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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
List of contents
PrefaceScatterplots and MapsProfiles and the Profile SpaceMasses and CentroidsChi-Square Distance and InertiaPlotting Chi-Square DistancesReduction of DimensionalityOptimal ScalingSymmetry of Row and Column AnalysesTwo-Dimensional MapsThree More ExamplesContributions to InertiaSupplementary PointsCorrespondence Analysis BiplotsTransition and Regression RelationshipsClustering Rows and ColumnsMultiway TablesStacked TablesMultiple Correspondence AnalysisJoint Correspondence AnalysisScaling Properties of MCASubset Correspondence AnalysisAnalysis of Matches MatricesAnalysis of Square TablesCorrespondence Analysis of NetworksData RecodingCanonical Correspondence AnalysisCo-Inertia and Co-Correspondence AnalysisAspects of Stability and InferencePermutation TestsAppendix A: Theory of Correspondence AnalysisAppendix B: Computation of Correspondence AnalysisAppendix C: Bibliography of Correspondence AnalysisAppendix D: Glossary of TermsAppendix E: EpilogueIndex
About the author
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.
Summary
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
