Abstract
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.
Keywords
Affiliated Institutions
Related Publications
Experimental Design: Procedures for the Behavioral Sciences
Chapter 1. Research Strategies and the Control of Nuisance Variables Chapter 2. Experimental Designs: an Overview Chapter 3. Fundamental Assumptions in Analysis of Variance Chap...
Quality by Experimental Design.
THE PHILOSOPHY OF EXPERIMENTATION Why Design Experiments? Organizing the Experiment The Neglected Response Variable STATISTICAL EXPERIMENTAL DESIGN The Factorial 2-Level Design ...
Statistical power analysis: a simple and general model for traditional and modern hypothesis tests
1. The Power of Statistical Tests. 2. A Simple and General Model for Power Analysis. 3. Power Analyses for Minimum-Effect Tests. 4. Using Power Analyses. 5. Correlation and Regr...
CANOCO Reference Manual and CanoDraw for Windows User's Guide: Software for Canonical Community Ordination (version 4.5)
Canoco is a software package for multivariate data analysis, with an emphasis on dimesional reduction (ordination), regression analysis, and the combination of the two, constrai...
Analysis of Longitudinal Data
1. Introduction 2. Design considerations 3. Exploring longitudinal data 4. General linear models 5. Parametric models for covariance structure 6. Analysis of variance methods 7....
Publication Info
- Year
- 2002
- Type
- book
- Citations
- 10558
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.1017/cbo9780511806384