Keith Tan, CFA

LEVEL II

Adjusted R-squared is a modified version of the R-squared measure of how well the regression model fits the data in a simple linear regression study. It takes into account the number of independent variables in the model and adjusts the R-squared value accordingly.

R-squared represents the percentage of the variance in the dependent variable that is explained by the independent variable. A higher R-squared value indicates a better fitting model, as it suggests that the independent variable is having a strong effect on the dependent variable. However, R-squared can be inflated if the model includes more independent variables, even if they are not contributing significantly to the model.

Adjusted R-squared is calculated by subtracting the proportion of the total sum of squares that is not explained by the model from the R-squared value. This helps to correct for the inflation of R-squared due to the inclusion of additional independent variables.

Adjusted R-squared may be used over R-squared in cases where the model includes a large number of independent variables and it is important to determine which variables are contributing significantly to the model. It is a more accurate measure of the fit of the model, as it takes into account the number of independent variables and adjusts the R-squared value accordingly.