statsmodels.sandbox.distributions.extras.SkewNorm_gen.ppf SkewNorm_gen.ppf(q, *args, **kwds) Percent point function (inverse of cdf) at q of the given RV. Parameters: q : array_like lower 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 : array_like qu

statsmodels.sandbox.distributions.extras.SkewNorm_gen.logcdf SkewNorm_gen.logcdf(x, *args, **kwds) Log of the cumulative distribution function at x of the given RV. 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 scale parameter (default=1) Returns: logcdf : array_like

statsmodels.discrete.discrete_model.CountModel.pdf CountModel.pdf(X) The probability density (mass) function of the model.

statsmodels.sandbox.distributions.extras.SkewNorm_gen.fit_loc_scale SkewNorm_gen.fit_loc_scale(data, *args) Estimate loc and scale parameters from data using 1st and 2nd moments. Parameters: data : array_like Data to fit. arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). Returns: Lhat : float Estimated location parameter for the data. Shat : float Estimated scale parameter for the data.

statsmodels.regression.quantile_regression.QuantReg.information QuantReg.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.tsa.vector_ar.var_model.VARProcess.long_run_effects VARProcess.long_run_effects() [source] Compute long-run effect of unit impulse

statsmodels.regression.quantile_regression.QuantReg.score QuantReg.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.discrete.discrete_model.NegativeBinomialResults.summary NegativeBinomialResults.summary(yname=None, xname=None, title=None, alpha=0.05, yname_list=None) 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

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

statsmodels.discrete.discrete_model.LogitResults.summary2 LogitResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') Experimental function to summarize regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replaces the default title alp