I am trying to make some sense out of the results of a linear regression model. I have a dependent variable X, and, say, 3 independent variables Y1 Y2 Y3. I set up 5 models :
(m1) X ~ Y1
(m2) X ~ Y1 + Y2
(m3) X ~ Y1 + Y3
(m4) X ~ Y1 + Y2 + Y3
(m5) X ~ Y1 + Y2*Y3
In models 1 to 3, all independent variables are significant.
In model 4, when I include both Y2 and Y3 in the model, they both turn un-significant. However, when I include an interaction between Y2 and Y3 (m5), all variables main effects and the interaction turn significant.
I am wondering whether I am overfitting the model, or if there might be one logical way to interpret the changes of significances for Y2 and Y3 in these analyses (m4 vs m5).
 Y2 is numeric, Y3 is factor (3 levels). The parameters' sign are the following : (m5) main Y2 : +, main Y3 : +, Y2*Y3 : -, Y2*Y3 : -,