7.2 Making Sense of Adjusted R-squared

The adjusted R-squared value is a modification of the R-squared value that penalizes the addition of predictor variables that do not significantly improve the model’s fit. The formula for adjusted R-squared is: \[1 - (1-R-squared) * (n-1)/(n-p-1)\] where n is the number of observations and p is the number of predictor variables. Compared to R-squared, adjusted R-squared is always lower, and it is lower by more when the number of predictor variables is higher. If the adjusted R-squared notably smaller than plain old R-squared, it is a sign that the model is overfitting the data.