Drawing on the author's 45 years of experience in multivariate analysis, Correspondence Analysis in Practice, Third Edition, shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. CA and its variants, subset CA, multiple CA and joint CA, translate two-way and multi-way tables into more readable graphical forms - ideal for applications in the social, environmental and health sciences, as well as marketing, economics, linguistics, archaeology, and more.
Autorentext
Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra, Barcelona, Spain, where he teaches a course, amongst others, on Data Visualization. He has authored and co-edited nine books and 80 journal articles and book chapters, mostly on correspondence analysis, the latest being Visualization and Verbalization of Data in 2015. He has given short courses in fifteen countries to environmental scientists, sociologists, data scientists and marketing professionals, and has specialized in statistics in ecology and social science.
Zusammenfassung
Drawing on the author's 45 years of experience in multivariate analysis, Correspondence Analysis in Practice, Third Edition, shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. CA and its variants, subset CA, multiple CA and joint CA, translate two-way and multi-way tables into more readable graphical forms ideal for applications in the social, environmental and health sciences, as well as marketing, economics, linguistics, archaeology, and more.
Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra, Barcelona, Spain, where he teaches a course, amongst others, on Data Visualization. He has authored and co-edited nine books and 80 journal articles and book chapters, mostly on correspondence analysis, the latest being Visualization and Verbalization of Data in 2015. He has given short courses in fifteen countries to environmental scientists, sociologists, data scientists and marketing professionals, and has specialized in statistics in ecology and social science.
Inhalt
Preface
Scatterplots and Maps
Profiles and the Profile Space
Masses and Centroids
Chi-Square Distance and Inertia
Plotting Chi-Square Distances
Reduction of Dimensionality
Optimal Scaling
Symmetry of Row and Column Analyses
Two-Dimensional Maps
Three More Examples
Contributions to Inertia
Supplementary Points
Correspondence Analysis Biplots
Transition and Regression Relationships
Clustering Rows and Columns
Multiway Tables
Stacked Tables
Multiple Correspondence Analysis
Joint Correspondence Analysis
Scaling Properties of MCA
Subset Correspondence Analysis
Analysis of Matches Matrices
Analysis of Square Tables
Correspondence Analysis of Networks
Data Recoding
Canonical Correspondence Analysis
Co-Inertia and Co-Correspondence Analysis
Aspects of Stability and Inference
Permutation Tests
Appendix A: Theory of Correspondence Analysis
Appendix B: Computation of Correspondence Analysis
Appendix C: Bibliography of Correspondence Analysis
Appendix D: Glossary of Terms
Appendix E: Epilogue
Index