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Modern Statistics for the Life Sciences

Alan Grafen and Rosie Hails

Price: £29.99 (paper)
ISBN-13: 978-0-19-925231-2
Publication date: 21 March 2002
368 pages, 14 line illus., 246x171 mm

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Reviews

''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' -

Description

Teaches the reader the language of model formulae, universally employed by statisticians today, and found in all major computer statistics packages.
Employs the General Linear Model (GLMs), a powerful tools to analyse data that incorporates a large array of traditional methods
Gives a firm conceptual grounding in GLMs, allowing statistics to be presented as a meaningful whole and enabling more material to be analysed in a given period of time
Focuses on concepts required by life sciences students using statistics (e.g. marginality, random effects, multiplicity, instead of those required by mathematics students inventing them (e.g. sufficiency, theory of distributions, mathematical proofs)
Online Resource Centre: www.oup.com/uk/grafenhails, containing: · Language-specific supplements in PDF format (Minitab, SAS and SPSS) · All the datasets used in the book, in Minitab, SAS, SPSS and plain text formats · A chapter-by-chapter, page-by-page response by the authors to queries from readers · A section providing support for teachers, including PowerPoint presentations and practical worksheets

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 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 - this book 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.

Readership: Second and third year undergraduates, MSc students, and postgraduate researchers in the Life Sciences; also a useful resource for students of other non-mathematics-based disciplines using statistics: geographers, psychologists, epidemiologists.

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

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

Authors, editors, and contributors

Alan Grafen, Oxford University and
Rosie Hails, Zoology Department, St John's College, Oxford

Links to web resources and related information

Companion web site

More in the same subject area:
Life sciences: general issues
Human biology & related topics
Psychology
Probability & statistics

The specification in this catalogue, including without limitation price, format, extent, number of illustrations, and month of publication, was as accurate as possible at the time the catalogue was compiled. Occasionally, due to the nature of some contractual restrictions, we are unable to ship a specific product to a particular territory. Jacket images are provisional and liable to change before publication.