You are consulting for a large real estate firm. You have been asked to construct a model that can predict listing prices based on square footages for home in the city you'be been

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week_5_t_assignment.xlsx
Home>Mathematics homework help>You are consulting for a large real estate firm. You have been asked to construct a model that can predict listing prices based on square footages for home in the city you'be been

Regression Output

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.9796
R Square 0.9596
Adjusted R Square 0.9592
Standard Error 9.5100
Observations 105
ANOVA
df SS MS F Significance F
Regression 1 221452.232 221452.232 2448.600 1.30E-73
Residual 103 9315.357 90.440
Total 104 230767.589
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -191.5796 8.3913 -22.831 0.0000 -208.2218 -174.9375 -208.222 -174.937
Sq Footage 0.1856 0.0038 49.483 0.0000 0.1781 0.1930 0.178 0.193
Predicted 198.1

Data

Price Sq Footage
125 1600
125.9 1700
139.9 1700
147.4 1700
154.3 1900
155.4 1900
164.1 1900
166.2 1900
166.5 1900
171.6 1900
172.4 1900
172.7 1900
173.1 1900
173.6 2000
173.6 2000
175 2000
175.6 2000
176 2000
176.3 2000
177.1 2000
179 2000
179 2000
180.4 2100
182.4 2100
182.7 2100
186.7 2100
187 2100
188.1 2100
188.3 2100
188.3 2100
188.3 2100
188.3 2100
189.4 2100
190.9 2100
192.2 2100
192.6 2100
192.9 2100
194.4 2100
198.3 2100
198.9 2100
199 2100
199.8 2100
205.1 2200
206 2200
207.1 2200
207.5 2200
207.5 2200
209 2200
209.3 2200
209.3 2200
209.7 2200
209.7 2200
213.6 2200
216 2200
216.8 2200
217.8 2200
220.9 2200
221.1 2200
221.5 2200
222.1 2200
224 2300
224.8 2300
227.1 2300
227.1 2300
228.4 2300
232.2 2300
233 2300
234 2300
236.4 2300
236.8 2300
240 2300
242.1 2300
243.7 2300
244.6 2300
245.4 2300
246 2300
246.1 2300
247.7 2400
251.4 2400
252.3 2400
253.2 2400
254.3 2400
257.2 2400
263.1 2400
263.2 2400
266.6 2400
269.2 2400
269.9 2400
270.8 2500
271.8 2500
273.2 2500
281.3 2500
289.8 2500
292.4 2500
293.7 2500
294 2600
294.3 2600
294.5 2600
307.8 2600
310.8 2600
312.1 2700
312.1 2700
326.3 2900
327.2 2900
345.3 2900

Scatter Plot of Price vs. Sq Footage

Price 1600 1700 1700 1700 1900 1900 1900 1900 1900 1900 1900 1900 1900 2000 2000 2000 2000 2000 2000 2000 2000 2000 2100 2100 2100 2100 2100 2100 2100 2100 2100 2100 2100 2100 2100 2100 2100 2100 2100 2100 2100 2100 2200 2200 2200 2200 2200 2200 2200 2200 2200 2200 2200 2200 2200 2200 2200 2200 2200 2200 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2300 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2400 2500 2500 2500 2500 2500 2500 2500 2600 2600 2600 2600 2600 2700 2700 2900 2900 2900 125 125.9 139.9 147.4 154.30000000000001 155.4 164.1 166.2 166.5 171.6 172.4 172.7 173.1 173.6 173.6 175 175.6 176 176.3 177.1 179 179 180.4 182.4 182.7 186.7 187 188.1 188.3 188.3 188.3 188.3 189.4 190.9 192.2 192.6 192.9 194.4 198.3 198.9 199 199.8 205.1 206 207.1 207.5 207.5 209 209.3 209.3 209.7 209.7 213.6 216 216.8 217.8 220.9 221.1 221.5 222.1 224 224.8 227.1 227.1 228.4 232.2 233 234 236.4 236.8 240 242.1 243.7 244.6 245.4 246 246.1 247.7 251.4 252.3 253.2 254.3 257.2 263.10000000000002 263.2 266.60000000000002 269.2 269.89999999999998 270.8 271.8 273.2 281.3 289.8 292.39999999999998 293.7 294 294.3 294.5 307.8 310.8 312.10000000000002 312.10000000000002 326.3 327.2 345.3

Sq Footage

Price

1

2

3

4

5

6

7

8

9

A

B

C

Multiple R

0.9796

R Square

0.9596

Adjusted R Square

0.9592

Standard Error

9.5100

Observations

105

SUMMARY OUTPUT

Regression Statistics