Analysis of Variance and Covariance
Analysis of Variance and Covariance
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.
- Pictorial representations of designs and tabulated descriptions help readers differentiate between models and evaluate designs
- Worked examples of the principal models and illustrative examples of applications are developed through this book, bringing the statistics to life
- Chapter on troubleshooting problems, addresses frequently asked questions on data collection, practicalities of analysing data and interpretation of results
Reviews & endorsements
"This is an authoritatively written book aimed at people who already have a good grasp of analysis of (co)variance using fixed factor an(c)ova, who are not afraid of algebraic notation and who wish to understand the background to the comprehensive range of study designs described which incorporate covariates and random factors."
Peter Watson, Psychological Medicine
Product details
October 2007Paperback
9780521684477
304 pages
228 × 153 × 16 mm
0.496kg
62 b/w illus. 95 tables
Available
Table of Contents
- 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.
