Welcome to EC 320: Introduction to Econometrics (Fall 2019) at the University of Oregon.
This course introduces the statistical techniques that help economists learn about the world using data. Using calculus and introductory statistics, students will cultivate a working understanding of the theory underpinning regression analysis—how it works, why it works, and when it can lead us astray. As the course progresses, students will apply the insights of theory to work with and learn from actual data using R, a statistical programming language. My goal is for students to leave the course with marketable skills in data analysis and—most importantly—a more sophisticated understanding of the notion that correlation does not necessarily imply causation.
The HTML versions of the lecture slides allow you to view animations and interactive features, provided that you have an internet connection. The PDF slides don't require an internet connection, but they cannot display the animations or interactive features.
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Introduction to
R
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Introduction to
R Markdown
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Regression Analysis
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Hypothesis Testing
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Hypothesis Testing and Omitted-Variable Bias
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Happy Thanksgiving! No lab
For supplemental lecture documents, problem sets, and other materials, please see Canvas.
I am indebted to Ed Rubin (@edrubin) for his generous contribution of course materials. I also source some material from Nick Huntington-Klein (@NickCH-K), who maintains a trove of resources for learning causal inference.