Chia C Chong <[EMAIL PROTECTED]> wrote in message
news:9rn4vc$8v2$[EMAIL PROTECTED]...
>
> "Glen" <[EMAIL PROTECTED]> wrote in message
> [EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> > "Chia C Chong" <[EMAIL PROTECTED]> wrote in message
> news:<9rjs94$lht$[EMAIL PROTECTED]>...
> > > I have 2 variables and would like to test whether these 2 variables are
> > > correlated or not. What statistical tests that I should use?? I would
> guess
> > > sth like joint pdf tests but can somebody give me some suggestions to
> start
> > > with??
> >
> > Are the observations numbers or categories, or something else?
> > If they are categorical, are the categories ordered?
> >
> > Are we talking linear correlation or some more general association
> > (e.g. a monotonic relationship)? Are the variables observed over
> > time or space (or otherwise likely to be correlated with themselves)?
> >
> > In essence, what's your model for the variables (if any)?
> >
> > Glen
>
> The observations were numbers. To be specified, the 2 variables are DELAY
> and ANGLE. So, basically I am looking into some raw measurement data
> captured in the real environment and after post-proceesing these data, I
> will have information in these two domains.
>
> I do not know whether there are linearly correlated or sth else but, by
> physical mechanisms, there should be some kind of correlation between them.
> They are observed over the TIME domain.
If you're wanting to measure monotonic association, the Speaman correlation
has much to recommend it (including high efficiency against the Pearson when
the data are bivariate normal - with resulting linear association).
If you want to measure linear association, then the Pearson is generally the
way to go,
though Spearman is less influenced by extreme observations, so even here it has
something to recommend it.
If you want to measure some more general dependence, then I'm no expert on it,
but you may be on the right track trying to estimate the bivariate
distribution -
perhaps with kernel density estimation, unless you have some more knowledge
about the process (the more outside information you can put in, the easier it
should
be to identify if something is happening).
I'd probably suggest not trying to group the data and do a chi-squared measure
of
association (you're throwing away the ordering, where most of the information
will be), except perhaps just as an exploratory technique that's fast.
If one of the variables is more like a predictor and the other more like a
response,
you might consider looking at nonparametric regression approaches (smoothing,
basically). Most packages will at least do loess these days.
If the variables aren't expected to reasonably fall into a functional-type
relationship
(maybe all the points lie on an arc that's 3/4 of a circle or something), then
you could look at some of the methods that find principal curves.
Glen
=================================================================
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
http://jse.stat.ncsu.edu/
=================================================================
