statsmodels.emplike.descriptive.DescStatUV.test_mean DescStatUV.test_mean(mu0, return_weights=False) [source] Returns - 2 x log-likelihood ratio, p-value and weights for a hypothesis test of the mean. Parameters: mu0 : float Mean value to be tested return_weights : bool If return_weights is True the funtion returns the weights of the observations under the null hypothesis. Default is False Returns: test_results : tuple The log-likelihood ratio and p-value of mu0

statsmodels.emplike.descriptive.DescStatUV.test_kurt DescStatUV.test_kurt(kurt0, return_weights=False) [source] Returns -2 x log-likelihood and the p-value for the hypothesized kurtosis. Parameters: 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 The log-likelihood ratio and p-value of kurt0

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.emplike.descriptive.DescStatUV.plot_contour DescStatUV.plot_contour(mu_low, mu_high, var_low, var_high, mu_step, var_step, levs=[0.2, 0.1, 0.05, 0.01, 0.001]) [source] Returns a plot of the confidence region for a univariate mean and variance. Parameters: mu_low : float Lowest value of the mean to plot mu_high : float Highest value of the mean to plot var_low : float Lowest value of the variance to plot var_high : float Highest value of the variance to plot mu_step : flo

statsmodels.emplike.descriptive.DescStatUV.ci_var DescStatUV.ci_var(lower_bound=None, upper_bound=None, sig=0.05) [source] Returns the confidence interval for the variance. Parameters: lower_bound : float The minimum value the lower confidence interval can take. The p-value from test_var(lower_bound) must be lower than 1 - significance level. Default is .99 confidence limit assuming normality upper_bound : float The maximum value the upper confidence interval can take. The p-value from t

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

statsmodels.emplike.descriptive.DescStatUV.ci_mean DescStatUV.ci_mean(sig=0.05, method='gamma', epsilon=1e-08, gamma_low=-10000000000, gamma_high=10000000000) [source] Returns the confidence interval for the mean. Parameters: sig : float significance level. Default is .05 method : str Root finding method, Can be ?nested-brent? or ?gamma?. Default is ?gamma? ?gamma? Tries to solve for the gamma parameter in the Lagrange (see Owen pg 22) and then determine the weights. ?nested brent? uses

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.DescStatMV.test_corr DescStatMV.test_corr(corr0, return_weights=0) [source] Returns -2 x log-likelihood ratio and p-value for the correlation coefficient between 2 variables Parameters: corr0 : float Hypothesized value to be tested return_weights : bool If true, returns the weights that maximize the log-likelihood at the hypothesized value

statsmodels.emplike.descriptive.DescStatMV.mv_test_mean DescStatMV.mv_test_mean(mu_array, return_weights=False) [source] Returns -2 x log likelihood and the p-value for a multivariate hypothesis test of the mean Parameters: mu_array : 1d array Hypothesized values for the mean. Must have same number of elements as columns in endog return_weights : bool If True, returns the weights that maximize the likelihood of mu_array. Default is False. Returns: test_results : tuple The log-likelih