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 mult
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 multHinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
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
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