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Practical Experiment Designs: for Engineers and Scientists
 
 
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Practical Experiment Designs: for Engineers and Scientists [Paperback]

William J. Diamond (Author)
4.5 out of 5 stars  See all reviews (2 customer reviews)

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Book Description

0471390542 978-0471390541 February 5, 2001 3
Most books cover the subject from a statistical or theoretical point of view. Ideal for working engineers, this book uses real-world examples and boils statistical theory and analysis down to its simplest form.
* Features new and updated material, including cases and a larger focus on multivariate analysis.
* Uses simple analysis tools for practical implementation on the job.
* Targets experiment planning as the groundwork for quality experiments.

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Product Details

  • Paperback: 448 pages
  • Publisher: Wiley; 3 edition (February 5, 2001)
  • Language: English
  • ISBN-10: 0471390542
  • ISBN-13: 978-0471390541
  • Product Dimensions: 9.4 x 7.3 x 0.9 inches
  • Shipping Weight: 1.9 pounds (View shipping rates and policies)
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Best Sellers Rank: #1,533,464 in Books (See Top 100 in Books)

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16 of 18 people found the following review helpful:
5.0 out of 5 stars Title says it all, August 2, 2001
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Steven Marks "Prog Harpo" (Petaluma, CA United States) - See all my reviews
(REAL NAME)   
This review is from: Practical Experiment Designs: for Engineers and Scientists (Paperback)
This book is a great introduction to ballanced orthogonal experimental designs. The so-called Scientific Method (tweak one parameter while holding the other variables fixed) is not used that much where there are complicated interactions between variables. This Designed Experimental (DOX) approach is used more frequently in the Semiconductor industry. This approach envolves a series of experiments where all the variables are potentially changed form one trial to another, but in such a way that one can resolved the effects of one variable from another AND understand interactions and non-linear effects. It is simply the best way to optimize a complicated system.

OK, if you have SAS or RS/1 or ...packages you may ask yourself "why bother learning the theory." I would say for 2 reasons - if you need to manually intervene or supplement an existing experiment AND seeing some more designs that are very effecient (John's 3/4 designs and Plackett-Burman designs) for initial screening that is not ususally part of the packages.

Compared to Box, Box, Hunter this book is easier to read, has a huge summary fold out sheet and is geared to a practicing engineer or scientist not a professional mathematician. Best introduction and most likely the only book you will ever need on the subject.

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2 of 2 people found the following review helpful:
4.0 out of 5 stars Bought as text, September 15, 2008
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This review is from: Practical Experiment Designs: for Engineers and Scientists (Paperback)
I bought this book as a required text for a Design of Experiments (DOE) class. The explanations are pretty straight forward. Answers to problems are in back of the book, a plus.
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Inside This Book (learn more)
First Sentence:
The information covered under Introduction to Experiment Design has been divided into two chapters so that the reader unfamiliar with statistics will not be overwhelmed with a huge mass of new concepts. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
population mean tensile strength, multivariable experiments, randomly chosen levels, multilevel experiments, simple comparative experiments, unlabeled columns, factorial block, defining contrast, rinse time, contrast columns, red impurity, crude sum, proper sample size, correct sample size, matrix experiment, ternative hypothesis, treatment combinations, testing variance, matrix designs, beta error, alpha risk, random strategy, alpha error, complete factorial, experiment design
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Irregular Hadamard, New York, Matrix Type Page, Design The Resolution, North Scituate, Example of Saturated Resolution, Treatment Results, Treatment Trial, Trials Variables, Block Combinations, Component Number of Number Variables Configurations, Event Fig, Example of Resolution, Quantitative Variables, Trial Sum, Variable Low Level High Level, Variables Trials
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