Statistical Principles for the Design of Experiments

1. Auflage 2012
ISBN: 978-0-521-86214-1
Verlag: Cambridge University Press

Buch, Englisch, Band 36, 588 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 1286 g

Reihe: Cambridge Series in Statistical and Probabilistic Mathematics

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.

Autoren/Hrsg.

Mead, R.

Gilmour, S. G.

Mead, A.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Forschungsmethodik, Wissenschaftliche Ausstattung
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Interdisziplinäres Wissenschaften Wissenschaften Interdisziplinär Naturwissenschaften, Technik, Medizin

Inhaltsverzeichnis

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.

Über Autor(innen)

Gilmour, S. G.
S. G. Gilmour is Professor of Statistics in the Southampton Statistical Sciences Research Institute at the University of Southampton.

Mead, R.
R. Mead is Emeritus Professor of Applied Statistics at the University of Reading.

Mead, A.
A. Mead is Senior Teaching Fellow in the School of Life Sciences at the University of Warwick.