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:

x1array_like, 1-D or 2-D: first of the two independent samples, see notes for 2-D case
x2array_like, 1-D or 2-D: second of the two independent samples, see notes for 2-D case
valuefloat: 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 under the Null hypothesis. The test statistic is x1_mean - x2_mean - value.
alphafloat: significance level for the confidence interval, coverage is 1-alpha
alternativestr: This specifies the alternative hypothesis for the test that corresponds to the confidence interval. The alternative hypothesis, H1, has to be one of the following

‘two-sided’: H1: difference in means not equal to value (default) ‘larger’ : H1: difference in means larger than value ‘smaller’ : H1: difference in means smaller than value
usevarstr, ‘pooled’: Currently, only ‘pooled’ is implemented. If pooled, then the standard deviation of the samples is assumed to be the same. see CompareMeans.ztest_ind for different options.
ddofint: Degrees of freedom use in the calculation of the variance of the mean estimate. In the case of comparing means this is one, however it can be adjusted for testing other statistics (proportion, correlation)