I've run a glm (gaussian family) with a*b as independent variables. At first, I run two separate models (like
glm(y~b)), in which the
a was not significant and the
b it was. Then, I combined them together in
glm(y~a*b). Combining them, I have both the independent variables significant, even if
a less significant than
b. How is it possible? Can have anything to do with the intercept? Shall I try to remove the intercept from the interaction model?
I attached the scripts of the model with just a (ci$TOT), the model with just b (ci$salinity), the model with interaction and the same model with interaction but without intercept.