On Wed, 20 Feb 2002 22:21:38 -0000, "Chia C Chong" <[EMAIL PROTECTED]> wrote:
[snip, various discussion before] > > I have an example of data of 2 RVs. When I tested the correlation between > them, by simply find the correlation coefficient, it shows that the > correlation coefficient is so small and therefore, I could say that these > two RVs are uncorrelated,or better still, not linearly correlated. Right! > However, > when I plotted the scatter plot of them, it is clearly shown that one of the > varriable does dependent on the other variable in some kind of pattern, is > just that there are not lineraly dependent, hence the almost zero > correlation coeffiicent. So, I am just wonder whether any kind of tests that > I could use to test dependency between 2 varaibles... Construct a test that checks for features. What features? Well, what features characterize your *observed* dependency, in a generalized way? -- you do want a description that would presumably have a chance for describing some future set of data. The null hypothesis is that the joint density is merely the product of the separate densities. For a picture: a greytone backdrop changes just gradually, as you move in any direction. Distinct lines or blotches are 'dependencies' -- whenever they are more distinct than would 'arise by chance.' The best test to detect vague blotches would not be the best to detect sharp spots, and that would be different from detecting lines. As I wrote before , > > > > So there is an infinite variety of tests conceivable. > > So the *useful* test is the one that avoids 'Bonferroni correction," > > because it is the one you perform because > > you have some reason for it. > > -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =================================================================
