Abstract #301238

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JSM 2003 Abstract #301238
Activity Number: 456
Type: Invited
Date/Time: Thursday, August 7, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Quality & Productivity
Abstract - #301238
Title: Analysis Considerations in Industrial Split-Plot Experiments When the Responses are Nonnormal
Author(s): Timothy Robinson*+ and Raymond H. Myers and Douglas C. Montgomery
Companies: University of Wyoming and Virginia Polytechnic Institute and State University and Arizona State University
Address: 335 Ross Hall, Laramie, WY, 82070,
Keywords: split-plot ; nonnormal ; mixed models
Abstract:

Nonnormal responses are common in many industrial experiments. When there are factors whose levels are difficult and/or costly to control, the experiment is typically run within a split-plot context. Industrial split-plot experiments have received a great deal of attention in the literature in the normal response, linear model setting. Generalized linear models have been proposed for the analysis of completely randomized designs when the response is non-normal. When one uses a completely randomized design, it is implied that the responses are independent. Split-plot experimentation implies that responses within a given whole plot are correlated. o account for this correlation, generalized linear mixed models can be used for the analysis. Generalized linear mixed models fall under two headings: population-averaged models and batch-specific models. The pros and cons of each type of analysis are discussed within the context of examples.

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