A Comprehensive Guide to Factorial Two-Level Experimentation

  • Authors
  • Robert Mee

Table of contents

  1. Front Matter
    Pages 1-16
  2. Full Factorial Designs

    1. Front Matter
      Pages 1-1
    2. Robert W. Mee
      Pages 27-74
    3. Robert W. Mee
      Pages 75-114
    4. Robert W. Mee
      Pages 115-142
  3. Fractional Factorial Designs

  4. Additional Topics

    1. Front Matter
      Pages 396-396
    2. Robert W. Mee
      Pages 415-435
    3. Robert W. Mee
      Pages 437-468
  5. Appendices and Tables

  6. Back Matter
    Pages 1-30

About this book


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)


Excel Variance analysis of variance chemistry design distribution experiment marketing modeling neural networks processing quality search engine marketing (SEM) statistics transport

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