Representing a large auto dealer, a buyer attends car auctions. To help with the bidding, the buyer built a regression equation to predict the resale value of cars purchased at the auction. The equation is given below. Estimated Resale Price ($) = 20,000 - 2,050 Age (year), with ? = 0.46 and s, = $3,300 Use this information to complete parts (a) through (c) below. (a) Which is more predictable: the resale value of one two-year-old car, or the average resale value of a collection of 16 cars, all of which are two years old? O A. The average of the 16 cars is more predictable by default because it is impossible to predict the value of a single observation. O B. The resale value of one two-year-old car is more predictable because only one car will contribute to the error. OC. The average of the 16 cars is more predictable because the averages have less variation. O D. The resale value of one two-year-old car is more predictable because a single observation has no variation. (b) According the buyer's equation, what is the estimated resale value of a two-year-old car? The average resale value of a collection of 16 cars, each two years old? The estimated resale value of a two-year-old car is $. (Type an integer or a decimal. Do not round.) The average resale value of a collection of 16 cars, each two years old is $. (Type an integer or a decimal. Do not round.) (c) Could the prediction from this equation overestimate or underestimate the resale price of a car by more than $2,250? O A. No. Since $2,250 is less than the standard error of $3,300, it is impossible for the regression equation to be off by more than $2,250. O B. Yes. Since $2,250 is less than the standard error of $3,300, it is quite possible that the regression equation will be off by more than $2,250. OC. Yes. Since $2,250 is greater than the absolute value of the predicted slope, $2,050, it is quite possible that the regression equation will be off by more than $2,250. OD. No. Since $2,250 is greater than the absolute value of the predicted slope, $2,050, it is impossible for the regression equation to be off by more than $2,250.