The thing about my way of teaching stats is that my cool, novel examples get old really fast. Like, pop culture is on a thirty-day cycle. Anyway, YEARS ago, 2014 to be exact, I blogged about illustrating bimodal data distributions using baby name data from the Social Security Administration. Specifically, when an older name comes back into vogue, it has a bimodal distribution when you plot time on the X axis. I stand by this example. However, all of the data illustrations come from 2014. When our traditionally aged college students were 6 or 7. Which makes this example feel a little stale. However, here is some data, courtesy of Redditor fevaah , showing old names that are back in vogue through 2025 ! And Eleanor, Violet, Nora, and Hazel are here, illustrating bimodal distributions for a new generation. If you used my old baby-name data, this is a quick, easy refresh for your lectures (or a fun new addition if you didn't). If this idea was help...
This tongue-in-cheek regression analysis found a way to predict the "Hipster Music Index" of a given artist by plotting # of Facebook shares of said artist's Pitchfork magazine review on they y-axis and Pitchfork magazine review score on the x-axis. If an artist falls above the linear regression line, they aren't "hipster". If they fall below the line, they are. For example, Kanye West is a Pitchfork darling but also widely shared on FB, and, thus demonstrating too much popular appeal to be a hipster darling (as opposed to Sun Kill Moon (?), who is beloved by both Pitchfork but not overly shared on FB). As instructors, we typically talk about the regression line as an equation for prediction, but Priconomics uses the line in a slightly different way in order to make predictions. Also, if you go to the source article, there are tables displaying the difference between the predicted Y-value (FB Likes) for a given artist versus the actual Y-value, which coul...