"Rich Ulrich" <[EMAIL PROTECTED]> wrote in message [EMAIL PROTECTED]">news:[EMAIL PROTECTED]... > On Wed, 20 Feb 2002 19:30:19 -0000, "Chia C Chong" > <[EMAIL PROTECTED]> wrote: > > > > > "Vadim and Oxana Marmer" <[EMAIL PROTECTED]> wrote in message > > [EMAIL PROTECTED]">news:[EMAIL PROTECTED]... > > > You can start with checking if they are correlated. It's simpler to do. If > > > you find that they are correlated then you have the answer to your > > > question. > > > If you find that they are uncorrelated and you have a reason to believe > > > that they may be not independent anyway then you can look for more > > > advanced tests. > > > > Can you give some examples of more advanced tests that can be used to test > > the depedency of data when there these data are uncorrelated?? > > You can check for an obvious non-linear (say quadratic) fit. > > WHAT is your 'reason to believe that they may be > not independent'? > Anything that makes any pattern, at all, is 'dependent.'
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. 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... CCC > > 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/ =================================================================
