statsmodels.emplike.descriptive.DescStatUV.ci_kurt DescStatUV.ci_kurt(sig=0.05, upper_bound=None, lower_bound=None) [source] Returns the confidence interval for kurtosis. Parameters: sig : float The significance level. Default is .05 upper_bound : float Maximum value of kurtosis the upper limit can be. Default is .99 confidence limit assuming normality. lower_bound : float Minimum value of kurtosis the lower limit can be. Default is .99 confidence limit assuming normality. Returns:

statsmodels.emplike.descriptive.DescStatUV.test_joint_skew_kurt DescStatUV.test_joint_skew_kurt(skew0, kurt0, return_weights=False) [source] Returns - 2 x log-likelihood and the p-value for the joint hypothesis test for skewness and kurtosis Parameters: skew0 : float Skewness value to be tested kurt0 : float Kurtosis value to be tested return_weights : bool If True, function also returns the weights that maximize the likelihood ratio. Default is False. Returns: test_results : tuple

statsmodels.sandbox.stats.runs.Runs.runs_test Runs.runs_test(correction=True) [source] basic version of runs test Parameters: correction: bool : Following the SAS manual, for samplesize below 50, the test statistic is corrected by 0.5. This can be turned off with correction=False, and was included to match R, tseries, which does not use any correction. pvalue based on normal distribution, with integer correction :

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.sandbox.regression.gmm.IVRegressionResults.spec_hausman IVRegressionResults.spec_hausman(dof=None) [source] Hausman?s specification test See also spec_hausman generic function for Hausman?s specification test

statsmodels.sandbox.distributions.transformed.LogTransf_gen.interval LogTransf_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 parame

statsmodels.regression.linear_model.OLSResults.summary OLSResults.summary(yname=None, xname=None, title=None, alpha=0.05) Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns: smry : Summary insta

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