I teach data analysis and modeling, with a heavy emphasis on business statistics, at the MBA level. The primary difficulty students have at this level is making data analysis relevant to themselves and their careers, as well as understanding basic probability theory and the fundamental logic of inferential statistics. To tie the course into the real world, we have a structured discussion of articles, so that students can find the strengths and weaknesses of data analysis, and how they may relate such information to decisions they will be expected to make. The class has multiple problem sets to make sure students get ample training, and a hand-written, comprehensive, and challenging final exam. The key goal of the course is to provide students with the foundation required to succeed in the MBA program, and to become informed producers and consumers of statistics in their subsequent careers.
Synergy Between Research and Teaching
Since I teach statistics, it is easy to use examples from my research to illustrate problems with hypothesis testing, confidence intervals, or other inferential statistics. Conversely, students will sometimes provide ideas for me to test, or feedback about how they regard certain issues, which will then spark new research questions. More directly, as part of a team of three, I developed VBA modules to help teach statistics, tested the tools with the help of the classes, and published the results in three pedagogical papers.