Abstract
Plackett-Burman designs and other two-level nonregular fractional factorial designs are popular for identifying the active factors from a large list of experimental factors. Data analysis for these designs is straightforward if interactions can be safely ignored. However, rather than viewing interactions as a hazard, methods have recently been proposed to identify and estimate active interaction effects via nonregular factorial designs. The purpose of this article is to suggest approaches that the author has found useful and to caution against unrealistic attempts for finding many interactions. If interactions are anticipated, the use of designs with more runs than double the number of factors is strongly encouraged.
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Robert W. Mee
Dr. Mee is the William and Sara Clark Professor of Business in the Department of Statistics, Operations, and Management Science at the University of Tennessee. His email address is [email protected].