Robust Design via Generalized Linear Models

Abstract

A single data transformation may fail to satisfy all the required properties necessary for an analysis. With generalized linear models (GLMs), the identification of the mean-variance relationship and the choice of the scale on which the effects are to be measured can be done separately, overcoming the shortcomings of the data-transformation approach. GLMs also provide an extension of the response surface approach. In this paper, we set out the current status of the GLM approach to the analysis of data from quality-improvement experiments and discuss its merits.

• Data Analysis
• Generalized Linear Model
• Model Checking
• Residuals
• Robust Design

Additional information

Notes on contributors

Youngjo Lee

Dr. Lee is a Professor in the Department of Statistics. His e-mail address is [email protected].

John A. Nelder

Dr. Nelder is a Visiting Professor in the Department of Mathematics. His e-mail address is [email protected].