7.13.3.2. statsmodels.emplike.descriptive.DescStatUV¶

class statsmodels.emplike.descriptive.DescStatUV(endog)[source]¶

A class to compute confidence intervals and hypothesis tests involving mean, variance, kurtosis and skewness of a univariate random variable.

Parameters:	endog : 1darray Data to be analyzed

Attributes

endog	(1darray) Data to be analyzed
nobs	(float) Number of observations

__init__(endog)[source]¶

Methods

__init__(endog)
ci_kurt([sig, upper_bound, lower_bound])	Returns the confidence interval for kurtosis.
ci_mean([sig, method, epsilon, gamma_low, ...])	Returns the confidence interval for the mean.
ci_skew([sig, upper_bound, lower_bound])	Returns the confidence interval for skewness.
ci_var([lower_bound, upper_bound, sig])	Returns the confidence interval for the variance.
plot_contour(mu_low, mu_high, var_low, ...)	Returns a plot of the confidence region for a univariate mean and variance.
test_joint_skew_kurt(skew0, kurt0[, ...])	Returns - 2 x log-likelihood and the p-value for the joint
test_kurt(kurt0[, return_weights])	Returns -2 x log-likelihood and the p-value for the hypothesized kurtosis.
test_mean(mu0[, return_weights])	Returns - 2 x log-likelihood ratio, p-value and weights for a hypothesis test of the mean.
test_skew(skew0[, return_weights])	Returns -2 x log-likelihood and p-value for the hypothesized skewness.
test_var(sig2_0[, return_weights])	Returns -2 x log-likelihoog ratio and the p-value for the