Part I · Quantitative Foundations
Module 03. Linear Regression Mechanics, OVB & Inference
Understand exactly what OLS does, why omitted variables bias it, and how to get the standard errors right.
Labour economics: the Mincer earnings equation and heteroskedasticity about 6 hours3 lessons
Module progress0/3 lessons · 0%
Lessons
3.1
OLS Mechanics: the Line of Best Fit
- · Explain OLS as the line that minimises the sum of squared residuals
- · Recover the same coefficients three ways: numerical optimisation, matrix formula and statsmodels
- · Interpret slopes, intercepts, residuals and R² in a Mincer earnings equation
120 min 9 Colab cellsVideo coming soon
3.2
Omitted Variable Bias and Partialling Out
- · Simulate an unobserved confounder and measure the omitted variable bias (OVB) it creates
- · Verify the OVB formula numerically
- · Use multiple regression to "close the backdoor" and recover the causal return to education
120 min 6 Colab cellsVideo coming soon
3.3
Statistical Inference and Robust Standard Errors
- · Read standard errors, t-statistics, p-values and confidence intervals from a regression
- · Simulate the false-positive rate of a test to see what a p-value promises
- · Diagnose heteroskedasticity and fix inference with robust (HC) standard errors
120 min 6 Colab cellsVideo coming soon