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Abstract
Supersaturated designs offer a potentially useful way to investigate many factors with very few experimental runs. These designs are used to investigate m factors with n experimental runs, where m > n−1. We evaluate several methods for analyzing a broad range of supersaturated designs and provide a basic explanation of these procedures. We show that the contrasts of a supersaturated design follow a permuted multivariate hypergeometric distribution, which may be approximated with a normal distribution. The analysis methods presented are based on methods for unreplicated fractional factorial designs. Two contrast-based analysis methods are presented, and the assumptions of the underlying model are described for a wide range of supersaturated designs.
Additional information
Notes on contributors
Don R. Holcomb
Dr. Holcomb is a Master Black Belt. He is a Member of ASQ. His email address is [email protected].
Douglas C. Montgomery
Dr. Montgomery is a Professor of Industrial Engineering. He is a Fellow of ASQ. His email address is [email protected].
W. Matthew Carlyle
Dr. Carlyle is an Associate Professor of Operations Research.
