In logistic regression, we trying to get a final outcome which is either,
a) TRUE / FALSE
b) Between 1 to 0
Perhaps the following equation would paint the picture a little better.
y = 1 / e^- (a + b1x1 + b2x2 + x3b3...)
As you can see from the equation above, we will be getting a result 0 to 1 or true / false, depending on how you calculate it.
We will be using our data earlier and call a very important function in R, glm with family parameter set to binomial as shown below :-
And the result is as follows. Here can can see that degree is a strong influence of whether a person gets high salary or not.
Residue deviance - is lack of fit of your model taken as a whole.
Fisher scoring iteration is to say how a model is estimated.
As for the interpretation of results, here is one great link.