Generalized linear models for the analysis of taguchi‐type experiments

John Ashworth Nelder · Y. Lee

179 citations0/0 files storedApplied Stochastic Models and Data Analysis

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Optimal Experimental Design Methods Statistical Methods and Applications Statistical Methods and Bayesian Inference

Abstract

Abstract Recent interest in Taguchi's methods have led to developments in joint analysis of the mean and dispersion from designed experiments. A commonly used method is the analysis of variance of the transformed data. However, a single transformation cannot necessarily produce the Normality, constancy of variance and linearity of systematic effects for the mean and dispersion models. We describe the use of generalized linear models for the analysis of such experiments and illustrate the methods with a data set.

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1959Use of Half-Normal Plots in Interpreting Factorial Two-Level Experiments 1988Signal-to-Noise Ratios, Performance Criteria, and Transformations 1985Quality engineering in japan

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1992Metamodels for simulation input-output relations 1998Generalized linear models for the analysis of quality‐improvement experiments 1996A Generalized Linear Modeling Approach to Robust Design 2003Robust Design via Generalized Linear Models 1997Generalized Linear Models for Quality-Improvement Experiments 2002Analysis of Functional Responses from Robust Design Studies 2008Another view of dual response surface modeling and optimization in robust parameter design 2011Variance Function Regression in Hierarchical Age-Period-Cohort Models

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