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

C. Patrick Doncaster 0521684471

Review

'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.' Psychological Medicine

'This book presents details of the analysis of variance for a compendium of designs with up to three treatment factors. The book has a good discussion of practical situations where each design may be useful, ranging from the authors' interests in ecology to more conventional examples from agricultural and medical research. … the book has many strengths and I am happy to recommend it.' Experimental Agriculture

'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.

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