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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

Taal
Jaar van publicatie
2010
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(Hardcover)
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Titel
Cambridge Series in Statistical and Probabilistic Mathematics: Data Analysis and Graphics Using R
Ondertitel
An Example-Based Approach - Third Edition
Taal
Engels
Jaar van publicatie
2010
Formaat
Hardcover
Aantal pagina's
549
ISBN10
0521762936
ISBN13
9780521762939
Reeks
Beoordeling
4,2 van 5
Aantekening
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.