Data Analysis and Graphics Using R(ISBN=9780521861168) mobi 下载 网盘 caj lrf pdf txt 阿里云

　　Join the revolution ignited by the ground-breaking R system!

Starting with an introduction to R, covering standard regression

methods, then presenting more advanced topics, this book guides

users through the practical and powerful tools that the R system

provides. The emphasis is on hands-on analysis, graphical display

and interpretation of data. The many worked examples, taken from

real-world research, are accompanied by commentary on what is done

and why. A website provides computer code and data sets, allowing

readers to reproduce all analyses. Updates and solutions to

selected exercises are also available. Assuming only basic

statistical knowledge, the book is ideal for research scientists,

final-year undergraduate or graduate level students of applied

statistics, and practising statisticians. It is both for learning

and for reference. This revised edition reflects changes in R since

2003 and has new material on survival analysis, random coefficient

models, and the handling of high-dimensional data.

Preface

1. A brief introduction to R

2. Styles of data analysis

3. Statistical models

4. An introduction to formal inference

5. Regression with a single predictor

6. Multiple linear regression

7. Exploiting the linear model framework

8. Generalized linear models and survival analysis

9. Time series models

10. Multi-level models and repeated measures

11. Tree-based classification and regression

12. Multivariate data exploration and discrimination

13. Regression on principal component or discriminant scores

14. The R system - additional topics

Epilogue - models

References

Index of R symbols and functions

Index of terms

Index of names

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书籍介绍

Join the revolution ignited by the ground-breaking R system! Starting with an introduction to R, covering standard regression methods, then presenting more advanced topics, this book guides users through the practical and powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display and interpretation of data. The many worked examples, taken from real-world research, are accompanied by commentary on what is done and why. A website provides computer code and data sets, allowing readers to reproduce all analyses. Updates and solutions to selected exercises are also available. Assuming only basic statistical knowledge, the book is ideal for research scientists, final-year undergraduate or graduate level students of applied statistics, and practising statisticians. It is both for learning and for reference. This revised edition reflects changes in R since 2003 and has new material on survival analysis, random coefficient models, and the handling of high-dimensional data.