4.5 Consider the multiple regression model fit to the house price data. URIA SEIS FUR EXERULISES JUI TABLE B.4 Property Valuation Data y x₁ X₂ x3 X₂ X₂ 25.9 0.9980 7 29.5 4.9176 1.0 3.4720 1.0 1.0 2.2750 5.0208 3.5310 1.5000 27.9 4.5429 1.1750 25.9 4.5573 1.0 4.0500 1.2320 29.9 5.0597 1.0 4.4550 1.1210 29.9 3.8910 1.0 4.4550 0.9880 30.9 5.8500 1.2400 28.9 5.8980 1.0 5.6039 1.0 9.5200 5.8282 1.0 6.4350 1.5010 35.9 1.2250 31.5 5.3003 1.0 4.9883 1.5520 31.0 0.9750 30.9 6.2712 1.0 5.5200 5.9592 1.0 6.6660 5.0500 1.0 1.1210 30.0 5.0000 1.0200 36.9 8.2464 1.5 5.1500 1.6640 41.9 6.6969 1.5 6.9020 1.4880 40.5 7.7841 1.5 7.1020 1.3760 43.9 7.8000 1.5000 9.0384 1.0 37.5 5.9894 1.0 37.9 7.5422 1.5 44.5 8.7951 1.5 5.5200 1.2560 2.0 5.0000 1.6900 1.0 9.8900 1.8200 2.0 37.9 6.0831 1.5 6.7265 1.6520 1.0 9.1500 1.7770 2.0 38.9 8.3607 1.5 36.9 8.1400 1.0 8.0000 1.5040 2.0 7 3 45.8 9.1416 1.5 7.3262 1.8310 1.5 8 4 31 b. Construct and interpret a plot of the residuals versus the predicted response. 1.0 2.0 1.0 1.0 1.0 1.0 1.0 0.0 2.0 1.0 1.0 2.0 0.0 2.0 1.5 1.0 165 15 79666676666767668687 6 6 7 6 6 5 6 5 8 M 6 । 4433 NSATMmmmm بیا بیا بیا بیا بیا بیا بیا برا را با ر ا دا بیا بیا بیا بیا با ما با ما با 3 3 3 3 2 3 2 4 3 3 3 51 3 3 3 3 3 6 3 8 4 42 Suttons85386388532852886 40 54 42 30 30 46 50 17 40 50 44 48 5 0000OOOOOOOHOTO OHOHOHOO 1 1
4.5 Consider the multiple regression model fit to the house price data. URIA SEIS FUR EXERULISES JUI TABLE B.4 Property Valuation Data y x₁ X₂ x3 X₂ X₂ 25.9 0.9980 7 29.5 4.9176 1.0 3.4720 1.0 1.0 2.2750 5.0208 3.5310 1.5000 27.9 4.5429 1.1750 25.9 4.5573 1.0 4.0500 1.2320 29.9 5.0597 1.0 4.4550 1.1210 29.9 3.8910 1.0 4.4550 0.9880 30.9 5.8500 1.2400 28.9 5.8980 1.0 5.6039 1.0 9.5200 5.8282 1.0 6.4350 1.5010 35.9 1.2250 31.5 5.3003 1.0 4.9883 1.5520 31.0 0.9750 30.9 6.2712 1.0 5.5200 5.9592 1.0 6.6660 5.0500 1.0 1.1210 30.0 5.0000 1.0200 36.9 8.2464 1.5 5.1500 1.6640 41.9 6.6969 1.5 6.9020 1.4880 40.5 7.7841 1.5 7.1020 1.3760 43.9 7.8000 1.5000 9.0384 1.0 37.5 5.9894 1.0 37.9 7.5422 1.5 44.5 8.7951 1.5 5.5200 1.2560 2.0 5.0000 1.6900 1.0 9.8900 1.8200 2.0 37.9 6.0831 1.5 6.7265 1.6520 1.0 9.1500 1.7770 2.0 38.9 8.3607 1.5 36.9 8.1400 1.0 8.0000 1.5040 2.0 7 3 45.8 9.1416 1.5 7.3262 1.8310 1.5 8 4 31 b. Construct and interpret a plot of the residuals versus the predicted response. 1.0 2.0 1.0 1.0 1.0 1.0 1.0 0.0 2.0 1.0 1.0 2.0 0.0 2.0 1.5 1.0 165 15 79666676666767668687 6 6 7 6 6 5 6 5 8 M 6 । 4433 NSATMmmmm بیا بیا بیا بیا بیا بیا بیا برا را با ر ا دا بیا بیا بیا بیا با ما با ما با 3 3 3 3 2 3 2 4 3 3 3 51 3 3 3 3 3 6 3 8 4 42 Suttons85386388532852886 40 54 42 30 30 46 50 17 40 50 44 48 5 0000OOOOOOOHOTO OHOHOHOO 1 1
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:4.5 Consider the multiple regression model fit to the house price data.
UAIA SEIS FUR EXERLIDES
331
TABLE B.4 Property Valuation Data
y
X₁
X₂
X3
Xx₂
X₂
X₂
25.9
4.9176 1.0
3.4720
0.9980
4
29.5
5.0208
1.0
3.5310
1.5000
4
27.9
4.5429
1.0
2.2750
1.1750
3
25.9
4.5573
1.0
4.0500
1.2320
3
29.9
1.0
4.4550
1.1210
3
5.0597
29.9 3.8910 1.0
4.4550
0.9880
3
30.9 5.8980 1.0
5.8500
1.2400
3
28.9
5.6039
9.5200
1.5010
3
35.9
1.0
1.0
5.3003 1.0 4.9883
5.8282
6.4350
1.2250
3
31.5
1.5520
3
31.0 6.2712 1.0
5.5200
0.9750
2
30.9
6.6660
1.1210
3
30.0
5.9592 1.0
5.0500 1.0
8.2464 1.5 5.1500
5.0000
1.0200
2
36.9
1.6640
4
41.9
6.6969 1.5
6.9020
1.4880
3
40.5
7.7841 1.5
7.1020
1.3760
3
43.9
9.0384 1.0
7.8000
1.5000
3
0
37.5
5.9894 1.0 5.5200
1.2560
2.0
3
1
37.9
7.5422 1.5
5.0000 1.6900
1.0
3
22
0
44.5
8.7951 1.5
9.8900
1.8200
2.0
4
50 1
37.9
1.6520
1.0
3
44
0
38.9
6.0831 1.5 6.7265
8.3607 1.5 9.1500
1.7770
2.0
4 48 1
36.9
1.5040
2.0
7 3
8.1400 1.0 8.0000
9.1416 1.5 7.3262
3
0
45.8
1.8310
1.5 8 4
31
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b. Construct and interpret a plot of the residuals versus the predicted response.
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2.0
1.0
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1.0
1.0
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0.0
2.0
1.0
1.0
2.0
0.0
2.0
1.5
1.0
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