Logistic Regression Results Counterintuitive

logistic_regression
statistics

#1

All,

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.


#2

Hi @Arindam,

First, I didn’t really get your statement - "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."
Please elaborate.

Also, it will be good if you can share the cross-tabulation between the variables and outcome along with the respective coefficient which you mentioned here. It’ll be easier to visualize the problem.

Cheers!


#3

hello @Arindam,

Can you post the model output image from R here??