An Introduction to Applied Multivariate Analysis with R (Use R)

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9781441996497: An Introduction to Applied Multivariate Analysis with R (Use R)
Présentation de l'éditeur :

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.

Biographie de l'auteur :

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.

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Brian S. Everitt, Torsten Hothorn
Edité par Springer-Verlag New York Inc., United States (2011)
ISBN 10 : 1441996494 ISBN 13 : 9781441996497
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Description du livre Springer-Verlag New York Inc., United States, 2011. Paperback. État : New. 2011 ed.. 231 x 155 mm. Language: English . Brand New Book. 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. N° de réf. du libraire AAU9781441996497

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Brian S. Everitt, Torsten Hothorn
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Description du livre Springer-Verlag New York Inc., United States, 2011. Paperback. État : New. 2011 ed.. 231 x 155 mm. Language: English . Brand New Book. 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. N° de réf. du libraire AAU9781441996497

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Description du livre Springer-Verlag New York Inc. Paperback. État : new. BRAND NEW, An Introduction to Applied Multivariate Analysis with R, Brian S. Everitt, Torsten Hothorn, 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. N° de réf. du libraire B9781441996497

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ISBN 10 : 1441996494 ISBN 13 : 9781441996497
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Description du livre Springer, 2011. État : New. 2011. 2011th Edition. Paperback. The majority of data sets collected by researchers in all disciplines are multivariate. This book comprehensively covers a variety of multivariate analysis techniques using R. It provides extensive examples of R code used to apply the multivariate techniques. Series: Use R! Num Pages: 274 pages, 92 black & white illustrations, 21 black & white tables, biography. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 236 x 155 x 16. Weight in Grams: 424. . . . . . . N° de réf. du libraire V9781441996497

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Description du livre Springer. État : New. 2011. 2011th Edition. Paperback. The majority of data sets collected by researchers in all disciplines are multivariate. This book comprehensively covers a variety of multivariate analysis techniques using R. It provides extensive examples of R code used to apply the multivariate techniques. Series: Use R! Num Pages: 274 pages, 92 black & white illustrations, 21 black & white tables, biography. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 236 x 155 x 16. Weight in Grams: 424. . . . . . Books ship from the US and Ireland. N° de réf. du libraire V9781441996497

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Description du livre Springer Verlag, 2011. Paperback. État : Brand New. 273 pages. 9.25x6.00x0.75 inches. In Stock. N° de réf. du libraire __1441996494

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