John Howard, a Mobile, Alabama, real estate developer, has devised a regression model to help determine residential housing prices in South Alabama. The model was developed using recent sales in a particular neighborhood. The price (Y) of the house is based on the size (square footage = X) of the house. The model is:

Y = 13 comma 97313,973 + 38.5038.50X.

The coefficient of correlation for the model is 0.630.63.

a) Using the above model, the selling price of a house that is 1 comma 9601,960 square feet = $ nothing (enter a whole number).

b) A 1 comma 9601,960-square-foot house recently sold for $102 comma 000102,000, which is different than the predicted value. This is (possible not, possible) as the forecast represents (average, actual) value.

c) To make this model more realistic, additional quantitative variables that could be included in a multiple regression model are (select the choice that has all the factors that are quantifiable):

A. The age of the house, the location of the house, and the size of the garage.

B. The size of the lot, the number of bedrooms, and the layout of the rooms.

C. The age of the house, the number of bedrooms, and the size of the lot.