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A First Course in Statistical Programming with R

$53.00

textbook
  • Date Published: January 2008
  • availability: Temporarily unavailable - available from October 2014
  • format: Paperback
  • isbn: 9780521694247

$53.00
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About the Authors
  • This is the only introduction you'll need to start programming in R, the open-source language that is free to download, and lets you adapt the source code for your own requirements. Co-written by one of the R Core Development Team, and by an established R author, this book comes with real R code that complies with the standards of the language. Unlike other introductory books on the ground-breaking R system, this book emphasizes programming, including the principles that apply to most computing languages, and techniques used to develop more complex projects. Learning the language is made easier by the frequent exercises and end-of-chapter reviews that help you progress confidently through the book. Solutions, datasets and any errata will be available from the book's web site. The many examples, all from real applications, make it particularly useful for anyone working in practical data analysis.

    • First book to teach basic computer programming in R, the language of choice for statistics and data analysis
    • Authors are recognized and trusted: John Braun is co-author of the successful book Data Analysis and Graphics Using R; Duncan Murdoch is a member of the R Core Development Team
    • End-of-chapter review questions plus over 150 exercises, data sets and solutions all available on the web
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    Reviews & endorsements

    "While it is rare to see in a book, denseness does not have to be difficult and this book is an example of that. The authors are terse and effective as they clearly demonstrate how to use the R package. If you lack the budget for the purchase of a commercial computational mathematics package, then R with this textbook provides a very low cost alternative for many classes."
    Charles Ashbacher, Journal of Recreational Mathematics

    " a useful introductory text..."
    Andrew Schaffner, The American Statistician

    "As an R novice, I appreciated the explanations. Several times, I reacted with 'Oh, that's how it is supposed to be done.' ... I like this book and I learned a lot. I am convinced that most readers (including regular R users and mature statisticians) will learn something useful from this book. Again, it is not a book for learning statistical data analysis in R, but it is the right book for a statistician to learn the structure of R, and it is a good book to study before (or after) learning data analysis. I highly recommend this book."
    Myron Hlynka, Technometrics

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    Product details

    • Date Published: January 2008
    • format: Paperback
    • isbn: 9780521694247
    • length: 172 pages
    • dimensions: 246 x 189 x 10 mm
    • weight: 0.392kg
    • contains: 39 b/w illus. 160 exercises
    • availability: Temporarily unavailable - available from October 2014
  • Table of Contents

    1. Getting started
    2. Introduction to the R language
    3. Programming statistical graphics
    4. Programming with R
    5. Simulation
    6. Computational linear algebra
    7. Numerical optimization
    Appendix. Review of random variables and distributions
    Index.

  • Instructors have used or reviewed this title for the following courses

    • Advanced Applied Econometrics
    • Advanced GIS and Spatial Analysis
    • Advanced Methods for Economic Evaluation
    • Applied bioinformatics
    • Hierarchical linear model
    • Introduction to Statistical Computing
    • Limited Dependent Variable Model
    • Linear Models and Data Analysis; Laboratory in Statistical Computation ll
    • Marine community ecology (lab portion of course)
    • Probability and Statistics ll
    • Programming for GIS
    • Programming in R for Biology
    • Psychological Statistics
    • Quant Methods for Geography
    • Research Techniques
    • Statistical Computing & EDA
    • Statistical Computing with R
    • Statistical Programming
    • Statistics and Probability (with Excel)
    • Stats for Earth Sciences
    • The Statistics of Causal Inference in the Social Sciences
  • Authors

    W. John Braun, University of Western Ontario
    W. John Braun is an Associate Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. He is also a co-author, with John Maindonald, of Data Analysis and Graphics Using R, 2nd edition (Cambridge University Press, 2007).

    Duncan J. Murdoch, University of Western Ontario
    Duncan J. Murdoch is an Associate Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. He is a member of the R Development Core Team and was columnist and column editor of the statistical computing column of Chance 1999-2000.

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