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Data Analysis and Graphics Using R : An Example-Based Approach - John Maindonald

Data Analysis and Graphics Using R

An Example-Based Approach

Hardcover

Published: 6th May 2010
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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 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.

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

Preface
Content - how the chapters fit together
A brief introduction to R
Styles of data analysis
Statistical models
A review of inference concepts
Regression with a single predictor
Multiple linear regression
Exploiting the linear model framework
Generalized linear models and survival analysis
Time series models
Multi-level models, and repeated measures
Tree-based classification and regression
Multivariate data exploration and discrimination
Regression on principal component or discriminant scores
The R system - additional topics
Graphs in R
Epilogue
Index of R symbols and functions
Index of authors
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9780521762939
ISBN-10: 0521762936
Series: Cambridge Series in Statistical and Probabilistic Mathematics
Audience: Tertiary; University or College
Format: Hardcover
Language: English
Number Of Pages: 525
Published: 6th May 2010
Publisher: CAMBRIDGE UNIV PR
Country of Publication: GB
Dimensions (cm): 26.1 x 18.7  x 3.7
Weight (kg): 1.3
Edition Number: 3
Edition Type: Revised