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Modern Statistics for the Life Sciences: Learn How to Analyse Your Experiments

TextbookHandbook / Manual

By: Alan Grafen(Author), Rosie Hails(Author)

351 pages, Figs, tabs

Oxford University Press

Paperback | Mar 2002 | #127751 | ISBN: 0199252319
Availability: Usually dispatched within 5 days Details
NHBS Price: £39.99 $52/€45 approx

About this book

Modern Statistics for the Life Sciences 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 first time. The computer revolution has finally made it possible to teach life sciences undergraduates how to use the statistics they really need to know – Modern Statistics for the Life Sciences provides the course materials needed to fulfil that possibility. This text presents the fundamental statistical concepts without being tied to 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.

"The book is well laid out and concepts are very well explained by making effective use of diagrams and geometric representations. There are many analyses of example data sets to ilustrate the application the methods and the interpretation of the output."
Biometrics 59, 200-209, March 2003

"it is a stepping-stone between one's first statistics course and what one really needs as a professional biologist. That said, it is the best stepping-stone on the market."
Trends in Ecology and Evolution, 2003

"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


Contents

Why use this book

1: An introduction to the analysis of variance
2: Regression
3: Models, parameters and GLMs
4: Using more than one explanatory variable
5: Designing experiments - keeping it simple
6: Combining continuous and categorical variables
7: Interactions - getting more complex
8: Checking the models A: Independence
9: Checking the models B: The other three assumptions
10: Model selection I: Principles of model choice and designed experiments
11: Model selection II: Data sets with several explanatory variables
12: Random effects
13: Categorical data
14: What lies beyond?

Answers to exercises
Revision section: The basics
Appendix I: The meaning of p-values and confidence intervals
Appendix II: Analytical results about variances of sample means
Appendix III: Probability distributions
Bibliography


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