Part A- Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output is provided.

The regression equation is Price = 15.2 - 0.0101 Mileage - 1.55 Age

Predictor	Coef	SE Coef	T	P
Constant	15.2174	0.6112	24.90	0.000
Mileage	-0.01005	0.01977	-0.51	0.616
Age	-1.5466	0.3508	-4.41	0.000

S = 1.39445 R-Sq = 89.0% R-Sq(adj) = 88.0%

One of the cars in the sample was a 5-year-old Hyundai Elantra with 87.1 (thousands) of miles being sold for $6,000. What is the residual for the car?

Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output is provided.

The regression equation is Price = 15.2 - 0.0101 Mileage - 1.55 Age

Predictor	Coef	SE Coef	T	P
Constant	15.2174	0.6112	24.90	0.000
Mileage	-0.01005	0.01977	-0.51	0.616
Age	-1.5466	0.3508	-4.41	0.000

S = 1.39445 R-Sq = 89.0% R-Sq(adj) = 88.0%

One of the cars in the sample was a 5-year-old Hyundai Elantra with 87.1 (thousands) of miles being sold for $6,000. What is the residual for the car?

A. 5

B. 570

C. -570

D. -880

E. 87.1

F. 880

Part B- For the relationship between Calories and Carbs, r-squared was = 88.8%. Describe what r-squared means, in general, and what r-squared of 88.8% means.

A. 88% of total y variability is unexplained by the relationship between carbs and calories.

B. 88% of total y variability has been explained by the relationship between carbs and calories.

C. 12% of total y variability has been explained by the relationship between carbs and calories.

D. 88% of the ordered pairs are within 1 standard deviation of the line of best fit.

Part C- For the relationship between Calories and Carbs, here is the equation of the line of best fit: Regression Equation: Calories = 2.36 + 4.432 Carbs

What would you predict for the number of Calories if a cereal had 28 Carbs?