Predicting sale prices of homes. Real-estate investors, home buyers, and homeowners often use the appraised value of property as a basis for predicting the sale of that property. Data on sale prices and total appraised value of 78 residential properties sold recently in an upscale Tampa, Florida, neighborhood named Hunter’s Green are saved in the HOMES file. Selected observations are listed in the table on p. 672.

a. Propose a straight-line model to relate the appraised property value (x) to the sale price (y) for residential properties in this neighborhood.

b. A MINITAB scatterplot of the data with the least squared line is shown at the top of the printout on p. 671.
Does it appear that a straight-line model will be an appropriate fit to the data?

c. A MINITAB simple linear regression printout is also shown at the bottom of the printout on p. 671. Find the equation of the least squared line. Interpret the estimated slope and y-intercept in the words of the problem.

d. Locate the test statistic and p-value for testing H0: b1 = 0 against Ha: b1 7 0. Is there sufficient evidence (at a = .01) of a positive linear relationship between apprised property value (x) and sale price (y)?

e. Locate and interpret practically the values of r and r2 on the printout.

f. Locate and interpret practically the 95% prediction interval for sale price (y) on the printout.

Transcribed Image Text:

SALEPRICE MINITAB output for Exercise 11.136 Scatterplot of SALEPRICE vs APPVALUE $2,000,000- $1,500,000 - $1,000,000- $500,000 - $0- $0 $300,000 $600,000 $900,000 $1,200,000 $1,500,000 APPVALUE