I'm very sorry if this should be obvious, I'm just feeling a little lost with this assignment..

I have four independent variables X1,X2,X3,X4 plus a constant, modelled against Y. I know X4 to be heavily correlated already, it's mostly a control variable. I've checked for multicollinearity. There are 52 observations. These are the results with only one independent variable at a time:

X1 X2 X3 X4
coefficient 0.77 -0.32 0.34 0.95
p-value 0.0001 0.03 0.027 0.00005
adjusted r-squared 0.567 0.0632 0.074 0.645

And these are the results when combined with X4:

(X1, X4) (X2, X4) (X3, X4)
coefficient (0.43, 0.64) (-0.34, 0.96) (-0.03, 0.93)
p-value (0.00001, 0.0000000084) (0.000031, 0.00000001) (0.73, 0.000006)
adjusted r-squared 0.757 0.747 0.63

I'm not sure if it's relevant, but the constant terms is varying positive and negative, sometimes with a significant p-value and sometimes not.

My question is: X2 only has 0.06 adjusted r-squared with Y by itself, while X4 has 0.645 by itself. But combined, r-squared increases to 0.747. Does that mean something is wrong with my model? Or that the tiny variance in Y that X2 explains (6%) is not included in X4, so that X2 is actually a significant variable in the model? Is 0.1 increase in r-squared even enough to say the combined model is better? Please help!