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AN INTRODUCTION TO MULTIVARIATE STATISTICAL ANALYSIS, 3RD ED

Author : Theodore W. Anderson

Publisher : John Wiley & Sons

Page : 0 pages

File Size : 42,78 MB

Release : 2009

Category :

ISBN :

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Book Description

Market_Desc: Designed primarily as a text for a two-semester course in multivariate statistics Special Features: · Incorporates the advice and comments of the readers of the first two editions as well as extensively classroom-tested techniques and calculations· Two new chapters have been added, along with a number of new sections· Updating has been methodically executed when and where appropriate.· An accompaning ftp provides tables and data· New exercises have been added throughout. About The Book: For more than four decades An Introduction to Multivariate Statistical Analysis has been an invaluable text for students and a resource for professionals wishing to acquire a basic knowledge of multivariate statistical analysis. Since the previous edition, the field has grown significantly. This updated and improved Third Edition familiarizes readers with these new advances, elucidating several aspects that are particularly relevant to methodology and comprehension.



An Introduction to Multivariate Statistical Analysis

Author : T. W. Anderson

Publisher : Wiley-Interscience

Page : 721 pages

File Size : 21,9 MB

Release : 2003-07-25

Category : Business & Economics

ISBN : 9780471360919

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Book Description

Perfected over three editions and more than forty years, this field- and classroom-tested reference: * Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. * Treats all the basic and important topics in multivariate statistics. * Adds two new chapters, along with a number of new sections. * Provides the most methodical, up-to-date information on MV statistics available.



Multivariate Statistical Analysis

Author : Parimal Mukhopadhyay

Publisher : World Scientific Publishing Company

Page : 568 pages

File Size : 23,74 MB

Release : 2008-11-25

Category : Mathematics

ISBN : 9813107111

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Book Description

This textbook presents a classical approach to some techniques of multivariate analysis in a simple and transparent manner. It offers clear and concise development of the concepts; interpretation of the output of the analysis; and criteria for selection of the methods, taking into account the strengths and weaknesses of each. With its roots in matrix algebra, for which a separate chapter has been added as an appendix, the book includes both data-oriented techniques and a reasonable coverage of classical methods supplemented by comments about robustness and general practical applicability. It also illustrates the methods of numerical calculations at various stages.This self-contained book is ideal as an advanced textbook for graduate students in statistics and other disciplines like social, biological and physical sciences. It will also be of benefit to professional statisticians.The author is a former Professor of the Indian Statistical Institute, India.



Introduction to Multivariate Statistical Analysis in Chemometrics

Author : Kurt Varmuza

Publisher : CRC Press

Page : 328 pages

File Size : 42,94 MB

Release : 2016-04-19

Category : Mathematics

ISBN : 1420059491

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Book Description

Using formal descriptions, graphical illustrations, practical examples, and R software tools, Introduction to Multivariate Statistical Analysis in Chemometrics presents simple yet thorough explanations of the most important multivariate statistical methods for analyzing chemical data. It includes discussions of various statistical methods, such as





Introduction to Multivariate Analysis

Author : Chris Chatfield

Publisher : CRC Press

Page : 262 pages

File Size : 26,51 MB

Release : 1981-05-15

Category : Mathematics

ISBN : 9780412160400

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Book Description

This book provides an introduction to the analysis of multivariate data.It describes multivariate probability distributions, the preliminary analysisof a large -scale set of data, princ iple component and factor analysis, traditional normal theory material, as well as multidimensional scaling andcluster analysis.Introduction to Multivariate Analysis provides a reasonable blend oftheory and practice. Enough theory is given to introduce the concepts andto make the topics mathematically interesting. In addition the authors discussthe use (and misuse) of the techniques in pra ctice and present appropriatereal-life examples from a variety of areas includ ing agricultural research, soc iology and crim inology. The book should be suitable both for researchworkers and as a text for students taking a course on multivariate analysi



Methods of Multivariate Analysis

Author : Alvin C. Rencher

Publisher : John Wiley & Sons

Page : 739 pages

File Size : 44,15 MB

Release : 2003-04-14

Category : Mathematics

ISBN : 0471461725

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Book Description

Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Methods of Multivariate Analysis was among those chosen. When measuring several variables on a complex experimental unit, it is often necessary to analyze the variables simultaneously, rather than isolate them and consider them individually. Multivariate analysis enables researchers to explore the joint performance of such variables and to determine the effect of each variable in the presence of the others. The Second Edition of Alvin Rencher's Methods of Multivariate Analysis provides students of all statistical backgrounds with both the fundamental and more sophisticated skills necessary to master the discipline. To illustrate multivariate applications, the author provides examples and exercises based on fifty-nine real data sets from a wide variety of scientific fields. Rencher takes a "methods" approach to his subject, with an emphasis on how students and practitioners can employ multivariate analysis in real-life situations. The Second Edition contains revised and updated chapters from the critically acclaimed First Edition as well as brand-new chapters on: Cluster analysis Multidimensional scaling Correspondence analysis Biplots Each chapter contains exercises, with corresponding answers and hints in the appendix, providing students the opportunity to test and extend their understanding of the subject. Methods of Multivariate Analysis provides an authoritative reference for statistics students as well as for practicing scientists and clinicians.



Multivariate Statistical Methods

Author : Bryan F.J. Manly

Publisher : CRC Press

Page : 227 pages

File Size : 11,31 MB

Release : 2016-11-03

Category : Mathematics

ISBN : 1498728995

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Book Description

Multivariate Statistical Methods: A Primer provides an introductory overview of multivariate methods without getting too deep into the mathematical details. This fourth edition is a revised and updated version of this bestselling introductory textbook. It retains the clear and concise style of the previous editions of the book and focuses on examples from biological and environmental sciences. The major update with this edition is that R code has been included for each of the analyses described, although in practice any standard statistical package can be used. The original idea with this book still applies. This was to make it as short as possible and enable readers to begin using multivariate methods in an intelligent manner. With updated information on multivariate analyses, new references, and R code included, this book continues to provide a timely introduction to useful tools for multivariate statistical analysis.



An Introduction to Applied Multivariate Analysis with R

Author : Brian Everitt

Publisher : Springer Science & Business Media

Page : 284 pages

File Size : 48,91 MB

Release : 2011-04-23

Category : Mathematics

ISBN : 1441996508

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Book Description

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.



Matrix-Based Introduction to Multivariate Data Analysis

Author : Kohei Adachi

Publisher : Springer

Page : 298 pages

File Size : 10,19 MB

Release : 2016-10-11

Category : Mathematics

ISBN : 9811023417

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Book Description

This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter. This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on singular value decomposition among theorems in matrix algebra. The book begins with an explanation of fundamental matrix operations and the matrix expressions of elementary statistics, followed by the introduction of popular multivariate procedures with advancing levels of matrix algebra chapter by chapter. This organization of the book allows readers without knowledge of matrices to deepen their understanding of multivariate data analysis.