I've read through the most popular threads concerning confounding variables, but I haven't been able to find an answer to my specific question. Sorry for the wall of text, I hope it's clear enough. Thanks in advance for your time!
I'm attempting to build a predictive multiple regression model for $weight$ using a dataset with a large number of (mostly categorical) variables.
During my exploratory analysis I stumbled across the following problem. A simple scatter plot of my response vs $age$ revealed that there was no simple linear relation between these two variables. In fact, it seems like it would be possible to fit a straight line with a positive slope through the observations below a certain age. For older observations, there does not seem to be a relation between $age$ and $weight$. Of course this makes sense if you consider the fact that human beings stop growing after a certain age.
My question is: will it always be possible to perform a transformation on my response/predictors, so that I can fit a linear model on my data? Is there a boxcox-like procedure for predictor variables? I've attempted a log, square root and even cubed root transformation of age, but none of those seem to be the magic fix.
If I cannot find a useful transformation, does this then indicate that there is a confounding variable I should include in the model (or subset my data for)? There are plenty of examples on how adding a categorical variable (e.g. gender) could reveal a hidden association. In my case however, one possible categorical variable would simply be 'adult vs child'. Would it make sense to use an age category (adult/child) as a categorical factor in a model where $age$ is also my continuous predictor? While it would be strange to use the same variable twice (in a sense), this would allow me to properly capture the relationship I'm seeing.
Simple regression models of my other predictor variables were often only significant when performed on a certain age group. If I cannot fit an adequate curve on $age$, I will not be able to account for these differences between adults and children in my final multiple regression model, unless I use the categorical 'adult-child' variable. I would however lose out on the positive relation between $weight$ and $age$ (continuous) for children.
EDIT: Some additional information on why I believe I need to transform $age$: while the simple linear regression model of $weight$ ~ $age$ is significant, it has a low $R^2$ and the residual plot shows a parabola-like pattern.