Statistical design of experiments is useful in virtually every quantitative field. Two-level factorial designs, which are the focus of this book, provide the most efficient plans for exploring the effects of many factors at once. Engineers, physical scientists, and all who conduct experiments will find this book indispensible. The analysis of 50 published experiments from diverse fields gives the book broad appeal.

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Factorial designs enable researchers to experiment with many factors. The 50 published examples re-analyzed in this guide attest to the prolific use of two-level factorial designs. As a testimony to this universal applicability, the examples come from diverse fields:

Analytical Chemistry

Animal Science

Automotive Manufacturing

Ceramics and Coatings

Chromatography

Electroplating

Food Technology

Injection Molding

Marketing

Microarray Processing

Modeling and Neural Networks

Organic Chemistry

Product Testing

Quality Improvement

Semiconductor Manufacturing

Transportation

Focusing on factorial experimentation with two-level factors makes this book unique, allowing the only comprehensive coverage of two-level design construction and analysis. Furthermore, since two-level factorial experiments are easily analyzed using multiple regression models, this focus on two-level designs makes the material understandable to a wide audience. This book is accessible to non-statisticians having a grasp of least squares estimation for multiple regression and exposure to analysis of variance.

Robert W. Mee is Professor of Statistics at the University of Tennessee. Dr. Mee is a Fellow of the American Statistical Association. He has served on the Journal of Quality Technology (JQT) Editorial Review Board and as Associate Editor for Technometrics. He received the 2004 Lloyd Nelson award, which recognizes the year s best article for practitioners in JQT.

"This book contains a wealth of information, including recent results on the design of two-level factorials and various aspects of analysis The examples are particularly clear and insightful." (William Notz, Ohio State University

"One of the strongest points of this book for an audience of practitioners is the excellent collection of published experiments, some of which didn t come out as expected A statistically literate non-statistician who deals with experimental design will have plenty of motivation to read this book, and the payback for the effort will be substantial." (Max Morris, Iowa State University)

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