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Statistical Principles for the Design of Experiments: Applications to Real Experiments

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Series: Cambridge Series in Statistical and Probabilistic Mathematics Volume: 36

By: R Mead (Author), SG Gilmour (Author), A Mead (Author)

572 pages, 200 b/w illustrations, 400 tables

Cambridge University Press

Hardback | Sep 2012 | Edition: 2 | #199576 | ISBN-13: 9780521862141
Availability: In stock
Clearance price: £17.50 £71.99 (Save £54.49) $22/€21 approx

About this book

Statistical Principles for the Design of Experiments: Applications to Real Experiments is about the statistical principles behind the design of effective experiments and focuses on the practical needs of applied statisticians and experimenters engaged in design, implementation and analysis. Emphasising the logical principles of statistical design, rather than mathematical calculation, the authors demonstrate how all available information can be used to extract the clearest answers to many questions. The principles are illustrated with a wide range of examples drawn from real experiments in medicine, industry, agriculture and many experimental disciplines. Numerous exercises are given to help the reader practise techniques and to appreciate the difference that good design can make to an experimental research project.

Based on Roger Mead's excellent Design of Experiments, this new edition is thoroughly revised and updated to include modern methods relevant to applications in industry, engineering and modern biology. It also contains seven new chapters on contemporary topics, including restricted randomisation and fractional replication.

Reviews of the first edition:

"Simply superlative [...] concepts and principles are presented with clarity, balance, and thoroughness."
- Ecology

"A systematic and comprehensive coverage of statistical principles as applied to the design of scientific experiments [...] The book is well-written and contains very useful information that is relevant to a variety of scientific disciplines."
- Choice


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Contents

1. Introduction
2. Elementary ideas of blocking: the randomised complete block design
3. Elementary ideas of treatment structure
4. General principles of linear models for the analysis of experimental data
5. Experimental units
6. Replication
7. Blocking and control
8. Multiple blocking systems and crossover designs
9. Multiple levels of information
10. Randomisation
11. Restricted randomisation
12. Experimental objectives, treatments and treatment structures
13. Factorial structure and particular forms of effects
14. Fractional replication
15. Incomplete block size for factorial experiments
16. Quantitative factors and response functions
17. Multifactorial designs for quantitative factors
18. Split unit designs
19. Multiple experiments and new variation
20. Sequential aspects of experiments and experimental programmes
21. Designing useful experiments


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