statsmodels.stats.weightstats._zconfint_generic statsmodels.stats.weightstats._zconfint_generic(mean, std_mean, alpha, alternative) [source] generic normal-confint to save typing

statsmodels.robust.norms.AndrewWave.psi AndrewWave.psi(z) [source] The psi function for Andrew?s wave The analytic derivative of rho Parameters: z : array-like 1d array Returns: psi : array psi(z) = sin(z/a) for |z| <= a*pi psi(z) = 0 for |z| > a*pi

statsmodels.robust.robust_linear_model.RLM.deviance RLM.deviance(tmp_results) [source] Returns the (unnormalized) log-likelihood from the M estimator.

statsmodels.regression.linear_model.OLS.whiten OLS.whiten(Y) [source] OLS model whitener does nothing: returns Y.

Regression with Discrete Dependent Variable Regression models for limited and qualitative dependent variables. The module currently allows the estimation of models with binary (Logit, Probit), nominal (MNLogit), or count (Poisson) data. See Module Reference for commands and arguments. Examples # Load the data from Spector and Mazzeo (1980) spector_data = sm.datasets.spector.load() spector_data.exog = sm.add_constant(spector_data.exog) # Logit Model logit_mod = sm.Logit(spector_data.endog, spe

statsmodels.stats.diagnostic.kstest_normal statsmodels.stats.diagnostic.kstest_normal(x, pvalmethod='approx') Lillifors test for normality, Kolmogorov Smirnov test with estimated mean and variance Parameters: x : array_like, 1d data series, sample pvalmethod : ?approx?, ?table? ?approx? uses the approximation formula of Dalal and Wilkinson, valid for pvalues < 0.1. If the pvalue is larger than 0.1, then the result of table is returned ?table? uses the table from Dalal and Wilkinson, w

statsmodels.regression.quantile_regression.QuantRegResults.centered_tss static QuantRegResults.centered_tss() [source]

statsmodels.sandbox.regression.gmm.IVGMM.score IVGMM.score(params, weights, epsilon=None, centered=True)

statsmodels.genmod.generalized_estimating_equations.GEEResults.split_resid static GEEResults.split_resid() Returns the residuals, the endogeneous data minus the fitted values from the model. The residuals are returned as a list of arrays containing the residuals for each cluster.

statsmodels.nonparametric.kde.KDEUnivariate.sf static KDEUnivariate.sf() [source] Returns the survival function evaluated at the support. Notes Will not work if fit has not been called.