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Design and Analysis of Experiments
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Learn How to Achieve Optimal Industrial Experimentation Through four editions, Douglas Montgomery has provided statisticians, engineers, scientists, and managers with the most effective approach for learning how to design, conduct, and analyze experiments that optimize performance in products and processes. Now, in this fully revised and enhanced Fifth Edition, Montgomery has improved his bestselling text by focusing even more sharply on factorial and fractional factorial design and presenting new analysis techniques (including the generalized linear model). There is also expanded coverage of experiments with random factors, response surface methods, experiments with mixtures, and methods for process robustness studies. The book also illustrates two of today's most powerful software tools for experimental design: DesignExpert(r) and Minitab(r). Throughout the text, You'll find output from these two programs, along with detailed discussion on how computers are currently used in the analysis and design of experiments. You'll also learn 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 Other important textbook features: * Student version of DesignExpert(r) software is available.
highly regarded text on topics important in both engineering and agriculture and many other areas of scientific research
Doug Montgomery got excellent statistical training at VPI. He also has a wealth of practical experience from his consulting work. His books on regression, statistical design and response surfaces are all well written and understandable to engineers. This 4th edition published in 1997 still contains all the useful information on factorial and fractional factorial designs. Chapter 14 is a wonderful uptodate chapter that covers important process optimization topics not often covered in traditional design of experiments books. This includes response surface methods, mixture experiments, evolutionary operation and Taguchi methods. It does not require high level mathematics.
See Experiments by Wu and Hamada if you want a high mathematical level of presentation.
Grad student's review
I am a graduate student at ASU. This book is part of the required course materials, since Montgomery is affiliated with ASU. The quality of this textbook is what you may find in a mediocre $40 statistics book. I have not seen statistics dealt with in a more confusing manner. Explanations are incomplete, graphs meant to illustrate concepts are not labeled, symbols and functions are used without defining them, and there are errors in the examples. My understanding was that D. Montgomery has been in this field for a long time. However, basic principles, like including units with the data, are ignored. I expected by the 6th attempt these problems would have been worked out. The other reviews do not make sense to me, since my classmates feel a similar frustration with the book.
Comprehensive Introduction to the Design of Experiments
This text was used in a graduate course on the Design of Experiments. It has a great wealth of examples that span many different fields of industry, from pharmaceuticals to "tool and die" manufacturing. This text, when used along with a statistical software package, made research projects easy to design, implement, analyze and present to others. I found the chapters on reliability, determining sample size for mixed models (random and fixed variables), very helpful in my research.
Peptide nucleic acids, morpholinos and related antisense biomolecules Christopher Janson, Matthew During 

Probability and Statistics in Engineering Connie M. Borror, David M. Goldsman, Douglas C. Montgomery, William W. Hines 
Design and Analysis of Gauge R&R Studies: Making Decisions with Confidence Intervals in Random and Mixed Anova Models Connie M. Borror, Douglas C. Montgomery, Richard K. Burdick 