Biostatistics with R: An Introductory Guide for Field Biologists
Cambridge University Press, 7/30/2020
EAN 9781108727341, ISBN10: 1108727344
Paperback, 384 pages, 24.4 x 17 x 2.2 cm
Language: English
Originally published in English
Biostatistics with R provides a straightforward introduction on how to analyse data from the wide field of biological research, including nature protection and global change monitoring. The book is centred around traditional statistical approaches, focusing on those prevailing in research publications. The authors cover t-tests, ANOVA and regression models, but also the advanced methods of generalised linear models and classification and regression trees. Chapters usually start with several useful case examples, describing the structure of typical datasets and proposing research-related questions. All chapters are supplemented by example datasets, step-by-step R code demonstrating analytical procedures and interpretation of results. The authors also provide examples of how to appropriately describe statistical procedures and results of analyses in research papers. This accessible textbook will serve a broad audience, from students, researchers or professionals looking to improve their everyday statistical practice, to lecturers of introductory undergraduate courses. Additional resources are provided on www.cambridge.org/biostatistics.
1. Basic statistical terms, sample statistics
2. Testing hypotheses, goodness-of-fit test
3. Contingency tables
4. Normal distribution
5. Student's T distribution
6. Comparing two samples
7. Nonparametric methods for two samples
8. One-way analysis of variance (ANOVA) and Kruskal–Wallis test
9. Two-way analysis of variance
10. Data transformations for analysis of variance
11. Hierarchical ANOVA, split-plot ANOVA, repeated measurements
12. Simple linear regression
dependency between two quantitative variables
13. Correlation
relationship between two quantitative variables
14. Multiple regression and general linear models
15. Generalised linear models
16. Regression models for nonlinear relationships
17. Structural equation models
18. Discrete distributions and spatial point patterns
19. Survival analysis
20. Classification and regression trees
21. Classification
22. Ordination
Appendix 1. First steps with R software.