Interval estimation for conformance proportions of multiple quality characteristics

Abstract

A conformance proportion is an important and useful index to assess industrial quality improvement. Statistical confidence limits for a conformance proportion are usually required not only to perform statistical significance tests, but also to provide useful information for determining practical significance. In this article, we propose approaches for constructing statistical confidence limits for a conformance proportion of multiple quality characteristics. Under the assumption that the variables of interest are distributed with a multivariate normal distribution, we develop an approach based on the concept of a fiducial generalized pivotal quantity (FGPQ). Without any distribution assumption on the variables, we apply some confidence interval construction methods for the conformance proportion by treating it as the probability of a success in a binomial distribution. The performance of the proposed methods is evaluated through detailed simulation studies. The results reveal that the simulated coverage probability (cp) for the FGPQ-based method is generally larger than the claimed value. On the other hand, one of the binomial distribution-based methods, that is, the standard method suggested in classical textbooks, appears to have smaller simulated cps than the nominal level. Two alternatives to the standard method are found to maintain their simulated cps sufficiently close to the claimed level, and hence their performances are judged to be satisfactory. In addition, three examples are given to illustrate the application of the proposed methods.

Keywords:

• fiducial inference
• generalized pivotal quantity
• process capability index
• tolerance interval
• quality control

Acknowledgments

The authors thank the editor and two referees for their constructive comments and suggestions that resulted in a much improved article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Funding

This research is partially supported by the National Science Council of ROC under grant [NSC 102-2118-M-002-004-MY2].