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Design of comparative experiments

By: Bailey, R.A.
Series: Cambridge Series in Statistical and Probabilistic Mathematics; 25. Publisher: Cambridge Cambridge University Press 2008Description: xiv, 330 p.ISBN: 0521683572; 9780511611483.Subject(s): Statistical and probabilistic mathematicsDDC classification: 519.5 Online resources: E-Book Summary: Design of Comparative Experiments develops a coherent framework for thinking about factors that affect experiments and their relationships, including the use of Hasse diagrams. These diagrams are used to elucidate structure, calculate degrees of freedom and allocate treatment sub-spaces to appropriate strata. Good design considers units and treatments first, and then allocates treatments to units. Based on a one-term course the author has taught since 1989, the book is ideal for advanced undergraduate and beginning graduate courses.This book should be on the shelf of every practicing statistician who designs experiments.
List(s) this item appears in: Design Thinking | VR_VSL e-Book collection
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Item type Current location Collection Call number Status Date due Barcode
Electronic Resources Vikram Sarabhai Library
Electronic Resources
Non-fiction 519.5 B2D3 (Browse shelf) Not for loan ER000495

Table of contents:

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

Design of Comparative Experiments develops a coherent framework for thinking about factors that affect experiments and their relationships, including the use of Hasse diagrams. These diagrams are used to elucidate structure, calculate degrees of freedom and allocate treatment sub-spaces to appropriate strata. Good design considers units and treatments first, and then allocates treatments to units. Based on a one-term course the author has taught since 1989, the book is ideal for advanced undergraduate and beginning graduate courses.This book should be on the shelf of every practicing statistician who designs experiments.

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