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Description: |
viii, 942 pages : illustrations ; 25 cm |
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Bibliography Note: |
Includes bibliographical references (pages 873-876) and index. |
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Contents Note: |
Contents: Preface -- 1. Getting started -- 2. Essentials of the R language -- 3. Data input -- 4. Dataframes -- 5. Graphics -- 6. Tables -- 7. Mathematics -- 8. Classical tests -- 9. Statistical modelling -- 10. Regression -- 11. Analysis of variance -- 12. Analysis of covariance -- 13. Generalized linear models -- 14. Count data -- 15. Count data in tables -- 16. Proportion data -- 17. Binary response variables -- 18. Generalized additive models -- 19. Mixed-effects models -- 20. Non-linear regression -- 21. Tree models -- 22. Time series analysis -- 23. Multivariate statistics -- 24. Spatial statistics -- 25. Survival analysis -- 26. Simulation models -- 27. Changing the look of graphics -- References and further reading -- Index. |
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Summary, Etc. Note: |
Summary: The R language is recognised as one of the most powerful and flexible statistical software packages, and it enables the user to apply many statistical techniques that would be impossible without such software to help implement such large data sets. |