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Response Surface Methodology Process and Product Optimization Using Designed Experiments

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ISBN-10: 0471581003

ISBN-13: 9780471581000

Edition: 1st 1995

Authors: Raymond H. Myers, Douglas C. Montgomery

List price: $110.00
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Book details

List price: $110.00
Edition: 1st
Copyright year: 1995
Publisher: John Wiley & Sons, Incorporated
Publication date: 9/12/1995
Binding: Hardcover
Pages: 728
Size: 6.42" wide x 9.76" long x 1.53" tall
Weight: 2.530
Language: English

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…    

Building Empirical Models
Two-Level Factorial Designs
Two-Level Fractional Factorial Designs
Process Improvement with Steepest Ascent
The Analysis of Response Surfaces
Experimental Designs for Fitting Response Surfaces - I
Experimental Designs for Fitting Response Surfaces - II
Miscellaneous Response Surface Topics
Response Surface Methods and Taguchi's Robust Parameter Design
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 [beta]
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 x[subscript r + 1] for the Sequential Development of a D-Optimal Design)