statsmodels.tsa.arima_model.ARIMAResults.tvalues static ARIMAResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.tsa.arima_model.ARIMA.information ARIMA.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.regression.linear_model.OLSResults.normalized_cov_params OLSResults.normalized_cov_params()

statsmodels.sandbox.distributions.transformed.squaretg statsmodels.sandbox.distributions.transformed.squaretg = Distribution based on a non-monotonic (u- or hump-shaped transformation) the constructor can be called with a distribution class, and functions that define the non-linear transformation. and generates the distribution of the transformed random variable Note: the transformation, it?s inverse and derivatives need to be fully specified: func, funcinvplus, funcinvminus, derivplus, der

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

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

statsmodels.miscmodels.count.PoissonZiGMLE.loglikeobs PoissonZiGMLE.loglikeobs(params)

statsmodels.sandbox.regression.try_catdata.convertlabels statsmodels.sandbox.regression.try_catdata.convertlabels(ys, indices=None) [source] convert labels based on multiple variables or string labels to unique index labels 0,1,2,...,nk-1 where nk is the number of distinct labels

statsmodels.sandbox.distributions.transformed.Transf_gen.std Transf_gen.std(*args, **kwds) Standard deviation of the distribution. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: std : float standard deviation of the distribution