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Data Analysis and Graphics Using R
An Example-Based Approach

3rd Edition

$85.00

Part of Cambridge Series in Statistical and Probabilistic Mathematics

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

$85.00
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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 practicing 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
    Read more

    Reviews & endorsements

    "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 an easy-to-read and an understandable reference on the use of R for practical data analysis."
    R News

    "The style of the book is a commendable "learn by example" - each of the many statistical techniques is centered on real-world examples. The collective of topics is eclectic and the book also comes with extensive R code."
    Carl James Schwarz, Biometrics

    See more reviews

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    Product details

    • Edition: 3rd Edition
    • Date Published: June 2010
    • format: Hardback
    • isbn: 9780521762939
    • length: 549 pages
    • dimensions: 260 x 183 x 30 mm
    • weight: 1.3kg
    • 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.

  • general resources

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    General ResourcesAuthors' websitelinkn/aSort Order general resources general resources general resources general resources

    These resources are provided free of charge by Cambridge University Press with permission of the author of the corresponding work, but are subject to copyright. You are permitted to view, print and download these resources for your own personal use only, provided any copyright lines on the resources are not removed or altered in any way. Any other use, including but not limited to distribution of the resources in modified form, or via electronic or other media, is strictly prohibited unless you have permission from the author of the corresponding work and provided you give appropriate acknowledgement of the source.

    If you are having problems accessing these resources please email cflack@cambridge.org

  • 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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