Experiments: Planning, Analysis, and Parameter Design Optimization

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9780471255116: Experiments: Planning, Analysis, and Parameter Design Optimization
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A modern and highly innovative guide to industrial experimental design

The past two decades have seen major progress in the use of statistically designed experiments for product and process improvement. In this new work, Jeff Wu and Michael Hamada, two highly recognized researchers in the field, introduce some of the newest discoveries in the design and analysis of experiments as well as their applications to system optimization, robustness, and treatment comparisons in the diverse fields of engineering, technology, agriculture, biology, and medicine.

Drawing on examples from their impressive roster of industrial clients (including GM, Ford, AT&T, Lucent Technologies, and Chrysler), Wu and Hamada modernize accepted methodologies, while presenting many cutting-edge topics for the first time in a single, easily accessible source. These include robust parameter design, reliability improvement, analysis of nonnormal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays. Other features include:
* Coverage of parameter design for system improvement first introduced by Taguchi in the mid-1980s
* An innovative approach to the treatment of design tables
* A discussion of new computing techniques, including graphical methods, generalized linear models, and Bayesian computing via Gibbs samplers
* Each chapter motivated by a real experiment
* Extensive case studies, including goals, data, and experimental plans
* More than 80 data sets as well as hundreds of charts, tables, and figures

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About the Author:

C. F. JEFF WU, PhD, is H. C. Carver Professor, Department of Statistics and Industrial and Operations Engineering, University of Michigan, Ann Arbor. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and a recipient of the COPSS Award and numerous other awards and prizes. He is the author of about 100 published papers.

MICHAEL HAMADA, PhD, is a technical staff member in the Statistical Sciences Group at Los Alamos National Laboratory in New Mexico. He has published 30 papers and has won the Technometrics Wilcoxon Prize and the ASQC Brumbaugh Award.

Review:

"...this book will become a classic text on experimental design." (IIE Transactions on Quality & Reliability Engineering, June 2006)

"Wu and Hamada's book also possesses all the elements it needs to become a classic...this book is a great reference for both researchers and practitioners.” (Interfaces, September-October 2003)

"It is an impressive volume that seems likely to become a standard text." (Short Book Reviews, Vol. 20, No. 3, December 2000)

"I agree with the authors that this book is an excellent reference resource for experienced design-of-experiments practitioners." (Technometrics, Vol. 42, No. 4, May 2001)

"This is a marvelous book that gives a lucid and eminently readable account of he latest and most significant developments in experimental design...thoroughly recommended to anyone who wishes to acquire a sound knowledge of the subject..." (Mathematical Reviews, 2002a)

"A one sentence review of this text would report that it contains a wealth of information...an impressive text. I would highly recommend this as a reference book..." (Journal of Quality Technology, Vol. 34, No. 1, January 2002)

"the book will serve as an excellent reference for practicing statiscians ... (Zentralblatt MATH, Vol.964, No.14, 2001)

"If you are interested in industrial experiments and want an up-to-date, definitive reference written by authors who have contributed much to this field, then Experiments: Planning, Analysis, and Parameter Design Optimization is an essential addition to your library." (Journal of the American Statistical Association, Vol. 97, No. 458, June 2002)

"...goes more deeply into certain subjects than do other available books... this is the book one will turn to for answering more complex questions." (Mathematical Geology, Vol. 35, No. 2, February 2003)

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