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Winner of the 2009 Ziegel Prize
Optimum Experimental Designs, with SAS
Anthony Atkinson, Alexander Donev, and Randall Tobias
528 pages
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numerous line illustrationss
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240x168mm
978-0-19-929660-6
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Paperback
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24 May 2007
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- Extremely timely
- Experienced author team
- Numerous figures
- End of chapter notes on further reading
- Ideal for students and researchers in Statistics, and experimentalists in the Medical, Pharmaceutical and Chemical Industries
- Supporting Companion Website with SAS program codes, problems and solutions,
Experiments on patients, processes or plants all have random error, making statistical methods essential for their efficient design and analysis. This book presents the theory and methods of optimum experimental design, making them available through the use of SAS programs. Little previous statistical knowledge is assumed. The first part of the book stresses the importance of models in the analysis of data and introduces least squares fitting and simple optimum experimental designs. The second part presents a more detailed discussion of the general theory and of a wide variety of experiments. The book stresses the use of SAS to provide
hands-on solutions for the construction of designs in both standard and non-standard situations. The mathematical theory of the designs is developed in parallel with their construction in SAS, so providing motivation for the development of the subject. Many chapters cover self-contained topics drawn from science, engineering and pharmaceutical investigations, such as response surface designs, blocking of experiments, designs for mixture experiments and for nonlinear and generalized linear models. Understanding is aided by the provision of "SAS tasks" after most chapters as well as by more traditional exercises and a fully supported website. The authors are leading experts in key fields and this book is ideal for statisticians and scientists in academia, research and the process and
pharmaceutical industries.
Readership: Students and researchers in statistics, and experimentalists in the medical, pharmaceutical and chemical industries.
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Anthony Atkinson, London School of Economics, Alexander Donev, School of Mathematics University of Manchester, and Randall Tobias, SAS Institute Inc.
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"...the book will be a very valuable aid for all researchers and students who have to deal with, or are interested in, the efficient design of statistical experiments ... [it offers] a very apt blend of theoretical development and practical examples ... It is the view towards applications and design calculations which makes this book unique." - Friedrich Pukelsheim, International Statistical Review, August 2007
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Preface
I Background
1: Introduction
2: Some key ideas
3: Experimental strategies
4: The choice of a model
5: Models and least squares
6: Criteria for a good experiment
7: Standard designs
8: The analysis of experiments
II Theory and applications
9: Optimum design theory
10: Criteria of optimality
11: D-optimum designs
12: Algorithms for the construction of exact D-optimum designs
13: Optimum experimental design with SAS
14: Experiments with both qualitative and quantitative factors
15: Blocking response surface designs
16: Mixture experiments
17: Nonlinear models
18: Bayesian optimum designs
19: Design augmentation
20: Model checking and designs for discriminating between models
21: Compound design criteria
22: Generalized linear models
23: Response transformation and structured variances
24: Time-dependent models with correlated observations
25: Further topics
26: Exercises
Bibliography
Author index
Subject index
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The specification in this catalogue, including without limitation price, format, extent, number of illustrations, and month of publication, was as accurate as possible at the time the catalogue was compiled. Occasionally, due to the nature of some contractual restrictions, we are unable to ship a specific product to a particular territory. Jacket images are provisional and liable to change before publication.
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