At the end of the course, the successful student will be able to:

* understand the language of statistics, up through simple regression.
* evaluate causal statistical hypotheses through an appropriate critical lens.
* use the statistical package R.

This course introduces students to the testing of causal hypotheses with statistical methods, focusing on social-science applications.  Causal inference in this setting requires melding the language of mathematical statistics with the reality of human decision-making.  Successfully testing a social-science hypothesis thus requires both facility with the mathematics, as well an understanding of how people actually behave.  This course gives students an introduction to the math, and experience in translating the math to reality.

The course is timed according to the scripted schedule of the masters’ program in Public Choice, and is tailored to the needs of that program.  As a result, non-PCP students should be aware that the course will proceed at a substantially faster pace than other courses.

The first week is the program’s Math Camp, consisting of 10 lecture hours (no quarters) and nightly group homework assignments.  The following three weeks form the Statistics component, consisting of 24 lecture hours (no quarters), twice-a-week group homework assignments, and a final exam. 

The final mark comes from the Statistics component alone: 50% weight on homework, and 50% on the exam.