Video Highlights: Generalized Additive Models – Allowing for some wiggle room in your models

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In this video presentation, we’ll unpack GAMs as an extension of generalized linear models, learn about the role of splines in these models, and explore the many choices available to define and fit these models. We’ll be using data on traffic stops to investigate racially-biased policing in South Carolina as a motivating example, and we’ll get a chance to try out the related R code so that you have the basic tools needed to try out GAMs in your own research context.

The presenter is Sara Stoudt, a lecturer in the Statistical & Data Sciences program at Smith College. She received her PhD in statistics from the University of California, Berkeley where she was also a Berkeley Institute for Data Science, and her BA in Mathematics with an emphasis on Statistics from Smith College. Her research focuses on ecological applications of statistics and statistics communication.

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