I have struggled with the "because it is" and vague answers on this topic for a while and I was hoping someone could actually give a real reason why using an interaction term in regression is better than doing two regressions.
For example:
While doing a GLM to see how environmental factors predict a species probability of use between seasons, what benefit is gained by using one data set while including a seasonal interaction factor instead of two regressions using data from one season in each.
Any guidance on this would be appreciated.
For example:
While doing a GLM to see how environmental factors predict a species probability of use between seasons, what benefit is gained by using one data set while including a seasonal interaction factor instead of two regressions using data from one season in each.
Any guidance on this would be appreciated.