What happens to R2 as independent variables are added to the regression model?

When independent variables are added to a regression model, the coefficient of determination, denoted as R², typically increases. R² measures the proportion of variance in the dependent variable that can be explained by the independent variables in the model.

As new independent variables are introduced, they provide additional information that can explain more of the variation in the dependent variable. Even if the new variables are not significantly improving the model, R² will either increase or remain unchanged since it cannot decrease when new variables are added. This property is due to the nature of how R² is calculated—it is based on the sum of squares of residuals and the total sum of squares.

Therefore, the correct answer highlights that the addition of independent variables generally leads to an increase in R² or at least keeps it the same, reflecting that the model's explanatory power with respect to the dependent variable cannot diminish with the inclusion of more predictors.