Essentials Of Multivariate Data Analysis
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| Author | : Neil H. Spencer |
| Publisher | : CRC Press |
| Total Pages | : 186 |
| Release | : 2013-12-17 |
| Genre | : Mathematics |
| ISBN | : 1466584793 |
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
| Author | : Neil H. Spencer |
| Publisher | : CRC Press |
| Total Pages | : 188 |
| Release | : 2013-12-17 |
| Genre | : Mathematics |
| ISBN | : 1466584785 |
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.
| Author | : Klaus Backhaus |
| Publisher | : Springer Nature |
| Total Pages | : 618 |
| Release | : 2023-06-28 |
| Genre | : Business & Economics |
| ISBN | : 3658404116 |
Data can be extremely valuable if we are able to extract information from them. This is why multivariate data analysis is essential for business and science. This book offers an easy-to-understand introduction to the most relevant methods of multivariate data analysis. It is strictly application-oriented, requires little knowledge of mathematics and statistics, demonstrates the procedures with numerical examples and illustrates each method via a case study solved with IBM’s statistical software package SPSS. Extensions of the methods and links to other procedures are discussed and recommendations for application are given. An introductory chapter presents the basic ideas of the multivariate methods covered in the book and refreshes statistical basics which are relevant to all methods. For the 2nd edition, all chapters were checked and calculated using the current version of IBM SPSS. Contents Introduction to empirical data analysis Regression analysis Analysis of variance Discriminant analysis Logistic regression Contingency analysis Factor analysis Cluster analysis Conjoint analysis The original German version is now available in its 17th edition. In 2015, this book was honored by the Federal Association of German Market and Social Researchers as “the textbook that has shaped market research and practice in German-speaking countries”. A Chinese version is available in its 3rd edition. On the website www.multivariate-methods.info, the authors further analyze the data with Excel and R and provide additional material to facilitate the understanding of the different multivariate methods. In addition, interactive flashcards are available to the reader for reviewing selected focal points. Download the Springer Nature Flashcards App and use exclusive content to test your knowledge.
| Author | : Mukhopadhyay Parimal |
| Publisher | : World Scientific Publishing Company |
| Total Pages | : 568 |
| Release | : 2008-11-25 |
| Genre | : Mathematics |
| ISBN | : 9813107111 |
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.
| Author | : Daniel J. Denis |
| Publisher | : John Wiley & Sons |
| Total Pages | : 384 |
| Release | : 2020-04-14 |
| Genre | : Mathematics |
| ISBN | : 1119549930 |
A practical source for performing essential statistical analyses and data management tasks in R Univariate, Bivariate, and Multivariate Statistics Using R offers a practical and very user-friendly introduction to the use of R software that covers a range of statistical methods featured in data analysis and data science. The author— a noted expert in quantitative teaching —has written a quick go-to reference for performing essential statistical analyses and data management tasks in R. Requiring only minimal prior knowledge, the book introduces concepts needed for an immediate yet clear understanding of statistical concepts essential to interpreting software output. The author explores univariate, bivariate, and multivariate statistical methods, as well as select nonparametric tests. Altogether a hands-on manual on the applied statistics and essential R computing capabilities needed to write theses, dissertations, as well as research publications. The book is comprehensive in its coverage of univariate through to multivariate procedures, while serving as a friendly and gentle introduction to R software for the newcomer. This important resource: Offers an introductory, concise guide to the computational tools that are useful for making sense out of data using R statistical software Provides a resource for students and professionals in the social, behavioral, and natural sciences Puts the emphasis on the computational tools used in the discovery of empirical patterns Features a variety of popular statistical analyses and data management tasks that can be immediately and quickly applied as needed to research projects Shows how to apply statistical analysis using R to data sets in order to get started quickly performing essential tasks in data analysis and data science Written for students, professionals, and researchers primarily in the social, behavioral, and natural sciences, Univariate, Bivariate, and Multivariate Statistics Using R offers an easy-to-use guide for performing data analysis fast, with an emphasis on drawing conclusions from empirical observations. The book can also serve as a primary or secondary textbook for courses in data analysis or data science, or others in which quantitative methods are featured.
| Author | : W. J. Krzanowski |
| Publisher | : Wiley-Blackwell |
| Total Pages | : 296 |
| Release | : 1994 |
| Genre | : Mathematics |
| ISBN | : |
The second of a two-part work on multivariate analysis, this volume covers more specialized techniques; topics include discriminant analysis, cluster analysis, path analysis, graphical modelling, latent variable techniques, repeated measures analysis and growth curve models. Modern problems and techniques, such as handling of high dimensional data and the use of neural networks are featured and the book concludes with a discussion of strategic aspects of multivariate analysis.
| Author | : Thomas Cleff |
| Publisher | : Springer |
| Total Pages | : 488 |
| Release | : 2019-07-10 |
| Genre | : Business & Economics |
| ISBN | : 303017767X |
This textbook will familiarize students in economics and business, as well as practitioners, with the basic principles, techniques, and applications of applied statistics, statistical testing, and multivariate data analysis. Drawing on practical examples from the business world, it demonstrates the methods of univariate, bivariate, and multivariate statistical analysis. The textbook covers a range of topics, from data collection and scaling to the presentation and simple univariate analysis of quantitative data, while also providing advanced analytical procedures for assessing multivariate relationships. Accordingly, it addresses all topics typically covered in university courses on statistics and advanced applied data analysis. In addition, it does not limit itself to presenting applied methods, but also discusses the related use of Excel, SPSS, and Stata.
| Author | : J.D. Jobson |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 646 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1461209552 |
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.
| Author | : Bruce L. Brown |
| Publisher | : John Wiley & Sons |
| Total Pages | : 404 |
| Release | : 2011-11-01 |
| Genre | : Mathematics |
| ISBN | : 1118131614 |
An insightful guide to understanding and visualizing multivariate statistics using SAS®, STATA®, and SPSS® Multivariate Analysis for the Biobehavioral and Social Sciences: A Graphical Approach outlines the essential multivariate methods for understanding data in the social and biobehavioral sciences. Using real-world data and the latest software applications, the book addresses the topic in a comprehensible and hands-on manner, making complex mathematical concepts accessible to readers. The authors promote the importance of clear, well-designed graphics in the scientific process, with visual representations accompanying the presented classical multivariate statistical methods . The book begins with a preparatory review of univariate statistical methods recast in matrix notation, followed by an accessible introduction to matrix algebra. Subsequent chapters explore fundamental multivariate methods and related key concepts, including: Factor analysis and related methods Multivariate graphics Canonical correlation Hotelling's T-squared Multivariate analysis of variance (MANOVA) Multiple regression and the general linear model (GLM) Each topic is introduced with a research-publication case study that demonstrates its real-world value. Next, the question "how do you do that?" is addressed with a complete, yet simplified, demonstration of the mathematics and concepts of the method. Finally, the authors show how the analysis of the data is performed using Stata®, SAS®, and SPSS®. The discussed approaches are also applicable to a wide variety of modern extensions of multivariate methods as well as modern univariate regression methods. Chapters conclude with conceptual questions about the meaning of each method; computational questions that test the reader's ability to carry out the procedures on simple datasets; and data analysis questions for the use of the discussed software packages. Multivariate Analysis for the Biobehavioral and Social Sciences is an excellent book for behavioral, health, and social science courses on multivariate statistics at the graduate level. The book also serves as a valuable reference for professionals and researchers in the social, behavioral, and health sciences who would like to learn more about multivariate analysis and its relevant applications.
| Author | : Brian Everitt |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 284 |
| Release | : 2011-04-23 |
| Genre | : Mathematics |
| ISBN | : 1441996508 |
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.
