Any help would be very much appreciated. I have run a binary logistic regression model. My dependent variable is impaired or not-impaired (following stroke), I have two predictors one is age the other is an EEG variable called theta power. Both predictors are continuous variables. I have checked the assumptions and there is no indication for multicolinearity (both variables VIF=1.154, tolerance =0.866).In addition there is no indication that the model violates the assumption of linearity (I ran a check using linear regression).
My results show that the model is significant (Chi-square=13.779, df=2, sig=0.001). The sample is not large, only 31, however I only included two predictors with this in mind.
One of my predictors has an enormous Exp(B) value and confidence intervals to go along with it.
Age: B=0.084, S.E.=0.046, Wald=3.364, df=1, sig=0.067, Exp(B)=1.087, 95%C.I. for Exp(B)=0.994-1.189
ThetaPower: B=16.259, S.E.=8.019, Wald=4.112, df=1, sig=0.043, Exp(B)=11516574.29, 95%C.I. for Exp(B)=1.721-7.705+E13
There is only a very small variance in theta values (0.012) compared to age (202.034), could this be the reason for the extremely large odds ratio and confidence intervals? Is there anyway I can 'fix' this to keep this variable in my model and report the statistics in a more meaningful way?