its not empty model. Its a model with 5 variables (I posted a picture here)

What does my y* mean? Its the left side of equation:

y* = β0 + β1*X1 + β2*X2 + ... + βn*Kn

where y* = ln (p / (1-p))

so ln (p / (1-p)) = β0 + β1*X1 + β2*X2 + ... + βn*Kn (this is logit model)

and then p = e e^y* / (1 + e^y*)

and when I have in a model constant with coefficient 0,1042 (X1 to Xn are zero value) then

ln (p / (1-p)) = 0,1042

p = e^0,1042 / (1 + e^0,1042) = 0,526

It means that when I dont have any discriminant variables (x1....xn) then a probability that a observation will 1 (company will fail) is 52,6 %. And would be correct because you dont have any discriminant variables a therefore you cannot predict a company´s status

However in the second example (in attachment) there you cannot say that without discriminant variables (x1...xn) exists a cca 50 % probability that a observation will 1 because constatn coefficient is -2,8561

and then p = e^(-2,8561) / (1 + e^(-2,8561) = 0,054

so without discriminant variables there exists a 5,4 % probability that observation will 1 and this is not corrent, isnt it ?? Because when I horse-sense "tells me" that when I dont have any discriminant variables then must always exist 50 % probability that observation will 1 (or 50% probability that observation will 0)

So can I generaly say that in logit model, there a constant coefficient MUST BE allways close to zero ?? (regardless of any other outcome as for example "correctly predicted cases)