Response Surface Methodology: Process and Product Optimization Using Designed Experiments

Now updated and revised From the reviews of the First Edition ..."Truly a book that can be read by practitioners Anyone who deals with designing experiments, the statistical analysis and modeling of data, and especially product or process improvement, including optimization, should have this book as a reference." Technometrics "An excellent book for practitioners. Ownership is a professional necessity." Journal of the American Statistical Association Identifying and fitting an appropriate response surface model from experimental data requires knowledge of statistical experimental design fundamentals, regression modeling techniques, and elementary optimization methods. This book integrates these three topics into a comprehensive, state-of-the-art presentation of response surface methodology (RSM). This new second edition has been substantially rewritten and updated to include new topics and material, new examples, and to more fully illustrate modern applications of RSM. The authors have made the computer a more integral part of their presentation, employing the most common and useful software packages. They bring an applied focus to the subject of RSM, emphasizing methods that are useful in industry for product and process design and development. Features include: Coverage of two-level factorial and fractional factorial design, and empirical modeling of RSM Optimization techniques useful in RSM, including multiple responses Classical and modern response surface designs, including computer-generated designs The RSM approach to robust parameter design and process robustness studies Comprehensive treatment of mixture experiments Revised and expanded end-of-chapter problems, an extensive reference section, and valuable technical appendices on RSM Supported by Design-Expert software Response Surface Methodology develops the underlying theory of RSM, describes the assumptions and conditions necessary to successfully apply it, and provides comprehensive and authoritative discussion of current topics for statisticians, engineers, and students.

Table of Contents:
Preface. Introduction. Building Empirical Models. Two--Level Factorial Designs. Two--Level Fractional Factorial Designs. Process Improvement with Steepest Ascent. The Analysis of Second--Order Response Surfaces. Experimental Designs for Fitting Response Surfaces--I. Experimental Designs for Fitting Response Surfaces--II. Advanced Response Surface Topics I. Advanced Response Surface Topics II. Robust Parameter Design and Process Robustness Studies. Experiments with Mixtures. Other Mixture Design and Analysis Techniques. Continuous Process Improvement with Evolutionary Operation. Appendix 1: Variable Selection and Model--Building in Regression. Appendix 2: Multicollinearity and Biased Estimation in Regression. Appendix 3: Robust Regression. Appendix 4: Some Mathematical Insights into Ridge Analysis. Appendix 5: Moment Matrix of a Rotatable Design. Appendix 6: Rotatability of a Second--Order Equiradial Design. Appendix 7: Relationship Between D--Optimality and the Volume of a Joint Confidence Ellipsoid on s. Appendix 8: Relationship Between Maximum Prediction Variance in a Region and the Number of Parameters. Appendix 9: The Development of Equation (8.21). Appendix 10: Determination of Data Augmentation Result (Choice of xr+1 for the Sequential Development of a D--Optimal Design). Index.

About the Author :
RAYMOND H. MYERS, PhD, is Professor Emeritus in the Department of Statistics at Virginia Polytechnic Institute and State University. DOUGLAS C. MONTGOMERY, PhD, is Professor in the Department of Industrial Engineering at Arizona State University.

Review :
"...illustrates very well modeling experimental data using regression and response surface ideas...I recommend it highly." (Journal of Statistical Computation and Simulation, January 2006) "A textbook for a graduate level course on the optimization of response surfaces under experiment." (SciTech Book News, Vol. 26, No. 2, June 2002) "...highly recommended for purchase by all readers of this journal..." (Technometrics, Vol. 44, No. 3, August 2002) "This book remains an excellent choice as a textbook...as well as for use by practitioners..." (Journal of the American Statistical Association, December 2002)