statsmodels.stats.weightstats.DescrStatsW.ztost_mean DescrStatsW.ztost_mean(low, upp) [source] test of (non-)equivalence of one sample, based on z-test TOST: two one-sided z-tests null hypothesis: m < low or m > upp alternative hypothesis: low < m < upp where m is the expected value of the sample (mean of the population). If the pvalue is smaller than a threshold, say 0.05, then we reject the hypothesis that the expected value of the sample (mean of the population) is outside of

statsmodels.robust.norms.TrimmedMean.psi TrimmedMean.psi(z) [source] The psi function for least trimmed mean The analytic derivative of rho Parameters: z : array-like 1d array Returns: psi : array psi(z) = z for |z| <= c psi(z) = 0 for |z| > c

statsmodels.genmod.cov_struct.Autoregressive.summary Autoregressive.summary() [source]

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.median SkewNorm2_gen.median(*args, **kwds) Median 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 is 0. scale : array_like, optional Scale parameter, Default is 1. Returns: median : float The median of the distribution. See also stats.distributions.

statsmodels.robust.norms.RobustNorm class statsmodels.robust.norms.RobustNorm [source] The parent class for the norms used for robust regression. Lays out the methods expected of the robust norms to be used by statsmodels.RLM. Parameters: None : : Some subclasses have optional tuning constants. See also statsmodels.rlm, and Notes Currently only M-estimators are available. References PJ Huber. ?Robust Statistics? John Wiley and Sons, Inc., New York, 1981. DC Montgomery, EA Peck. ?Introd

statsmodels.sandbox.regression.gmm.GMMResults.predict GMMResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data structu

statsmodels.genmod.families.family.InverseGaussian.resid_anscombe InverseGaussian.resid_anscombe(endog, mu) [source] The Anscombe residuals for the inverse Gaussian distribution Parameters: endog : array Endogenous response variable mu : array Fitted mean response variable Returns: resid_anscombe : array The Anscombe residuals for the inverse Gaussian distribution as defined below Notes resid_anscombe = log(endog/mu)/sqrt(mu)

statsmodels.sandbox.distributions.transformed.Transf_gen.logsf Transf_gen.logsf(x, *args, **kwds) Log of the survival function of the given RV. Returns the log of the ?survival function,? defined as (1 - cdf), evaluated at x. Parameters: x : array_like quantiles 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

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

statsmodels.iolib.summary.Summary.add_table_2cols Summary.add_table_2cols(res, title=None, gleft=None, gright=None, yname=None, xname=None) [source] add a double table, 2 tables with one column merged horizontally Parameters: res : results instance some required information is directly taken from the result instance title : string or None if None, then a default title is used. gleft : list of tuples elements for the left table, tuples are (name, value) pairs If gleft is None, then a de