Cambridge series in statistical and probabilistic mathematics
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
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Includes bibliographical references (pages 346-351) and indexes
CONTENTS NOTE
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1. A brief introduction to R -- 2. Styles of data analysis -- 3. Statistical models -- 4. Introduction to formal inference -- 5. Regression with a single predictor -- 6. Multiple linear regression -- 7. Exploiting the linear model framework -- 8. Logistic regression and other generalised linear models -- 9. Multi-level models, time series and repeated measures -- 10. Tree-based classification and regression -- 11. Multivariate data exploration and discrimination -- 12. The R system -- additional topics -- 13. Epilogue -- models -- App. S-plus differences
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SUMMARY OR ABSTRACT
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Text explaining basic statistical methods in the R programming language through extensive use of examples