I ran a logistic Regression on a set of variables coded by me in R both categorical and continuous with a binary event as dependent variable.
Now post modelling, I observe a set of categorical variables showing negative sign which I presume is to understand that if that categorical variable occurs high number of times then the probability of the dependent variable occurring is low.
But when I see the % of occurrence of that independent variable I see the reverse trend happening. hence the result seems to be counter intuitive. Any reason why this could happen. I have tried explaining below with a pseudo example.
Dependent Variable - E Predictors: 1. Categorical Var - Cat1 with 2 levels (0,1) 2. Continuous Var - Con1 3. Categorical Var - Cat2 with 2 levels (0,1) Post Modelling: Say all are significant and the coefficients are like below, Cat1 - (-0.6) Con1- (0.3) Cat2 - (-0.4)
But when I calculate the % of occurrence of Event E on Cat 1, I observe that the % of occurrence is high when Cat1 is 1, which I think is counter intuitive.
Pls help in understanding this.