🚀 go-pugleaf

Please forgive my statistically untutored way of speaking, but I have what I
think should be a relatively simple hypothesis testing problem, which I (not
being a statistician) don't quite know how to approach.

There is a process (unmodelable) which I can measure in two different ways,
giving me values, call them X and Y, for different states of the underlying
process.  I would like to make statements of the form: 

  When X is high, Y is (usually) low.  ("high" and "low" can be interchanged)
	(note the asymmetry, if X is low, we don't care about Y)

I need a statistical testing method that gives me a measure of my confidence in
such a statement.  If necessary, I can put in threshholds defining "high" and
"low", but I'd prefer not to (or to get them out of the method).  I have a
reasonable number of data points (about 300).  Doing a regression analysis
seems inappropriate, since (a) different values of X can be fundamentally (ie,
ignoring noise) associated with the same value of Y and v.v.  (since the
underlying process state in multidimensional) and (b) the choice of regression
model is not clear (since I have no real model for the process).  Any and all
suggestions would be most helpful, especially references.

Thanks very much.
-- 
Sean Philip (Shlomo) Engelson		Im eshkachekh Yerushalayim, 
Yale Department of Computer Science		Tishkach yemini;  
Box 2158 Yale Station			Tidbak leshoni l.chiki
New Haven, CT 06520				Im lo ezkerekhi...