# 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 brents method to find the confidence intervals but must maximize the likelihhod ratio on every iteration. gamma is generally much faster. If the optimizations does not converge, try expanding the gamma_high and gamma_low variable. gamma_low (float) – Lower bound for gamma when finding lower limit. If function returns f(a) and f(b) must have different signs, consider lowering gamma_low. gamma_high (float) – Upper bound for gamma when finding upper limit. If function returns f(a) and f(b) must have different signs, consider raising gamma_high. epsilon (float) – When using ‘nested-brent’, amount to decrease (increase) from the maximum (minimum) of the data when starting the search. This is to protect against the likelihood ratio being zero at the maximum (minimum) value of the data. If data is very small in absolute value (<10 ** -6) consider shrinking epsilon When using ‘gamma’, amount to decrease (increase) the minimum (maximum) by to start the search for gamma. If fucntion returns f(a) and f(b) must have differnt signs, consider lowering epsilon. Interval – Confidence interval for the mean tuple