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A straightforward introduction to a wide range of statistical methods for field biologists, using thoroughly explained R code.
Table des matières
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.
A propos de l'auteur
Jan Lepš is Professor of Ecology in the Department of Botany, Faculty of Science, University of South Bohemia, České Budějovice, Czech Republic. His main research interests include plant functional ecology, particularly the mechanisms of species coexistence and stability, and ecological data analysis. He has taught many ecological and statistical courses and supervised more than 80 student theses, from undergraduate to PhD.Petr Šmilauer is Associate Professor of Ecology in the Department of Ecosystem Biology, Faculty of Science, University of South Bohemia, České Budějovice, Czech Republic. His main research interests are multivariate statistical analysis, modern regression methods and the role of arbuscular mycorrhizal symbiosis in the functioning of plant communities. He is co-author of multivariate analysis software Canoco 5, CANOCO for Windows 4.5 and TWINSPAN for Windows.
Résumé
A straightforward introduction on how to analyse data from the field of biological research and conservation. All chapters are supplemented by thoroughly explained R code demonstrating interpretation of the results. An ideal reference for students, researchers and professionals, as well as lecturers of undergraduate courses.
