BMET 23A03 - Econometrics and Data Analysis using R

This course offers a practical, hands-on introduction to econometrics and data analysis using R. It introduces the quantitative techniques that guide evidence-based analysis in the social sciences and economics. For instance, we will ask whether candidates with different names are treated equally in hiring, whether students from working-class backgrounds are graded differently than students from upper-class backgrounds for the same performance, whether women receive lower starting salary offers than men with comparable qualifications, and whether specific policies like minimum wage laws and mentoring programs actually change outcomes.

This course develops a comprehensive framework that students can use to examine whether the predictions of social and economic theories are supported by empirical evidence. At an abstract level, the course will provide students with the tools to address questions such as:

When can we conclude that A causes B?

When is it sufficient to know that A is correlated with B?

How certain should we be of our empirical conclusions?

When are the results of a study externally valid?

How should we read a graph?

The approach is somewhat formal, as the course makes use of econometric theorems and techniques. However, it is grounded in real-world applications. Students will be provided with tools and data sets to answer applied questions from the social sciences and economics. Many applications will be illustrated using the R statistical software and the RStudio graphical interface, and the students will be expected to learn the basics of data manipulation and regression analysis.

We will clean and explore real-world datasets, conduct different types of exploratory data analysis (scatter plots, facet wraps, histograms, boxplots), and systematically learn how to design and simple and multiple linear regression models, logit regressions, interpret results, and produce reproducible analyses.