statsmodels.stats.weightstats.DescrStatsW.nobs static DescrStatsW.nobs() [source] alias for number of observations/cases, equal to sum of weights

statsmodels.regression.linear_model.OLSResults.resid_pearson static OLSResults.resid_pearson() Residuals, normalized to have unit variance. Returns: An array wresid/sqrt(scale) :

statsmodels.discrete.discrete_model.BinaryModel.initialize BinaryModel.initialize() Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.

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

statsmodels.regression.quantile_regression.QuantRegResults.fittedvalues static QuantRegResults.fittedvalues()

statsmodels.discrete.discrete_model.DiscreteResults.f_test DiscreteResults.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 hypotheses to t

statsmodels.tsa.varma_process.VarmaPoly.vstack VarmaPoly.vstack(a=None, name='ar') [source] stack lagpolynomial vertically in 2d array

statsmodels.sandbox.regression.gmm.IVGMMResults.pvalues static IVGMMResults.pvalues()

statsmodels.discrete.discrete_model.NegativeBinomial.information NegativeBinomial.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.graphics.mosaicplot.mosaic statsmodels.graphics.mosaicplot.mosaic(data, index=None, ax=None, horizontal=True, gap=0.005, properties= at 0x2ac652f5b6e0>, labelizer=None, title='', statistic=False, axes_label=True, label_rotation=0.0) [source] Create a mosaic plot from a contingency table. It allows to visualize multivariate categorical data in a rigorous and informative way. Parameters: data : dict, pandas.Series, np.ndarray, pandas.DataFrame The contingency table that contai