Many variables have an impact on determining the price of a house. A few of these are size of the house (square feet), lot size, and number of bathrooms. Information for a random sample of homes for sale in the Statesboro, Georgia, area was obtained from the Internet. Regression output modeling the asking price with square footage and number of bathrooms gave the following result.

a) Write the regression equation.
b) How much of the variation in home asking prices is accounted for by the model?
c) Explain in context what the coefficient of Area means.
d) The owner of a construction firm, upon seeing this model, objects because the model says that the number of bathrooms has no effect on the price of the home. He says that when he adds another bathroom, it increases the value. Is it true that the number of bathrooms is unrelated to house price?

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Dependent Variable is: Asking Price s_ 67013 R-Sq-71.1% R-Sq(adj)-64.6% Predictor Coeff Intercept -152037 Baths Area SE(Coeff) t-ratio P-value 85619 40826 -1.78 0.110 0.23 .82 3.00 0.015 9530 139.87 46.67 Analysis of Variance Source Regression 2 99303550067 49651775033 11.06 0.004 Residu 40416679100 4490742122 Total DF MS F P-value 11 1.39720E 11