern . For now is always set to 1.

statsmodels.discrete.discrete_model.BinaryResults.pvalues static BinaryResults.pvalues()

statsmodels.discrete.discrete_model.BinaryResults.prsquared static BinaryResults.prsquared()

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

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

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