This book is about the power of statistical experiments. In the past, books on experimental design focused almost entirely on manufacturing problems. In contrast, although this book is relevant to manufacturing and includes useful examples in that area, its emphasis is on applications to marketing and service operations. The authors provide a fresh and practical treatment of the key topics in designing and analyzing experiments. Testing in the business world is commonplace, and the usual approach is to change one factor at a time while holding other factors constant. This approach may seem logical and appealing, but, as the authors explain, it is highly inefficient, and may lead to wrong conclusions. The better method, the authors propose, is to test all factors simultaneously. Doing so not only reduces the costs of experimenting but also provides the decision-maker with better information. Throughout the book, the authors illustrate concepts with practical examples. In addition, the book includes a set of 13 separate real-world cases based on the actual implementation of experimental design methods.
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Johannes Ledolter is the C. Maxwell Stanley Professor of Management Sciences at the University of Iowa, and Professor of Statistics at the Vienna University of Economics and Business Administration. His books include Introduction to Regression Modeling (with Bovas Abraham) and Achieving Quality Through Continual Improvement (with Claude W. Burrill). Arthur J. Swersey is Professor of Operations Research at the Yale School of Management.
“Ledolter and Swersey succeed in conveying their passion for experimental design and sharing the power and practical value of these methods. The real-world examples used in the book are excellent.”—Bovas Abraham, University of Waterloo
“The authors present a wealth of interesting examples, many of which come from marketing, to minimize mathematical formalism and to help students learn how experimental design methods work and why they are so potentially useful. At last we have an accessible and relevant resource to learn about these powerful ideas.”—Bert Gunter, Principal Biostatistician, Genentech
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