In the episode, Don brings Charlie a notebook that was found on the body. It
contains horse racing data and equations. Charlie determines that the equations
were designed to pick the SECOND place winner, not first place. Parts of these
equations use the “logit” function, a specific probability function that uses
logarithms and odds ratios. Because the logit function can get pretty
complicated, this activity lays its foundations, namely the relationship between
probability, odds, and odds ratios.
This is a nice way to introduce the building block for the logistic regression model, which relates a set of predictor variables to an outcome variable that is binary (e.g. buyer/non-buyer). Unlike a linear regression model where the (numerical) outcome variable is a linear function of the predictors, here the relationship is between the logit of the (binary) outcome variable and the predictors.