Experimental Design and Data Analysis for Biologists

Nonfiction, Science & Nature, Nature, Environment, Ecology, Mathematics, Science
Cover of the book Experimental Design and Data Analysis for Biologists by Gerry P. Quinn, Michael J. Keough, Cambridge University Press
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Author: Gerry P. Quinn, Michael J. Keough ISBN: 9781107085664
Publisher: Cambridge University Press Publication: March 21, 2002
Imprint: Cambridge University Press Language: English
Author: Gerry P. Quinn, Michael J. Keough
ISBN: 9781107085664
Publisher: Cambridge University Press
Publication: March 21, 2002
Imprint: Cambridge University Press
Language: English

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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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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