R2 will continue to increase, even if you are overfitting the data. You
might be better served by adjusted R2
http://en.wikipedia.org/wiki/Coeffic..._determination

If you have n data points, you can always perfectly fit (R2=1) a polynomial
of degree n-1 to that data, but it will be chasing the noise in the data
instead of the signal and be totally useless for extrapolation and frequently
useless for interpolation. With less extreme polynomials, you may still
overfit the data.

A famous qoute (attributed to various persons from Poincare on) says that
"With four parameters I can fit an elephant; with five I can make it wag its
tail."

Jerry

"Dellie" wrote:

I'm a newbie to this group, so please excuse me if this isn't the right
place.

I have a number of column charts with data representing 12 months. Most
often, these data are not linear. I have been fitting trendlines (ok by
experimentation) and can get very high R2's. But, when I go to project
out only 1 month, it seems that the better the fit, the more extreme
the projection. If I back off a polynomial model by 1, the projection
may reverse itself. Back off by another 1, and the projection looks
(yes I'm just looking and not getting into the stats behind the
regressions) more "reasonable". The behavior of the projections seems
to be erratic and the best regression fit makes it appear the a steep
increase or decrease is coming. Anyone care to comment? Thank you so
much for your time.