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^