A used car dealership owner is interested in studying the relationship between the age of the car and the price. He asked his assistant to fit a linear regression model to quantify the relationship. The assistant regressed age on price (i.e., fit a linear regression model with age as the response and price as the predictor variable) and reported that thecoefficient of determination (i.e., R2) for the model is 65%. But the owner is interested in knowing the variation in sale price explained by the age. That is, coefficient of determination for the regression line with price as the response and age as the predictor.

0 Amount of variation in sale price explained by the age is 65% 0 Amount of variation in sale price explained by the age is 50% 0 Amount of variation in sale price explained by the age is 100% 0 Amount of variation in sale price explained by the age is 35% 0 We cannot nd it without having the data 0 Amount of variation in sale price explained by the age is 153% 0 Amount of variation in sale price explained by the age is 53%