Answer: All of the above are correct

The coefficient of determination (R²) has the following properties: 1. **Range**: R² takes values between 0% and 100% inclusive (0 ≤ R² ≤ 1). A value of 0% means the model explains none of the variability, while 100% means it explains all variability. 2. **Effect of additional variables**: R² generally increases when additional independent variables are added to the regression model, even if those variables are not statistically significant. This is why adjusted R² is often used to penalize for adding irrelevant variables. 3. **Ordinary Least Squares (OLS)**: OLS estimation maximizes R². The OLS method finds the parameter estimates that minimize the sum of squared residuals, which is equivalent to maximizing R². Since all three statements A, B, and C are true, the correct answer is D: All of the above are correct. The coefficient of determination (R²) measures the proportion of variability in the dependent variable that is explained by the independent variables in the regression model: R² = SS_REG / SS_TOT = 1 - (SS_RES / SS_TOT), where SS_REG is the regression sum of squares, SS_RES is the residual sum of squares, and SS_TOT is the total sum of squares.

Author: Nikitesh Somanthe