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

Attributes:¶

std_meandiff_pooledvar

variance assuming equal variance in both data sets

std_meandiff_separatevar

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
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