One of the biggest factors in determining the value of a home is the square footage. The accompanying data represent the square footage and selling price (in thousands of dollars) for a random sample of homes for sale in a certain region.

(a) Which variable is the explanatory variable?

-Selling Price

-Square Footage

(b) Determine the linear correlation coefficient between square footage and asking price.

r=____

(c) Is there a linear relation between square footage and asking price?

-Yes

-No

(d) Find the least-squares regression line treating square footage as the explanatory variable.

y=_____x+_____

(e) Interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete the right choice. SELECT ONE

a) For every additional square foot, the selling price increases by ____ thousand dollars, on average.

b) For a house that is 0 square feet, the predicted selling price is ____ thousand dollars.

c) For a house that is sold for $0, the predicted square footage is ____.

d) For every additional thousand dollars in selling price, the square footage increases by ____ square feet, on average.

e) It is not appropriate to interpret the slope.

(f) Is it reasonable to interpret the y-intercept? Why? Select the correct choice below and, if necessary, fill in the answer box to complete the right choice. SELECT ONE

a) Noa house of ____ square feet is outside the scope of the model

b) Noa house of _____ square feet is not possible and outside the scope of the model.

c) Noa house of _____ square feet is not possible.

d) Yesa house of _____ square feet is possible and within the scope of the model.

e) More information about the houses is necessary before deciding.

(g) One home that is 1499 square feet is sold for $280 thousand. Is this home's price above or below average for a home of this size?

The home's price is (below ,above) the average price. The average price of a home that is 1499 square feet is

$____thousand.