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Experimntl Design Data Anl Biol 1ed by Gerry…
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Experimntl Design Data Anl Biol 1ed (edition 2002)

by Gerry P Quinn (Author)

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421449,866 (3.13)2
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.… (more)
Member:DeclanM
Title:Experimntl Design Data Anl Biol 1ed
Authors:Gerry P Quinn (Author)
Info:Cambridge University Press (2002), Edition: 1, 556 pages
Collections:Your library
Rating:
Tags:statistics

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Experimental Design and Data Analysis for Biologists by Gerry P. Quinn

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I bought this book because someone recommended it to me as a companion to Sokal's Biometry, and he was right. I kept both books together in my shelf and I use them quite often. Quinn's book covers many aspects untouched by Sokal's book like smoothing functions, mutivariate statistics and others. It just gives concise explanations to many methods and provide tons of references. Together with Sokal's Biometry it must be in any biology library. ( )
  amarcobio | Sep 11, 2013 |
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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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