I recently held a free webinar in our The Craft of Statistical Analysis program about Binary, Ordinal, and Nominal Logistic Regression.

It was a record crowd and we didn’t get through everyone’s questions, so I’m answering some here on the site. They’re grouped by topic, and you will probably get more out of it if you watch the webinar recording. It’s free.

The following questions refer to this logistic regression model:

#### Q: So the link function is everything on the left of the equals sign?

Yes, exactly. The right side looks pretty much like every other regression equation you’ve seen.

But the left side has a link function instead of Y. Since P is the conditional mean of Y, this ugly mess is simply a function of the mean. That’s the definition of a link function — a function of the mean of Y.

And although it looks ugly at first, it’s really not so bad once you learn more about logistic regression. It’s simply the natural log of the odds.

#### Q: What is the base of the log? e or 10 or maybe something else?

It’s *e*.

This is the natural log.

#### Q: Why isn’t there an error term in the logit model?

It’s because we’re only modeling the mean here, not each individual value of Y.

Logistic Regression is one type of Generalized Linear Model and they all have that same feature. Rather than model each value of Y with the predicted mean plus an error term, it simply models the predicted mean.

This is related to the two ways we can write a linear model. (See the next question, below). For generalized linear models, we can only use the second form AND we have to apply a link function to that predicted mean on the left.

#### Q: Is there an error term in that form of the linear model also?

(This question refers to these two ways of writing the linear regression model, which we reviewed at the beginning of the webinar):

Only the first has an error term, because here we are writing a model for each of the *i* individual values of Y. You’ll notice that not only does the error term have a subscript *i*, but so do all the Xs and Y.

The second one doesn’t need an error term, because the left side of the equation is not each value of Y, but the mean of Y at given values of X. That’s the value *on* the regression line.

We needed the error term in the first equation to move us up or down from the regression line to get to the actual data point. Here we are just staying on the regression line.

That’s what we do in generalized linear models, like logistic regression. Just model the regression line, but we are unable at all to model individual points.