# multivariate linear regression

Hi,

I am working on a multivariate linear regression of the form y = Ax.

I am seeing a great dispersion of y w.r.t x. For example, the
correlations between y and x are very small, even after using some
typical transformations like log, power.

I tried with simple linear regression, robust regression and ace and
avas package in R (or splus). I didn't see an improvement in the fit
and predictions over simple linear regression. (I also tried this with
transformed variables)

I am sure that some of you came across such data. How did you deal with
it?

Linear regressions are good for the data like y = x +
0.01Normal(mu,sigma2) i.e. a small noise (data observed in a lab). But
linear regressions are bad for large noise, like typical market (or
survey) data.

Thank you,
Nagu

.

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