# Can binning a continuous predictor or DV variable improve large data sets fit?

I read that averaging and binning a continuous predictor variable is in general a bad idea because it's always better to fit the continuous relationship through splines, poly and all of that. Sure, I agree, especially for smaller, accurately measured data sets.

But what about big data and exponential distributions, where noise is more frequent and we don't necessarily want to skew the coefficients towards the center of the distribution, where we have most of the observations (although less interesting for our analysis)? Doesn't binning the predictors and the response variable reduce noise and improve our analysis for the full distribution?

• What does "noise is more frequent" mean? Isn't there noise in every observation? How do you get more frequent than that? What exactly is it you're trying to achieve? Sep 17 '14 at 1:31
• Nov 18 '14 at 13:47

• Ok. I don't see why binning a predictor would improve the non-linear relationship. I do see that in my $R^2$ but I can't get the intuition... Sep 16 '14 at 23:27