binomial distributionlogisticregression
I've always thought of logistic regression as simply a special case of binomial regression where the link function is the logistic function (instead of, say, a probit function).
From reading the answers on another question I had, though, it sounds like I might be confused, and there is a difference between logistic regression and binomial regression with a logistic link.
What's the difference?
The picture below will probably answer your question.
A logistic regression model with 2 features creates a wave based on the logit link function.
Applying the decision rule (for example above 50%) transforms the wave to a separating hyperplane like that, but not similar to, one found in SVM. This is illustrated in the picture below. Note that this separating hyperplane is in feature space.
These pictures come from http://blog.data-miners.com/2014/03/lines-and-circles-and-logistic.html You can find an expose on om the subject.
This sounds like pseudostatistical gibberish to me. It may be that what he has in mind is the beta-binomial distribution, which is a way to account for greater variability in the response than 'ought' to occur with a binomial, but it's hard to say. The beta-binomial distribution would not be familiar to someone who has only taken a couple of applied statistics classes, but should not be exotic to a statistics professor.
The rest of his argument sounds like a Dunning-Kruger effect to me. That is where someone knows just a little bit about a topic, but is unaware of the breadth and depth of the issues or the potential caveats and complications, and therefore thinks that the topic is easy and obvious. The idea that the best way to forecast the election is to build one simple logistic regression model with the state polls is strikingly ignorant.
Best Answer
Logistic regression is a binomial regression with the "logistic" link function:
$$g(p)=\log\left(\frac{p}{1-p}\right)=X\beta$$
Although I also think logistic regression is usually applied to binomial proportions rather than binomial counts.