Correspondence Analysis in Practice
eBook - PDF

Correspondence Analysis in Practice

  1. 310 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Correspondence Analysis in Practice

About this book

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.

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Yes, you can access Correspondence Analysis in Practice by Michael Greenacre in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Chapman and Hall/CRC
Year
2017
eBook ISBN
9781498731782
Edition
3
Topic
Mathematics
Subtopic
Probability & Statistics
Index
Mathematics

Table of contents

  1. Cover
  2. Half Title
  3. Title Page
  4. Copyright Page
  5. Dedication
  6. Contents
  7. Preface
  8. 1 Scatterplots and Maps
  9. 2 Profiles and the Profile Space
  10. 3 Masses and Centroids
  11. 4 Chi-Square Distance and Inertia
  12. 5 Plotting Chi-Square Distances
  13. 6 Reduction of Dimensionality
  14. 7 Optimal Scaling
  15. 8 Symmetry of Row and Column Analyses
  16. 9 Two-Dimensional Displays
  17. 10 Three More Examples
  18. 11 Contributions to Inertia
  19. 12 Supplementary Points
  20. 13 Correspondence Analysis Biplots
  21. 14 Transition and Regression Relationships
  22. 15 Clustering Rows and Columns
  23. 16 Multiway Tables
  24. 17 Stacked Tables
  25. 18 Multiple Correspondence Analysis
  26. 19 Joint Correspondence Analysis
  27. 20 Scaling Properties of MCA
  28. 21 Subset Correspondence Analysis
  29. 22 Compositional Data Analysis
  30. 23 Analysis of Matched Matrices
  31. 24 Analysis of Square Tables
  32. 25 Correspondence Analysis of Networks
  33. 26 Data Recoding
  34. 27 Canonical Correspondence Analysis
  35. 28 Co-Inertia and Co-Correspondence Analysis
  36. 29 Aspects of Stability and Inference
  37. 30 Permutation Tests
  38. Appendix A: Theory of Correspondence Analysis
  39. Appendix B: Computation of Correspondence Analysis
  40. Appendix C: Glossary of Terms
  41. Appendix D: Bibliography of Correspondence Analysis
  42. Appendix E: Epilogue
  43. Index