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, d2instances of DescrStatsW

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.

Attributes
std_meandiff_pooledvar

variance assuming equal variance in both data sets

std_meandiff_separatevar

Methods

dof_satt()

degrees of freedom of Satterthwaite for unequal variance

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

construct a CompareMeans object from data

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

Properties

std_meandiff_pooledvar

variance assuming equal variance in both data sets

std_meandiff_separatevar