I apologise for an earlier post in error. I realised I had expB rather than B in the prediction formula.

You dont normally look at the expB of the constant in isolation of the other variables so without knowing the rest of your model it is not clear.

However a large ExpB simply means you have a large B and pushes predictions towards 1. In this case constant b0=log(9148.758) = 9.12

You will know that to predict your outcome

outcome = 1/(1 + (exp(-(b0 + b1 * x1 +b2 * x2 ....))))

Lets ignore all the other variables and just consider the constant

For large b0, exp(-b0) -> 0 so outcome -> 1

For b0=0 exp(b0)=1 so outcome =0.5

For large negatve b0, exp(-b0) -> infinity so outcome -> 0

In your case without any other variables exp(-9.12) = 0.00019 so 1/1+exp(-b0) is almost 1

Therefore your large expB for the constant means that in the absence of your other variables (which we dont know) your predictions are pushed towards 1.

Regarding Chi-squared all you need are two variables with two or more groups each and counts or frequencies for each combination of groups