R Book

ISBN-10: 0470973927
ISBN-13: 9780470973929
Edition: 2nd 2012
List price: $65.50 Buy it from $50.85
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Description: The R language is recognized as one of the most powerful and flexible statistical software packages, and it enables the user to apply many statistical techniques that would be impossible without such software to help implement such large data sets.  More...

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Book details

List price: $65.50
Edition: 2nd
Copyright year: 2012
Publisher: John Wiley & Sons, Limited
Publication date: 12/7/2012
Binding: Hardcover
Pages: 1076
Size: 7.75" wide x 10.00" long x 1.75" tall
Weight: 3.630
Language: English

The R language is recognized as one of the most powerful and flexible statistical software packages, and it enables the user to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R is becoming essential both to carry out research and to understand it, as more and more people present their results in the context of R. This edition introduces the advantages of the R environment, in a user–friendly format, to beginners and intermediate users in a range of disciplines, from science and engineering to medicine and economics. The format enables it to be either read as a text, or dipped–into as a reference manual. The early chapters assume no background in statistics or computing, and introduce the reader to the basic concepts involved. In this way the reader is introduced to the assumptions that lie behind the tests, fostering a critical approach to statistical modeling. These early chapters have been thoroughly updated to take account of the way language has evolved since the publication of the first edition. Subsequent chapters examine more advanced topics, cementing what is learnt in the opening chapters, as well as benefiting more intermediate readers. Throughout the book, the reader’s experience is furthered by practical guidance and the inclusion of numerous worked examples. 

Preface
Getting Started
Essentials of the R Language
Data Input
Dataframes
Graphics
Tables
Mathematics
Classical Tests
Statistical Modelling
Regression
Analysis of Variance
Analysis of Covariance
Generalized Linear Models
Count Data
Count Data in Tables
Proportion Data
Binary Response Variables
Generalized Additive Models
Mixed-Effects Models
Non-linear Regression
Meta-analysis xxx
Bayesian statistics xxx
Tree Models
Time Series Analysis
Multivariate Statistics
Spatial Statistics
Survival Analysis
Simulation Models
Changing the Look of Graphics
References and Further Reading
Index

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