Lectures and discussion notes

1. Lecture 1: Data Exploration
2. Lecture 2: Plotting
3. Optional Probability Lecture
4. Lecture 3: Estimation and Standard Error
5. Lecture 4: Confidence Intervals
6. Lecture 5: Hypothesis Testing
7. Lecture 6: Hypothesis Testing II
8. Lecture 7: Bootstrapping
9. Lecture 8: Causality
10. Lecture 9: Simple Linear Regression
11. Lecture 10: Regression Inference
12. Lecture 11: Multiple Linear Regression
13. Lecture 12: Model Selection
14. Lecture 13: Logistic Regression
15. Lecture 14: Classification Error Metrics
16. Lecture 15: Additive Models

Lecture 1: Data Exploration

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Lecture 2: Plotting

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Optional Probability Lecture

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Lecture 3: Estimation and Standard Error

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Lecture 4: Confidence Intervals

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Lecture 5: Hypothesis Testing

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Lecture 6: Hypothesis Testing II

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Lecture 7: Bootstrapping

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Lecture 8: Causality

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Lecture 9: Simple Linear Regression

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Lecture 10: Regression Inference

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Lecture 11: Multiple Linear Regression

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Lecture 12: Model Selection

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Lecture 13: Logistic Regression

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Lecture 14: Classification Error Metrics

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Lecture 15: Additive Models

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