Question
In: Statistics and Probability
Price (in K) Sqft Age Features CornerCODE Corner_Label 310.0 2650 13 7 0 NO 313.0 2600...
| Price (in K) | Sqft | Age | Features | CornerCODE | Corner_Label |
| 310.0 | 2650 | 13 | 7 | 0 | NO |
| 313.0 | 2600 | 9 | 4 | 0 | NO |
| 320.0 | 2664 | 6 | 5 | 0 | NO |
| 320.0 | 2921 | 3 | 6 | 0 | NO |
| 304.9 | 2580 | 4 | 4 | 0 | NO |
| 295.0 | 2580 | 4 | 4 | 0 | NO |
| 285.0 | 2774 | 2 | 4 | 0 | NO |
| 261.0 | 1920 | 1 | 5 | 0 | NO |
| 250.0 | 2150 | 2 | 4 | 0 | NO |
| 249.9 | 1710 | 1 | 3 | 0 | NO |
| 242.5 | 1837 | 4 | 5 | 0 | NO |
| 232.0 | 1880 | 8 | 6 | 0 | NO |
| 230.0 | 2150 | 15 | 3 | 0 | NO |
| 228.5 | 1894 | 14 | 5 | 0 | NO |
| 222.0 | 1928 | 18 | 8 | 0 | NO |
| 223.0 | 1830 | 16 | 3 | 0 | NO |
| 220.5 | 1767 | 16 | 4 | 0 | NO |
| 216.0 | 1630 | 15 | 3 | 1 | YES |
| 218.9 | 1680 | 17 | 4 | 1 | YES |
| 204.5 | 1725 | 13 | 3 | 0 | NO |
| 204.5 | 1500 | 15 | 4 | 0 | NO |
| 202.5 | 1430 | 10 | 3 | 0 | NO |
| 202.5 | 1360 | 12 | 4 | 0 | NO |
| 195.0 | 1400 | 16 | 2 | 1 | YES |
| 201.0 | 1573 | 17 | 6 | 0 | NO |
| 191.0 | 1385 | 22 | 2 | 0 | NO |
| 274.5 | 2931 | 28 | 3 | 1 | YES |
| 260.3 | 2200 | 28 | 4 | 0 | NO |
| 230.0 | 2277 | 30 | 4 | 0 | NO |
| 235.0 | 2000 | 37 | 3 | 0 | NO |
| 207.0 | 1478 | 53 | 3 | 1 | YES |
| 207.0 | 1713 | 30 | 4 | 1 | YES |
| 197.2 | 1326 | 25 | 4 | 0 | NO |
| 197.5 | 1050 | 22 | 2 | 1 | YES |
| 194.9 | 1464 | 34 | 2 | 0 | NO |
| 190.0 | 1190 | 41 | 1 | 0 | NO |
| 192.6 | 1156 | 37 | 1 | 0 | NO |
| 194.0 | 1746 | 30 | 2 | 0 | NO |
| 192.0 | 1280 | 28 | 1 | 0 | NO |
| 175.0 | 1215 | 43 | 3 | 0 | NO |
| 177.0 | 1121 | 46 | 4 | 0 | NO |
| 177.0 | 1050 | 48 | 1 | 0 | NO |
| 179.9 | 1733 | 43 | 6 | 0 | NO |
| 178.1 | 1299 | 40 | 6 | 0 | NO |
| 177.5 | 1140 | 36 | 3 | 1 | YES |
| 172.0 | 1181 | 37 | 4 | 0 | NO |
| 320.0 | 2848 | 4 | 6 | 0 | NO |
| 264.9 | 2440 | 11 | 5 | 0 | NO |
| 240.0 | 2253 | 23 | 4 | 0 | NO |
| 234.9 | 2743 | 25 | 5 | 1 | YES |
| 230.0 | 2180 | 17 | 4 | 1 | YES |
| 228.9 | 1706 | 14 | 4 | 0 | NO |
| 225.0 | 1948 | 10 | 4 | 0 | NO |
| 217.5 | 1710 | 16 | 4 | 0 | NO |
| 215.0 | 1657 | 15 | 4 | 0 | NO |
| 213.0 | 2200 | 26 | 4 | 0 | NO |
| 210.0 | 1680 | 13 | 4 | 0 | NO |
| 209.9 | 1900 | 34 | 3 | 0 | NO |
| 200.5 | 1565 | 19 | 3 | 0 | NO |
| 198.4 | 1543 | 20 | 3 | 0 | NO |
| 192.5 | 1173 | 6 | 4 | 0 | NO |
| 193.9 | 1549 | 5 | 4 | 0 | NO |
| 190.5 | 1900 | 3 | 3 | 0 | NO |
| 188.5 | 1560 | 8 | 5 | 1 | YES |
| 186.0 | 1365 | 10 | 2 | 0 | NO |
| 185.5 | 1258 | 7 | 4 | 1 | YES |
| 184.9 | 1314 | 5 | 2 | 0 | NO |
| 180.0 | 1338 | 2 | 3 | 1 | YES |
| 180.9 | 997 | 4 | 4 | 0 | NO |
| 180.5 | 1275 | 8 | 5 | 0 | NO |
| 180.0 | 1030 | 4 | 1 | 0 | NO |
| 178.0 | 1027 | 5 | 3 | 0 | NO |
| 177.9 | 1007 | 19 | 6 | 0 | NO |
| 176.0 | 1083 | 22 | 4 | 0 | NO |
| 182.3 | 1320 | 18 | 5 | 0 | NO |
| 174.0 | 1348 | 15 | 2 | 0 | NO |
| 172.0 | 1350 | 12 | 2 | 0 | NO |
| 166.9 | 837 | 13 | 2 | 0 | NO |
| 234.5 | 3750 | 10 | 4 | 1 | YES |
| 202.5 | 1500 | 7 | 3 | 1 | YES |
| 198.9 | 1428 | 40 | 2 | 0 | NO |
| 187.0 | 1375 | 28 | 1 | 0 | NO |
| 183.0 | 1080 | 20 | 3 | 0 | NO |
| 182.0 | 900 | 23 | 3 | 0 | NO |
| 175.0 | 1505 | 16 | 2 | 1 | YES |
| 167.0 | 1480 | 19 | 4 | 0 | NO |
| 159.0 | 1142 | 10 | 0 | 0 | NO |
| 212.0 | 1464 | 7 | 2 | 0 | NO |
| 315.0 | 2116 | 25 | 3 | 0 | NO |
| 177.5 | 1280 | 14 | 3 | 0 | NO |
| 171.0 | 1159 | 23 | 0 | 0 | NO |
| 165.0 | 1198 | 10 | 4 | 0 | NO |
| 163.0 | 1051 | 15 | 2 | 0 | NO |
| 289.4 | 2250 | 40 | 6 | 0 | NO |
| 263.0 | 2563 | 17 | 2 | 0 | NO |
| 174.9 | 1400 | 45 | 1 | 1 | YES |
| 238.0 | 1850 | 5 | 5 | 1 | YES |
| 221.0 | 1720 | 5 | 4 | 0 | NO |
| 215.9 | 1740 | 4 | 3 | 0 | NO |
| 217.9 | 1700 | 6 | 4 | 0 | NO |
| 210.0 | 1620 | 6 | 4 | 0 | NO |
| 209.5 | 1630 | 6 | 4 | 0 | NO |
| 210.0 | 1920 | 8 | 4 | 0 | NO |
| 207.0 | 1606 | 5 | 4 | 0 | NO |
| 205.0 | 1535 | 7 | 5 | 1 | YES |
| 208.0 | 1540 | 6 | 2 | 1 | YES |
| 202.5 | 1739 | 13 | 3 | 0 | NO |
| 200.0 | 1715 | 8 | 3 | 0 | NO |
| 199.0 | 1305 | 5 | 3 | 0 | NO |
| 197.0 | 1415 | 7 | 4 | 0 | NO |
| 199.5 | 1580 | 9 | 3 | 0 | NO |
| 192.4 | 1236 | 3 | 4 | 0 | NO |
| 192.2 | 1229 | 6 | 3 | 0 | NO |
| 192.0 | 1273 | 4 | 4 | 0 | NO |
| 191.9 | 1165 | 7 | 4 | 0 | NO |
| 181.6 | 1200 | 7 | 4 | 1 | YES |
| 178.9 | 970 | 4 | 4 | 1 | YES |
1.) Make a multiple regression model using these potential numerical predictor variables and, at most, one categorical dummy variable.
2.)Write the sample multiple regression equation for the “final best” model you have developed.
3.) Look at the set of residual plots, cut and paste them into the report, and briefly comment on the appropriateness of your fitted model.
Solutions
Expert Solution
| (1) Regression Analysis | |||||||||
| R² | 0.739 | ||||||||
| Adjusted R² | 0.729 | n | 117 | ||||||
| R | 0.859 | k | 4 | ||||||
| Std. Error | 19.795 | Dep. Var. | Price (in K) | ||||||
| ANOVA table | |||||||||
| Source | SS | df | MS | F | p-value | ||||
| Regression | 124,004.2121 | 4 | 31,001.0530 | 79.12 | 9.90E-32 | ||||
| Residual | 43,885.2558 | 112 | 391.8326 | ||||||
| Total | 167,889.4679 | 116 | |||||||
| Regression output | confidence interval | ||||||||
| variables | coefficients | std. error | t (df=112) | p-value | 95% lower | 95% upper | std. coeff. | ||
| Intercept | 111.6367 | 0.000 | |||||||
| Sqft | 0.0586 | 0.0038 | 15.292 | 3.46E-29 | 0.0510 | 0.0662 | 0.807 | ||
| Age | -0.2079 | 0.1517 | -1.371 | .1732 | -0.5084 | 0.0926 | -0.068 | ||
| Features | 2.2579 | 1.4453 | 1.562 | .1210 | -0.6057 | 5.1215 | 0.083 | ||
| CornerCODE | -10.2273 | 4.7006 | -2.176 | .0317 | -19.5409 | -0.9138 | -0.105 | ||
| (2) The regression equation is Price (in K) = 111.6367 + 0.0586 Sqft - 0.2079 Age + 2.2579 Features - 10.2273 CornerCODE | |||||||||
| The normal probability plot of residuals shows a straight line pattern, so the assumptions of simple linear regression are satisfied. | ||||||||
| R^2 = 0.729 means the model accounts for 72.9% of the variation in Price (in K) on the basis of the predictor variables. The model is a good fit. | ||||||||
| Age and Features have p- values > 0.05 and are therefore not significant. Dropping these from the model, we can get the final regression equation as Price (in K) = 111.3012 + 0.0617 Sqft - 11.0245 CornerCODE | ||||||||
| Regression Analysis | ||||||||
| R² | 0.727 | |||||||
| Adjusted R² | 0.722 | n | 117 | |||||
| R | 0.852 | k | 2 | |||||
| Std. Error | 20.066 | Dep. Var. | Price (in K) | |||||
| ANOVA table | ||||||||
| Source | SS | df | MS | F | p-value | |||
| Regression | 121,986.6611 | 2 | 60,993.3305 | 151.48 | 7.92E-33 | |||
| Residual | 45,902.8068 | 114 | 402.6562 | |||||
| Total | 167,889.4679 | 116 | ||||||
| Regression output | confidence interval | |||||||
| variables | coefficients | std. error | t (df=114) | p-value | 95% lower | 95% upper | std. coeff. | |
| Intercept | 111.3012 | 0.000 | ||||||
| Sqft | 0.0617 | 0.0036 | 17.330 | 9.90E-34 | 0.0546 | 0.0688 | 0.849 | |
| CornerCODE | -11.0245 | 4.7516 | -2.320 | .0221 | -20.4374 | -1.6115 | -0.114 | |
orchestra answered 2 years agoRelated Solutions
Price (in K) Sqft Age Features CornerCODE Corner_Label 310.0 2650 13 7 0 NO 313.0 2600...
Price (in K)
Sqft
Age
Features
CornerCODE
Corner_Label
310.0
2650
13
7
0
NO
313.0
2600
9
4
0
NO
320.0
2664
6
5
0
NO
320.0
2921
3
6
0
NO
304.9
2580
4
4
0
NO
295.0
2580
4
4
0
NO
285.0
2774
2
4
0
NO
261.0
1920
1
5
0
NO
250.0
2150
2
4
0
NO
249.9
1710
1
3
0
NO
242.5
1837
4
5
0
NO
232.0
1880
8
6
0
NO...
Price (in K) Sqft 310.0 2650 313.0 2600 320.0 2664 320.0 2921 304.9 2580 295.0 2580...
Price (in K)
Sqft
310.0
2650
313.0
2600
320.0
2664
320.0
2921
304.9
2580
295.0
2580
285.0
2774
261.0
1920
250.0
2150
249.9
1710
242.5
1837
232.0
1880
230.0
2150
228.5
1894
222.0
1928
223.0
1830
220.5
1767
216.0
1630
218.9
1680
204.5
1725
204.5
1500
202.5
1430
202.5
1360
195.0
1400
201.0
1573
191.0
1385
274.5
2931
260.3
2200
230.0
2277
235.0
2000
207.0
1478
207.0
1713
197.2
1326
197.5
1050
194.9
1464
190.0
1190
192.6
1156
194.0
1746...
Price (in K) Sqft 310.0 2650 313.0 2600 320.0 2664 320.0 2921 304.9 2580 295.0 2580...
Price (in K)
Sqft
310.0
2650
313.0
2600
320.0
2664
320.0
2921
304.9
2580
295.0
2580
285.0
2774
261.0
1920
250.0
2150
249.9
1710
242.5
1837
232.0
1880
230.0
2150
228.5
1894
222.0
1928
223.0
1830
220.5
1767
216.0
1630
218.9
1680
204.5
1725
204.5
1500
202.5
1430
202.5
1360
195.0
1400
201.0
1573
191.0
1385
274.5
2931
260.3
2200
230.0
2277
235.0
2000
207.0
1478
207.0
1713
197.2
1326
197.5
1050
194.9
1464
190.0
1190
192.6
1156
194.0
1746...
HouseID Age(years) Size(sqft) Bedrooms Price($1,000) 1 7 1580 3 132.0 2 8 1744 3 123.9 3...
HouseID
Age(years)
Size(sqft)
Bedrooms
Price($1,000)
1
7
1580
3
132.0
2
8
1744
3
123.9
3
5
1863
3
159.1
4
7
1824
4
126.0
5
3
1924
4
128.3
6
10
1707
4
145.4
7
5
1898
3
126.1
8
5
2063
4
128.4
9
7
1641
3
147.4
10
7
1524
3
121.5
11
4
2144
3
167.7
12
9
1518
2
109.2
13
8
1645
3
132.6
14
5
2741
4
212.3
15
6
1718
3
111.8...
X = [ 6, 0, 13, 10, 7, 16, 13 ] (a) mode (b) median (c)...
X = [ 6, 0, 13, 10, 7, 16, 13 ]
(a) mode
(b) median
(c) mean
(d) 20% trimmed mean
(e) range
(f) interquartile range
(g) sample standard deviation
(h) winsorized sample standard deviation
6 5 4 5 0 0 13 48 6 1 0 7 2 0 1 1...
6
5
4
5
0
0
13
48
6
1
0
7
2
0
1
1
0
2
11
5
11
27
4
0
6
Create Standard Deviation Chart (Normal Distribution
Curve)
7- What is the price of a bond with the following features? Face Value = $1,000...
7- What is the price of a bond with the following features?
Face Value = $1,000
Coupon Rate = 3% (stated as an ANNUAL rate)
Semiannual coupon payments
Maturity = 6 years
YTM = 5.2% (Stated as an APR)
State your answer to the nearest penny (e.g., 984.25)
Principles of Marketing, 15th Edition, Philip T Kotler, Gary Armstrong ISBN-10: 0-13-308404-3 ISBN-13: 978-0-13-308404-7 I need...
Principles of Marketing, 15th Edition, Philip T Kotler, Gary
Armstrong ISBN-10: 0-13-308404-3 ISBN-13: 978-0-13-308404-7 I need
the answer to this book.
1. Expalin why brand equity is important to the seller. Does
ESPN have strong brand equity? How does its brand equity relate to
its brand value?
Price Rooms Neighborhood 309.6 7 0 307.4 8 0 340.3 9 0 346.5 12 0 298.2...
Price
Rooms
Neighborhood
309.6
7
0
307.4
8
0
340.3
9
0
346.5
12
0
298.2
6
0
337.8
9
0
324.1
10
0
313.2
8
0
327.8
9
0
325.3
8
0
308.5
6
1
381.3
13
1
337.4
10
1
346.2
10
1
342.4
9
1
323.7
8
1
329.6
8
1
343.6
9
1
360.7
11
1
348.3
9
1
Predict the selling price for a house with nine rooms that is
located in the east-side neighborhood....
Data: 7,-5, -8, 7, 9, 15, 0, 2, 13, 8, 6, -2, 4 (a) Mean= Mode=...
Data: 7,-5, -8, 7, 9, 15, 0, 2, 13, 8, 6, -2, 4 (a) Mean= Mode=
median= (b) Variance= Standard deviation= (c) Range=
IQR(Interquartilerange)= (d) Mid-Range= Mid-Hinge=
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