In quality, regression serves to identify substitutes for true characteristics that are hard to observe and to find the root causes of technically challenging process problems. It is a major topic in data science, but oddly, the most extensive coverage I could find in the literature on quality is in Shewhart’s first book, from 1931! Later books, including Shewhart’s second, discuss it briefly or not at all. The ASQC, forerunner of the ASQ, published an 80-page guide on how to use regression analysis in quality control in 1985, but has not updated it since.
Regression analysis has been around for almost 140 years and has grown massively in scope, capabilities, and dataset size. Perhaps, it is time for professionals involved with quality to take another look at it.
Sep 3 2024
Using Regression to Improve Quality | Part I – What for?
In quality, regression serves to identify substitutes for true characteristics that are hard to observe and to find the root causes of technically challenging process problems. It is a major topic in data science, but oddly, the most extensive coverage I could find in the literature on quality is in Shewhart’s first book, from 1931! Later books, including Shewhart’s second, discuss it briefly or not at all. The ASQC, forerunner of the ASQ, published an 80-page guide on how to use regression analysis in quality control in 1985, but has not updated it since.
Regression analysis has been around for almost 140 years and has grown massively in scope, capabilities, and dataset size. Perhaps, it is time for professionals involved with quality to take another look at it.
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By Michel Baudin • Data science, Tools • 0 • Tags: Quality, regression, Statistical Process Control