Abstract
The capability index is a simple and quantitative way to describe process capability. The index Cpm is an indicator, involving specifications, target value, process mean and standard deviation, for evaluating the capability of a process. When Cpm is applied to evaluate a process, estimating the confidence interval of Cpm is important for statistical inference on the process. Calculating the confidence interval for a process index usually needs the assumption about the underlying distribution. Bootstrapping is a nonparametric, but computer intensive, estimation method. Several types of bootstrap confidence intervals have been developed over the years. In the present paper we report the results of a simulation study on the behavior of the 95% bias-corrected and accelerated (BCa) percentile bootstrap confidence interval for estimating Cpm when the data are correlated and follow a Burr distribution, which is used to represent various population distributions. Effects of sample size, process capability, population distribution and correlation on the BCa bootstrap interval of Cpm are simultaneously studied. A detailed discussion of the simulation results is presented and some conclusions are provided.
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