scipy.stats._result_classes.BinomTestResult.
proportion_ci#
- BinomTestResult.proportion_ci(confidence_level=0.95, method='exact')[source]#
- Compute the confidence interval for - statistic.- Parameters:
- confidence_levelfloat, optional
- Confidence level for the computed confidence interval of the estimated proportion. Default is 0.95. 
- method{‘exact’, ‘wilson’, ‘wilsoncc’}, optional
- Selects the method used to compute the confidence interval for the estimate of the proportion: - ‘exact’ :
- Use the Clopper-Pearson exact method [1]. 
- ‘wilson’ :
- ‘wilsoncc’ :
 - Default is - 'exact'.
 
- Returns:
- ciConfidenceIntervalobject
- The object has attributes - lowand- highthat hold the lower and upper bounds of the confidence interval.
 
- ci
 - References [1]- C. J. Clopper and E. S. Pearson, The use of confidence or fiducial limits illustrated in the case of the binomial, Biometrika, Vol. 26, No. 4, pp 404-413 (Dec. 1934). - Examples - >>> from scipy.stats import binomtest >>> result = binomtest(k=7, n=50, p=0.1) >>> result.statistic 0.14 >>> result.proportion_ci() ConfidenceInterval(low=0.05819170033997342, high=0.26739600249700846)