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Data Analysis and Graphics Using R
An Example-Based Approach

3rd Edition

£70.00

Part of Cambridge Series in Statistical and Probabilistic Mathematics

  • Date Published: May 2010
  • availability: In stock
  • format: Hardback
  • isbn: 9780521762939

£ 70.00
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Description
Contents
Resources
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About the Authors
  • 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.

    • Practical, hands-on, example-based approach deals with real-world issues
    • Extensive use of graphs for exploration of data and interpretation of analyses
    • R code, data sets, updates and exercise solutions, all provided on companion website
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    Reviews & endorsements

    From reviews of previous edition: 'The strength of the book is in the extensive examples of practical data analysis with complete examples of the R code necessary to carry out the analyses … I would strongly recommend the book to scientists who have already had a regression or a linear models course and who wish to learn to use R … I give it a strong recommendation to the scientist or data analyst who wishes to have an easy-to-read and an understandable reference on the use of R for practical data analysis.' R News

    From reviews of previous edition: 'This book does an excellent job of describing the basics of a variety of statistical tools, both classical and modern, through examples from a wide variety of disciplines … the book's writing style is very readable, with clear explanations and precise introductions of all topics and terminology … the book also provides a wealth of examples from various physical and social sciences, engineering, and medicine that have been effectively chosen to illustrate not only the basics of the statistical methods, but also some of the interesting subtleties of the analyses that may require careful interpretation and discussion … I believe that they have … created a readable book that is rich with clear explanations and illustrative examples of the capability of a diverse set of tools. The packaging of the material with the R language is natural, and the extensive web pages of resources complement the book's usefulness for a road audience of statisticians and practitioners.' Biometrics

    From reviews of previous edition: 'This book does an excellent job of describing the basics of a variety of statistical tools, both classical and modern, through examples from a wide variety of disciplines … With its focus on ideas and concepts, rather than an extensive formula-based presentation, the book finds a nice balance between discussing statistical concepts and teaching the basics of the freely-available statistical package R … a readable book that is rich with clear explanations and illustrative examples of the capability of a diverse set of tools. The packaging of the material with the R language is natural, and the extensive web pages of resources complement the book's usefulness for a broad audience of statisticians and practitioners.' Journal of the American Statistical Association

    From reviews of previous edition: '... a very useful book that can be recommended for applied statisticians and other scientists who want to use R for data analysis, and as a textbook for an applied statistics course using R.' Journal of Applied Statistics

    From reviews of previous edition: '... an excellent intermediate-level text ... Though a bit more terse than Dalgaard's Introductory Statistics with R, Maindonald and Braun's exposition of the R language is nonetheless first rate.' DM Review Online

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    Product details

    • Edition: 3rd Edition
    • Date Published: May 2010
    • format: Hardback
    • isbn: 9780521762939
    • length: 549 pages
    • dimensions: 260 x 183 x 30 mm
    • weight: 1.22kg
    • contains: 150 b/w illus. 12 colour illus. 40 tables
    • availability: In stock
  • Table of Contents

    Preface
    Content - how the chapters fit together
    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
    Index of R symbols and functions
    Index of authors.

  • Resources for

    Data Analysis and Graphics Using R

    John Maindonald, W. John Braun

    General Resources

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  • Authors

    John Maindonald, Australian National University, Canberra
    John Maindonald is Visiting Fellow at the Mathematical Sciences Institute at the Australian National University. He has collaborated extensively with scientists in a wide range of application areas, from medicine and public health to population genetics, machine learning, economic history, and forensic linguistics.

    W. John Braun, University of Western Ontario
    W. John Braun is Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. He has collaborated with biostatisticians, biologists, psychologists, and most recently has become involved with a network of forestry researchers.

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