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Design and Analysis of Experiments 7th Edition

17 customer reviews
ISBN-13: 978-0470128664
ISBN-10: 0470128666
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From the Back Cover

Master the experimental techniques that achieve optimal performance.

Across a wide range of fields—from industrial engineering to business and statistics—Douglas Montgomery’s Design and Analysis of Experiments has been a foundational work for students and professionals needing to design, conduct, and analyze experiments for optimizing performance in products and processes.

Like its bestselling predecessors, the Seventh Edition shows you how to use statistically designed experiments to:

  • 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

Placing a strong focus on the use of the computer, the new edition makes much more use of optimal designs and includes even more software examples than before, taken from the three most dominant programs in the field: Minitab, Design-Expert V7, and JMP. You’ll also find additional material discussing the latest developments in robust design and factorial designs along with fresh examples and exercises that illustrate the use of designed experiments in service and transactional organizations.

About the Author

Douglas C. Montgomery, Regents' Professor of Industrial Engineering and Statistics at Arizona State University, received his B.S., M.S., and Ph.D. degrees from Virginia Polytechnic Institute, all in engineering. From 1969 to 1984, he was a faculty member of the School of Industrial & Systems Engineering at the Georgia Institute of Technology; from 1984 to 1988, he was at the University of Washington, where he held the John M. Fluke Distinguished chair of Manufacturing Engineering, was Professor of Mechanical Engineering, and Director of the Program in industrial Engineering. He has authored and coauthored many technical papers as well as twelve other books. Dr. Montgomery is a Stewart Medalist of the American Society for Quality, and has also received the Brumbaugh Award, the William G. Hunter Award, and the Shewell Award(twice) from the ASQ.


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Product Details

  • Hardcover: 680 pages
  • Publisher: Wiley; 7 edition (July 28, 2008)
  • Language: English
  • ISBN-10: 0470128666
  • ISBN-13: 978-0470128664
  • Product Dimensions: 8.3 x 1.1 x 10 inches
  • Shipping Weight: 2.7 pounds
  • Average Customer Review: 3.7 out of 5 stars  See all reviews (17 customer reviews)
  • Amazon Best Sellers Rank: #157,017 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

8 of 9 people found the following review helpful By Charles G Schreiber on February 25, 2011
Format: Hardcover Verified Purchase
Design of Experiments is probably the single most important process improvement tool available. Most engineers think they understand experimental methods, and most six sigma black belts believe they understand process improvement, but without a thorough understanding of DOE, their skills are a mirage. First, DOE is a complicated method, built on many statistical foundations. Don't think you can get by with just a cursory view of the subject, and trying to understand the concepts without the proper statistical background will be difficult, although some calculus in shown, it definitely is not needed. That said, everything you need to know is in this book. The examples are simple, the calculations are easy to follow and manually repeat, and they progressively take you to more difficult concepts. The text is very well written, but this is one of those subjects which requires you to read and re-read concepts to get a full understanding. I also recommend the solutions manual with does an excellent job of showing additional examples, but you will need Minitab or Design Expert to fully appreciate them (manual calculations quickly become impossible). If you have heard about DOE and just want an overview, just do a search on-line. If you are charged with any aspect of process improvement, study this book! If you are a grad student, and this is a required text, make sure to work the examples and end of chapter problems. The concepts, after a little effort, are actually easy to understand, but they support and build on each other. If you put in the effort, this could be the most practical and valuable course you have ever taken.
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10 of 13 people found the following review helpful By Michael R. Nasuta on February 5, 2010
Format: Hardcover Verified Purchase
you'll probably need to keep another stats book on-hand because this leaves out a lot of material. The majority of distributions other than the main 4 are excluded with no mention. Not too many examples, but there are a lot of problems at the end of each chapter (no answers, unless you buy the solutions manual, but that doesn't have very many and the work shown is very minimal).
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4 of 5 people found the following review helpful By Tom O. on February 13, 2012
Format: Hardcover
Doug knows his stuff, but his development relies too heavily on symbols. Symbols don't mean much to most people. I've been a teacher long enough to know that a picture is worth a thousand equations. And not just one picture, but many pictures that actually illustrate the meaning of the symbols. If you use this book, just ask people to translate into pictures all the symbols,and they'll all be confused because they don't actually know what the symbols represent. This is the kind of book that will reduce you to a robot. For example, if you want to truly understand complex analysis, you should read Visual Complex Analysis by Tristan Needham. If you're accustomed to pushing symbols around on a piece of paper like a robot, Needham's book will show you what true understanding is. For example, when blocking, why not show a scatter plot with the points in the same block actually enclosed together so people can see what blocking means in terms of a diagram? Or when blocking with 2 variables, whey not show the blocked output more or less lying on the same plane? No imagination! Apply the logic of Needham's book to this one and you'll develop a true understanding of this subject. Sorry, Doug, I've seen too many people push symbols around without real comprehension of their meaning, and this book just contributes to this regrettable state of scientific education. There is a book by George Cobb that is far more insightful than this one, and, of course, the one by Box is far superior.
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2 of 3 people found the following review helpful By David C. Anderson on December 27, 2012
Format: Hardcover
It's unfortunate that so many people are using this book in classrooms as I found it to be extremely light on practical value, and extremely heavy on pure theory. The author seems to be more concerned with putting the nuances of his knowledge on display than with getting the reader up to speed on how to put this very practical art to use. Put simply, the book reads much more like a journal article than a text for instruction on the subject. This is evidenced by the scarcity of real-life examples, intermixed with a host of sigmas and unnecessary formulas. There are many places where otherwise simple concepts which could be explained in a paragraph (and backed up with a few examples) are seemingly intentionally ambiguated with pages of theory thus seemingly intentionally convoluting the issue. Things I understood before even reading the book became a blur after reading. Take the introduction of 2-factor factorials for example (ch. 6). The actual calculation of these factorials is very simple, and involves nothing more than some basic arithmetic. To read this book, though, one would think that calculating them is total rocket science. Everywhere there is a chance to say "it's easy" or "it's just like this but with a slight twist" instead the author is explaining things in an array of unnecessarily complex terms.

Also, the few examples from each chapter that *are* given apparently are coming from multiple types of stats packages which obfuscates the reading even further as each page seems to contain formats, fonts, and nomenclature that are inconsistent with the last. Sometimes it's design expert, sometimes it's JMP, sometimes it's minitab - which of course all have different names for things, making this quite a puzzle to put together. Not everything is bad in the book, but I have to say there was much more bad than good. Overall I give it a D+ for classroom use.
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