Question

In: Economics

Download RegHousePrice.xlsx from the course Blackboard site. The first variable measures the price of the house,...

Download RegHousePrice.xlsx from the course Blackboard site. The first variable measures the price of the house, and this is followed by the number of bedrooms, the size of the house (in sq. ft.), and the size of the lot (i.e. yard; also in sq. ft.). a. Build and estimate a regression model to predict the selling price of a house. b. Evaluate the model: be sure to mention the goodness of fit, the sign of the coefficients, the statistical significance of the coefficients, and the economic plausibility of the results. c. Suppose your house is 1,600 sq. ft., there are four bedrooms, and the lot size is 5,000 sq. ft. According to your model, what should its price be?

Price Bedrooms House Size Lot Size
124100 3 1290 3900
218300 4 2080 6600
117800 3 1250 3750
168300 3 1550 4650
120400 3 1360 4050
159200 3 1450 4200
158000 4 2110 6600
73800 2 1270 4200
142500 4 1940 6300
160100 3 1290 4050
199200 4 2190 6900
179200 4 2030 6300
153800 3 1310 4350
150900 4 2300 7200
180100 4 1870 5700
132600 4 1920 6000
147200 4 1530 4500
149800 3 1350 4200
151500 3 1590 5100
132800 4 1680 5100
115300 3 1370 4200
196600 4 2130 6450
217400 4 1840 5700
106100 3 1600 4950
220900 4 2330 7200
162000 4 2290 6900
179000 4 2270 6900
107700 4 1910 5550
136900 4 2150 6450
115400 3 1230 3600
118500 3 1410 4500
208600 5 2360 7200
186700 4 2320 7050
131800 4 1530 4950
149400 3 1280 3900
155600 4 1690 5250
160300 3 1560 4800
131200 4 1810 5550
107300 3 1240 4050
109700 3 1320 4200
203100 4 1870 5700
144800 4 1920 6000
150400 3 1520 4800
96400 2 1070 3450
153500 3 1570 4800
139900 4 2260 7050
146900 4 1970 6000
136800 3 1360 4200
96400 3 1290 4050
148400 3 1550 5100
143100 2 1220 3750
191800 5 2330 7350
102000 3 1460 4500
147500 3 1410 4350
184300 4 2300 7050
178100 4 2220 6750
267800 5 2980 9150
245700 5 2950 9000
107000 3 1550 4800
137700 4 2010 6150
88900 3 1570 4800
98700 4 1660 5100
181200 4 2310 7350
199500 4 2200 6750
162400 4 1590 4950
125500 3 1360 4350
165400 4 2310 7350
209400 5 2790 8400
129800 4 1540 4950
192000 4 1780 5400
124700 3 1320 4350
147300 4 1780 5250
154700 4 1980 6000
122200 4 1590 5100
125000 4 1830 5850
253200 5 2340 7500
157800 3 1540 4800
123700 3 1200 3750
125500 4 1560 4650
130000 4 1520 4650
179800 4 2070 6150
150200 4 1840 5700
160900 4 1950 5850
153200 3 1280 4050
204200 4 2310 7050
215800 4 2380 7200
159700 3 1580 4800
180800 4 2140 6600
178800 5 2300 7050
120200 3 1370 4500
134200 4 1590 5100
134800 3 1480 4650
161500 4 1870 5700
155400 3 1520 4500
113200 3 1250 3750
180500 3 1320 3900
218100 5 2980 9000
117500 3 1570 4950
157400 3 1560 5100
155900 4 1620 4800

Solutions

Expert Solution

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.748329963
R Square 0.559997733
Adjusted R Square 0.546247662
Standard Error 25022.70761
Observations 100
ANOVA
df SS MS F Significance F
Regression 3 76501718347.31 25500572782.44 40.73 0.00
Residual 96 60109046052.69 626135896.38
Total 99 136610764400.00
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 37718 14177 3 0 9577 65858 9577 65858
Bedrooms 2306 6994 0 1 -11577 16189 -11577 16189
House Size 74 53 1 0 -31 179 -31 179
Lot Size -4 17 0 1 -38 29 -38 29
RESIDUAL OUTPUT
Observation Predicted Price Residuals
1 123459.9635 640.0364917
2 172678.3073 45621.69269
3 121142.6587 -3342.658699
4 139504.2959 28795.70409
5 128006.1725 -7606.172498
6 134038.3176 25161.68239
7 174907.2115 -16907.21149
8 118358.8117 -44558.8117
9 163585.8893 -21085.88933
10 122805.3961 37294.60392
11 179541.8211 19658.17889
12 170272.6019 8927.398125
13 122982.1973 30817.80267
14 186405.3349 -35505.33491
15 161003.3826 19096.61736
16 163409.0881 -30809.08808
17 140979.008 6220.991953
18 126608.637 23191.36299
19 140512.4659 10987.53414
20 149505.2592 -16705.25922
21 128094.5731 -12794.57313
22 177047.715 19552.28495
23 158774.4785 58625.52154
24 141910.0014 -35810.00135
25 188634.2391 32265.76091
26 186971.5017 -24971.50171
27 185485.5656 -6485.565592
28 164629.8223 -56929.82231
29 178533.6512 -41633.65117
30 120311.29 -4911.290011
31 129757.3105 -11257.3105
32 193169.2241 15430.77591
33 188545.8385 -1845.838462
34 139015.3058 -7215.305751
35 122716.9954 26683.00455
36 149593.6599 6006.340149
37 139592.6965 20707.30346
38 157200.1417 -26000.14171
39 119090.5558 -11790.55578
40 124379.7328 -14679.73282
41 161003.3826 42096.61736
42 163409.0881 -18609.08808
43 136620.8243 13779.1757
44 106772.2877 -10372.28766
45 140335.6646 13164.3354
46 184088.0301 -44188.0301
47 167123.9284 -20223.92838
48 127351.6051 9448.394934
49 122805.3961 -26405.39608
50 137540.5936 10859.40638
51 116607.6737 26492.3263
52 190285.7525 1514.247521
53 133472.1508 -31472.1508
54 130411.8779 17088.12206
55 187059.9023 -2759.902341
56 182425.2927 -4325.292723
57 230723.8672 37076.13279
58 229149.5305 16550.46954
59 138849.7285 -31849.72848
60 169441.2332 -31741.23319
61 140335.6646 -51435.6646
62 148019.3231 -49319.3231
63 186493.7355 -5293.735537
64 180939.3566 18560.6434
65 143473.1141 18926.88589
66 126697.0376 -1197.037634
67 186493.7355 -21093.73554
68 219880.3112 -10480.31123
69 139758.2738 -9958.273811
70 155625.805 36374.19504
71 123725.1654 974.8346071
72 156280.3724 -8980.372393
73 167866.8964 -13166.89644
74 142818.5467 -20618.54668
75 157376.943 -32376.94297
76 190374.1531 62825.84689
77 138106.7604 19693.23958
78 117427.8184 6272.181602
79 142553.3448 -17053.3448
80 139581.4726 -9581.472555
81 173899.0415 5900.958452
82 158774.4785 -8574.478458
83 166292.5597 -5392.559689
84 122062.428 31137.57198
85 187802.8704 16397.1296
86 192349.0794 23450.92061
87 141078.6327 18621.36734
88 177136.1157 3663.884327
89 189365.9832 -10565.98316
90 126785.4383 -6585.438262
91 142818.5467 -8618.54668
92 134303.5195 496.4805072
93 161003.3826 496.6173611
94 137929.9592 17470.04083
95 121142.6587 -7942.658699
96 125688.8677 54811.13231
97 231378.4346 -13278.43464
98 139681.0972 -22181.09717
99 138283.5617 19116.43832
100 146356.5857 9543.414275

a.   Price = 37718 + 2306 * bedroom + 74*housesize - 4*lotsize

b. R2 = 0.56

p-value > alpha, hence insignificant

Coefficient has correct sign for bedroom and housesize but not lotsize

Hence, it is not a very good fit.

c. Suppose your house is 1,600 sq. ft., there are four bedrooms, and the lot size is 5,000 sq. ft.

Price = 37718 + 2306 * bedroom + 74*housesize - 4*lotsize

= 37718 + 2306 * 4 + 74*1600 - 4*5000

= 37718 + 9224 + 118400 - 20000

=145342


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