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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 #312334
Title: Computational Skills for Multivariable Thinking in Introductory Statistics
Author(s): Anny-Claude Joseph* and Bryan Adams and Kevin Cummiskey and Daniel Baller and Bryan Jonas
Companies: United States Military Academy and United States Military Academy and United States Military Academy and United States Military Academy and United States Military Academy
Keywords: Data visualization; Education; Introductory Statistics; Statistical Computing; Confounding; Statistical Investigation Labs

Since the publishing of Nolan and Temple Lang's “Computing in the Statistics Curriculum” in 2010, the American Statistical Association published updated recommendations for introductory, undergraduate statistics courses. These recommendations place developing multivariable thinking at the center of such courses. Students develop multivariable thinking when they analyze real data in the context of investigating research questions of interest, which typically involve complex relationships between many variables. Proficiency in a statistical programming language facilitates the development of multivariable thinking by giving students tools to investigate complex data on their own. However, learning a programming language in an introductory course is difficult for many students. In this talk, we recommend a set of computational skills for introductory courses, demonstrate them using R tidyverse, and describe a statistical investigation lab to develop computational skills and multivariable thinking.

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

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