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Activity Number: 447 - Multivariable Thinking Across the Curriculum
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistics and Data Science Education
Abstract #312869
Title: Including Multivariable Thinking in a First Course in Statistics
Author(s): Nathan Tintle*
Companies: Dordt University
Keywords: multivariable; algebra-based; teaching; visualization; GAISE; simulation-based inference

The recently updated GAISE college guidelines now talk about giving students experience with multivariable thinking. But, what does this new language mean for first courses in Statistics? In this talk, I will discuss a handful of ways to help students experience multivariable thinking in a first, algebra-based course in Statistics, in a manner that is synergistic with standard learning outcomes for the course. We will contrast the historical approach to multivariable thinking in first courses with emerging approaches which leverage big data/data science, simulation-based inference and the overarching statistical method. We will discuss data visualization, multivariable modelling, causation and confounding, and sources of variation, among others. Freely available web applets and case-studies will be used to concretely demonstrate the way these approaches would look in the classroom.

Authors who are presenting talks have a * after their name.

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