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Type of bind: Hardcover
Dewey Decimal Number: 519.5
EAN num: 9780471718130
ISBN number: 0471718130
Label: Wiley-Interscience
Manufacturer: Wiley-Interscience
Quantity: 1
Page Count: 664
Printing Date: June 14, 2005
Publishing house: Wiley-Interscience
Sale Popularity Level: 411114
Studio: Wiley-Interscience
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Editor's Notes and Comments:
Product Description:
A Classic adapted to modern times
Rewritten and updated, this new edition of Statistics for Experimenters adopts the same approaches as the landmark First Edition by teaching with examples, readily understood graphics, and the appropriate use of computers. Catalyzing innovation, problem solving, and discovery, the Second Edition provides experimenters with the scientific and statistical tools needed to maximize the knowledge gained from research data, illustrating how these tools may best be utilized during all stages of the investigative process. The authors’ practical approach starts with a problem that needs to be solved and then examines the appropriate statistical methods of design and analysis.
Providing even greater accessibility for its users, the Second Edition is thoroughly revised and updated to reflect the changes in techniques and technologies since the publication of the classic First Edition.
Among the new topics included are: - Graphical Analysis of Variance
- Computer Analysis of Complex Designs
- Simplification by transformation
- Hands-on experimentation using Response Service Methods
- Further development of robust product and process design using split plot arrangements and minimization of error transmission
- Introduction to Process Control, Forecasting and Time Series
- Illustrations demonstrating how multi-response problems can be solved using the concepts of active and inert factor spaces and canonical spaces
- Bayesian approaches to model selection and sequential experimentation
An appendix featuring Quaquaversal quotes from a variety of sources including noted statisticians and scientists to famous philosophers is provided to illustrate key concepts and enliven the learning process.
All the computations in the Second Edition can be done utilizing the statistical language R. Functions for displaying ANOVA and lamba plots, Bayesian screening, and model building are all included and R packages are available online. All theses topics can also be applied utilizing easy-to-use commercial software packages.
Complete with applications covering the physical, engineering, biological, and social sciences, Statistics for Experimenters is designed for individuals who must use statistical approaches to conduct an experiment, but do not necessarily have formal training in statistics. Experimenters need only a basic understanding of mathematics to master all the statistical methods presented. This text is an essential reference for all researchers and is a highly recommended course book for undergraduate and graduate students.
User popularity level:
Rated by buyers 
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I am a mathematical statistician and I appreciate and understand modern books on experimental designs such as the book by Wu and Hamada. However, the very first edition of this book became an immediate classic because George Box is a genius and is from a rare breed of statisticians who have strong theoretical and practical experience in statistical methods and in this case statistical design. Stu Hunter and Bill Hunter are two other statisticians with strong applied backgrounds in engineering and other applications of experimental design. Before you can appreciate the theory you need to understand the theory. The very first edition of this book presented the concepts beautifully. This was a great help to me as I had learned the theory and the construction of factorial designs, fractional factorial designs and incomplete block designs, but never had a clear understanding of when to use them until I read this book. Other important simple designs of great practical importance are also covered extremely well.
I wrote a review of the very first edition of this text. Justin Hunter appreciated it so much that he wrote a very touching email to me on this and he was very kind to send me a complimentary copy of the second edition. Justin is the son of Bill Hunter. Unfortunately Bill past away before this second edition was conceived. I believe it was partly as a tribute to Bill that George Box and Stu Hunter put together this revised edition. The spirit and philosophy of the very first edition has been maintained and since the very first edition had appear way back in 1978 the production of an updated edition is welcome and way over due.
Please read the book review by Justin Hunter. He is very upfront about his bias for his father but what he writes is honest and comes from an interesting and unique perspective.
Rated by buyers -
Superb! If you are involved, in any way, with science or engineering, you need this book on your shelf (after you have carefully read it twice). My only complaint is that I found out about it circuitously reading Prof. Box's "Improving Almost Anything"; I was curious what the often cited BHH reference was. I think someone should have a word with the publisher's marketing department; if we don't know about it, how are we supposed to buy it?
Rated by buyers 
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I have found it very difficult to identify a good book on study design for the biomedical industry as most are too focused on engineering/manufacturing applications. Though this book does discuss many different study designs and their theoretical underpinnings well, it does not do a very good job of explaining the issues of sample size and power. These issues are critical to designing studies in biologic settings.
If one is planning experiments on animals, it is mandatory to design studies that minimize the number of animals required to answer the scientific question at hand--given pre-specified levels of statistical power and effect size. Similarly, when experiments are conducted on humans (i.e. clinical trials) it is important to include enough patients so that adequate precision is obtained. I wish this book discussed better how to find the appropriate number of replications required to optimize experimental conclusions (like the interactions terms in a factorial experiment, for instance).
Rated by buyers -
This is the best applied book in any scientific or mathematical subject that I have ever read. The reviewers who are looking for equations and the typical assumptions-theory-proof presentation just picked up the wrong book.
If you're interested in applying experimental design to real-world problems, this book is indispensable. The authors help you get inside the math and really understand the important and often profound issues. It is easy to write a book that regurgitates equations and proofs; it is a major accomplishment to bring to bear decades of practical insights.
I still need to read the 2nd edition in detail and I plan to spend as much time as needed. Based on my brief reading of selected sections, the authors have retained the same style which has made their 1st edition a classic.
Rated by buyers 
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As a text book, this book will drive students crazy. Despite the typos which can be found not occasionally, I found the authors wrote most of the examples using large paragraphs but hard to comprehend.Pretty often the results they generated comes from nowhere. No clear deduction, some times even no equations, just tell you some meanlingless number. The authors must have treated students as experienced as they are, therefore they omit most of the important details for a student to follow. Obviously, by using computer software, they can easily get answers from problems they made without bothering write down how they can get it by pencil and paper.
My suggestion is that if ever you want to learn some statistics, get some books which are clearly written and well illustrated. This book is obviously overrated.
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