Turn data into models,
one regression at a time.

One outcome, one explanatory variable, one line. Meet the population regression model, fit it by ordinary least squares, learn to read the slope as a marginal effect, measure how well the line fits with R², and reshape the relationship with logs and elasticities. Everything else in econometrics is this idea, scaled up.

1. 📐
   Level 1 · ▶ NEXT The Regression Model ★★★ ◆◆
2. 📉
   Level 2 Ordinary Least Squares ★★★ ◆◆ 🧩 Builds on L01
3. 📏
   Level 3 Slopes, Fits & Residuals ★★★ ◆◆ 🧩 Builds on L02
4. 🎯
   Level 4 Goodness of Fit: R² ★★★ ◆◆ 🧩 Builds on L03
5. 🧮
   Level 5 Logs, Elasticities & Functional Form ★★★ ◆◆◆ 🧩 Builds on L04
6. 🎓 👑
   Level 6 · EXAM Simple Regression Exam ★★★ ◆◆◆ 🧩 Builds on L05

Real questions have more than one cause. Add explanatory variables so each slope is a partial effect — the effect of one thing holding the others fixed. See how leaving a relevant variable out biases what's left (omitted-variable bias), fold categories in with dummy variables, and bend the model with interactions and quadratics.

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   Level 7 The Multiple Regression Model ★★★ ◆◆◆ 🧩 Builds on L06
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   Level 8 Partialling Out: Ceteris Paribus ★★★ ◆◆◆ 🧩 Builds on L07
3. 🕳️
   Level 9 Omitted Variable Bias ★★★ ◆◆◆ 🧩 Builds on L08
4. 🚦
   Level 10 Dummy Variables ★★★ ◆◆◆ 🧩 Builds on L09
5. ✳️
   Level 11 Interactions & Quadratics ★★★ ◆◆◆ 🧩 Builds on L10
6. 🎓 👑
   Level 12 · EXAM Multiple Regression Exam ★★★ ◆◆◆◆ 🧩 Builds on L11

An estimate without a standard error is a guess with no error bars. Lay out the classical assumptions, see why OLS is unbiased and — under Gauss–Markov — the best you can do, then test claims: the t-test for one coefficient, the F-test for several, and what to do when heteroskedasticity threatens every standard error you just computed.

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   Level 13 The Classical Assumptions ★★★ ◆◆◆ 🧩 Builds on L12
2. 📊
   Level 14 Standard Errors & Gauss–Markov ★★★ ◆◆◆◆ 🧩 Builds on L13
3. ⚖️
   Level 15 The t Test & Confidence Intervals ★★★ ◆◆◆◆ 🧩 Builds on L14
4. 🧪
   Level 16 The F Test ★★★ ◆◆◆◆ 🧩 Builds on L15
5. 📶
   Level 17 Heteroskedasticity ★★★ ◆◆◆◆ 🧩 Builds on L16
6. 🎓 👑
   Level 18 · EXAM Inference Exam ★★★ ◆◆◆◆ 🧩 Builds on L17

Data that arrives in order breaks the 'random sample' story. Learn to regress one time series on another without fooling yourself, handle trends and seasonality, spot the serial correlation that inflates your confidence, and build an autoregressive forecast you can actually check out of sample. Then put it all together and graduate the range.

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   Level 19 Time Series Regression ★★★ ◆◆◆ 🧩 Builds on L18
2. 🌊
   Level 20 Trends & Seasonality ★★★ ◆◆◆◆ 🧩 Builds on L19
3. 🔗
   Level 21 Serial Correlation ★★★ ◆◆◆◆ 🧩 Builds on L20
4. 🔮
   Level 22 Forecasting ★★★ ◆◆◆◆ 🧩 Builds on L21
5. 🏗️
   Level 23 Building a Credible Model ★★★ ◆◆◆◆ 🧩 Builds on L22
6. 🏆 👑
   Level 24 · EXAM The Final Exam ★★★ ◆◆◆◆◆ 🧩 Builds on L23