statsmodels.tsa.arima_model.ARMAResults.mafreq static ARMAResults.mafreq() [source] Returns the frequency of the MA roots. This is the solution, x, to z = abs(z)*exp(2j*np.pi*x) where z are the roots.

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

statsmodels.regression.linear_model.OLSResults.HC3_se static OLSResults.HC3_se() See statsmodels.RegressionResults

statsmodels.genmod.generalized_estimating_equations.GEEResults.llf static GEEResults.llf()

statsmodels.genmod.generalized_estimating_equations.GEEResults.initialize GEEResults.initialize(model, params, **kwd)

statsmodels.tsa.arima_model.ARMAResults.normalized_cov_params ARMAResults.normalized_cov_params()

statsmodels.discrete.discrete_model.BinaryModel.cdf BinaryModel.cdf(X) The cumulative distribution function of the model.

Graphics Goodness of Fit Plots gofplots.qqplot(data[, dist, distargs, a, ...]) Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution. gofplots.qqline(ax, line[, x, y, dist, fmt]) Plot a reference line for a qqplot. gofplots.qqplot_2samples(data1, data2[, ...]) Q-Q Plot of two samples? quantiles. gofplots.ProbPlot(data[, dist, fit, ...]) Class for convenient construction of Q-Q, P-P, and probability plots. Boxplots boxplots.violinplot(data[, ax, labels, ...]) Make a v

statsmodels.regression.linear_model.RegressionResults.bse static RegressionResults.bse() [source]

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