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

d2 (d1,) –

Notes

The result for the statistical tests and the confidence interval are independent of the user specified ddof.

TODO: Extend to any number of groups or write a version that works in that case, like in SAS and SPSS.

Methods

dof_satt()

degrees of freedom of Satterthwaite for unequal variance

from_data(data1, data2[, weights1, …])

construct a CompareMeans object from data

std_meandiff_pooledvar()

variance assuming equal variance in both data sets

std_meandiff_separatevar()

summary([use_t, alpha, usevar, value])

summarize the results of the hypothesis test

tconfint_diff([alpha, alternative, usevar])

confidence interval for the difference in means

ttest_ind([alternative, usevar, value])

ttest for the null hypothesis of identical means

ttost_ind(low, upp[, usevar])

test of equivalence for two independent samples, base on t-test

zconfint_diff([alpha, alternative, usevar])

confidence interval for the difference in means

ztest_ind([alternative, usevar, value])

z-test for the null hypothesis of identical means

ztost_ind(low, upp[, usevar])

test of equivalence for two independent samples, based on z-test