Logistic regression (Episode 9)

Logistic regression is a beautiful tool for modeling a binary dependent variable, although many more complex extensions exist. In the show, we will speak about the generalized linear model family, logit and probit functions, interpretations, and practicalities.

Resources:

● McCullagh, Peter, and John A. Nelder. Generalized linear models. Routledge, 1983.

● Faraway, Julian J. Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. Chapman and Hall/CRC, 2016. (http://https://julianfaraway.github.io/faraway/ELM/)