SUMMARY OUTPUT
Regression Statistics
Multiple R	0.711381
R Square	0.506063
Adjusted R Square	0.504574
Standard Error	68.35
Observations	999
ANOVA
	df	SS	MS	F	Significance F
Regression	3	4762483	1587494	339.8092	7.2E-152
Residual	995	4648363	4671.722
Total	998	9410846
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	31.43778	9.04627	3.47522	0.000532	13.68583	49.18974	13.68583	49.18974
1792	0.10542	0.005897	17.8759	3.42E-62	0.093847	0.116992	0.093847	0.116992
3	-13.2806	3.504473	-3.78962	0.00016	-20.1576	-6.40362	-20.1576	-6.40362
1.5	19.91528	4.733624	4.207196	2.82E-05	10.62625	29.20432	10.62625	29.20432

1. Estimate the regression model below.

The real estate agent is confused as to why the sign on bedrooms in your regression from Part 1 is negative. He says, Homes with more bedrooms go down in price? That makes no sense.

2. Explain why it makes perfect sense for bedrooms in the model above to have a negative sign. (Hint: think about what you are controlling in the regression above.)

3. Use the model estimates to predict the price of a 1700 square foot home with 3 bedrooms and 2.5 baths.

The couple would prefer a home with central air. They can either buy a house with central air or have central air installed for $25,000.

4.Add Air to the regression you ran in Part 1 and estimate the model below. Note: you will have to first create a dummy variable and recode Air before running the regression.

5.Interpret the results of a t-test on the coefficient for Air. Is it statistically significant? Explain.

6.Does the model in Part 4 do a better job of predicting home prices compared to the model in Part 1? Explain.

7.Based on the results from Parts 4 and 5, on average, will it be cheaper to purchase a 1700 square foot, 3 bedroom, 2.5 bath home with central air or purchase a 1700 square foot, 3 bedroom, 2.5 bath without central air and install it for $25,000? Explain.