If there are two interaction terms in a single model, does that mess up the interpretation of it? For example, Gender*Weight and Gender*Height. Say the model is Y~Weight+Height +Gender +Gender*Weight+Gender*Height. Would I simply interpret it as usual? That is, "The difference in mean response for a one unit increase in weight differs between the genders by the value of the interaction coefficient between weight and gender for a subpopulation of people with Height=some fixed value"? I've heard that having multiple interactions isn't good because it might complicate the interpetability. I'm not sure how though which is why I'm asking. Also, isn't the power higher in a model with two interaction if there are in fact two interactions vs having a seperate model for each interaction term?