When running a regression with a categorical independent variable, we get results for each level of the variable except for the base, which we can choose.
Now I've always had a hard time on how to interpret these results.
Say we have a study of aneurysm locations. They can be located in, say, 10 different areas.
We want to see if smokers develop aneurysms in other areas than non-smokers. We have our dependent variable (smoker, no/yes) and our independent variable of location with 10 levels.
If we run the regression we might get a significant hit on 3 locations. But this is compared to the base location which let's say is level "5".
So yes, smokers are significantly more likely to get aneurysms in location 1, 2 and 3 compared to location 5. But this doesn't answer my research question of "which areas are smokers more likely to develop aneurysms in?".
What I would like to do is to maybe make a "dummy level" to my categorical variable in which half the patients have that location and half do not and then use that as base to see if ANY of the 10 true levels have a significantly higher risk for smokers. I don't want to compare the levels to each other and I want every level included and not for one of them to be used as a base.
Is there some way to do this or am I using the wrong model to answer my research question?
I assume splitting the categorical variable into 10 dummy variables is probably not so smart.