# An Introduction to Applied Multivariate Analysis with R

## Benefits

• Comprehensively covers a wide variety of multivariate analysis techniques using R

• Covers the use of Rs graphical capabilities for interpretation of multivariate data

• Provides extensive examples of R code used to apply the multivariate techniques to multivariate data

Textbook

Part of the Use R book series (USE R)

1. Front Matter
Pages i-xiv
2. Brian Everitt, Torsten Hothorn
Pages 1-24
3. Brian Everitt, Torsten Hothorn
Pages 25-60
4. Brian Everitt, Torsten Hothorn
Pages 61-103
5. Brian Everitt, Torsten Hothorn
Pages 105-134
6. Brian Everitt, Torsten Hothorn
Pages 135-161
7. Brian Everitt, Torsten Hothorn
Pages 163-200
8. Brian Everitt, Torsten Hothorn
Pages 201-224
9. Brian Everitt, Torsten Hothorn
Pages 225-257
10. Back Matter
Pages 259-273

### Introduction

The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos.

An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.

### Keywords

Multivariate Analysis R applications Statistical Inference

#### Authors and affiliations

1. 1.Professor EmeritusKing's College LondonLondonUnited Kingdom
2. 2.Institut f"ur StatistikUniversit"at M"unchenM"unchenGermany

Brian Everitt is Professor Emeritus at King's College, London. He is the author of over 50 books on statistics.

Torsten Hothorn is Professor of Biostatistics in the Faculty of Mathematics, Computer Science and Statistics at Ludwig-Maximilians-Universität München in Germany.

### Bibliographic information

• Book Title An Introduction to Applied Multivariate Analysis with R
• Authors Brian Everitt
Torsten Hothorn
• Series Title Use R
• DOI https://doi.org/10.1007/978-1-4419-9650-3
• Publisher Name Springer, New York, NY
• eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
• Softcover ISBN 978-1-4419-9649-7
• eBook ISBN 978-1-4419-9650-3
• Edition Number 1
• Number of Pages XIV, 274
• Number of Illustrations 92 b/w illustrations, 0 illustrations in colour
• Topics
• Buy this book on publisher's site
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