Basic Statistics in Multivariate Analysis

2013-03-07
Basic Statistics in Multivariate Analysis

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  • Author: Karen A. Randolph
  • Publisher: Oxford University Press
  • ISBN: 0199764042
  • Category : Mathematics
  • Languages : en
  • Pages : 224

This pocket guide introduces readers to linear regression analysis, analysis of variance and covariance, and path analysis with an emphasis on the basic statistics. It prepares doctoral students and early career social work researchers with limited statistics exposure in the use of multivariate methods by providing an easy-to-understand presentation.

Multivariate Statistical Analysis

1991
Multivariate Statistical Analysis

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  • Author: Sam Kash Kachigan
  • Publisher:
  • ISBN:
  • Category : Mathematics
  • Languages : en
  • Pages : 330

This classic book provides the much needed conceptual explanations of advanced computer-based multivariate data analysis techniques: correlation and regression analysis, factor analysis, discrimination analysis, cluster analysis, multi-dimensional scaling, perceptual mapping, and more. It closes the gap between spiraling technology and its intelligent application, fulfilling the potential of both.

An Introduction to Applied Multivariate Analysis with R

2011-04-23
An Introduction to Applied Multivariate Analysis with R

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  • Author: Brian Everitt
  • Publisher: Springer Science & Business Media
  • ISBN: 1441996508
  • Category : Mathematics
  • Languages : en
  • Pages : 284

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.

Basic Statistics in Multivariate Analysis

2013
Basic Statistics in Multivariate Analysis

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  • Author: Karen A. Randolph
  • Publisher:
  • ISBN: 9780199332533
  • Category : Multivariate analysis
  • Languages : en
  • Pages :

This pocket guide introduces readers to linear regression analysis, analysis of variance and covariance, and path analysis with an emphasis on the basic statistics. It prepares doctoral students and early career social work researchers with limited statistics exposure in the use of multivariate methods by providing an easy-to-understand presentation.

Applied Multivariate Statistical Analysis (Classic Version)

2018-03-18
Applied Multivariate Statistical Analysis (Classic Version)

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  • Author: Richard A. Johnson
  • Publisher: Pearson
  • ISBN: 9780134995397
  • Category : Multivariate analysis
  • Languages : en
  • Pages : 808

This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price. Please visit www.pearsonhighered.com/math-classics-series for a complete list of titles. For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduate-level courses in Experimental Design and Statistics. Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable introduction to the statistical analysis of multivariate observations. Its primary goal is to impart the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analyzing multivariate data, the text assumes two or more statistics courses as a prerequisite.

Introduction to Multivariate Analysis

1981-05-15
Introduction to Multivariate Analysis

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  • Author: Chris Chatfield
  • Publisher: CRC Press
  • ISBN: 9780412160400
  • Category : Mathematics
  • Languages : en
  • Pages : 262

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

2003-04-14
Methods of Multivariate Analysis

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  • Author: Alvin C. Rencher
  • Publisher: John Wiley & Sons
  • ISBN: 0471461725
  • Category : Mathematics
  • Languages : en
  • Pages : 739

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.

Modern Multivariate Statistical Techniques

2009-03-02
Modern Multivariate Statistical Techniques

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  • Author: Alan J. Izenman
  • Publisher: Springer Science & Business Media
  • ISBN: 0387781897
  • Category : Mathematics
  • Languages : en
  • Pages : 757

This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.

Introduction to Multivariate Statistical Analysis in Chemometrics

2016-04-19
Introduction to Multivariate Statistical Analysis in Chemometrics

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  • Author: Kurt Varmuza
  • Publisher: CRC Press
  • ISBN: 9781420059496
  • Category : Science
  • Languages : en
  • Pages : 336

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 principal component analysis, regression analysis, classification methods, and clustering. Written by a chemometrician and a statistician, the book reflects the practical approach of chemometrics and the more formally oriented one of statistics. To enable a better understanding of the statistical methods, the authors apply them to real data examples from chemistry. They also examine results of the different methods, comparing traditional approaches with their robust counterparts. In addition, the authors use the freely available R package to implement methods, encouraging readers to go through the examples and adapt the procedures to their own problems. Focusing on the practicality of the methods and the validity of the results, this book offers concise mathematical descriptions of many multivariate methods and employs graphical schemes to visualize key concepts. It effectively imparts a basic understanding of how to apply statistical methods to multivariate scientific data.

Multivariate Statistical Methods

2016-11-03
Multivariate Statistical Methods

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  • Author: Bryan F.J. Manly
  • Publisher: CRC Press
  • ISBN: 1498728995
  • Category : Mathematics
  • Languages : en
  • Pages : 227

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.