In: Statistics and Probability
The accompanying table shows a portion of a data set that refers to the property taxes owed by a homeowner (in $) and the size of the home (in square feet) in an affluent suburb 30 miles outside New York City.
Taxes |
Size |
21987 |
2352 |
17343 |
2362 |
18279 |
1776 |
15645 |
1118 |
43954 |
5712 |
33653 |
2592 |
15105 |
2134 |
16749 |
1905 |
18239 |
2024 |
16048 |
1389 |
15135 |
1381 |
36016 |
3028 |
31083 |
2771 |
42000 |
3374 |
14370 |
1556 |
38953 |
3971 |
25373 |
3934 |
22971 |
2321 |
16169 |
3557 |
29272 |
2878 |
a. Estimate the sample regression equation that enables us to predict property taxes on the basis of the size of the home. (Round your answers to 2 decimal places.)
TaxesˆTaxes^ = + Size.
b. Interpret the slope coefficient.
As Property Taxes increase by 1 dollar, the size of the house increases by 6.91 ft.
As Size increases by 1 square foot, the property taxes are predicted to increase by $6.91.
c. Predict the property taxes for a
1,200-square-foot home. (Round coefficient estimates to at
least 4 decimal places and final answer to 2 decimal
places.)
TaxesˆTaxes^