Hardback : HK$840.00
Analysis of variance (ANOVA) is a core technique for analysing data in the Life Sciences. In this reference book, the gap between statistical theory and practical data analysis is bridged by presenting a comprehensive set of tables for all standard models of analysis of variance and covariance with up to three treatment factors. This 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.
Analysis of variance (ANOVA) is a core technique for analysing data in the Life Sciences. In this reference book, the gap between statistical theory and practical data analysis is bridged by presenting a comprehensive set of tables for all standard models of analysis of variance and covariance with up to three treatment factors. This 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.
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.
A concise, systematic introduction to the principles of analysis of variance for post-graduates and professionals.
C. PATRICK DONCASTER is a Reader in Ecology in the School of Biological Sciences at the University of Southampton.
'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
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