Data Analysis and Graphics Using R : An example-based approach / John Maindonald and W. John Braun.

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Material type: Text

TextLanguage: English Series: Cambridge series in statistical and probabilistic mathematics ; 10 | Cambridge series on statistical and probabilistic mathematics ; 10.Publication details: Cambridge ; New York : Cambridge University Press, 2016.Edition: 3rd edDescription: xxvi, 525 p., [12] p. of plates ; ill. (some col.) ; 27 cmISBN:

9780521762939 (hb)
0521762936

Subject(s):

DDC classification:

519.50285 22 MAI

Online resources:

Contents:

1. A brief introduction to R -- 2. Styles of data analysis -- 3. Statistical models -- 4. A review of inference concepts -- 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 -- 15. Graphs in R; Epilogue --

Summary: "Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), 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 third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests"--Provided by publisher.

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Holdings
Item type	Current library	Collection	Call number	Status	Barcode
General Books	CUTN Central Library Sciences	Non-fiction	519.50285 MAI (Browse shelf(Opens below))	Available	41934
General Books	CUTN Central Library Sciences	Non-fiction	519.50285 MAI (Browse shelf(Opens below))	Available	41935
General Books	CUTN Central Library Sciences	Non-fiction	519.50285 MAI (Browse shelf(Opens below))	Available	41936
General Books	CUTN Central Library Sciences	Non-fiction	519.50285 MAI (Browse shelf(Opens below))	Available	41937

1. A brief introduction to R --
2. Styles of data analysis --
3. Statistical models --
4. A review of inference concepts --
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 --
15. Graphs in R; Epilogue --

"Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), 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 third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests"--Provided by publisher.

Includes bibliographical references and indexes.

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