Analysis of Variance and Covariance
How to Choose and Construct Models for the Life Sciences

A concise, systematic introduction to the principles of analysis of variance for post-graduates and professionals.

C. Patrick Doncaster (Author), Andrew J. H. Davey (Author)

9780521684477, Cambridge University Press

Paperback, published 30 August 2007

304 pages, 62 b/w illus. 95 tables
22.8 x 15.3 x 1.6 cm, 0.496 kg

'My overall impression is that this text can provide a useful reference for researchers needing a quick refresher on typical design and analysis issues and/or a check on the use of an appropriate design and/or analysis. It does a good job reminding the reader of the complicated issues that can arise and where to be especially cautious. It simplifies some aspects of design and ANOVA but does not attempt to sidestep around or ignore potentially difficult issues, such as unbalanced designs and post hoc pooling of error terms. Will I happily keep this book on my shelf? Yes, most definitely. Although not a stand-alone text on experimental design, it is a useful and usable reference tool.' The American Statistician

Analysis of variance (ANOVA) is a core technique for analysing data in the Life Sciences. This reference book bridges the gap between statistical theory and practical data analysis by presenting a comprehensive set of tables for all standard models of analysis of variance and covariance with up to three treatment factors. The book will serve as a tool to help post-graduates and professionals define their hypotheses, design appropriate experiments, translate them into a statistical model, validate the output from statistics packages and verify results. The systematic layout makes it easy for readers to identify which types of model best fit the themes they are investigating, and to evaluate the strengths and weaknesses of alternative experimental designs. In addition, a concise introduction to the principles of analysis of variance and covariance is provided, alongside worked examples illustrating issues and decisions faced by analysts.

Preface
Introduction to analysis of variance
Introduction to model structures
Part I. Model Structures: 1. One-factor designs
2. Nested designs
3. Fully replicated factorial designs
4. Randomised block designs
5. Split plot designs
6. Repeated measures designs
7. Unreplicated designs
Part II. Further Topics: 8. Further topics
9. Choosing experimental designs
10. Best practice in presentation of the design
11. Troubleshooting problems during analysis
Glossary
Categories of model
Bibliography
Index of all ANOVA models with up to three factors
Index.

Subject Areas: Biology, life sciences [PS], Mathematical modelling [PBWH], Data analysis: general [GPH]