statsmodels.stats.outliers_influence.OLSInfluence.ess_press static OLSInfluence.ess_press() [source] (cached attribute) error sum of squares of PRESS residuals

statsmodels.stats.outliers_influence.OLSInfluence.dffits_internal static OLSInfluence.dffits_internal() [source] (cached attribute) dffits measure for influence of an observation based on resid_studentized_internal uses original results, no nobs loop

statsmodels.stats.outliers_influence.OLSInfluence.dffits static OLSInfluence.dffits() [source] (cached attribute) dffits measure for influence of an observation based on resid_studentized_external, uses results from leave-one-observation-out loop It is recommended that observations with dffits large than a threshold of 2 sqrt{k / n} where k is the number of parameters, should be investigated. Returns: dffits: float : dffits_threshold : float References Wikipedia

statsmodels.stats.outliers_influence.OLSInfluence.dfbetas static OLSInfluence.dfbetas() [source] (cached attribute) dfbetas uses results from leave-one-observation-out loop

statsmodels.stats.outliers_influence.OLSInfluence.det_cov_params_not_obsi static OLSInfluence.det_cov_params_not_obsi() [source] (cached attribute) determinant of cov_params of all LOOO regressions uses results from leave-one-observation-out loop

statsmodels.stats.outliers_influence.OLSInfluence.cooks_distance static OLSInfluence.cooks_distance() [source] (cached attribute) Cooks distance uses original results, no nobs loop

statsmodels.stats.outliers_influence.OLSInfluence.cov_ratio static OLSInfluence.cov_ratio() [source] (cached attribute) covariance ratio between LOOO and original This uses determinant of the estimate of the parameter covariance from leave-one-out estimates. requires leave one out loop for observations

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

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

statsmodels.discrete.discrete_model.NegativeBinomialResults.resid static NegativeBinomialResults.resid() Residuals Notes The residuals for Count models are defined as where . Any exposure and offset variables are also handled.