This paper is from Session 3F: Statistical computing and communication
which comes under Topic 3: Statistics education at the post-secondary level
Paper 3F1 (Friday 13th, 11:00-12:30)
Improving Statistical Communication in Statistical Computing Courses
Presenter
- Hunter Glanz (California Polytechnic State University, United States)
Co-author
- Shannon Pileggi (California Polytechnic State University, United States)
Abstract
Statistical computing courses often revolve around a statistically-oriented programming language and involve curriculum motivated by problems in the field of Statistics. Programming-focused learning objectives can facilitate an excessively large focus on merely completing a particular programming task or recovering a certain table of results, when the course should be just as much about Statistics. In the midst of wondrously more computing ability by Statistics students in recent years, there must remain the ability to communicate about the methods implemented and results visualized. With the advent of RStudio, Jupyter Notebooks, and other tools, reincorporating statistical communication into your statistical computing course has never been easier.