Handbook of Applied Multivariate Statistics and Mathematical Modeling
Handbook of Applied Multivariate Statistics and Mathematical Modeling

Author: Howard E.A. Tinsley

Publisher: Academic Press

Published: 2000-05-22

Total Pages: 751

ISBN-13: 0080533566

DOWNLOAD EBOOK

Multivariate statistics and mathematical models provide flexible and powerful tools essential in most disciplines. Nevertheless, many practicing researchers lack an adequate knowledge of these techniques, or did once know the techniques, but have not been able to keep abreast of new developments. The Handbook of Applied Multivariate Statistics and Mathematical Modeling explains the appropriate uses of multivariate procedures and mathematical modeling techniques, and prescribe practices that enable applied researchers to use these procedures effectively without needing to concern themselves with the mathematical basis. The Handbook emphasizes using models and statistics as tools. The objective of the book is to inform readers about which tool to use to accomplish which task. Each chapter begins with a discussion of what kinds of questions a particular technique can and cannot answer. As multivariate statistics and modeling techniques are useful across disciplines, these examples include issues of concern in biological and social sciences as well as the humanities.

Applied Multivariate Statistics with R
Applied Multivariate Statistics with R

Author: Daniel Zelterman

Publisher: Springer Nature

Published: 2023-01-20

Total Pages: 469

ISBN-13: 3031130057

DOWNLOAD EBOOK

Now in its second edition, this book brings multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source shareware program R, Dr. Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays; linear algebra; univariate, bivariate and multivariate normal distributions; factor methods; linear regression; discrimination and classification; clustering; time series models; and additional methods. He uses practical examples from diverse disciplines, to welcome readers from a variety of academic specialties. Each chapter includes exercises, real data sets, and R implementations. The book avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary. New to this edition are chapters devoted to longitudinal studies and the clustering of large data. It is an excellent resource for students of multivariate statistics, as well as practitioners in the health and life sciences who are looking to integrate statistics into their work.

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

DOWNLOAD EBOOK

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

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

DOWNLOAD EBOOK

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.

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

DOWNLOAD EBOOK

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 Statistical Analysis
Applied Multivariate Statistical Analysis

Author: Wolfgang Karl Härdle

Publisher: Springer

Published: 2015-02-26

Total Pages: 581

ISBN-13: 3662451719

DOWNLOAD EBOOK

Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners. All chapters include practical exercises that highlight applications in different multivariate data analysis fields. All of the examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate Statistical Analysis offers the following new features: A new chapter on Variable Selection (Lasso, SCAD and Elastic Net) All exercises are supplemented by R and MATLAB code that can be found on www.quantlet.de. The practical exercises include solutions that can be found in Härdle, W. and Hlavka, Z., Multivariate Statistics: Exercises and Solutions. Springer Verlag, Heidelberg.

Applied Multivariate Analysis
Applied Multivariate Analysis

Author: Neil H. Timm

Publisher: Springer Science & Business Media

Published: 2007-06-21

Total Pages: 709

ISBN-13: 0387227717

DOWNLOAD EBOOK

This book provides a broad overview of the basic theory and methods of applied multivariate analysis. The presentation integrates both theory and practice including both the analysis of formal linear multivariate models and exploratory data analysis techniques. Each chapter contains the development of basic theoretical results with numerous applications illustrated using examples from the social and behavioral sciences, and other disciplines. All examples are analyzed using SAS for Windows Version 8.0.

Applied Multivariate Research
Applied Multivariate Research

Author: Lawrence S. Meyers

Publisher: SAGE Publications

Published: 2016-10-28

Total Pages: 938

ISBN-13: 1506329780

DOWNLOAD EBOOK

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.

Essentials of Multivariate Data Analysis
Essentials of Multivariate Data Analysis

Author: Neil H. Spencer

Publisher: CRC Press

Published: 2013-12-17

Total Pages: 186

ISBN-13: 1466584793

DOWNLOAD EBOOK

Since most datasets contain a number of variables, multivariate methods are helpful in answering a variety of research questions. Accessible to students and researchers without a substantial background in statistics or mathematics, Essentials of Multivariate Data Analysis explains the usefulness of multivariate methods in applied research. Unlike m

Modern Multivariate Statistical Techniques
Modern Multivariate Statistical Techniques

Author: Alan J. Izenman

Publisher: Springer Science & Business Media

Published: 2009-03-02

Total Pages: 757

ISBN-13: 0387781897

DOWNLOAD EBOOK

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.