Measuring correlation or dependence between two data sets Is there any statistical test or measure to evaluation the degree of correlation or dependence between two sets of data-points ?
 A: Sorry, it's still not clear - do you mean each data set has an x and a y, or that there's one x and one y?
You should give an example consisting of the first few data points to make it clear. Failing that, a few numbers like the first few data points would help.
It sounds kind of like the second, so that's what the first part of my answer covers. Then I discuss the first.
I) one x, one y
For measuring linear dependence between continuous x and y, there's the commonly used Pearson correlation, which has an associated statistical test (though it's not the only possible measure)
For monotonic dependence, there's Spearman's rho and Kendall's tau, which each have tests associated with them.
http://en.wikipedia.org/wiki/Correlation_and_dependence
http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient
http://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient
http://en.wikipedia.org/wiki/Kendall_tau
There are various other measures of association used in other circumstances.
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II) one (x, y) pair vs another (x,y) pair
If you mean that your first set of data is two dimensional $ (x_{1,i},y_{1,i}) \,\, $ ,  and your second data set is two dimensional  $ (x_{2,i},y_{2,i}) \,\, $ ,  then there are various ways to measure dependence or association depending on what kind of association you're trying to measure.
For example, one thing people might do is canonical correlation analysis. But there are a host of other things one might do, depending on what you're trying to achieve.
