Wiley-Blackwell Response Surface Methodology: Process and Product Optimization Using Designed Experiments (Wiley Series in Probability and Statistics)

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.