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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
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Searching... | 30000010335774 | QA276.45.R3 C73 2013 | Open Access Book | Book | Searching... |
On Order
Summary
Summary
Hugely successful and popular text presenting an extensive and comprehensive guide for all R users
The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research.
This edition:
Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user.
Praise for the first edition:
'...if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.' ( The American Statistician , August 2008)
'The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book...' ( Professional Pensions, July 2007)Author Notes
Michael J. Crawley FRS, Department of Biological Sciences, Imperial College of Science, Technology and Medicine
Author of three bestselling Wiley statistics titles and five life science books.
Reviews 1
Choice Review
Crawley (Imperial College London) offers readers a 950-page comprehensive reference manual for what is perhaps becoming the most powerful and flexible statistical software environment, the R language. The R Book is aimed at undergraduates, postgraduates, and professionals in science, engineering, and medicine, and is also ideal for students and professionals in statistics and related fields. The book assumes no background in statistics or computing and introduces the advantages of the R environment, detailing its applications in a wide range of disciplines. One weakness of the book, however, is that many of the graphs were apparently color-coded and the text frequently refers to the colors, but the book was printed in black and white. Primarily for professionals in statistics and related fields. Summing Up: Recommended. Upper-division undergraduates through professionals. C. Tappert Pace University
Table of Contents
Preface | p. vii |
1 Getting Started | p. 1 |
2 Essentials of the R Language | p. 9 |
3 Data Input | p. 97 |
4 Dataframes | p. 107 |
5 Graphics | p. 135 |
6 Tables | p. 183 |
7 Mathematics | p. 195 |
8 Classical Tests | p. 279 |
9 Statistical Modelling | p. 323 |
10 Regression | p. 387 |
11 Analysis of Variance | p. 449 |
12 Analysis of Covariance | p. 489 |
13 Generalized Linear Models | p. 511 |
14 Count Data | p. 527 |
15 Count Data in Tables | p. 549 |
16 Proportion Data | p. 569 |
17 Binary Response Variables | p. 593 |
18 Generalized Additive Models | p. 611 |
19 Mixed-Effects Models | p. 627 |
20 Non-linear Regression | p. 661 |
21 Meta-analysis xxx | |
22 Bayesian statistics xxx | |
23 Tree Models | p. 685 |
24 Time Series Analysis | p. 701 |
25 Multivariate Statistics | p. 731 |
26 Spatial Statistics | p. 749 |
27 Survival Analysis | p. 787 |
28 Simulation Models | p. 811 |
29 Changing the Look of Graphics | p. 827 |
References and Further Reading | p. 873 |
Index | p. 877s |