Series: Cambridge Series in Statistical and Probabilistic Mathematics Volume: 25
344 pages, 175 line diagrams, 110 tables, 130 figures
This book should be on the shelf of every practising statistician who designs experiments. Good design considers units and treatments first, and then allocates treatments to units. It does not choose from a menu of named designs. This approach requires a notation for units that does not depend on the treatments applied. Most structure on the set of observational units, or on the set of treatments, can be defined by factors. This book develops a coherent framework for thinking about factors and their relationships, including the use of Hasse diagrams.
These are used to elucidate structure, calculate degrees of freedom and allocate treatment subspaces to appropriate strata. Based on a one-term course the author has taught since 1989, the book is ideal for advanced undergraduate and beginning graduate courses. Examples, exercises and discussion questions are drawn from a wide range of real applications: from drug development, to agriculture, to manufacturing.
'Rosemary Bailey has made wonderful contributions to applications and theory of the design of statistical experiments. She has woven these and her love of the history and philosophy of the subject into an accessible textbook. A terrific achievement.' Persi Diaconis, Stanford University 'This is 'the beauty and joy of experimental design': a mathematically beautiful and eloquently written treatise by the master!' Geert Molenberghs, Universiteit Hasselt, Belgium 'A definitive treatment. Rothamsted experimental design lucidly expounded from a modern viewpoint.' Terry Speed, The Walter & Eliza Hall Institute of Medical Research, Australia 'This excellent book clearly presents elegant, general and simplifying theory, combining valuable practical advice with a large number of real examples. It treats the design of comparative experiments with a unique approach not seen in other books ...A must-read for anyone designing experiments or wanting to learn about the design of experiments.' Ching-Shui Cheng, University of California, Berkeley
Preface; 1. Forward look; 2. Unstructured experiments; 3. Simple treatment structure; 4. Blocking; 5. Factorial treatment structure; 6. Row-column designs; 7. Experiments on people and animals; 8. Small units inside large units; 9. More about Latin squares; 10. The calculus of factors; 11. Incomplete-block designs; 12. Factorial designs in incomplete blocks; 13. Fractional factorial designs; 14. Backward look; Exercises; Sources of examples, Questions and exercises; Further reading; Bibliography; Index.
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R. A. Bailey has been Professor of Statistics at Queen Mary, University of London since 1994. She is a fellow of the Institute of Mathematical Statistics and a past president of the International Biometric Society, British Region. This book reflects her extensive experience teaching design of experiments and advising on its application. Her book Association Schemes was published by Cambridge University Press in 2004.