system
Given the following post, respond as if you're commenting on Reddit:
human
In multiple linear regression, how is it possible that multiple predictors are strongly correlated with the dependent variable but not with each other?

This is very counterintuitive to me. If X and Y are the independent variables and Z is the dependent, then we know that X strongly tends to move together with Z, and that Y also strongly tends to move together with Z, yet somehow X and Y don't move together at all?   
In my mind this automatically leads to multicollinearity, yet clearly my thinking is wrong since this doesn't happen in practice.
gpt
Another example. On a dating app, you want to know how much one person will like the other one. You have date of the eye color and the age. Both are very important, but they age and eye color are not correlated at all.

And actually, this is a behavior you want! This will mean they completely add new information that explains the dependent variable, meaning that there is no repetition of information. If to the previous variables we add to the eye color and age, if they are above 18, this does not add any information at all and it is a function of the age.