Biostatistics with R - An Introductory Guide for Field Biologists
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| Verlag | Cambridge University Press |
| Auflage | 2020 |
| Seiten | 382 |
| Format | 17,3 x 24,5 x 1,9 cm |
| Gewicht | 770 g |
| Artikeltyp | Englisches Buch |
| EAN | 9781108727341 |
| Bestell-Nr | 10872734UA |
A straightforward introduction to a wide range of statistical methods for field biologists, using thoroughly explained R code.
Klappentext:
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 t heir everyday statistical practice, to lecturers of introductory undergraduate courses. Additional resources are provided on www.cambridge.org/biostatistics.
Inhaltsverzeichnis:
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.