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Abstract:
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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.
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