Column name Description Price ($1000s) Price of the house (in $1000s) Bath Number of bathrooms Area Area of the house (in square feet) Lot House lot size (in acres) Central cooling 1 if the house has central cooling and 0 if not Bed Number of bedrooms Heat pump 1 if the house has a heat pump and 0 if not Model 1 Regression Statistics Multiple R 0 53 R Square 0 28 Adjusted R Square 0 28 Standard Error 191 66 Observations 98 ANOVA df SS MS F Significance F Regression 1 1403819 33 1403819 33 38 22 1 5355E 08 Residual 96 3526382 81 36733 15 Total 97 4930202 14 Coefficients Standard Error t Stat P value Lower 95 Upper 95 Lower 95 0 Upper 95 0 Intercept 244 62 54 55 4 48 2 02661E 05 136 34 352 90 136 34 352 90 lot 551 73 89 25 6 18 1 5355E 08 374 58 728 89 374 58 728 89 Model 2 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 16 497225 2 23 24137 1 8125E 16 Residual 91 1946850 981 21393 97 Total 97 4930202 141 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 391949 229 24 90 69 229 24 90 69 bath 46 77 24 03 1 95 0 054708 0 96 94 51 0 96 94 51 area 0 11 0 02 4 49 0 000021 0 06 0 15 0 06 0 15 lot 330 75 74 50 4 44 0 000025 182 76 478 74 182 76 478 74 Central cooling 88 35 30 43 2 90 0 004624 27 92 148 79 27 92 148 79 bed 12 40 26 13 0 47 0 636116 64 30 39 50 64 30 39 50 Heat pump 39 52 39 90 0 99 0 324538 39 73 118 78 39 73 118 78 Model 3 Regression Statistics Multiple R 0 77 R Square 0 60 Adjusted R Square 0 58 Standard Error 145 67 Observations 98 ANOVA df SS MS F Significance F Regression 4 2956746 708 739186 7 34 83451 9 34467E 18 Residual 93 1973455 433 21219 95 Total 97 4930202 141 Coefficients Standard Error t Stat P value Lower 95 Upper 95 Lower 95 0 Upper 95 0 Intercept 86 52 58 61 1 48 0 143233 202 90 29 86 202 90 29 86 bath 44 85 21 99 2 04 0 044235 1 18 88 51 1 18 88 51 area 0 10 0 02 4 42 0 000027 0 05 0 14 0 05 0 14 lot 331 29 74 18 4 47 0 000022 183 98 478 61 183 98 478 61 Central cooling 90 98 30 16 3 02 0 003297 31 09 150 87 31 09 150 87 1 use the models you estimated in problem 1 through 3 (we'll call these model 1 , model 2 and model 3 , respectively) to predict the value of a particular home This home has two bathrooms, a total area of 2400 square feet, a lot size of 0 8 acres, central cooling, three bedrooms and a heat pump What are the predicted prices for this house (in dollars) using model 1, model 2 and model 3 (i e , predict three different prices using each model one at a time) 2 What is the 95 confidence interval for the average value of a house with the features given in this problem using model 3 (assume Distance Value 0673) (hint you already calculated the center of this confidence interval using model 3 in part a)

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Column name Description Price ($1000s) Price of the house (in $1000s) Bath Number of bathrooms Area Area of the house (in square feet) Lot House

Column name	Description
Price ($1000s)	Price of the house (in $1000s)
Bath	Number of bathrooms
Area	Area of the house (in square feet)
Lot	House lot size (in acres)
Central_cooling	1 if the house has central cooling and 0 if not
Bed	Number of bedrooms
Heat_pump	1 if the house has a heat pump and 0 if not

Model 1

Regression Statistics
Multiple R	0.53
R Square	0.28
Adjusted R Square	0.28
Standard Error	191.66
Observations	98

ANOVA
	df	SS	MS	F	Significance F
Regression	1	1403819.33	1403819.33	38.22	1.5355E-08
Residual	96	3526382.81	36733.15
Total	97	4930202.14

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	244.62	54.55	4.48	2.02661E-05	136.34	352.90	136.34	352.90
lot	551.73	89.25	6.18	1.5355E-08	374.58	728.89	374.58	728.89

Model 2

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	2983351.16	497225.2	23.24137	1.8125E-16
Residual	91	1946850.981	21393.97
Total	97	4930202.141

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	-69.27	80.53	-0.86	0.391949	-229.24	90.69	-229.24	90.69
bath	46.77	24.03	1.95	0.054708	-0.96	94.51	-0.96	94.51
area	0.11	0.02	4.49	0.000021	0.06	0.15	0.06	0.15
lot	330.75	74.50	4.44	0.000025	182.76	478.74	182.76	478.74
Central_cooling	88.35	30.43	2.90	0.004624	27.92	148.79	27.92	148.79
bed	-12.40	26.13	-0.47	0.636116	-64.30	39.50	-64.30	39.50
Heat_pump	39.52	39.90	0.99	0.324538	-39.73	118.78	-39.73	118.78

Model 3

Regression Statistics
Multiple R	0.77
R Square	0.60
Adjusted R Square	0.58
Standard Error	145.67
Observations	98

ANOVA
	df	SS	MS	F	Significance F
Regression	4	2956746.708	739186.7	34.83451	9.34467E-18
Residual	93	1973455.433	21219.95
Total	97	4930202.141

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	-86.52	58.61	-1.48	0.143233	-202.90	29.86	-202.90	29.86
bath	44.85	21.99	2.04	0.044235	1.18	88.51	1.18	88.51
area	0.10	0.02	4.42	0.000027	0.05	0.14	0.05	0.14
lot	331.29	74.18	4.47	0.000022	183.98	478.61	183.98	478.61
Central_cooling	90.98	30.16	3.02	0.003297	31.09	150.87	31.09	150.87

1. use the models you estimated in problem 1 through 3 (we'll call these "model 1", "model 2" and "model 3", respectively) to predict the value of a particular home. This home has two bathrooms, a total area of 2400 square feet, a lot size of 0.8 acres, central cooling, three bedrooms and a heat pump. What are the predicted prices for this house (in dollars) using model 1, model 2 and model 3? (i.e., predict three different prices using each model one at a time)

2. What is the 95% confidence interval for the average value of a house with the features given in this problem using model 3 (assume Distance Value = .0673)? (hint: you already calculated the center of this confidence interval using model 3 in part a)