Typically the first assessment of how well a regression mod

. Typically, the first assessment of how well a regression model predicts is based on R square (the coefficient of determination). The higher the R square, the more of the variation in observed Y-values is explained by the variation in observed X-values.

Suppose you want to find out if there’s a model that is a better predictor of wins than runs scored. You ask your staff to come up with alternate models. It turns out that when the X variable is the number of gallons of beer sold during a game, wins are predicted with an R Square of 0.7750.

            Would you stop using the runs scored/wins model and use the beer sold/wins model instead? Why or why not?

Solution

No you wont use the beer sold/wins model instead since R² is not the only measure that needs to be looked at.

R² basically tells us the % of variance that is being accounted by the predictor variables

We also need to check the significance of the model and the predictor variable

If the model is significant then only we will use that model and then consider the significance of the predictor variable.

It can be found by looking at the p value of the model and the variable coefficient.

The model wthat is significant and with a higher R² value should be used