How to interpret positive interaction in regression model if I have one predictor that has positive coefficient and one that has negative coefficient? I have a regression model with two predictors (stigma and social support), interaction (stigma*social support) and one dependent variable (depression). Stigma has positive coefficient (0.16), social support has negative coefficient (-0.23) and interaction is positive (0.13). This is a regression model: Y=11.9 + 0.16*stigma - 0.23*soc.support + 0.13 (stigma*soc.support)
I would appreciate if someone could help me interpret interaction term.
 A: Here, there are two contrasting effects of social support (negative) and stigma (positive) variable on your dependent variable depression $Y$. We can see from the coefficients that the effect of the absolute effect of having social support is somewhat stronger than receiving stigma in affecting one's depression.
Holding the level of stigma constant, for every unit increase in social support, depression becomes a positive linear function with stigma. Even though social support reduces overall depression (which is $11.9$ without any social support or stigma), the interaction effect of stigma is counter-acting against it and resulting in a $0.29$ unit increase in depression with every unit increase in stigma. So if stigma is sufficiently large, it will still result in higher depression.
i.e. $Y=11.67+0.29* stigma$
Similarly, holding social support constant, for every unit increase in stigma, depression becomes a negative linear function with social support. Even though stigma increases the overall depression score, the interaction between the two variables results in a opposite effect and if we have sufficient social support, depression scores will start to decrease.
i.e. $Y=12.06-0.10 *soc.support$
Hope that the explanation is clear enough and this helps!
