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Cambridge Series in Statistical and Probabilistic Mathematics: Data Analysis and Graphics Using R

An Example-Based Approach - Third Edition

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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.

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Cambridge Series in Statistical and Probabilistic Mathematics: Data Analysis and Graphics Using R, John Maindonald, W. John Braun

Sprache
Erscheinungsdatum
2010
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Titel
Cambridge Series in Statistical and Probabilistic Mathematics: Data Analysis and Graphics Using R
Untertitel
An Example-Based Approach - Third Edition
Sprache
Englisch
Erscheinungsdatum
2010
Einband
Hardcover
Seitenzahl
549
ISBN10
0521762936
ISBN13
9780521762939
Reihe
Bewertung
4,2 von 5 Sternen
Beschreibung
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.