Problem 1 Regression:

Regression Statistics
Multiple R	0.533609
R Square	0.284739
Adjusted R Square	0.277288
Standard Error	191.659
Observations	98
ANOVA
	df	SS	MS	F	Significance F
Regression	1	1403819	1403819	38.21668	1.54E-08
Residual	96	3526383	36733.15
Total	97	4930202
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	244.6221	54.55061	4.484314	2.03E-05	136.34	352.9042	136.34	352.9042
lot	551.7341	89.24899	6.181964	1.54E-08	374.5762	728.8919	374.5762	728.8919

Problem 2 Regression:

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	497225.2	23.24137	1.81E-16
Residual	91	1946851	21393.97
Total	97	4930202
	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

Using just one number from each of your outputs, compare the regression from problem 1 to Problem 2 regression. Which do you think explains home prices better and why, and which number did you use for this comparison?