Units

1. Artificial Intelligence in Econometrics and Statistics: Roles, Boundaries, Authentication and Privacy 2. Data Cleaning and Preparation: Missing Data, Outliers, and Coding 3. Exploratory Data Analysis and Visualization: Graphing and Interpretation with Artificial Intelligence 4. Linear Regression: Model Building, Coefficient Interpretation and Assumptions 5. Regression Diagnostics and Violations: Collinearity, Heteroscedasticity, Autocorrelation 6. Hypothesis Testing and p-value: Significance, Power, and Multiple Comparisons 7. p-hacking, HARKing and Statistical Integrity: Pitfalls in the Age of Artificial Intelligence 8. Time Series Analysis: Stationarity, ARIMA and Forecasting 9. Causation and Policy Analysis: DiD, IV, RDD and Artificial Intelligence 10. Code Generation and Reproducibility: R/Python, Versioning and Reproducibility 11. Report Writing, Communication, and Ethics: Presenting Results and Communicating Boundaries

Unit 10 / 11

Code Generation and Reproducibility: R/Python, Versioning and Reproducibility

Gains:

Ability to verify by reading and testing the R/Python analysis code produced by artificial intelligence and establish a reproducible project with seed, version lock and folder structure
Ability to use version control (Git), literate programming (R Markdown/Quarto/Jupyter) and environment management with artificial intelligence support, making analysis reproducible from start to finish
Ability to perform unit testing and sanity checking to catch silent errors in AI code (misjoin, leak, missed filter)

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