Analyzing Experiments with Correlated Multiple Responses

Abstract

Statistically designed experiments have been employed extensively to improve product or process quality and to make products and processes robust. In this paper, we consider experiments with correlated multiple responses whose means, variances, and correlations depend on experimental factors. Analysis of these experiments consists of modeling distributional parameters in terms of the experimental factors and finding factor settings which maximize the probability of being in a specification region, i.e., all responses are simultaneously meeting their respective specifications. The proposed procedure is illustrated with three experiments from the literature.

• Factorial Designs
• Fractional Factorial Designs
• Multivariate Normal Distribution
• Robust Parameter Design

Additional information

Notes on contributors

Chih-Hua Chiao

Dr. Chiao is an Associate Professor in the Department of Business Mathematics. His email address is [email protected].

Michael Hamada

Dr. Hamada is a Technical Staff Member in Statistical Sciences.