Learning regression models, in reverse

Understanding regression models is a core skill for economists. It takes some time to learn the mechanics of different regression models, but even then many students struggle with understanding what a significant model 'looks like'. What if you could teach (or learn) about regression models, not by starting with some data that you are given and running the model, but by trying to create the data that would lead to a particular result?

It sounds dodgy, because certainly we don't want to encourage people to make up their own data. However, being able to visualise what it takes for a model to be statistically significant at different levels of significance is important. And now you can do exactly this, at this website. It has a very simple point-and-click interface, and is supported by learning exercises that you can try for yourself.

There is also a working paper that briefly describes the app, by Luke Froeb (Vanderbilt University). Here's what he says about the website:

The app "inverts" the usual pedagogy. Rather than teaching students how to run regressions on data, it asks them to create data to achieve a given outcome, like a statistically significant line. Exercises are designed to give students an intuitive feel for the relationship between data and regression, and to show them how regression is used.

There's hours of fun to be had, playing with making your own data and trying to get it to fit increasingly tricky scenarios. Enjoy!

[HT: Marginal Revolution]