I ran a logistic Regression on a set of variables 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 occurence is high when Cat1 is 1, which I think is counter intuitive.

Pls help in understanding this.


1 Answer 1


There are at least two possibilities:

  1. The parameterization of the categorical variable may not be what you think it is. SAS PROC LOGISTIC, in particular, has defaults that are (to me and many other) counter intuitive in many cases. R can also do some counterintuitive things with factors.

  2. If that isn't it, then bear in mind that your logistic regression has other variables included. So, it is controlling for those variables. This has been discussed here multiple times.

  • $\begingroup$ Thanks for the answer. The variables were coded by me hence the first possibility I guess is ruled out. Also I ran in R with all variables created by me. I understand the controlling part but still doesn't the sign indicate type of correlation which is followed in many other continuous variables I have but seems counter intuitive in categorical ones I have. Any links if you could provide of the discussions would be great help. $\endgroup$
    – Arindam
    Feb 9, 2016 at 12:25

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