Experimental Design and Data Analysis for Biologists

An essential textbook for any biologist needing to design experiments, sample programs or analyse the resulting data.

Gerry P. Quinn (Author), Michael J. Keough (Author)

9780521009768, Cambridge University Press

Paperback, published 21 March 2002

553 pages, 125 b/w illus. 85 tables
24.6 x 19 x 3 cm, 1.16 kg

'… an essential textbook for students and researchers in biology needing to design experiments, sampling programs or analyze the resulting data.' Folia Geobotanica

An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software.

1. Introduction
2. Estimation
3. Hypothesis testing
4. Graphical exploration of data
5. Correlation and regression
6. Multiple regression and correlation
7. Design and power analysis
8. Comparing groups or treatments - analysis of variance
9. Multifactor analysis of variance
10. Randomized blocks and simple repeated measures: unreplicated two-factor designs
11. Split plot and repeated measures designs: partly nested anovas
12. Analysis of covariance
13. Generalized linear models and logistic regression
14. Analyzing frequencies
15. Introduction to multivariate analyses
16. Multivariate analysis of variance and discriminant analysis
17. Principal components and correspondence analysis
18. Multidimensional scaling and cluster analysis
19. Presentation of results.

Subject Areas: Life sciences: general issues [PSA], Biology, life sciences [PS], Applied mathematics [PBW]