stats.proportion.proportions_ztost()

statsmodels.stats.proportion.proportions_ztost

statsmodels.stats.proportion.proportions_ztost(count, nobs, low, upp, prop_var='sample') [source]

Equivalence test based on normal distribution

Parameters:

count : integer or array_like the number of successes in nobs trials. If this is array_like, then the assumption is that this represents the number of successes for each independent sample nobs : integer the number of trials or observations, with the same length as count. low, upp : float equivalence interval low < prop1 - prop2 < upp prop_var : string or float in (0, 1) prop_var determines which proportion is used for the calculation of the standard deviation of the proportion estimate The available options for string are ‘sample’ (default), ‘null’ and ‘limits’. If prop_var is a float, then it is used directly.

Returns:

pvalue : float pvalue of the non-equivalence test t1, pv1 : tuple of floats test statistic and pvalue for lower threshold test t2, pv2 : tuple of floats test statistic and pvalue for upper threshold test

Notes

checked only for 1 sample case