Modern Statistics for the Life Sciences

Model Formulae Represent A Powerful Methodology For Describing, Discussing, Understanding, And Performing That Large Part Of Statistical Tests Known As Linear Statistics. The Book Aims To Put This Methodology Firmly Within The Grasp Of Undergraduates. 1. Introduction To 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 I: Independence -- 9. Checking The Models Ii: The Other Three Asumptions -- 10. Model Selection I: Principles Of Model Choice And Designed Experiments -- 11. Model Selection Ii: Datasets With Several Explanatory Variables -- 12. Random Effects -- 13. Categorical Data -- 14. What Lies Beyond? -- 15. Answers To Exercises -- App. 1. Meaning Of P-values And Confidence Intervals -- App. 2. Analytical Results About Variances And Sample Means -- App. 3. Probability Distributions. Alan Grafen, Rosie Hails. Includes Bibliographical References (p. [343]-344) And Index.