statsmodels.stats.proportion.proportion_confint(count, nobs, alpha=0.05, method='normal')
confidence interval for a binomial proportion
ci_low, ci_upp – lower and upper confidence level with coverage (approximately) 1-alpha. When a pandas object is returned, then the index is taken from the
float, ndarray, or pandas Series or DataFrame
Beta, the Clopper-Pearson exact interval has coverage at least 1-alpha, but is in general conservative. Most of the other methods have average coverage equal to 1-alpha, but will have smaller coverage in some cases.
The ‘beta’ and ‘jeffreys’ interval are central, they use alpha/2 in each tail, and alpha is not adjusted at the boundaries. In the extreme case when
count is zero or equal to
nobs, then the coverage will be only 1 - alpha/2 in the case of ‘beta’.
The confidence intervals are clipped to be in the [0, 1] interval in the case of ‘normal’ and ‘agresti_coull’.
Method “binom_test” directly inverts the binomial test in scipy.stats. which has discrete steps.
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© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.