Mike Lynch manages a real estate firm in Myrtle Beach, South Carolina, and would like to construct a model to help him predict the selling price of beach properties for his customers based on the age of the house in years. Mike has collected a random sample of home sales from the area and generated a regression model using Excel, which is shown in the following table. Selling price for homes were recorded in thousands of dollars.

Anova df SS Regression 1 178208.8925 Residual 28 1797115.274 Total 29 1975324.167 Coefficients Standard Error Intercept 859.5990541 142.9768355 Age -10.2005605 6.121647288

a. Predict the selling price for a 16-year-old home.

b. Compute the coefficient of determination and interpret its meaning.

c. Do the sample data provide evidence that the model is useful for predicting average selling price based on the age of the home using a = 0.01?

d. Construct a 98% confidence interval around the sample slope and interpret its meaning.

AppendixLO1