Regression Statistics
Multiple R	0.78
R Square	0.61
Adjusted R Square	0.58
Standard Error	146.27
Observations	98
ANOVA
	df	SS	MS	F	Significance F
Regression	6	2983351.2	497225.19	23.24137	1.81E-16
Residual	91	1946851	21393.967
Total	97	4930202.1
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	-69.27	80.53	-0.86	0.39	-229.24	90.69	-229.24	90.69
bath	46.77	24.03	1.95	0.05	-0.96	94.51	-0.96	94.51
area	0.11	0.02	4.49	0.00	0.06	0.15	0.06	0.15
lot	330.75	74.50	4.44	0.00	182.76	478.74	182.76	478.74
Central_cooling	88.35	30.43	2.90	0.00	27.92	148.79	27.92	148.79
bed	-12.40	26.13	-0.47	0.64	-64.30	39.50	-64.30	39.50
Heat_pump	39.52	39.90	0.99	0.32	-39.73	118.78	-39.73	118.78

1. Using just one number from each of your outputs, compare the regression from problem 1 to this new regression. Which do you think explains home prices better and why, and which number did you use for this comparison?
2. According to the coefficients from this new regression, what is the equation you would use to predict the price of a home?
3. If you find two houses that differ only in that one of them has an additional floor (that amounts to 1200 extra square feet in size), with one more bedroom and two more bathrooms as well, what is the expected difference in price (in dollars) between these two houses according to this regression?
4. Which of the coefficients in this regression are significant at the 5% level, and which are weakly significant (i.e., significant at the 10% level but not the 5% level)?