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Response Surface Methodology: Process and Product Optimization Using Designed Experiments [NOOK Book]

Overview

Identifying 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 third edition has been substantially rewritten and updated with new topics and material, new examples and exercises, and to more fully illustrate modern applications of...
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Response Surface Methodology: Process and Product Optimization Using Designed Experiments

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Overview

Identifying 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 third edition has been substantially rewritten and updated with new topics and material, new examples and exercises, and to more fully illustrate modern applications of RSM. Working with the most useful software packages, the authors bring an applied focus that emphasizes models useful in industry for product and process design and development.
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Editorial Reviews

Booknews
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)
From The Critics
A textbook for a graduate level course on the optimization of response surfaces under experiment. The authors describe two-level factorial and fractional factorial designs, the method of steepest ascent, second-order response surfaces, designs for fitting response surface models, and experiments that involve mixtures. The second edition uses Design Expert version 6 for much of the computing. Annotation c. Book News, Inc., Portland, OR (booknews.com)
From the Publisher
“This new third edition has been substantially rewritten and updated with new topics and material, new examples and exercises, and to more fully illustrate modern applications of RSM.Working with the most useful software packages, the authors bring an applied focus that emphasizes models useful in industry for product and process design and development.” (Zentralblatt Math, 1 October 2013)

"The third edition of a well-regarded text on response surface methodology. Christine M. Anderson-Cook, has been added … [bringing] an applied perspective to the material." (Mathematical Reviews, December 2009)

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Product Details

  • ISBN-13: 9781118210475
  • Publisher: Wiley
  • Publication date: 9/20/2011
  • Series: Wiley Series in Probability and Statistics , #705
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 3
  • Pages: 704
  • File size: 13 MB
  • Note: This product may take a few minutes to download.

Meet the Author

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.

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Table of Contents

Preface xi

1 Introduction 1

1.1 Response Surface Methodology, 1

1.2 Product Design and Formulation (Mixture Problems), 10

1.3 Robust Design and Process Robustness Studies, 10

1.4 Useful References on RSM, 11

2 Building Empirical Models 13

2.1 Linear Regression Models, 13

2.2 Estimation of the Parameters in Linear Regression Models, 14

2.3 Properties of the Least Squares Estimators and Estimation of s2, 22

2.4 Hypothesis Testing in Multiple Regression, 24

2.5 Confidence Intervals in Multiple Regression, 31

2.6 Prediction of New Response Observations, 35

2.7 Model Adequacy Checking, 36

2.8 Fitting a Second-Order Model, 47

2.9 Qualitative Regressor Variables, 55

2.10 Transformation of the Response Variable, 58

3 Two-Level Factorial Designs 73

3.1 Introduction, 73

3.2 The 22 Design, 74

3.3 The 23 Design, 86

3.4 The General 2k Design, 96

3.5 A Single Replicate of the 2k Design, 96

3.6 The Addition of Center Points to the 2k Design, 109

3.7 Blocking in the 2k Factorial Design, 114

3.8 Split-Plot Designs, 121

4 Two-Level Fractional Factorial Designs 135

4.1 Introduction, 135

4.2 The One-Half Fraction of the 2k Design, 136

4.3 The One-Quarter Fraction of the 2k Design, 148

4.4 The General 2k2p Fractional Factorial Design, 154

4.5 Resolution III Designs, 158

4.6 Resolution IV and V Designs, 167

4.7 Fractional Factorial Split-Plot Designs, 168

4.8 Summary, 172

5 Process Improvement with Steepest Ascent 181

5.1 Determining the Path of Steepest Ascent, 182

5.2 Consideration of Interaction and Curvature, 189

5.3 Effect of Scale (Choosing Range of Factors), 193

5.4 Confidence Region for Direction of Steepest Ascent, 195

5.5 Steepest Ascent Subject to a Linear Constraint, 198

5.6 Steepest Ascent in a Split-Plot Experiment, 202

6 The Analysis of Second-Order Response Surfaces 219

6.1 Second-Order Response Surface, 219

6.2 Second-Order Approximating Function, 220

6.3 A Formal Analytical Approach to the Second-Order Model, 223

6.4 Ridge Analysis of the Response Surface, 235

6.5 Sampling Properties of Response Surface Results, 242

6.6 Multiple Response Optimization, 253

6.7 Further Comments Concerning Response Surface Analysis, 264

7 Experimental Designs for Fitting Response Surfaces—I 281

7.1 Desirable Properties of Response Surface Designs, 281

7.2 Operability Region, Region of Interest, and Model Inadequacy, 282

7.3 Design of Experiments for First-Order Models, 285

7.4 Designs for Fitting Second-Order Models, 296

8 Experimental Designs for Fitting Response Surfaces—II 349

8.1 Designs that Require a Relatively Small Run Size, 350

8.2 General Criteria for Constructing, Evaluating, and Comparing Experimental Designs, 362

8.3 Computer-Generated Designs in RSM, 386

8.4 Some Final Comments Concerning Design Optimality and Computer-Generated Design, 405

9 Advanced Topics in Response Surface Methodology 417

9.1 Effects of Model Bias on the Fitted Model and Design, 417

9.2 A Design Criterion Involving Bias and Variance, 420

9.3 Errors in Control of Design Levels, 432

9.4 Experiments with Computer Models, 435

9.5 Minimum Bias Estimation of Response Surface Models, 442

9.6 Neural Networks, 446

9.7 RSM for Non-Normal Responses—Generalized Linear Models, 449

9.8 Split-Plot Designs for Second-Order Models, 466

10 Robust Parameter Design and Process Robustness Studies 483

10.1 Introduction, 483

10.2 What is Parameter Design?, 483

10.3 The Taguchi Approach, 486

10.4 The Response Surface Approach, 495

10.5 Experimental Designs for RPD and Process Robustness Studies, 525

10.6 Dispersion Effects in Highly Fractionated Designs, 537

11 Experiments with Mixtures 557

11.1 Introduction, 557

11.2 Simplex Designs and Canonical Mixture Polynomials, 560

11.2.1 Simplex Lattice Designs, 560

11.3 Response Trace Plots, 576

11.4 Reparameterizing Canonical Mixture Models to Contain a Constant Term (b0), 577

12 Other Mixture Design and Analysis Techniques 589

12.1 Constraints on the Component Proportions, 589

12.2 Mixture Experiments Using Ratios of Components, 617

12.3 Process Variables in Mixture Experiments, 621

12.4 Screening Mixture Components, 641

Appendix 1 Moment Matrix of a Rotatable Design 655

Appendix 2 Rotatability of a Second-Order Equiradial Design 661

References 665

Index 677

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