statsmodels.stats.weightstats.DescrStatsW class statsmodels.stats.weightstats.DescrStatsW(data, weights=None, ddof=0) [source] descriptive statistics and tests with weights for case weights Assumes that the data is 1d or 2d with (nobs, nvars) observations in rows, variables in columns, and that the same weight applies to each column. If degrees of freedom correction is used, then weights should add up to the number of observations. ttest also assumes that the sum of weights corresponds to th

statsmodels.genmod.cov_struct.GlobalOddsRatio.summary GlobalOddsRatio.summary() [source]

statsmodels.stats.power.TTestIndPower class statsmodels.stats.power.TTestIndPower(**kwds) [source] Statistical Power calculations for t-test for two independent sample currently only uses pooled variance Methods plot_power([dep_var, nobs, effect_size, ...]) plot power with number of observations or effect size on x-axis power(effect_size, nobs1, alpha[, ratio, ...]) Calculate the power of a t-test for two independent sample solve_power([effect_size, nobs1, alpha, ...]) solve for any one p

statsmodels.miscmodels.count.PoissonOffsetGMLE.predict PoissonOffsetGMLE.predict(params, exog=None, *args, **kwargs) After a model has been fit predict returns the fitted values. This is a placeholder intended to be overwritten by individual models.

statsmodels.sandbox.distributions.extras.SkewNorm_gen.moment SkewNorm_gen.moment(n, *args, **kwds) n?th order non-central moment of distribution. Parameters: n : int, n>=1 Order of moment. arg1, arg2, arg3,... : float The shape parameter(s) for the distribution (see docstring of the instance object for more information). kwds : keyword arguments, optional These can include ?loc? and ?scale?, as well as other keyword arguments relevant for a given distribution.

statsmodels.genmod.families.links.Log.deriv2 Log.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.tsa.arima_model.ARIMAResults.cov_params ARIMAResults.cov_params()

statsmodels.duration.hazard_regression.PHRegResults class statsmodels.duration.hazard_regression.PHRegResults(model, params, cov_params, covariance_type='naive') [source] Class to contain results of fitting a Cox proportional hazards survival model. PHregResults inherits from statsmodels.LikelihoodModelResults Parameters: See statsmodels.LikelihoodModelResults : Returns: **Attributes** : model : class instance PHreg model instance that called fit. normalized_cov_params : array The samp

statsmodels.sandbox.regression.gmm.IVRegressionResults.ess static IVRegressionResults.ess()

statsmodels.sandbox.regression.gmm.IVRegressionResults.ssr static IVRegressionResults.ssr()