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, solidunderstanding of response surface methodology. The book is easy toread, easy to understand, and very applicable. The examples areexcellent and facilitate learning of the concepts andmethods."
Journal of Quality Technology

Complete with updates that capture the important advances in thefield of experimental design, Response Surface Methodology,Third Edition successfully provides a basic foundation forunderstanding and implementing response surface methodology (RSM)in modern applications. The book continues to outline the essentialstatistical experimental design fundamentals, regression modelingtechniques, and elementary optimization methods that are needed tofit a response surface model from experimental data. With itswealth of new examples and use of the most up-to-date softwarepackages, this book serves as a complete and modern introduction toRSM and its uses across scientific and industrial research.

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

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

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

  • New material on experiments with computer models

  • Updated optimization techniques useful in RSM, includingmultiple responses

  • Thorough treatment of presented examples and experiments usingJMP 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 themost current RSM research

Assuming only a fundamental background in statistical models andmatrix algebra, Response Surface Methodology, Third Editionis an ideal book for statistics, engineering, and physical sciencescourses at the upper-undergraduate and graduate levels. It is alsoa valuable reference for applied statisticians and practicingengineers.


What people are saying - Write a review

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


Building Empirical Models
TwoLevel Factorial Designs
TwoLevel Fractional Factorial Designs
Process Improvement with Steepest Ascent
The Analysis of SecondOrder Response Surfaces
Experimental Designs for Fitting Response SurfacesI
Experimental Designs for Fitting Response SurfacesII
Advanced Topics in Response Surface Methodology
Robust Parameter Design and Process Robustness Studies
Experiments with Mixtures
a Constant Term b0
Other Mixture Design and Analysis Techniques
Moment Matrix of a Rotatable Design
Rotatability of a SecondOrder Equiradial Design

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