Hardcover. 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. A concise introduction to the principles of analysis of variance and covariance with worked examples. It 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. An essential reference for post-graduates and professionals. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Bibliographic Details

Title: Analysis of Variance and Covariance (Hardcover)
Publisher: Cambridge University Press, Cambridge
Publication Date: 2007
Language: English
ISBN 10: 052186562X
ISBN 13: 9780521865623
Binding: Hardcover
Edition: 1st Edition
Condition: new

About this title

Synopsis

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.

About the Author

C. PATRICK DONCASTER is a Reader in Ecology in the School of Biological Sciences at the University of Southampton.

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