The point estimate of the proportion, with the confidence interval as an attribute References Rao, JNK, Scott, AJ (1984) "On Chi-squared Tests For Multiway Contingency Tables with Proportions Estimated From Survey Data" Annals of Statistics 12:46-60. Reference Chart for Precision of Wilson Binomial Proportion Confidence Interval Posted on October 16, 2015 by BioStatMatt in R bloggers | 0 Comments [This article was first published on BioStatMatt » R , and kindly contributed to R-bloggers ]. Suppose n is the sample size, r the number of count of interested outcome, and p = r / n is so called binomial proportion (sample proportion). By default, "Clopper-Pearson" confidence intervals are calculated (via stats::binom.test). Coull, Approximate is better than "exact" for interval estimation of binomial proportions, American Statistician, 52:119–126, 1998. Further possibilities are "Wilson", "Agresti-Coull", and 95 percent confidence intervalの項が区間推定範囲。 > binom.test ( 3 , 100 ) Exact binomial test data : 3 and 100 number of successes = 3 , number of trials = 100 , p - value < 2.2e-16 alternative hypothesis : true probability of success is not equal to 0.5 95 percent confidence interval : 0.006229972 0.085176053 sample estimates : probability of success 0.03 Newcombe, Logit confidence intervals and the inverse sinh transformation, R.G. The erratic behavior of the coverage probability of the standard Wald confidence interval has previously been Interval Estimation for a Binomial Proportion Abstract We revisit the problem of interval estimation of a binomial proportion. This function calculates confidence intervals for a population proportion. A. Agresti and B.A. A confidence interval (CI) is a range of values, computed from the sample, which is