In this episode of Which Model When, Systems Engineer Mary Loveless goes house hunting; more specifically, house hunting in Cincinnati, Ohio. Together, Mary and Academic Manager Ruth Hummel use real estate data to determine how to predict a selling price for homes in The Queen City. First, Mary encourages you not to forget an often-overlooked step: running summary statistics to familiarize yourself with your data. (Mary is looking for, in particular, things like number of missing data, max, min and averages to help her build her model.) Next Mary and Ruth will tell us about different types of predictive models, and how to choose among them when working with your own data. Finally, the ladies show you how JMP’s Prediction Profiler can easily adjust your various features to show the effect it would have on other factors. But, I’m not going to give everything away. You’ll have to tune in to find out what home Mary ultimately chooses.