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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.
From the Back Cover
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.
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Most Helpful Customer Reviews
Format:Paperback
As an introductory book to multivariate analysis it does its job. The contents are well structured and the writting is clear, giving a nice view of the main statistical aproaches to multivariate analysis. All the explanations are supported by R code and illustrated with practical examples. I bought the book expecting more deep in some areas, but I can't complain given its an introductory book.
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
1 review
One of the most readable technical books I've ever encountered
15 Feb 2012
Format:Paperback
Multivariate statistics has applications in finance, machine learning and the analysis of experimental data. Most statistics book (e.g., statistics for engineers) provide only sparse coverage for multivariate statistics. Advanced books tend to be more difficult to learn from for those without advanced statistics backgrounds. This books is a fantastic introduction to the important area of multivariate statistics on a very approachable level.
This books is a real rarity. The authors are gifted teachers and writers. Their chapters cover the intuition behind the various multivariate statistical techniques, the basic mathematical foundation and application in R. I have seen some R statistics books that cover lots of statistics but the R examples are few and far between. This book provides solid R examples to illustrate the topic under discussion.
If you need to understand multivariate statistics and how to apply R libraries to solve your problems in multivariate statistics I don't think that you can go wrong with this book.
Someone with an advanced background in statistics and mathematics might miss proofs. There are places where the authors describe the foundation of a statistical technique without providing the associated proofs. However, for the practitioner, who wants to understand the technique and how to apply it, the level of this books is just right.
This book is available to University students whose libraries subscribe to Springer link. I started reading it via Springer link and liked it so much that I ordered a copy through the Springer link print on demand option (MyCopy), which is why you don't see an "Amazon Verified Purchase" notation.
This books is a real rarity. The authors are gifted teachers and writers. Their chapters cover the intuition behind the various multivariate statistical techniques, the basic mathematical foundation and application in R. I have seen some R statistics books that cover lots of statistics but the R examples are few and far between. This book provides solid R examples to illustrate the topic under discussion.
If you need to understand multivariate statistics and how to apply R libraries to solve your problems in multivariate statistics I don't think that you can go wrong with this book.
Someone with an advanced background in statistics and mathematics might miss proofs. There are places where the authors describe the foundation of a statistical technique without providing the associated proofs. However, for the practitioner, who wants to understand the technique and how to apply it, the level of this books is just right.
This book is available to University students whose libraries subscribe to Springer link. I started reading it via Springer link and liked it so much that I ordered a copy through the Springer link print on demand option (MyCopy), which is why you don't see an "Amazon Verified Purchase" notation.
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