Final answer:
The statement is true. Adding more predictor variables to a linear regression model can decrease the coefficient of determination (r²) as it introduces more variability and uncertainty into the model.
Step-by-step explanation:
As we add more predictor variables to a linear regression model, the coefficient of determination, represented by r², can indeed decrease. The coefficient of determination represents the percentage of variation in the dependent variable that can be explained by the independent variable(s) using the regression line. Adding more predictor variables can introduce more variability and uncertainty into the model, which can decrease the coefficient of determination.