statsmodels.discrete.discrete_model.ProbitResults.resid_generalized static ProbitResults.resid_generalized() [source] Generalized residuals Notes The generalized residuals for the Probit model are defined

statsmodels.discrete.discrete_model.DiscreteResults.llnull static DiscreteResults.llnull() [source]

statsmodels.sandbox.distributions.transformed.Transf_gen.fit Transf_gen.fit(data, *args, **kwds) Return MLEs for shape, location, and scale parameters from data. MLE stands for Maximum Likelihood Estimate. Starting estimates for the fit are given by input arguments; for any arguments not provided with starting estimates, self._fitstart(data) is called to generate such. One can hold some parameters fixed to specific values by passing in keyword arguments f0, f1, ..., fn (for shape parameters)

statsmodels.tsa.vector_ar.var_model.VARResults.plot_forecast VARResults.plot_forecast(steps, alpha=0.05, plot_stderr=True) [source] Plot forecast

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

statsmodels.regression.linear_model.OLSResults.summary OLSResults.summary(yname=None, xname=None, title=None, alpha=0.05) Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns: smry : Summary insta

statsmodels.tsa.arima_model.ARIMAResults.summary2 ARIMAResults.summary2(title=None, alpha=0.05, float_format='%.4f') Experimental summary function for ARIMA Results Parameters: title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals float_format: string : print format for floats in parameters summary Returns: smry : Summary instance This holds the summary table and text, which

statsmodels.sandbox.distributions.extras.NormExpan_gen.isf NormExpan_gen.isf(q, *args, **kwds) Inverse survival function at q of the given RV. Parameters: q : array_like upper tail probability 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: x : ndarray or scalar Quantil

statsmodels.graphics.gofplots.ProbPlot.qqplot ProbPlot.qqplot(xlabel=None, ylabel=None, line=None, other=None, ax=None, **plotkwargs) [source] Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution or the quantiles of another ProbPlot instance. Parameters: xlabel, ylabel : str or None, optional User-provided lables for the x-axis and y-axis. If None (default), other values are used depending on the status of the kwarg other. line : str {?45?, ?s?, ?r?, q?} or None, opti

statsmodels.tsa.arima_model.ARIMAResults.conf_int ARIMAResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interval. ?Defaul