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Data Analysis and Graphics Using R
An Example-based Approach
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Modern statistical software systems provide sophisticated tools for researchers who need to manipulate and display their data. Using such systems requires training both in the software itself and in the statistical methods that it relies on. Concentrating on the freely available R system, this book demonstrates recently implemented approaches and methods in statistical analysis. The authors introduce elementary concepts in statistics through examples of real-world data analysis drawn from the authors' experience, both as teachers and as consultants. R code and data sets for all examples are av...
Modern statistical software systems provide sophisticated tools for researchers who need to manipulate and display their data. Using such systems requires training both in the software itself and in the statistical methods that it relies on. Concentrating on the freely available R system, this book demonstrates recently implemented approaches and methods in statistical analysis. The authors introduce elementary concepts in statistics through examples of real-world data analysis drawn from the authors' experience, both as teachers and as consultants. R code and data sets for all examples are available on the Internet. This emphasis on practical methodology combined with a tutorial approach makes the book accessible to anyone with a knowledge of undergraduate statistics, whether an upper-graduate student, a researcher, or a practising scientist or statistician. The methods demonstrated are suitable for use in a wide variety of disciplines, from social sciences to medicine, engineering and science.
Table of contents:
Introduction; 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; Appendix: S-plus differences; Bibliography; Acknowledgements; Index.
Extensive examples illustrate the use of R, a statistical computing environment, and the modern statistical methods that can be used with it. Starting with elementary concepts, the authors proceed to introduce advanced topics of use to researchers or students in medicine, science, engineering and the social sciences. R code and data sets for all examples will be available on the Web.
Text explaining basic statistical methods in the R programming language through extensive use of examples.
Table of contents:
Introduction; 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; Appendix: S-plus differences; Bibliography; Acknowledgements; Index.
Extensive examples illustrate the use of R, a statistical computing environment, and the modern statistical methods that can be used with it. Starting with elementary concepts, the authors proceed to introduce advanced topics of use to researchers or students in medicine, science, engineering and the social sciences. R code and data sets for all examples will be available on the Web.
Text explaining basic statistical methods in the R programming language through extensive use of examples.