Data Analysis and Graphics Using R(I***N=9780521861168) pdf mobi 下载 kindle 115盘 chm 阿里云 rb

　　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 ***ysis, 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 ***yses. Updates and soluti*** 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 ***ysis, random coefficient

models, and the handling of high-dimensional data.

Preface

1. A brief introduction to R

2. Styles of data ***ysis

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 ***ysis

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 functi***

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 ***ysis, 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 ***yses. Updates and soluti*** 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 ***ysis, random coefficient models, and the handling of high-dimensional data.