Part III\ Researchers would tike to see if a difference in price exists between homes located on a waterfiont and those that are not (after controlling for the other variables in the model). Using the variable Waterfront, create a dummy variable in Excel called W where:\

W=1

if home is located on a waterfont\ W

=0

otherwise\ The regression model becomes:\

P=\\\\beta _(0)+\\\\beta _(1) Living. Area +\\\\beta _(2) Bedrooms +\\\\beta _(3) Bathrooms +\\\\alpha _(0)W+\\\\epsi

\ Estimate the regression above. Copy and paste your Excel results below.\ Carry out a t-test for the coefficient on the dummy variable

W

. Interpret the results in the context of the problem. Use an alpha-level of

10%

.\ Interpret the coefficient on the dummy variable

W

in the context of the problem.\ Predict the selling price (in

$1,000s

) of a non-waterfont home with 1800 square feet, 3 bedrooms, and 2 bathrooms.\ Predict the selling price (in

$1,000s

) of a waterfont home with 1800 square feet, 3 bedrooms, and 2 bathrooms.\ PartV\ Researchers would like to see if the relationship between the living area and the price of the house differs between homes located on a waterfront and those that are not. Create a dummy variable interaction term in Excel using the dummy variable

W

interacted with living area:\ WhLixingatea\ The regression model becomes:\

P=\\\\beta _(0)+\\\\beta _(1) Living.Area +\\\\beta _(2) Bedrooms +\\\\beta _(3) Bathrooms +\\\\alpha _(0)W+\\\\alpha _(1)W** Living.Area +\\\\epsi

\ Estimate the regression above. Copy and paste your Excel resultis below.\ Carry out a t-test for the coeficient on the dummy interaction term. Interpret the results in the context of the problem. Use an alpha-level of

10%

.

Part III Researchers would the to see if a difference in price exists between homes located on a waterfront and those that are not (after controlling for the other variables in the model). Using the variable Waterfront, create a dummy variable in Excel called W where: W=1ifhomeislocatedonawaterfiont W=0otherwise The regression model becomes: P=0+1Living.Area+2Bedrooms+3Bathrooms+0W+ 6. Estimate the regression above. Copy and paste your Excel resulks below. 7. Carry out a t-test for the coeficient on the dummy variable W. Interpret the results in the context of the problem. Use an alpha-level of 10%. 8. Interpret the coefficient on the dummy variable W in the context of the problem. 9. Predict the selling price (in $1,000s ) of a non-waterfront home with 1800 square feet, 3 bedrooms, and 2 bathrooms. 10. Predict the selling price (in $1,000s ) of a waterfont home with 1800 square feet, 3 bedrooms, and 2 bathrooms. Part IV Researchers would like to see if the relationship between the living area and the price of the house differs between homes located on a waterfiont and those that are not. Create a dummy variable interaction term in Excel using the dummy variable W interacted with living area: Whixingatea The regression model becomes: P=0+1Living.Area+2Bedrooms+3Bathrooms+0W+1WLiving.Area+ 11. Estimate the regression above. Copy and paste your Excel results below. 12. Carry out a t-test for the coeficient on the dummy interaction term. Interpret the results in the context of the problem. Use an alpha-level of 10%