I have the following model:
glm(Yi~Xi,Di, data=data, family=binomial(link="logit") )
where Di is a dummy variable
the output of this function gives me the estimates for Beta0, Beta1 and Beta2. Lest's suppose Beta1 is >0 and Beta2 is <0
Which is the correct way to interpret this result?
I can say that X1 (the variable associated to Beta1) determines an increment of the logit(pi). Di the opposite. Is it right?
Then, if I calculate the exponential of these coefficients, what should I day? "Beta1 determines an increment of the odds" or "Beta1 determines an increment of the odds ratio"?
