By: Peter J Diggle and Amanda G Chetwynd
172 pages, 4 colour plates, b/w photos, b/w figures, maps, tables
An antidote to technique-oriented service courses, this book studiously avoids the recipe-book style and keeps algebraic details of specific statistical methods to the minimum extent necessary to understand the underlying concepts. The text aims to give the reader a clear understanding of how core statistical ideas of experimental design, modelling and data analysis are integral to the scientific method.
Aimed primarily at beginning postgraduate students across a range of scientific disciplines (albeit with a bias towards the biological, environmental and health sciences), it therefore assumes some maturity of understanding of scientific method, but does not require any prior knowledge of statistics, or any mathematical knowledge beyond basic algebra and a willingness to come to terms with mathematical notation.
Any statistical analysis of a realistically sized data-set requires the use of specially written computer software. An Appendix introduces the reader to the open-source software R, whilst the book's web-page includes downloadable data and R code that enables the reader to reproduce all of the analyses in the book and, with easy modifications, to adapt the code to analyse their own data if they wish.
4. Exploratory data analysis
5. Experimental design
6. Simple comparative experiments
7. Statistical modelling
8. Survival analysis
9. Time series analysis
10. Spatial statistics
Appendix: The R computing environment
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