Making Sense of Multivariate Data Analysis

An Intuitive Approach

Author: John Spicer

Publisher: SAGE

ISBN: 9781412904018

Category: Mathematics

Page: 233

View: 883

'This book is a helpful guide to reading and understanding multivariate data analysis results in social and psychological research' --C. Y. Joanne Peng, Indiana University at Bloomington 'This book serves as a resource for readers who want to have an overall view of what encompasses multivariate analyses. The author has discussed some important issues rather philosophically (e.g., theory vs. data analysis). These points are valuable even for readers who have extensive training with multivariate analyses' --Jenn-Yun Tein, Arizona State University

Multivariate Data Analysis - in Practice

An Introduction to Multivariate Data Analysis and Experimental Design

Author: Kim H. Esbensen,Dominique Guyot,Frank Westad,Lars P. Houmoller

Publisher: Multivariate Data Analysis

ISBN: 9788299333030

Category: Experimental design

Page: 598

View: 4321

"Multivariate Data Analysis - in practice adopts a practical, non-mathematical approach to multivariate data analysis. The book's principal objective is to provide a conceptual framework for multivariate data analysis techniques, enabling the reader to apply these in his or her own field. Features: Focuses on the practical application of multivariate techniques such as PCA, PCR and PLS and experimental design. Non-mathematical approach - ideal for analysts with little or no background in statistics. Step by step introduction of new concepts and techniques promotes ease of learning. Theory supported by hands-on exercises based on real-world data. A full training copy of The Unscrambler (for Windows 95, Windows NT 3.51 or later versions) including data sets for the exercises is available. Tutorial exercises based on data from real-world applications are used throughout the book to illustrate the use of the techniques introduced, providing the reader with a working knowledge of modern multivariate data analysis and experimental design. All exercises use The Unscrambler, a de facto industry standard for multivariate data analysis software packages. Multivariate Data Analysis in Practice is an excellent self-study text for scientists, chemists and engineers from all disciplines (non-statisticians) wishing to exploit the power of practical multivariate methods. It is very suitable for teaching purposes at the introductory level, and it can always be supplemented with higher level theoretical literature."Résumé de l'éditeur.

Essentials of Multivariate Data Analysis

Author: Neil H. Spencer

Publisher: CRC Press

ISBN: 1466584785

Category: Mathematics

Page: 186

View: 7235

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 most books on multivariate methods, this one makes straightforward analyses easy to perform for those who are unfamiliar with advanced mathematical formulae. An easily understood dataset is used throughout to illustrate the techniques. The accompanying add-in for Microsoft Excel® can be used to carry out the analyses in the text. The dataset and Excel add-in are available for download on the book’s CRC Press web page. Providing a firm foundation in the most commonly used multivariate techniques, this text helps readers choose the appropriate method, learn how to apply it, and understand how to interpret the results. It prepares them for more complex analyses using software such as Minitab®, R, SAS, SPSS, and Stata.

Exploratory and Multivariate Data Analysis

Author: Michel Jambu

Publisher: Elsevier

ISBN: 0080923674

Category: Mathematics

Page: 432

View: 9136

With a useful index of notations at the beginning, this book explains and illustrates the theory and application of data analysis methods from univariate to multidimensional and how to learn and use them efficiently. This book is well illustrated and is a useful and well-documented review of the most important data analysis techniques. Key Features * Describes, in detail, exploratory data analysis techniques from the univariate to the multivariate ones * Features a complete description of correspondence analysis and factor analysis techniques as multidimensional statistical data analysis techniques, illustrated with concrete and understandable examples * Includes a modern and up-to-date description of clustering algorithms with many properties which gives a new role of clustering in data analysis techniques

Multivariate Data Analysis: Pearson New International Edition

Author: Joseph F. Hair,William C. Black,Barry J. Babin,Rolph E. Anderson

Publisher: Pearson Higher Ed

ISBN: 1292035110

Category: Business & Economics

Page: 752

View: 4177

For graduate and upper-level undergraduate marketing research courses. For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. Hair et. al provides an applications-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques. In this seventh revision, the organization of the chapters has been greatly simplified. New chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques.

Matrix-Based Introduction to Multivariate Data Analysis

Author: Kohei Adachi

Publisher: Springer

ISBN: 9811023417

Category: Mathematics

Page: 301

View: 527

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.

Applied Multivariate Data Analysis

Regression and Experimental Design

Author: John Jobson

Publisher: Springer

ISBN: 0387976604

Category: Business & Economics

Page: 622

View: 2754

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.

Multivariate Data Analysis

Author: Fionn Murtagh,Andre Heck

Publisher: Springer Science & Business Media

ISBN: 940093789X

Category: Mathematics

Page: 224

View: 9076

Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set of general and astronomical bibliographic references follows each chapter, providing valuable entry-points for research workers in all astronomical sub-disciplines. Although the applications considered focus on astronomy, the algorithms used can be applied to similar problems in other branches of science. Fortran programs are provided for many of the methods described.

Handbook of Univariate and Multivariate Data Analysis and Interpretation with SPSS

Author: Robert Ho

Publisher: CRC Press

ISBN: 1420011111

Category: Mathematics

Page: 424

View: 7125

Many statistics texts tend to focus more on the theory and mathematics underlying statistical tests than on their applications and interpretation. This can leave readers with little understanding of how to apply statistical tests or how to interpret their findings. While the SPSS statistical software has done much to alleviate the frustrations of social science professionals and students who must analyze data, they still face daunting challenges in selecting the proper tests, executing the tests, and interpreting the test results. With emphasis firmly on such practical matters, this handbook sets forth clear guidelines for performing specific statistical tests with SPSS and interpreting the output. The author clearly explains the purpose of each test and the research designs for which they are relevant, demonstrates the execution of the tests, and explains how to interpret the results obtained. The presentation covers not only the point-and-click Windowsä method of using SPSS, but also the syntax method, which provides users with the power and flexibility to deal with complex experimental designs. All chapters include the full SPSS output of the test being addressed and the step-by-step interpretation of the results. All of the data sets used in the examples along with the SPSS code are freely available for download at Armed with the knowledge, tools, and experience this handbook provides, social scientists, both student and professional, can maximize the utility of SPSS, choose the right statistical tests with confidence, and more accurately and appropriately interpret their results.

Applied Multivariate Data Analysis

Author: Brian S. Everitt,Graham Dunn

Publisher: Wiley

ISBN: 9780470711170

Category: Mathematics

Page: 354

View: 1918

Multivariate analysis plays an important role in the understanding of complex data sets requiring simultaneous examination of all variables. Breaking through the apparent disorder of the information, it provides the means for both describing and exploring data, aiming to extract the underlying patterns and structure. This intermediate-level textbook introduces the reader to the variety of methods by which multivariate statistical analysis may be undertaken. Now in its 2nd edition, 'Applied Multivariate Data Analysis' has been fully expanded and updated, including major chapter revisions as well as new sections on neural networks and random effects models for longitudinal data. Maintaining the easy-going style of the first edition, the authors provide clear explanations of each technique, as well as supporting figures and examples, and minimal technical jargon. With extensive exercises following every chapter, 'Applied Multivariate Data Analysis' is a valuable resource for students on applied statistics courses and applied researchers in many disciplines.

Chemometrics with R

Multivariate Data Analysis in the Natural Sciences and Life Sciences

Author: Ron Wehrens

Publisher: Springer Science & Business Media

ISBN: 9783642178412

Category: Science

Page: 286

View: 4457

"Chemometrics with R" offers readers an accessible introduction to the world of multivariate statistics in the life sciences, providing a complete description of the general data analysis paradigm, from exploratory analysis to modeling to validation. Several more specific topics from the area of chemometrics are included in a special section. The corresponding R code is provided for all the examples in the book; scripts, functions and data are available in a separate, publicly available R package. For researchers working in the life sciences, the book can also serve as an easy-to-use primer on R.

Advances in Multivariate Data Analysis

Proceedings of the Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, University of Palermo, July 5–6, 2001

Author: Hans-Hermann Bock,Marcello Chiodi,Antonio Mineo

Publisher: Springer Science & Business Media

ISBN: 3642171117

Category: Mathematics

Page: 281

View: 9935

The book presents a range of new developments in the theory and practice of multivariate statistical data analysis. Several contributions illustrate the use of multivariate methods in application fields such as economics, medicine, environment, and biology.

Applied Multivariate Data Analysis

Volume II: Categorical and Multivariate Methods

Author: J.D. Jobson

Publisher: Springer Science & Business Media

ISBN: 1461209218

Category: Mathematics

Page: 732

View: 9450

A Second Course in Statistics The past decade has seen a tremendous increase in the use of statistical data analysis and in the availability of both computers and statistical software. Business and government professionals, as well as academic researchers, are now regularly employing techniques that go far beyond the standard two-semester, introductory course in statistics. Even though for this group of users shorl courses in various specialized topics are often available, there is a need to improve the statistics training of future users of statistics while they are still at colleges and universities. In addition, there is a need for a survey reference text for the many practitioners who cannot obtain specialized courses. With the exception of the statistics major, most university students do not have sufficient time in their programs to enroll in a variety of specialized one-semester courses, such as data analysis, linear models, experimental de sign, multivariate methods, contingency tables, logistic regression, and so on. There is a need for a second survey course that covers a wide variety of these techniques in an integrated fashion. It is also important that this sec ond course combine an overview of theory with an opportunity to practice, including the use of statistical software and the interpretation of results obtained from real däta.

Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition

Author: Robert Ho

Publisher: CRC Press

ISBN: 1439890226

Category: Mathematics

Page: 600

View: 5044

Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows. New to the Second Edition Three new chapters on multiple discriminant analysis, logistic regression, and canonical correlation New section on how to deal with missing data Coverage of tests of assumptions, such as linearity, outliers, normality, homogeneity of variance-covariance matrices, and multicollinearity Discussions of the calculation of Type I error and the procedure for testing statistical significance between two correlation coefficients obtained from two samples Expanded coverage of factor analysis, path analysis (test of the mediation hypothesis), and structural equation modeling Suitable for both newcomers and seasoned researchers in the social sciences, the handbook offers a clear guide to selecting the right statistical test, executing a wide range of univariate and multivariate statistical tests via the Windows and syntax methods, and interpreting the output results. The SPSS syntax files used for executing the statistical tests can be found in the appendix. Data sets employed in the examples are available on the book’s CRC Press web page.

Exploratory Multivariate Analysis by Example Using R, Second Edition

Author: Francois Husson,Sebastien Le,Jérôme Pagès

Publisher: CRC Press

ISBN: 1315301865

Category: Mathematics

Page: 262

View: 4684

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis. The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualising objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical. They also illustrate the methods using examples from various fields, with related R code accessible in the FactoMineR package developed by the authors. The book has been written using minimal mathematics so as to appeal to applied statisticians, as well as researchers from various disciplines, including medical research and the social sciences. Readers can use the theory, examples, and software presented in this book in order to be fully equipped to tackle real-life multivariate data.

Analyzing Multivariate Data

Author: James M. Lattin,J. Douglas Carroll,Paul E. Green

Publisher: Duxbury Press

ISBN: 9780534349745

Category: Mathematics

Page: 556

View: 8006

Offering the latest teaching and practice of applied multivariate statistics, this text is perfect for students who need an applied introduction to the subject. Lattin, Carroll, and Green have created a text that speaks to the needs of applied students who have advanced beyond the beginning level, but are not advanced statistics majors. The text provides a three-part structure. First, the authors begin each major topic by developing students' statistical intuition through applications. Then, they providing illustrative examples for support. Finally, for those courses where it will be valuable, they describe relevant mathematical underpinnings with vectors and matrix algebra. Additionally, each chapter follows a standard format. This format begins by discussing a general set of research objectives, followed by illustrative examples of problems in different areas. Then it provides an explanation of how each method works, followed by a sample problem, application of the technique, and interpretation of results.