Section 1

1. Probability Distributions: Joint, Conditional, Marginal

2. Bayes Formula

3. Simpson's Paradox

4. Expectation, Variance, Covariance, Correlation

5. Probability -> Decisions


Section 2

1. Normal Distribution

2. Estimate Population Mean and Proportion Based on Samples

3. Sampling Distribution of Sample Mean and Sample Proportion

4. Confidence Interval, Hypothesis Testing

5. Construct Portfolios (Covariance, Correlation), Sharpe Ratio



Section 3

1. Simple Linear Regression (SLR) Model

2. Property of Least Squares

3. Information in Regression Table

4. Statistical Inference and Interpretation of Coefficients

5. Application of SLR: CAPM


Section 4

1. Multiple Linear Regression (MLR) Model

2. Interpretation, Visualization and Estimation

3. Inference about MLR: Uncertainty, T-test, and F-test

4. Demystifying R^2

5. Understand MLR vs. SLR (Correlation and Causation)

6. Compare MLRs (Model Selection)


Section 5

1. Categorical Variables (Dummy)

2. Case Study: Salary Discrimination (Compare Different MLRs)

3. Multiple Categories

4. Variable Interaction (Simplest Non-linear Model)

5. A Complex Example via Housing Data (Interactions and Dummies)


Advice: Focus on Intuition and Concepts.