Response Surface Methodology: Process and Product Optimization Using Designed Experiments

Front Cover
John Wiley & Sons, 2009 - Mathematics - 680 pages
1 Review
Praise for the Second Edition:

"This book [is for] anyone who would like a good, solid understanding of response surface methodology. The book is easy to read, easy to understand, and very applicable. The examples are excellent and facilitate learning of the concepts and methods."
Journal of Quality Technology

Complete with updates that capture the important advances in the field of experimental design, Response Surface Methodology, Third Edition successfully provides a basic foundation for understanding and implementing response surface methodology (RSM) in modern applications. The book continues to outline the essential statistical experimental design fundamentals, regression modeling techniques, and elementary optimization methods that are needed to fit a response surface model from experimental data. With its wealth of new examples and use of the most up-to-date software packages, this book serves as a complete and modern introduction to RSM and its uses across scientific and industrial research.

This new edition maintains its accessible approach to RSM, with coverage of classical and modern response surface designs. Numerous new developments in RSM are also treated in full, including optimal designs for RSM, robust design, methods for design evaluation, and experiments with restrictions on randomization as well as the expanded integration of these concepts into computer software. Additional features of the Third Edition include:

  • Inclusion of split-plot designs in discussion of two-level factorial designs, two-level fractional factorial designs, steepest ascent, and second-order models

  • A new section on the Hoke design for second-order response surfaces

  • New material on experiments with computer models

  • Updated optimization techniques useful in RSM, including multiple responses

  • Thorough treatment of presented examples and experiments using JMP 7, Design-Expert Version 7, and SAS software packages

  • Revised and new exercises at the end of each chapter

  • An extensive references section, directing the reader to the most current RSM research

Assuming only a fundamental background in statistical models and matrix algebra, Response Surface Methodology, Third Edition is an ideal book for statistics, engineering, and physical sciences courses at the upper-undergraduate and graduate levels. It is also a valuable reference for applied statisticians and practicing engineers.

  

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

Building Empirical Models
13
TwoLevel Factorial Designs
73
TwoLevel Fractional Factorial Designs
135
Process Improvement with Steepest Ascent
181
The Analysis of SecondOrder Response Surfaces
219
Experimental Designs for Fitting Response SurfacesI
281
Experimental Designs for Fitting Response SurfacesII
349
Advanced Topics in Response Surface Methodology
417
Robust Parameter Design and Process Robustness Studies
483
Experiments with Mixtures
557
a Constant Term b0
577
Other Mixture Design and Analysis Techniques
589
Moment Matrix of a Rotatable Design
655
Rotatability of a SecondOrder Equiradial Design
661
Index
677
Copyright

Common terms and phrases

About the author (2009)

Raymond H. Myers, PhD, is Professor Emeritus in the Department of Statistics at Virginia Polytechnic Institute and State University. He has over forty years of academic experience in the areas of experimental design and analysis, response surface analysis, and designs for nonlinear models. A Fellow of the American Statistical Society, Dr. Myers has authored or coauthored numerous journal articles and books, including Generalized Linear Models: With Applications in Engineering and the Sciences, also published by Wiley.

Douglas C. Montgomery, PhD, is Regents' Professor of Industrial Engineering and Statistics at Arizona State University. Dr. Montgomery has over thirty years of academic and consulting experience and has devoted his research to engineering statistics, specifically the design and analysis of experiments. He has authored or coauthored numerous journal articles and twelve books, including Generalized Linear Models: With Applications in Engineering and the Sciences; Introduction to Linear Regression Analysis, Fourth Edition; and Introduction to Time Series Analysis and Forecasting, all published by Wiley.

Christine M. Anderson-Cook, PhD, is Project Leader a t the Los Alamos National Laboratory, New Mexico. Dr. Anderson-Cook has over ten years of academic and consulting experience and has written numerous journal articles on the topics of design of experiments and response surface methodology.

Bibliographic information