GENERALIZED LINEAR-MODELS FOR QUALITY-IMPROVEMENT EXPERIMENTS

Since the early 1980s, industry has embraced the use of designed exper iments as an effective means for improving quality. For quality charac teristics not normally distributed, the practice of first transforming the data and then analyzing them by standard normal-based methods is well established. There is a natural alternative called generalized li near models (GLMs). This paper explains how GLMs achieve the intended goal of transformation while at the same time giving a wider class of models that can handle a range of applications. Moreover, the same ite rative strategy for data analysis that has been developed for normal d ata over the years, namely, the alternation between model selection an d model checking, extends easily to analyses with GLMs. The paper illu strates the ability of GLMs to handle many different types of data by the re-analysis of three quality-improvement experiments.