Beth Chance, Elena Keeling, Karen McGaughey, Jamie Bunting (Cal Poly, San Luis Obispo); Nathan Tintle (Dordt University)
In an increasingly data-centric world, where both consumers and creators of statistical information are required to understand the complex associations among the many variables that form the basis for good data-driven decisions, we pose the question how can teachers of statistics, at all levels and in all disciplinary settings, introduce and build facility with multivariable thinking? With the recent recommendation in the GAISE Guidelines (2016) to include multivariable thinking in the first course, the push to make the second course in statistics more appealing and accessible to an increasingly mathematically diverse audience, and increasingly amounts and depth of statistics being taught in ‘client’ disciplines, instructors need pedagogically-sound, conceptually-based methods and visualizations to develop multivariable thinking. The purpose of this session is to discuss ways we are approaching multivariable thinking in first and second courses in statistics, and how we coordinate these efforts with client departments. We will include a presentation about a new interdisciplinary project between Statistics and Biology instructors at Cal Poly – SLO.
- isi-stats.com (textbooks and applets; instructor resources, samples, videos, etc.)
- causeweb.org/stub (statistical thinking in undergraduate biology)
- causeweb.org/sbi (simulation-based inference blog)