Design of Comparative Experiments

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Cambridge University Press, Apr 17, 2008 - Mathematics
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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.

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Unstructured experiments
Simple treatment structure
Factorial treatment structure
Rowcolumn designs
Experiments on people and animals
Small units inside large units
More about Latin squares
Incompleteblock designs
Factorial designs in incomplete blocks
Fractional factorial designs
Backward look
Sources of examples questions and exercises

The calculus of factors

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About the author (2008)

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.

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