Logistic regression

In logistic regression, we trying to get a final outcome which is either,


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.


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