statsmodels.stats.weightstats.DescrStatsW.ztest_mean
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DescrStatsW.ztest_mean(value=0, alternative='two-sided')
[source] -
z-test of Null hypothesis that mean is equal to value.
The alternative hypothesis H1 is defined by the following ?two-sided?: H1: mean not equal to value ?larger? : H1: mean larger than value ?smaller? : H1: mean smaller than value
Parameters: value : float or array
the hypothesized value for the mean
alternative : string
The alternative hypothesis, H1, has to be one of the following
?two-sided?: H1: mean not equal to value (default) ?larger? : H1: mean larger than value ?smaller? : H1: mean smaller than value
Returns: tstat : float
test statisic
pvalue : float
pvalue of the t-test
Notes
This uses the same degrees of freedom correction as the t-test in the calculation of the standard error of the mean, i.e it uses
(sum_weights - 1)
instead ofsum_weights
in the denominator. See Examples below for the difference.Examples
z-test on a proportion, with 20 observations, 15 of those are our event
>>> x1 = [0, 1] >>> w1 = [5, 15] >>> d1 = smws.DescrStatsW(x1, w1) >>> d1.ztest_mean(0.5) (2.5166114784235836, 0.011848940928347452)
This differs from the proportions_ztest because of the degrees of freedom correction:
>>> smprop.proportions_ztest(15, 20., value=0.5) (2.5819888974716112, 0.009823274507519247).
We can replicate the results from
proportions_ztest
if we increase the weights to have artificially one more observation:>>> smws.DescrStatsW(x1, np.array(w1)*21./20).ztest_mean(0.5) (2.5819888974716116, 0.0098232745075192366)
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