Abstract
![CDATA[Simpson’s paradox arises when aggregated data suggest a certain relationship whereas the corresponding non-aggregated data suggest a contrary relationship. Here we relate this well-known paradox to generalised correlations. For a discrete bivariate model with at least one non-binary variable, multiple parameters must be zero in order for independence to hold. These parameters may be expressed in terms of bivariate moments, and interpreted as generalised correlations. Generalised correlations include ordinary Pearson correlation as a special case, but also enable assessment of non-linear relationships. For more than two dimensions, generalised correlations are no longer constrained to sharp bounds of 1 and -1 when linearity holds, so some of the questions accessible in the bivariate case are not accessible in higher dimensions. However generalised correlations provide useful insight into complexities of multivariate structure such as Simpson’s paradox.]]
Original language | English |
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Title of host publication | Current Research in Modelling, Data Mining & Quantitative Techniques |
Publisher | University of Western Sydney |
Number of pages | 11 |
ISBN (Electronic) | 0975159909 |
ISBN (Print) | 9780975159903 |
Publication status | Published - 2003 |
Event | Workshop on Advanced Research Methods - Duration: 1 Jan 2003 → … |
Conference
Conference | Workshop on Advanced Research Methods |
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Period | 1/01/03 → … |
Keywords
- correlation (statistics)
- Paradox (computer file)
- independence
- multivariate analysis
- mathematical statistics
- contingency tables