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Experimental Design and Data Analysis for Biologists | Zookal Textbooks | Zookal Textbooks
  • Author(s) Gerry P. Quinn / Michael J. Keough
  • Edition
  • Published21st March 2002
  • PublisherCambridge University Press
  • ISBN9780521009768
  • University course codes
    • University of Tasmania (UTAS):KSM309 - Quantitative Methods in Biology
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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  • Author(s) Gerry P. Quinn / Michael J. Keough
  • Edition
  • Published21st March 2002
  • PublisherCambridge University Press
  • ISBN9780521009768
  • University course codes
    • University of Tasmania (UTAS):KSM309 - Quantitative Methods in Biology
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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