A straightforward introduction to a wide range of statistical methods for field biologists, using thoroughly explained R code.
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Jan Lep is Professor of Ecology in the Department of Botany, Faculty of Science, University of South Bohemia, ¿eské Bud¿jovice, 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.
Inhaltsangabe
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.
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.
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.
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.
Rezensionen
'We will never have a textbook of statistics for biologists that satisfies everybody. However, this book may come closest. It is based on many years of field research and the teaching of statistical methods by both authors. All useful classic and advanced statistical concepts and methods are explained and illustrated with data examples and R programming procedures. Besides traditional topics that are covered in the premier textbooks of biometry/biostatistics (e.g. R. R. Sokal & F. J. Rohlf, J. H. Zar), two extensive chapters on multivariate methods in classification and ordination add to the strength of this book. The text was originally published in Czech in 2016. The English edition has been substantially updated and two new chapters 'Survival Analysis' and 'Classification and Regression Trees' have been added. The book will be essential reading for undergraduate and graduate students, professional researchers, and informed managers of natural resources.' Marcel Rejmánek, Department of Evolution and Ecology, University of California, Davis, CA, USA
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