Topics in Applied Multivariate Analysis
Topics in Applied Multivariate Analysis

Author: D. M. Hawkins

Publisher: Cambridge University Press

Published: 1982-04-22

Total Pages: 384

ISBN-13: 9780521243681

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Multivariate methods are employed widely in the analysis of experimental data but are poorly understood by those users who are not statisticians. This is because of the wide divergence between the theory and practice of multivariate methods. This book provides concise yet thorough surveys of developments in multivariate statistical analysis and gives statistically sound coverage of the subject. The contributors are all experienced in the theory and practice of multivariate methods and their aim has been to emphasize the major features from the point of view of applicability and to indicate the limitations and conditions of the techniques. Professional statisticians wanting to improve their background in applicable methods, users of high-level statistical methods wanting to improve their background in fundamentals, and graduate students of statistics will all find this volume of value and use.

An Introduction to Applied Multivariate Analysis
An Introduction to Applied Multivariate Analysis

Author: Tenko Raykov

Publisher: Routledge

Published: 2008-03-10

Total Pages: 514

ISBN-13: 113667599X

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This comprehensive text introduces readers to the most commonly used multivariate techniques at an introductory, non-technical level. By focusing on the fundamentals, readers are better prepared for more advanced applied pursuits, particularly on topics that are most critical to the behavioral, social, and educational sciences. Analogies betwe

Applied Multivariate Statistical Analysis and Related Topics with R
Applied Multivariate Statistical Analysis and Related Topics with R

Author: Lang WU

Publisher: EDP Sciences

Published: 2021-04-27

Total Pages: 238

ISBN-13: 2759826023

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Multivariate analysis is a popular area in statistics and data science. This book provides a good balance between conceptual understanding, key theoretical presentation, and detailed implementation with software R for commonly used multivariate analysis models and methods in practice.

Applied Multivariate Research
Applied Multivariate Research

Author: Lawrence S. Meyers

Publisher: SAGE Publications

Published: 2016-10-28

Total Pages: 938

ISBN-13: 1506329780

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Using a conceptual, non-mathematical approach, the updated Third Edition provides full coverage of the wide range of multivariate topics that graduate students across the social and behavioral sciences encounter. Authors Lawrence S. Meyers, Glenn Gamst, and A. J. Guarino integrate innovative multicultural topics in examples throughout the book, which include both conceptual and practical coverage of: statistical techniques of data screening; multiple regression; multilevel modeling; exploratory factor analysis; discriminant analysis; structural equation modeling; structural equation modeling invariance; survival analysis; multidimensional scaling; and cluster analysis.

An Introduction to Applied Multivariate Analysis with R
An Introduction to Applied Multivariate Analysis with R

Author: Brian Everitt

Publisher: Springer Science & Business Media

Published: 2011-04-23

Total Pages: 284

ISBN-13: 1441996508

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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.

Applied Multivariate Data Analysis
Applied Multivariate Data Analysis

Author: J.D. Jobson

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 646

ISBN-13: 1461209552

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An easy to read survey of data analysis, linear regression models and analysis of variance. The extensive development of the linear model includes the use of the linear model approach to analysis of variance provides a strong link to statistical software packages, and is complemented by a thorough overview of theory. It is assumed that the reader has the background equivalent to an introductory book in statistical inference. Can be read easily by those who have had brief exposure to calculus and linear algebra. Intended for first year graduate students in business, social and the biological sciences. Provides the student with the necessary statistics background for a course in research methodology. In addition, undergraduate statistics majors will find this text useful as a survey of linear models and their applications.

Mathematical Tools for Applied Multivariate Analysis
Mathematical Tools for Applied Multivariate Analysis

Author: Paul E. Green

Publisher: Academic Press

Published: 2014-05-10

Total Pages: 391

ISBN-13: 1483214044

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Mathematical Tools for Applied Multivariate Analysis provides information pertinent to the aspects of transformational geometry, matrix algebra, and the calculus that are most relevant for the study of multivariate analysis. This book discusses the mathematical foundations of applied multivariate analysis. Organized into six chapters, this book begins with an overview of the three problems in multiple regression, principal components analysis, and multiple discriminant analysis. This text then presents a standard treatment of the mechanics of matrix algebra, including definitions and operations on matrices, vectors, and determinants. Other chapters consider the topics of eigenstructures and linear transformations that are important to the understanding of multivariate techniques. This book discusses as well the eigenstructures and quadratic forms. The final chapter deals with the geometric aspects of linear transformations. This book is a valuable resource for students.

Applied Multivariate Statistical Analysis (Classic Version)
Applied Multivariate Statistical Analysis (Classic Version)

Author: Richard A. Johnson

Publisher: Pearson

Published: 2018-03-18

Total Pages: 808

ISBN-13: 9780134995397

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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.

Applied Multivariate Analysis
Applied Multivariate Analysis

Author: S. James Press

Publisher: Courier Corporation

Published: 2012-09-05

Total Pages: 706

ISBN-13: 0486139387

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Geared toward upper-level undergraduates and graduate students, this two-part treatment deals with the foundations of multivariate analysis as well as related models and applications. Starting with a look at practical elements of matrix theory, the text proceeds to discussions of continuous multivariate distributions, the normal distribution, and Bayesian inference; multivariate large sample distributions and approximations; the Wishart and other continuous multivariate distributions; and basic multivariate statistics in the normal distribution. The second half of the text moves from defining the basics to explaining models. Topics include regression and the analysis of variance; principal components; factor analysis and latent structure analysis; canonical correlations; stable portfolio analysis; classifications and discrimination models; control in the multivariate linear model; and structuring multivariate populations, with particular focus on multidimensional scaling and clustering. In addition to its value to professional statisticians, this volume may also prove helpful to teachers and researchers in those areas of behavioral and social sciences where multivariate statistics is heavily applied. This new edition features an appendix of answers to the exercises.

Multivariate Statistics:
Multivariate Statistics:

Author: Wolfgang Härdle

Publisher: Springer Science & Business Media

Published: 2007-07-27

Total Pages: 367

ISBN-13: 0387707840

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The authors have cleverly used exercises and their solutions to explore the concepts of multivariate data analysis. Broken down into three sections, this book has been structured to allow students in economics and finance to work their way through a well formulated exploration of this core topic. The first part of this book is devoted to graphical techniques. The second deals with multivariate random variables and presents the derivation of estimators and tests for various practical situations. The final section contains a wide variety of exercises in applied multivariate data analysis.