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

By: Material type: TextTextPublication 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: New Arrival Fy 2021-22 | Biology_Fy2021-22
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Holdings
Item type Current library Call number Status Date due Barcode
Books Books UE-Central Library 570.1519 L559 (Browse shelf(Opens below)) Available T16460
Books Books UE-Central Library 570.1519 L559 (Browse shelf(Opens below)) Available T16461
Books Books UE-Central Library 570.1519 L559 (Browse shelf(Opens below)) 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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