Statistical Principles for the Design of Experiments

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

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Description

This book 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.show more

Product details

  • Electronic book text
  • CAMBRIDGE UNIVERSITY PRESS
  • Cambridge University Press (Virtual Publishing)
  • Cambridge, United Kingdom
  • 200 b/w illus. 400 tables 80 exercises
  • 1139574884
  • 9781139574884

Table of 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.show more

About Roger Mead

R. Mead is Emeritus Professor of Applied Statistics at the University of Reading. S. G. Gilmour is Professor of Statistics in the Southampton Statistical Sciences Research Institute at the University of Southampton. A. Mead is Senior Teaching Fellow in the School of Life Sciences at the University of Warwick.show more