**Logistic regression**, also known as **logit regression**, or **logit model** is a probabilistic linear model for dichotomous data. The response variable is a binary variable (nominal variable), which means the variable has two categories or two values; True vs. False, or $1$ vs. $0$, or success vs. failure, with the probabilities of $\pi$ and $1-\pi$, respectively. Thus, the response variable follows a binomial distribution written as

where $\eta$ is the binomial denominator, which is for a binary variable $0$ or $1$ and $\pi$ is the probability of success.

For this part we rely on the following modules, libraries, and packages:

**Citation**

The E-Learning project SOGA-Py was developed at the Department of Earth Sciences by Annette Rudolph, Joachim Krois and Kai Hartmann. You can reach us via mail by soga[at]zedat.fu-berlin.de.

You may use this project freely under the Creative Commons Attribution-ShareAlike 4.0 International License.

Please cite as follow: *Rudolph, A., Krois, J., Hartmann, K. (2023): Statistics and Geodata Analysis
using Python (SOGA-Py). Department of Earth Sciences, Freie Universitaet Berlin.*