7 Refer to the accompanying table which was obtained using d

+++++7+++++

Refer to the accompanying table, which was obtained using data from homes sold. The response (y) variable is selling price (in dollars). The predictor (x) variables are LP (list price in dollars), LA (living area of the home in square feet), and LOT (lot size in acres). If only one predictor (x) variable is used to predict the selling price, which single variable is best? Why?

Select the best choice.

A. The best single predictor variable is LOT because the associated regression equation has the highest P-value of 0.040 and the lowest adjusted R2 of 0.198.

B. The best single predictor variable is LA because the associated regression equation has the lowest P-value of 0.000?

and the lowest adjusted R2 of 0.637 of the equations with a P-value of 0.000.

C. The best single predictor variable is LP because the associated regression equation has the lowest P-value of 0.000 and the highest adjusted R2 of 0.984.

D. None of the single predictor variables can be used to predict the selling price.

Predictor (x)

Variables

P-

Value

R2

Adjusted

R2

Regression Equation

LP, LA, LOT

0.000

0.985

0.984

y=22,601+0.888?

LP+2.1?

LA+1,413?

LP, LA

0.000

0.984

0.983

y=19,660+0.933?

LP?2.8?

LP, LOT

0.000

0.985

0.984

y=22,206+0.903?

+1,204?

LA, LOT

0.000

0.822

0.814

y=106,180+102.0?

LA+14,834?

LP

0.000

0.984

0.984

y=19,575+0.917?

LA

0.000

0.644

0.637

y=130,215+105.0?

LOT

0.040

0.214

0.198

y=322,037+16,239?

Predictor (x) Variables	P- Value	R2	Adjusted R2	Regression Equation
LP, LA, LOT	0.000	0.985	0.984	? y=22,601+0.888? LP+2.1? LA+1,413? LOT
LP, LA	0.000	0.984	0.983	? y=19,660+0.933? LP?2.8? LA
LP, LOT	0.000	0.985	0.984	? y=22,206+0.903? LP? +1,204? LOT
LA, LOT	0.000	0.822	0.814	? y=106,180+102.0? LA+14,834? LOT
LP	0.000	0.984	0.984	? y=19,575+0.917? LP
LA	0.000	0.644	0.637	? y=130,215+105.0? LA
LOT	0.040	0.214	0.198	? y=322,037+16,239? LOT

Solution

None of the single predictor variables can be used to predict the selling price.

Where there are more extraneous variables, each has its own importance.

Multiregression line preditcts always better,