This is a second course in statistics for undergraduate students in the life sciences, which will also be invaluable for many graduate students. It makes available to undergraduates methods hitherto used only by statistical sophisticates, namely model formulae and the General Linear Model. The computer revolution has finally made it possible to teach life sciences undergraduates the statistics they really need to know and how to use it - this book provides the course materials needed to fulfil that possibility.

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)
• Companion Web Site: 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

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

Ancillary material:
Companion website:
www.oup.com/booksites/biosciences/lifesci