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Data Analysis and Graphics Using R (edition 2003)

by John Maindonald

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701156,478 (3.6)1
Member:conceptDawg
Title:Data Analysis and Graphics Using R
Authors:John Maindonald
Info:Cambridge University Press (2003), Edition: 1st, Hardcover
Collections:Computer, Wishlist
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Data Analysis and Graphics Using R by John Maindonald

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Amazon.com Product Description (ISBN 0521813360, Hardcover)

Using modern statistical software systems requires training both in the software itself and in the underlying statistical methods. Concentrating on the freely available R system, this volume demonstrates recently implemented approaches and methods in statistical analysis. The authors introduce elementary concepts in statistics through examples of real-world data analysis drawn from their experience as teachers and as consultants. R code and data sets for all examples are available on the Internet. This emphasis on practical methodology combined with a tutorial approach makes the book accessible to anyone with a knowledge of undergraduate-level statistics. The methods demonstrated are suitable for use in a wide variety of disciplines, from social sciences to medicine, engineering and science.

(retrieved from Amazon Wed, 20 Apr 2011 19:28:53 -0400)

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"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.… (more)

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