Restricted randomization and multiple responses in industrial experiments

Two issues regarding designed experiments are discussed; restrictions on randomization and multiple responses. The former is typically related to hard-to-vary factors and factors appearing in separate stages of a process experiment. Randomization restrictions should be taken into account in the construction of the design as well as in the statistical modelling. In the paper, a case study of sausage production is discussed, having a split-plot model with correlated multiple responses. Multiple responses are handled in two ways, by principal component analysis (PCA) followed by ANOVA of the principal components, and by a newly developed alternative, the ‘50–50 MANOVA’. Multiple tests of correlated response variables are also described. Practical aspects of the planning, performing, response measurements, and statistical analysis are emphasized throughout. Hence, the paper aims to extend the utility of statistical methods in industry by linking design of experiments to multivariate analysis of the responses. Copyright © 2007 John Wiley & Sons, Ltd.

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