20) A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on 48 houses that had recently sold in the area. They want to predict the selling price of homes (in thousands of dollars) based on the size of the home (in square feet).

The regression equation is Price (in thousands) = 17.1 + 0.0643 Size (sq. ft.)

Predictor	Coef	SE Coef	T	P
Constant	17.06	24.59	0.69	0.491
Size (sq. ft.)	0.06427	0.01224	5.25	0.000

S = 48.5733 R-Sq = 37.5% R-Sq(adj) = 36.1%

Predicted Values for New Observations

Size (sq. ft.)	Fit	SE Fit	95% CI	95% PI
2000	145.61	7.07	(131.38, 159.83)	(46.80, 244.41)

Use the computer output to provide and interpret a 95% interval for the selling price of a single 2,000 square foot house in this portion of northern New York.

A. CI: (131.38, 159.83) We are 95% sure that the selling price of a 2,000 square foot house in this portion of northern New York is between $131,380 and $159,830.

B. PI: (46.80, 244.41) We are 95% sure that the selling price of a 2,000 square foot house in this portion of northern New York is between $46,800 and $244,410.

C. PI: (46.80, 244.41) We are 95% sure that the mean selling price of all 2,000 square foot houses in this portion of northern New York is between $46,800 and $244,410.

D. CI: (131.38, 159.83) We are 95% sure that the mean selling price of all 2,000 square foot houses in this portion of northern New York is between $131,380 and $159,830.

28) A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on 48 houses that had recently sold in the area. They want to predict the selling price of homes (in thousands of dollars) based on the size of the home (in square feet).

The regression equation is Price (in thousands) = 17.1 + 0.0643 Size (sq. ft.)

Predictor	Coef	SE Coef	T	P
Constant	17.06	24.59	0.69	0.491
Size (sq. ft.)	0.06427	0.01224	5.25	0.000

S = 48.5733 R-Sq = 37.5% R-Sq(adj) = 36.1%

Predicted Values for New Observations

Size (sq. ft.)	Fit	SE Fit	95% CI	95% PI
2000	145.61	7.07	(131.38, 159.83)	(46.80, 244.41)

Construct a 95% confidence interval for the population slope.

Note: t* = 2.013

A. -0.0197 to 0.0389

B. 0.0497 to 0.0789

C. -0.0397 to 0.0259

D. 0.0347 to 0.0849

E. 0.0397 to 0.0889

30) The least squares regression line is displayed on the provided scatterplot. Note that the points are displayed with numbers (each point having its own number), rather than points.

Which point has the most extreme negative residual? (type a number from 0 to 9).