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Design and Analysis of Experiments

Design and Analysis of Experiments - Free Ebook Download

Book Detail

Author/Editor(s): Douglas C. Montgomery
Publication Date: April 10, 2012
ISBN-10: 1118146921
ISBN-13: 978-1118146927
Language: English
Edition: 8
Publisher: Wiley
Size: 3.72 MB
Format: pdf

Book Description

A wide range of fields depend on experimentation to achieve optimal product and process performance. Now in its Eighth Edition. Douglas C. Montgomery's Design and Analysis of Experiments has served as the foundational text for students and professionals alike. Whether you are an engineer, statistician, or businessperson, learning the techniques needed to design, conduct, and analyze experiments shouldn't be left to chance. That's why Montgomery's text is recognized as the undisputed leader for helping to teach students and professionals how to use statistically designed experiments to do many of the following:
  • Obtain information for characterization and optimization of systems
  • Improve manufacturing processes
  • Design and develop new processes and products
  • Evaluate material alternatives in product design
  • Improve the field performance, reliability, and manufacturing aspects of products
  • Learn how to conduct experiments effectively and efficiently

New To This Edition
  • The residual maximum likelihood (REML) method is now emphasized throughout the book
  • 83 new homework problems (including in the areas of biochemistry and biotechnology)
  • Additional examples of single-factor experiments. such as a study involving chocolate consumption and cardiovascular health (Chapter 3)
  • New section on the random effects model (Chapter 3)
  • New material on non-regular fractions as alternatives to traditional minimum aberration fractions in 16 runs and analysis methods for those designs discussed and illustrated (Chapter 9)
  • New material on constructing factorial and fractional factorial designs using an optimal design tool (Chapter 9)
  • New topics and problems in the area of response surface, including designs that combine screening and optimization and use of optimal designs (Chapter 11)
  • New topics in nested and split-plot design, including the use of REML in the analysis (Chapter 14)

About the Author

Douglas C. Montgomery is Regent's Professor of Industrial Engineering and Statistics at Arizona State University and the author of numerous texts on statistics and quality management. Dr. Montgomery received his B.S., M.S., and Ph.D. from Virginia Polytechnic Institute. Dr. Montgomery taught at the Georgia Institute of Technology from 1969 until 1984. From 1984 until 1988 he held the John M. Fluke Distinguished Chair of Manufacturing Engineering position at the University of Washington. The author, and coauthor of many technical papers as well as twelve other books. Dr. Montgomery is a Stewart Medalist of the American Society for Quality. He has also received the Brumbaugh Award, the William G. Hunter Award, and the Shewell Award (twice) from the ASQ.


An excellent text that has greatly reinforced some course material that only provided a very preliminary exposure to design and analysis of experiments. With my initial course work, self-study of this text, and acquisition of a stats package (I'm using JMP) I'm now prepared to advocate the use of design of experiments for a test program in my workplace which I expect will provide a better answer for lower cost and time than our traditional approach of testing one threshold at a time to accept a developmental system. This text book was cited as a valuable resource in a July 2013 internal memo by the U.S. DoD Director of Operational Test and Evaluation which was another reason I chose this book from several alternative texts I'd researched.

-- William J Jones, Amazon Customer Reviews

The first time I opened this book it was the fifth edition and I still was an undergrad in food chemistry. There was a lot of math in it and I could not get what it was trying to tell me. The next time I opened this book, I had a graduate diploma in statistics and it was easier for me to get some concepts, but I was not able to fully understand it. Today, I'm a grad student of Statistics, I have a better background on linear algebra, Mathematical analysis, Probability, DoE and RSM. Concepts, and "symbols" now mean something to me. I can read several pages without stopping to re-read a concept and I fully understand what the author is trying to explain. If you are interested in DoE, I strongly recommend to start with linear algebra, probability and some inference. Only then, you will be able to fully understand this book.

--Christian Ponce, Amazon Customer Reviews
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