The Washington Post , and numerous other media outlets, recent wrote about an unintended consequence of COVID-19 and the sudden drop off in commercial flights: Fewer data points for weather forecasts ( PDF ). Due to the coronavirus, commercial flights are down: How does this affect weather forecasts? Data is constantly being collected from commercial flights, and that data is used to predict future weather: Ways to use in class: A conceptual example of multivariate modeling : Windspeed...temperature...humidity...lots of different data points, from lots of different elevations, come into play when making our best guess at the weather. This is a non-math, abstract way to discuss such multivariate models. A conceptual example of effect sizes/real-world effects: In the article, they clearly spell out the magnitude of the data loss. That is pretty easy to track since we can count the number of flights that have been canceled. More complex is determining the effect size of this data loss....

Eric Roston and Blacki Migliozzi put together some great data visualizations illustrating different factors that may or may not contribute to global climate change ( "What's Really Warming the World?" ). I couldn't capture it in this blog post, but the data is animated and interactive as to highlight change over time. Very slick. This got me thinking about multiple regression, which studies different variables (X 1 , X 2... ) that may or may not contribute to some outcome (Y), and how we can use this website as a conceptual example of multiple regression. Here, the graph features multiple "predictor"/X 1 , X 2 , X 3 , X 4 variables (greenhouse gasses, ozone, land use, aerosols) as well as the predicted/Y variable (global temperature). we can see the aerosols are likely a very poor predictor while greenhouse gasses are likely a good predictor. This page can also be used to explain plain old linear regression. This example compares one predictor/X...

This website is associated with Agresti, Franklin, and Klinenberg's text Statistics, The Art and Science of Learning from Data ( @artofstat ), and there are dozens of great interactives to share with your statistics students. Similar and useful interactives exist elsewhere, but it is nice to have such a thorough, one-stop-shop of great visuals. Below, I have included screengrabs of two of their interactive tools. They also explain chi-square distributions, central limit theorem, exploratory data analysis, multivariate relationships, etc. This interactive about linear regression let's you put in your own dots in the scatter plot, and returns descriptive data and the regression line, https://istats.shinyapps.io/ExploreLinReg/.  Show the difference between two populations (of your own creation), https://istats.shinyapps.io/2sample_mean/

This story is pretty easy to follow. Life expectancy varies by income level . The story becomes a good example for a statistics class because in the interview, the researcher describes a multivariate model. One in which multiple different independent variables (drug use, medical insurance, smoking, income, etc.) could be used to explain the disparity the exists in lifespan between people with different incomes. As such, this story could be used as an example of multivariate regression. And The Third Variable Problem. And why correlation isn't enough. In particular, this part of the interview (between interviewer Ari Shapiro and researcher Gary Burtless) refers to the underlying data as well as the Third Variable Problem as well as the amount to variability that can be assigned to the independent variables he lists). SHAPIRO: Why is this gap growing so quickly between life expectancy of rich and poor people? BURTLESS: We don't know. More affluent Americans tend to engage...