Response Surface Methodology : Process and Product Optimization Using Designed Experiments
by Myers, Raymond H.; Montgomery, Douglas C.; Anderson-Cook, Christine M.Edition: 3rd
Format: Hardcover
Pub. Date: 2009-01-14
Publisher(s): Wiley
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Summary
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.
Author Biography
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.
Table of Contents
| Preface | |
| Introduction | |
| Response Surface Methodology | |
| Approximating Response Functions | |
| The Sequential Nature of RSM | |
| Objectives and Typical Applications of RSM | |
| RSM and the Philosophy of Quality Improvement | |
| Product Design and Formulation (Mixture Problems) | |
| Robust Design and Process Robustness Studies | |
| Useful References on RSM | |
| Building Empirical Models | |
| Linear Regression Models | |
| Estimation of the Parameters in Linear Regression Models | |
| Properties of the Least Squares Estimators and Estimation of | |
| Hypothesis Testing in Multiple Regression | |
| Test for Significance of Regression | |
| Tests on Individual Regression Coefficients and Groups of Coefficients | |
| Confidence Intervals in Multiple Regression | |
| Confidence Intervals on the Individual Regression Coefficients, 38 | |
| A Joint Confidence Region on the Regression Coefficients | |
| Confidence Interval on the Mean Response | |
| Prediction of New Response Observations | |
| Model Adequacy Checking | |
| Residual Analysis | |
| Scaling Residuals | |
| Influence Diagnostics | |
| Testing for Lack of Fit | |
| Fitting a Second-Order Model | |
| Qualitative Regressor Variables | |
| Transformation of the Response Variable | |
| Exercises | |
| Two-Level Factorial Designs | |
| Introduction | |
| The Design | |
| The Design, 1 | |
| The General Design | |
| A Single Replicate of the Design | |
| The Addition of Center Points to the Design | |
| Blocking in the Factorial Design | |
| Blocking in the Replicated Design | |
| Confounding in the Design | |
| Split-Plot Designs | |
| Exercises | |
| Two-Level Fractional Factorial Designs | |
| Introduction | |
| The One-Half Fraction of the Design | |
| The One-Quarter Fraction of the Design | |
| The General Fractional Factorial Design | |
| Resolution III Designs | |
| Resolution IV and V Designs | |
| Fractional Factorial Split-Plot Designs | |
| Summary | |
| Exercises | |
| Process Improvement with Steepest Ascent | |
| Determining the Path of Steepest Ascent | |
| Development of the Procedure | |
| Practical Application of the Method of Steepest Ascent | |
| Consideration of Interaction and Curvature | |
| What About a Second Phase? | |
| What Happens Following Steepest Ascent? | |
| Effect of Scale (Choosing Range of Factors) | |
| Confidence Region for Direction of Steepest Ascent | |
| Steepest Ascent Subject to a Linear Constraint | |
| Steepest Ascent in a Split-Plot Experiment | |
| Exercises | |
| The Analysis of Second-Order Response Surfaces | |
| Second-Order Response Surface | |
| Second-Order Approximating Function | |
| The Nature of the Second-Order Function and Second-Order Surface | |
| Illustration of Second-Order Response Surfaces | |
| A Formal Analytical Approach to the Second-Order Model | |
| Location of the Stationary Point | |
| Nature of the Stationary Point (Canonical Analysis) | |
| Ridge Systems | |
| Role of Contour Plots | |
| Ridge Analysis of the Response Surface | |
| What Is the Value of Ridge Analysis? | |
| Mathematical Development of Ridge Analysis | |
| Sampling Properties of Response Surface Results | |
| Standard Error of Predicted Response | |
| Confidence Region on the Location of the Stationary Point | |
| Use and Computation of the Confidence Region on the Location of the Stationary Point | |
| Confidence Intervals on Eigenvalues in Canonical Analysis | |
| Multiple Response Optimization | |
| Further Comments Co | |
| Table of Contents provided by Publisher. All Rights Reserved. |
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