Session 3: Modelling Binary and Categorical Outcomes
Note
Date: Thursday, 7 May 2026, 16:00–19:00
Business question
Can we predict which firms are likely to exit the market in the next year?
Learning goals
- Understand when and why OLS breaks down for binary outcomes
- Run and interpret logistic regression with
glm() - Translate log-odds into meaningful business insights (predicted probabilities, marginal effects)
Dataset
bisnode-firms
- ~17,500 Hungarian firms in manufacturing and services
- Key variables: firm exit (binary), log sales, log employees, profit margin, firm age, liquidity, ownership structure.
- Download processed version (original source: Békés & Kézdi 2021)
Session outline
- Debrief: Take-home Task 2
- Input: why OLS fails for binary outcomes; logistic regression intuition |
- Break
- Live demo
- In-session exercise
- Debrief + Quarto skill: inline R code
Materials
| File | Description |
|---|---|
| Slides | Lecture slides — open in browser, press F for fullscreen |
| Lecture notes & demo | Live coding document built during the session |
| Exercise sheet | In-session exercise — predicting firm exit with logistic regression Classroom link |
| Exercise solution | An example solution for the exercise (added after session) |