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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/)
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By Alexander Schacht and Paolo EusebiLogistic 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/)