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Biostatistics with R : an introductory guide for field biologists / Jan Leps & Petr Smilauer

By: Material type: TextPublication details: UK : Cambridge University Press, 2020Description: 365 pISBN:
  • 9781108727341 (pbk)
Subject(s): DDC classification:
  • 570.1519 L559
Contents:
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. Non-parametric methods for two samples 8. Oneway 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 varaibles 13. Correlation: relationship between two quantitative variables 14. Multiple regression and general linear models 15. Generalized linear models 16. Regression models for non-linear relationships 17. Structural equation models 18. Discrete distribution and spatial point patterns 19. Survival analysis
List(s) this item appears in: Biology
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Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
Books UE-Central Library 570.1519 L559 (Browse shelf) Available T16460
Books UE-Central Library 570.1519 L559 (Browse shelf) Available T16461
Books UE-Central Library 570.1519 L559 (Browse shelf) Available T16462

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. Non-parametric methods for two samples
8. Oneway 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 varaibles
13. Correlation: relationship between two quantitative variables
14. Multiple regression and general linear models
15. Generalized linear models
16. Regression models for non-linear relationships
17. Structural equation models
18. Discrete distribution and spatial point patterns
19. Survival analysis

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