statsmodels.sandbox.distributions.extras.SkewNorm2_gen.interval SkewNorm2_gen.interval(alpha, *args, **kwds) Confidence interval with equal areas around the median. Parameters: alpha : array_like of float Probability that an rv will be drawn from the returned range. Each value should be in the range [0, 1]. arg1, arg2, ... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional location parameter,

Statistics stats This section collects various statistical tests and tools. Some can be used independently of any models, some are intended as extension to the models and model results. API Warning: The functions and objects in this category are spread out in various modules and might still be moved around. We expect that in future the statistical tests will return class instances with more informative reporting instead of only the raw numbers. Residual Diagnostics and Specification Tests dur

statsmodels.tools.tools.ECDF class statsmodels.tools.tools.ECDF(x, side='right') Return the Empirical CDF of an array as a step function. Parameters: x : array-like Observations side : {?left?, ?right?}, optional Default is ?right?. Defines the shape of the intervals constituting the steps. ?right? correspond to [a, b) intervals and ?left? to (a, b]. Returns: Empirical CDF as a step function. : Examples >>> import numpy as np >>> from statsmodels.distributions.empir

statsmodels.nonparametric.kernel_regression.KernelCensoredReg.aic_hurvich KernelCensoredReg.aic_hurvich(bw, func=None) Computes the AIC Hurvich criteria for the estimation of the bandwidth. Parameters: bw : str or array_like See the bw parameter of KernelReg for details. Returns: aic : ndarray The AIC Hurvich criteria, one element for each variable. func : None Unused here, needed in signature because it?s used in cv_loo. References See ch.2 in [1] and p.35 in [2].

statsmodels.stats.power.TTestPower.solve_power TTestPower.solve_power(effect_size=None, nobs=None, alpha=None, power=None, alternative='two-sided') [source] solve for any one parameter of the power of a one sample t-test for the one sample t-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be None, all others need numeric values. This test can also be used for a paired t-test, where effect size is defined in terms of the mean difference, and nobs is the number of p

statsmodels.distributions.empirical_distribution.ECDF class statsmodels.distributions.empirical_distribution.ECDF(x, side='right') [source] Return the Empirical CDF of an array as a step function. Parameters: x : array-like Observations side : {?left?, ?right?}, optional Default is ?right?. Defines the shape of the intervals constituting the steps. ?right? correspond to [a, b) intervals and ?left? to (a, b]. Returns: Empirical CDF as a step function. : Examples >>> import nu

statsmodels.sandbox.distributions.extras.NormExpan_gen.logsf NormExpan_gen.logsf(x, *args, **kwds) Log of the survival function of the given RV. Returns the log of the ?survival function,? defined as (1 - cdf), evaluated at x. 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

statsmodels.discrete.discrete_model.CountResults.predict CountResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data st

statsmodels.genmod.families.family.Family class statsmodels.genmod.families.family.Family(link, variance) [source] The parent class for one-parameter exponential families. Parameters: link : a link function instance Link is the linear transformation function. See the individual families for available links. variance : a variance function Measures the variance as a function of the mean probabilities. See the individual families for the default variance function. See also Link Functions

statsmodels.sandbox.regression.gmm.IVRegressionResults.f_test IVRegressionResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypothes