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Response Surface Methodology: Process and Product in Optimization Using Designed Experimentsby Raymond H Myers
Synopses & Reviews
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."
"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.
Response Surface Methodologydevelops 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.
Book News Annotation:
A practical guide to response surface methodology (RSM)--the process of identifying and fitting an appropriate response surface model from experimental data--for statisticians in fields including chemistry, engineering, quality control, computer science, industrial engineering, and the experimental sciences. While the opening chapter lays down the basic conceptual groundwork, the bulk of the volume is devoted to providing step-by-step guidance on the use of statistical and empirical modeling techniques that have proven their efficacy in industry. Numerous real-world examples illuminate critical points covered. Includes end-of-chapter problems.
Annotation c. Book News, Inc., Portland, OR (booknews.com)
Gets you quickly up and running with the full range of powerful statistical experimental design, modeling, and optimization techniques
Coauthored by widely recognized experts in the fields of quality control and the design of experiments, this book is a practical guide to Response Surface Methodology (RSM)—the process of identifying and fitting an appropriate response surface model from experimental data. While in the opening chapters the authors lay down the basic conceptual groundwork preliminary to a working understanding of the methods described, the bulk of the book is devoted to providing students and professionals with clear, step-by-step guidance on the use of powerful statistical and empirical modeling techniques that have proven their efficacy in industry. Throughout, numerous real-world examples help illuminate critical points covered, and chapter-end problems help you to gauge your command of the concepts and procedures described. Important topics covered include:
Providing clear, hands-on guidance to the application of some of today's most useful techniques, Response Surface Methodology is an essential working resource for process development and quality engineering professionals, engineering designers, product formulators, engineers, chemists, and all those whose professional activities involve the design of experiments.
Includes bibliographical references (p. 689-695) and 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.
Table of Contents
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 ß.
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).
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