1 / 29

Multivariate Data Plots

Explore non-conventional methods for visualizing multidimensional data, including Chernoff faces, Kruskal's Multidimensional Scaling, and Cartograms.

earlenea
Télécharger la présentation

Multivariate Data Plots

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Multivariate Data Plots

  2. Example of conventional analysis of multivariate data Example: A 2D sample of 100 observations is illustrated here using the two 1D cross-sectional histograms. Corr(x,y) = 0.04. Question: Can you guess the shape of the original sample?

  3. Cross-sectional/correlation analysis misses the big picture! The original 2D sample

  4. Analysis of marginal distributions has limited power • Nothing is better that a human eye • One wishes to “see” the whole picture, but we live in 3D world while the realistic data sets are coming from multi-dimensional spaces • Special visual methods have been developed to visualize multidimensional sets • We proceed with a brief review of different non-conventional methods to present multidimensional information for human visual analysis

  5. Chernoff faces Idea: We are good at reading facial emotions

  6. Chernoff faces Chernoff, H. (1973): The use of faces to represent statistical association, JASA, 68, pp. 361–368.

  7. Chernoff faces Example: Economy is going to recession

  8. Kruskal’s Multidimensional Scaling Idea: Redraw a p-dimensional point set in dimension q < p preserving as much pair-wise distances as possible.

  9. Kruskal’s Multidimensional Scaling • Consider a set of N objects (points ) in p-dimensional space • Matrix of similarities (or distances) is given by • The goal of MDS is to find such points x1,…,xNin q-dimensional space that • Kruskal stress is a measure of discrepancy between the true and estimated similarities (distances)

  10. Kruskal MDS results for distance between European cities (“eurodist”)

  11. Actual map of Europe

  12. Cartograms Idea: Draw a map with areas corresponding to the quantity of interest, NOT physical area. Michael T. Gastner and M. E. J. Newman, Proc. Natl. Acad. Sci. USA101, 7499-7504 (2004). http://www-personal.umich.edu/~mejn/cart/ http://www-personal.umich.edu/~mejn/cartograms/

  13. Good old physical map

  14. World Population

  15. Gross Domestic Product

  16. Child Mortality

  17. US Elections 2012 Mitt Romney Barak Obama

  18. US Elections 2012 by population Mitt Romney Barak Obama

  19. US Elections 2012 Mitt Romney Barak Obama

  20. US Elections 2012 by population Mitt Romney Barak Obama

  21. A Brief Summary of Multivariate Techniques

  22. Car marketing analysis Comfort Price

  23. Car marketing analysis Comfort Very good (and probably unrealistic) situation Price

  24. Car marketing analysis Comfort Very bad (and very realistic) situation Price

  25. Car marketing analysis Comfort Very bad (and very realistic) situation: A historic car Price

  26. Car marketing analysis Comfort Realistic (common) situations Price

  27. Car marketing analysis Comfort Price

  28. Car marketing analysis Comfort Quality Value Price

  29. Car marketing analysis Comfort Quality Value Good value! Bad value! Price

More Related