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Correspondence Analysis Handbook
This practical reference/text presents a complete introduction to the practice of data analysis - clarifying the geometrical language used, explaining the formulae, reviewing linear algebra and multidimensional Euclidean geometry, and including proofs of results. It is intended as either a self-study guide for professionals involved in experimental
Geometric Data Analysis
Geometric Data Analysis (GDA) is the name suggested by P. Suppes (Stanford University) to designate the approach to Multivariate Statistics initiated by Benzécri as Correspondence Analysis, an approach that has become more and more used and appreciated over the years. This book presents the full formalization of GDA in terms of linear algebra - the most original and far-reaching consequential feature of the approach - and shows also how to integrate the standard statistical tools such as Analysis of Variance, including Bayesian methods. Chapter 9, Research Case Studies, is nearly a book in itself; it presents the methodology in action on three extensive applications, one for medicine, one from political science, and one from education (data borrowed from the Stanford computer-based Educational Program for Gifted Youth ). Thus the readership of the book concerns both mathematicians interested in the applications of mathematics, and researchers willing to master an exceptionally powerful approach of statistical data analysis.
Correspondence Analysis and Data Coding with Java and R
Developed by Jean-Paul Benzerci more than 30 years ago, correspondence analysis as a framework for analyzing data quickly found widespread popularity in Europe. The topicality and importance of correspondence analysis continue, and with the tremendous computing power now available and new fields of application emerging, its significance is greater
Visualization and Verbalization of Data
Visualization and Verbalization of Data shows how correspondence analysis and related techniques enable the display of data in graphical form, which results in the verbalization of the structures in data. Renowned researchers in the field trace the history of these techniques and cover their current applications. The first part of the book explains the historical origins of correspondence analysis and associated methods. The second part concentrates on the contributions made by the school of Jean-Paul Benzécri and related movements, such as social space and geometric data analysis. Although these topics are viewed from a French perspective, the book makes them understandable to an international audience. Throughout the text, well-known experts illustrate the use of the methods in practice. Examples include the spatial visualization of multivariate data, cluster analysis in computer science, the transformation of a textual data set into numerical data, the use of quantitative and qualitative variables in multiple factor analysis, different possibilities of recoding data prior to visualization, and the application of duality diagram theory to the analysis of a contingency table.
Statistical Learning and Data Science
Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data wor
Correspondence Analysis Handbook
This practical reference/text presents a complete introduction to the practice of data analysis - clarifying the geometrical language used, explaining the formulae, reviewing linear algebra and multidimensional Euclidean geometry, and including proofs of results. It is intended as either a self-study guide for professionals involved in experimental research, or as a text for graduate level courses in multidimensional statistics.;The book features fully worked-out exercises, without the help of a computer, illustrating the constructions of correspondence analysis. It gives details of how to prepare, read and interpret computer results, including a complete FORTRAN program listing of the basic algorithms of factor analysis and classification. Sixteen case studies from medicine and biology, economics and the humanities, demonstrate a selection of data and interpretation of results. A foundation for agglomerative hierarchical clustering is provided.
Review of Marketing Research
The 'Review of Marketing Research' provides current articles by leading researchers and academicians in marketing, as well as material that is not only theoretically rigorous but that also offers richer detail, including literature reviews, empirical studies, and suggestions for future theory development.
Correspondence Analysis in Practice
Drawing on the author’s experience in social and environmental research, Correspondence Analysis in Practice, Second Edition shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. This completely revised, up-to-date edition features a didactic approach with self-contained chapters, extensive marginal notes, informative figure and table captions, and end-of-chapter summaries. New to the Second Edition • Five new chapters on transition and regression relationships, stacked tables, subset correspondence analysis, analysis of square tables, and canonical correspondence analysis • Substantially more figures and tabl...
Optimal Analysis of Qualitative Data with Illustrations
This is an introductory book on how to optimally analyze non-quantitative data, based on the author’s experiences over 60 years of research. The major message to the readers is that qualitative (non-quantitative) data are much more informative than quantitative data. This is good news for readers in applied areas of statistics such as those in the social sciences and marketing research, where qualitative data are everywhere. But how can one analyze qualitative data quantitatively and extract more information than from the sophisticated analysis of quantitative data? The key rests in illustrations of difficult topics in a way that anyone can understand. It is the author’s wish soon the use of AI will open a gate for simple means for optimal analysis of qualitative data, as illustrated throughout the book.
Handbook of Data Visualization
Visualizing the data is an essential part of any data analysis. Modern computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data displays. This book gives an overview of modern data visualization methods, both in theory and practice. It details modern graphical tools such as mosaic plots, parallel coordinate plots, and linked views. Coverage also examines graphical methodology for particular areas of statistics, for example Bayesian analysis, genomic data and cluster analysis, as well software for graphics.
