I'm wondering what the value is in taking a continuous predictor variable and breaking it up (e.g., into quintiles), before using it in a model. 

It seems to me that by binning the variable we lose information.

* Is this just so we can model non-linear effects?
* If we kept the variable continuous and it wasn't really a straight linear relationship would we need to come up with some kind of curve to best fit the data?