The DOI matches its Crossref record.
Experimental Design and Data Analysis for Biologists
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.
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“VIFs did not reveal collinearity problems, as indicated by the largest value being <4 (51) (event model 2.59; anticipation model 2.43; defection model 1.98; intergroup conflict type model 2.74; persistence model 2.03; reduced event model 2.72).”
From Oxytocin reactivity during intergroup conflict in wild chimpanzees (2017) · cites this paper as [51]
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Cited by
Sorted by citations
Generalized linear mixed models: a practical guide for ecology and evolution
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H., & White, J.-S. S. (2009) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2008.10.008 · Cited by 8,910
A protocol for data exploration to avoid common statistical problems: Data exploration
Zuur, A. F., Ieno, E. N., & Elphick, C. S. (2010) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2009.00001.x · Cited by 8,228
Effect size, confidence interval and statistical significance: a practical guide for biologists
Nakagawa, S., & Cuthill, I. C. (2007) · Biological Reviews · doi:10.1111/j.1469-185x.2007.00027.x · Cited by 3,835
“A confidence interval (CI) is usually interpreted as the range of values that encompass the population or ‘true’ value, estimated by a certain statistic, with a given probability (e.g. Cohen, 1990; Rice, 1995; Zar, 1999; Quinn & Keough, 2002).”
BackgroundSimple means to improve the interpretability of regression coefficients
Schielzeth, H. (2010) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2010.00012.x · Cited by 3,027
“Centring the input variables before fitting the model will largely remove this correlation (e.g. r = )0Æ12 and )0Æ05 for the same input variables; Aiken & West 1991; Neter et al. 1996; Quinn & Keough 2002).”
BackgroundMethodInfluentialBivariate line‐fitting methods for allometry
Warton, D. I., Wright, I. J., Falster, D. S., & Westoby, M. (2006) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1017/s1464793106007007 · Cited by 2,356
Metadata
- Authors
- Quinn, G. P., & Keough, M. J.
- Affiliations
- Monash University (AU)
- The University of Melbourne (AU)
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2002-03-21
- OpenAlex
- W1489838709
- Cited by
- 10,674
- References
- 0
- Citations by year
- 2026: 200 · 2025: 218 · 2024: 239 · 2023: 250 · 2022: 275 · 2021: 394 · 2020: 448 · 2019: 430 · 2018: 446 · 2017: 473 · 2016: 528 · 2015: 625 · 2014: 763 · 2013: 719 · 2012: 675
- FWCI
- 50.52
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- experimental design, statistical analysis, biology, hypothesis testing, Bayesian analysis, linear models, generalized linear models, logistic regression, analysis of variance, repeated measures, log-linear models, multivariate analysis, classification, ordination, exploratory data analysis, assumption checking
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