Modern Statistics for the Life Sciences by Alan GrafenModern Statistics for the Life Sciences by Alan Grafen

Modern Statistics for the Life Sciences

byAlan Grafen, Rosie Hails

Paperback | March 1, 2002

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This textbook teaches statistics in a different way. It is aimed at undergraduate students in the life sciences, and it will also be invaluable for many graduate students. It makes the powerful methods of model formulae and the General Linear Model accessible to undergraduates for the firsttime. The computer revolution has finally made it possible to teach life sciences undergraduates how to use the statistics they really need to know - this book provides the course materials needed to fulfil that possibility. This text presents the fundamental statistical concepts without being tiedto any one statistical package. Three supplements available on the web site provide all the information you need to conduct the analyses in either Minitab, SAS, or SPSS. All datasets are available on the web site.
Degrees in Experimental Psychology, Economics and Zoology have exposed Professor Alan Grafen to various different statistical traditions, and also to his main research interest in how adaptive complexity arises through natural selection. He has been interested in statistics since he was an undergraduate, learned mathematical theory of...
Title:Modern Statistics for the Life SciencesFormat:PaperbackDimensions:368 pages, 9.69 × 6.73 × 0.81 inPublished:March 1, 2002Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199252319

ISBN - 13:9780199252312

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Table of Contents

Why use this book1. An introduction to the analysis of variance2. Regression3. Models, parameters and GLMs4. Using more than one explanatory variable5. Designing experiments - keeping it simple6. Combining continuous and categorical variables7. Interactions - getting more complex8. Checking the models A: Independence9. Checking the models B: The other three assumptions10. Model selection I: Principles of model choice and designed experiments11. Model selection II: Data sets with several explanatory variables12. Random effects13. Categorical data14. What lies beyond?Answers to exercisesRevision section: The basicsAppendix I: The meaning of p-values and confidence intervalsAppendix II: Analytical results about variances of sample meansAppendix III: Probability distributionsBibliography

Editorial Reviews

"Grafen and Hails have written a very nice book...many examples also serve to highlight design or analysis errors that are commonly made and encourage constructive critism: learning from mistakes is, I think, a very powerful approach." Animal Behaviour 2003