Modern Statistics for the Life Sciences

Alan Grafen and Rosie Hails

Teaches the reader the language of model formulae, universally employed by statisticians today, and found in all computer statistics packages.
Employs General Linear Models (GLMs), powerful tools to analyse data using a large array of methods at the same time.
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.
Written to enable students to work through the text using the statistical language of their choice.
A companion website provides a wealth of worked exercises in the three statistical languages Minitab, SAS, and SPSS.
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).
Graphics are used to explain key concepts in a visual way, enabling students to understand ideas more easily.

Modern Statistics for the Life Sciences

Alan Grafen and Rosie Hails

Description

Model formulae represent a powerful methodology for describing, discussing, understanding, and performing the component of statistical tests known as linear statistics. It was developed for professional statisticians in the 1960s and has become increasingly available as the use of computers has grown and software has advanced. Modern Statistics for Life Scientists puts this methodology firmly within the grasp of undergraduates for the first time. The authors assume a basic knowledge of statistics--up to and including one and two sample t-tests and their non-parametric equivalents. They provide the conceptual framework needed to understand what the method does--but without mathematical proofs--and introduce the ideas in a simple and steady progression with worked examples and exercises at every stage.

This innovative text offers students a single conceptual framework for a wide range of tests-including t-tests, oneway and multiway analysis of variance, linear and polynomial regressions, and analysis of covariance-that are usually introduced separately. More importantly, it gives students a language in which they can frame questions and communicate with the computers that perform the analyses. A companion website, www.oup.com/grafenhails, provides a wealth of worked exercises in the three statistical languages; Minitab, SAS, and SPSS. Appropriate for use in statistics courses at undergraduate and graduate levels, Modern Statistics for the Life Sciences is also a helpful resource for students in non-mathematics-based disciplines using statistics, such as geography, psychology, epidemiology, and ecology.

Modern Statistics for the Life Sciences

Alan Grafen and Rosie Hails

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

Modern Statistics for the Life Sciences

Alan Grafen and Rosie Hails

Author Information

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 statistics as a graduate student, and encountered modern statistics in the package GLIM as a research student. The impetus to produce a systematic introduction for undergraduates to model formulae and the General Linear Model came from his appointment in 1989 to a lectureship in Quantitative Biology at Oxford University. Degrees in Zoology, Pest Management and Population Dynamics led Dr Rosie Hails toward the more quantitative areas of ecology. Most of her research career has developed the theme of the potential impacts of biological invasions, with reference to both natural invasions and genetically modified organisms. In the early 1990s, she was involved in the first experiments monitoring the behaviour and population dynamics of transgenic plants in natural habitats across the UK with Professor Mick Crawley. More recently, at the NERC Centre for Ecology and Hydrology in Oxford, her research themes have included the dynamics of wildlife diseases as well as plants. In moving to Oxford, Dr Hails became involved in teaching Professor Alan Grafen's undergraduate course, principally through a position at St Anne's College.