Measurement System Assessment via Generalized Inference

Abstract

Good measurement systems are an important requirement for a successful quality improvement or statistical process control program. A measurement system is assessed by performing a designed experiment known as a gauge repeatability and reproducibility (R & R) study. Confidence intervals for the parameters which describe measurement system quality are an important part of analyzing the data from a gauge R & R study. In this paper, we show how confidence intervals can easily be obtained using the recently developed generalized inference methodology, which can be calculated by exact numerical integration or can be approximated to any desired accuracy using simulation. The methodology is demonstrated on data from two gauge R & R studies based on two-way layouts. The approach is simple and general enough to extend the results to higher-way layouts.