statsmodels.discrete.discrete_model.BinaryResults.tvalues static BinaryResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.genmod.generalized_linear_model.GLMResults.pvalues static GLMResults.pvalues()

statsmodels.discrete.discrete_model.ProbitResults.prsquared static ProbitResults.prsquared()

statsmodels.discrete.discrete_model.NegativeBinomialResults class statsmodels.discrete.discrete_model.NegativeBinomialResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for NegativeBinomial 1 and 2 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 : f

statsmodels.tools.tools.add_constant statsmodels.tools.tools.add_constant(data, prepend=True, has_constant='skip') [source] This appends a column of ones to an array if prepend==False. Parameters: data : array-like data is the column-ordered design matrix prepend : bool True and the constant is prepended rather than appended. has_constant : str {?raise?, ?add?, ?skip?} Behavior if ``data?? already has a constant. The default will return data without adding another constant. If ?raise?,

statsmodels.duration.hazard_regression.PHReg.information PHReg.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.discrete.discrete_model.NegativeBinomialResults.llnull static NegativeBinomialResults.llnull()

statsmodels.tsa.arima_model.ARMAResults.hqic static ARMAResults.hqic() [source]

statsmodels.stats.proportion.binom_tost statsmodels.stats.proportion.binom_tost(count, nobs, low, upp) [source] exact TOST test for one proportion using binomial distribution Parameters: count : integer or array_like the number of successes in nobs trials. nobs : integer the number of trials or observations. low, upp : floats lower and upper limit of equivalence region Returns: pvalue : float p-value of equivalence test pval_low, pval_upp : floats p-values of lower and upper one-

statsmodels.sandbox.regression.gmm.GMM.score GMM.score(params, weights, epsilon=None, centered=True) [source]