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Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building, by George E.P. Box, William G. Hunter, J. Stua
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Introduces the philosophy of experimentation and the part that statistics play in experimentation. Emphasizes the need to develop a capability for ``statistical thinking'' by using examples drawn from actual case studies.
- Sales Rank: #270384 in Books
- Published on: 1978
- Original language: English
- Number of items: 1
- Dimensions: 9.47" h x 1.41" w x 6.36" l, .0 pounds
- Binding: Hardcover
- 653 pages
Review
"In fact, [Statistics for Experimenters] might be [one of ] the most readable...books on statistical methods...would be a wonderful background for someone who wants to move into quality control. For such practical purposes, it exceeds any five books on mathematical statistics. The first three chapters are a superb introduction to statistical thinking, something that does not occur anywhere in most texts." -- The UMAP Journal
"It is hoped...that experimenters in all fields will be encouraged to read this text. " -- Biometrics
"The authors are to be congratulated for having written an excellent book. Students fortunate enough to have this as a text will have the opportunity to learn much, and not the least from the review questions and numerous well-contracted problems. Engineers wishing to advance their elementary understanding of statistical design and analysis will be delighted with this work. The writing is literate, and the technical aspects impeccable." -- Journal of Quality Technology
From the Publisher
Introduces the philosophy of experimentation and the part that statistics play in experimentation. Emphasizes the need to develop a capability for ``statistical thinking'' by using examples drawn from actual case studies.
From the Inside Flap
Applied Regression Analysis Second Edition Norman Draper and Harry Smith, Jr. The revised, expanded Second Edition of this popular book by two authorities with more than 50 years of combined experience in the field. Draper and Smith offer a comprehensive introduction to fundamentals, concepts and applications. This Second Edition treats basic multiple linear regression, the examination of residuals, the planning of large regression studies, the theory of nonlinear estimation, the "practical" significance of regression, and much more. With many new exercises, examples, and references, this book is unusually versatile-a useful reference for professionals in many fields. 1981 (0 471-02995-5) 709 pp. Nonlinear Regression G. A. F. Seber and C. J. Wild Nonlinear Regression carefully blends the theory and practice of nonlinear regression and includes numerous computational examples and graphs throughout. Treats the broad aspects of model building and statistical inference, and serves as a thorough introduction to the topic for graduate students and as an accessible reference source for professionals. Most chapters are self-contained, and new and important material relating to the concept of curvature and its growing role in statistical inference is included. Ideal for those training to be statisticians, biometricians, and econometricians, as well as providing research scientists with up-to-date, readable access to their fields. 1989 (0 471-61760-1) 768 pp. Statistical Design for Research Leslie Kish The issues of data collection and selection, vital to statistics, yet often neglected, are described in-depth in Statistical Design For Research, as well as the ramifying effects of statistical design in the social and health sciences, education, and market research. Emphasizing research design development, this important work includes a discussion of key methods-experimental designs, survey sampling, and controlled investigations-plus ample tables, figures and problem sets. 1987 (0 471-08359-3) 296 pp.
Most helpful customer reviews
37 of 38 people found the following review helpful.
Outstanding book, but you should buy the newer edition, not this version
By Badger Turned Tarheel
All of the reviews on this book are generally consistent in their praise for the book and the authors. I do not have any points to add to the discussion other than this:
It is a credit to this version of Statistics for Experimenters that it has remained relevant throughout the years as a classic introductory text that has kept selling consistently since it was released in the 1970's. Nevertheless, unless you have a particular reason for purchasing this version, you should purchase the updated version(also available through Amazon).
The full title of the newer edition is:
Statistics for Experimenters: Design, Innovation, and Discovery, 2nd Edition
The 2nd edition, written in the same engaging and readable style as the 1st, contains virtually all of the content of the 1st edition plus advances in design of experiments that have happened since the 1st edition was published.
23 of 23 people found the following review helpful.
Immediate usability in practice.
By Kenneth R Litko
This is an excellently written book with clear examples of how to apply statistics to everyday experimental settings. Box delves deep enough into the underlying theory to give an engineer such as myself an appreciation for the "reality" of the mathematics, but sticks to concrete examples and putting theory into practice. Each chapter follows the previous one, but each is also reasonably self-contained. Terminology is easily clarified with a quick use of the comprehensive index.
Additionally, don't let the print date fool you... the book is timely.
29 of 31 people found the following review helpful.
classic text on design, well presented
By Michael R. Chernick
This book was published in 1978 but as other reviewers have noted its practical methods and advice are timeless. George Box and Stu Hunter are both very famous statisticians who are also great teachers and lecturers. Bill Hunter is now deceased. All three authors have made major contributions to the design of experiments. The book is written for practitioners and in the simplest language possible. Emphasis is placed on practical designs and not optimal designs because optimal designs are very sensitive to model specification.
It does not include the robust designs of Taguchi which came later and could easily be included if the authors choose to revise it.
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