statsmodels.stats.weightstats.ztost
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statsmodels.stats.weightstats.ztost(x1, low, upp, x2=None, usevar='pooled', ddof=1.0)
[source] -
Equivalence test based on normal distribution
Parameters: x1 : array_like
one sample or first sample for 2 independent samples
low, upp : float
equivalence interval low < m1 - m2 < upp
x1 : array_like or None
second sample for 2 independent samples test. If None, then a one-sample test is performed.
usevar : string, ?pooled?
If
pooled
, then the standard deviation of the samples is assumed to be the same. Onlypooled
is currently implemented.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
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