In a study, fast-food menu items were analyzed for their fat content (measured in grams) and calorie content. The goal is to predict the number of calories in a menu item from knowing its fat content. The least-squares regression line was computed and added to a scatterplot of the data:

The equation of the least-squares regression line is:
Calories = 204 + 11.4 × (Fat)
The correlation between Calories and Fat is r = .979. Hence, r2 = .958.
Finally, the average number of calories in menu items is 663, and the average fat content in menu items is 40 grams.

Question

In a study, fast-food menu items were analyzed for their fat content (measured in grams) and calorie content. The goal is to predict the number of calories in a menu item from knowing its fat content. The least-squares regression line was computed and added to a scatterplot of the data:



The equation of the least-squares regression line is:
Calories = 204 + 11.4 × (Fat)
The correlation between Calories and Fat is r = .979. Hence, r2 = .958.
Finally, the average number of calories in menu items is 663, and the average fat content in menu items is 40 grams.

konradzuse · Answer

The point indicated by * has 
 A. a negative value for the residual. 
B. a positive value for the residual. 
C. a zero value for the residual. 
D. a zero value for the correlation.

konradzuse · Answer

Not 100% what residual is, but I'm assuming it's B positive....?  Neg I assume would be under the line.....  Zero makes no sense either way....