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A Tutorial on Generalized Linear Models

Summary: Situations in which the observations are not normally distributed arise frequently in the quality engineering field. The standard approach to the analysis of such responses is to transform the response into a new quantity that behaves more like a normal random variable. An alternative approach is to use an analysis procedure based on the generalized linear model (GLM), where a nonnormal error distribution and a function that links the predictor to the response may be specified. We present an introduction to the GLM, and show how such models may be fit. We present the GLM as an analog to the normal theory linear model. The usefulness of this approach is illustrated with examples.

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  • Topics: Data Quality, Design of Experiments
  • Keywords: Non-normality,Data analysis,Design of experiments (DOE),Least squares,Linear models,Maximum likelihood estimate (MLE),Regression
  • Author: Myers, Raymond H.; Montgomery, Douglas C.
  • Journal: Journal of Quality Technology