statsmodels.stats.weightstats._tconfint_generic statsmodels.stats.weightstats._tconfint_generic(mean, std_mean, dof, alpha, alternative) [source] generic t-confint to save typing

statsmodels.stats.weightstats.ztost statsmodels.stats.weightstats.ztost(x1, low, upp, x2=None, usevar='pooled', ddof=1.0) [source] Equivalence test based on normal distribution Parameters: x1 : array_like one sample or first sample for 2 independent samples low, upp : float equivalence interval low < m1 - m2 < upp x1 : array_like or None second sample for 2 independent samples test. If None, then a one-sample test is performed. usevar : string, ?pooled? If pooled, then the stan

statsmodels.stats.weightstats.ztest statsmodels.stats.weightstats.ztest(x1, x2=None, value=0, alternative='two-sided', usevar='pooled', ddof=1.0) [source] test for mean based on normal distribution, one or two samples In the case of two samples, the samples are assumed to be independent. Parameters: x1, x2 : array_like, 1-D or 2-D two independent samples value : float In the one sample case, value is the mean of x1 under the Null hypothesis. In the two sample case, value is the differenc

statsmodels.stats.weightstats.zconfint statsmodels.stats.weightstats.zconfint(x1, x2=None, value=0, alpha=0.05, alternative='two-sided', usevar='pooled', ddof=1.0) [source] confidence interval based on normal distribution z-test Parameters: x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case value : float In the one sample case, value is the mean of x1 under the Null hypothesis. In the two sample case, value is the difference between mean of x1 and mean of x2

statsmodels.stats.weightstats.ttost_paired statsmodels.stats.weightstats.ttost_paired(x1, x2, low, upp, transform=None, weights=None) [source] test of (non-)equivalence for two dependent, paired sample TOST: two one-sided t tests null hypothesis: md < low or md > upp alternative hypothesis: low < md < upp where md is the mean, expected value of the difference x1 - x2 If the pvalue is smaller than a threshold,say 0.05, then we reject the hypothesis that the difference between the

statsmodels.stats.weightstats.ttost_ind statsmodels.stats.weightstats.ttost_ind(x1, x2, low, upp, usevar='pooled', weights=(None, None), transform=None) [source] test of (non-)equivalence for two independent samples TOST: two one-sided t tests null hypothesis: m1 - m2 < low or m1 - m2 > upp alternative hypothesis: low < m1 - m2 < upp where m1, m2 are the means, expected values of the two samples. If the pvalue is smaller than a threshold, say 0.05, then we reject the hypothesis t

statsmodels.stats.weightstats.ttest_ind statsmodels.stats.weightstats.ttest_ind(x1, x2, alternative='two-sided', usevar='pooled', weights=(None, None), value=0) [source] ttest independent sample convenience function that uses the classes and throws away the intermediate results, compared to scipy stats: drops axis option, adds alternative, usevar, and weights option Parameters: x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case alternative : string The altern

statsmodels.stats.weightstats.DescrStatsW class statsmodels.stats.weightstats.DescrStatsW(data, weights=None, ddof=0) [source] descriptive statistics and tests with weights for case weights Assumes that the data is 1d or 2d with (nobs, nvars) observations in rows, variables in columns, and that the same weight applies to each column. If degrees of freedom correction is used, then weights should add up to the number of observations. ttest also assumes that the sum of weights corresponds to th

statsmodels.stats.weightstats.CompareMeans class statsmodels.stats.weightstats.CompareMeans(d1, d2) [source] class for two sample comparison The tests and the confidence interval work for multi-endpoint comparison: If d1 and d2 have the same number of rows, then each column of the data in d1 is compared with the corresponding column in d2. Parameters: d1, d2 : instances of DescrStatsW Notes The result for the statistical tests and the confidence interval are independent of the user specifie

statsmodels.stats.stattools.omni_normtest statsmodels.stats.stattools.omni_normtest(resids, axis=0) [source] Omnibus test for normality Parameters: resid : array-like axis : int, optional Default is 0 Returns: Chi^2 score, two-tail probability :