statsmodels.stats.proportion.proportions_ztest statsmodels.stats.proportion.proportions_ztest(count, nobs, value=None, alternative='two-sided', prop_var=False) [source] test for proportions based on normal (z) test Parameters: count : integer or array_like the number of successes in nobs trials. If this is array_like, then the assumption is that this represents the number of successes for each independent sample nobs : integer the number of trials or observations, with the same length as

statsmodels.tsa.stattools.pacf statsmodels.tsa.stattools.pacf(x, nlags=40, method='ywunbiased', alpha=None) [source] Partial autocorrelation estimated Parameters: x : 1d array observations of time series for which pacf is calculated nlags : int largest lag for which pacf is returned method : ?ywunbiased? (default) or ?ywmle? or ?ols? specifies which method for the calculations to use: yw or ywunbiased : yule walker with bias correction in denominator for acovf ywm or ywmle : yule walke

statsmodels.stats.power.GofChisquarePower class statsmodels.stats.power.GofChisquarePower(**kwds) [source] Statistical Power calculations for one sample chisquare test Methods plot_power([dep_var, nobs, effect_size, ...]) plot power with number of observations or effect size on x-axis power(effect_size, nobs, alpha, n_bins[, ddof]) Calculate the power of a chisquare test for one sample solve_power([effect_size, nobs, alpha, ...]) solve for any one parameter of the power of a one sample ch

statsmodels.stats.moment_helpers.cov2corr statsmodels.stats.moment_helpers.cov2corr(cov, return_std=False) [source] convert covariance matrix to correlation matrix Parameters: cov : array_like, 2d covariance matrix, see Notes Returns: corr : ndarray (subclass) correlation matrix return_std : bool If this is true then the standard deviation is also returned. By default only the correlation matrix is returned. Notes This function does not convert subclasses of ndarrays. This requires

statsmodels.stats.power.GofChisquarePower.solve_power GofChisquarePower.solve_power(effect_size=None, nobs=None, alpha=None, power=None, n_bins=2) [source] solve for any one parameter of the power of a one sample chisquare-test for the one sample chisquare-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be None, all others need numeric values. n_bins needs to be defined, a default=2 is used. Parameters: effect_size : float standardized effect size, according to

statsmodels.miscmodels.count.PoissonOffsetGMLE.expandparams PoissonOffsetGMLE.expandparams(params) expand to full parameter array when some parameters are fixed Parameters: params : array reduced parameter array Returns: paramsfull : array expanded parameter array where fixed parameters are included Notes Calling this requires that self.fixed_params and self.fixed_paramsmask are defined. developer notes: This can be used in the log-likelihood to ... this could also be replaced by a m

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.pdf TransfTwo_gen.pdf(x, *args, **kwds) Probability density function at x of the given RV. 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 scale parameter (default=1) Returns: pdf : ndarray Probability density

statsmodels.genmod.generalized_estimating_equations.GEEMargins.summary GEEMargins.summary(alpha=0.05) [source] Returns a summary table for marginal effects Parameters: alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns: Summary : SummaryTable A SummaryTable instance

statsmodels.sandbox.stats.multicomp.GroupsStats.groupdemean GroupsStats.groupdemean() [source]

statsmodels.regression.linear_model.RegressionResults.mse_model static RegressionResults.mse_model() [source]