statsmodels.stats.weightstats._tstat_generic statsmodels.stats.weightstats._tstat_generic(value1, value2, std_diff, dof, alternative, diff=0) [source] generic ttest to save typing

statsmodels.genmod.families.links.nbinom.inverse_deriv nbinom.inverse_deriv(z) Derivative of the inverse of the negative binomial transform Parameters: z : array-like Usually the linear predictor for a GLM or GEE model Returns: The value of the inverse of the derivative of the negative binomial : link :

statsmodels.tsa.ar_model.ARResults.hqic static ARResults.hqic() [source]

statsmodels.duration.hazard_regression.PHRegResults.initialize PHRegResults.initialize(model, params, **kwd)

statsmodels.sandbox.stats.multicomp.catstack statsmodels.sandbox.stats.multicomp.catstack(args) [source]

statsmodels.discrete.discrete_model.LogitResults class statsmodels.discrete.discrete_model.LogitResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for Logit Model Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : float Akaike information criterion.

statsmodels.tsa.arima_model.ARIMAResults.bic static ARIMAResults.bic()

statsmodels.discrete.discrete_model.LogitResults.summary LogitResults.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 intervals Returns:

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

statsmodels.regression.linear_model.OLSResults.HC0_se static OLSResults.HC0_se() See statsmodels.RegressionResults