4.3 Consider the simple linear regression model fit to the solar energy data. DATA SETS FOR EXERCISES 555 TABLE B.2 Solar Thermal Energy Test Data y x₁ X₂ X3 X4 xs 271.8 783.35 33.53 40.55 16.66 13.20 264.0 748.45 36.50 36.19 16.46 14.11 238.8 684.45 34.66 37.31 17.66 15.68 230.7 827.80 33.13 32.52 17.50 10.53 251.6 860.45 35.75 33.71 16.40 11.00 257.9 875.15 34.46 34.14 16.28 11.31 263.9 909.45 34.60 34.85 16.06 11.96 266.5 905.55 35.38 35.89 15.93 12.58 229.1 756.00 35.85 33.53 16.60 10.66 239.3 769.35 35.68 33.79 16.41 10.85 258.0 793.50 35.35 34.72 16.17 11.41 257.6 801.65 35.04 35.22 15.92 11.91 267.3 819.65 34.07 36.50 16.04 12.85 267.0 808.55 32.20 37.60 16.19 13.58 259.6 774.95 34.32 37.89 16.62 14.21 240.4 711.85 31.08 37.71 17.37 15.56 227.2 694.85 35.73 37.00 18.12 15.83 196.0 638.10 34.11 36.76 18.53 16.41 278.7 774.55 34.79 34.62 15.54 13.10 272.3 757.90 35.77 35.40 15.70 13.63 267.4 753.35 36.44 35.96 16.45 14.51 254.5 704.70 37.82 36.26 17.62 15.38 224.7 666.80 35.07 36.34 18.12 16.10 181.5 568.55 35.26 35.90 19.05 16.73 227.5 653.10 35.56 31.84 16.51 10.58 253.6 704.05 35.73 33.16 16.02 11.28 263.0 709.60 36.46 33.83 15.89 11.91 265.8 726.90 36.26 34.89 15.83 12.65 263.8 697.15 37.20 36.27 16.71 14.06 b. Construct and interpret a plot of the residuals versus the predicted response.
4.3 Consider the simple linear regression model fit to the solar energy data. DATA SETS FOR EXERCISES 555 TABLE B.2 Solar Thermal Energy Test Data y x₁ X₂ X3 X4 xs 271.8 783.35 33.53 40.55 16.66 13.20 264.0 748.45 36.50 36.19 16.46 14.11 238.8 684.45 34.66 37.31 17.66 15.68 230.7 827.80 33.13 32.52 17.50 10.53 251.6 860.45 35.75 33.71 16.40 11.00 257.9 875.15 34.46 34.14 16.28 11.31 263.9 909.45 34.60 34.85 16.06 11.96 266.5 905.55 35.38 35.89 15.93 12.58 229.1 756.00 35.85 33.53 16.60 10.66 239.3 769.35 35.68 33.79 16.41 10.85 258.0 793.50 35.35 34.72 16.17 11.41 257.6 801.65 35.04 35.22 15.92 11.91 267.3 819.65 34.07 36.50 16.04 12.85 267.0 808.55 32.20 37.60 16.19 13.58 259.6 774.95 34.32 37.89 16.62 14.21 240.4 711.85 31.08 37.71 17.37 15.56 227.2 694.85 35.73 37.00 18.12 15.83 196.0 638.10 34.11 36.76 18.53 16.41 278.7 774.55 34.79 34.62 15.54 13.10 272.3 757.90 35.77 35.40 15.70 13.63 267.4 753.35 36.44 35.96 16.45 14.51 254.5 704.70 37.82 36.26 17.62 15.38 224.7 666.80 35.07 36.34 18.12 16.10 181.5 568.55 35.26 35.90 19.05 16.73 227.5 653.10 35.56 31.84 16.51 10.58 253.6 704.05 35.73 33.16 16.02 11.28 263.0 709.60 36.46 33.83 15.89 11.91 265.8 726.90 36.26 34.89 15.83 12.65 263.8 697.15 37.20 36.27 16.71 14.06 b. Construct and interpret a plot of the residuals versus the predicted response.
Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
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ISBN:9781337111348
Author:Bruce Crauder, Benny Evans, Alan Noell
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Chapter3: Straight Lines And Linear Functions
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Problem 12SBE: Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
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Transcribed Image Text:4.3 Consider the simple linear regression model fit to the solar energy data.
DATA SETS FOR EXERCISES 555
TABLE B.2 Solar Thermal Energy Test Data
y
x₁
X₂
X3
X4
Xs
271.8
783.35
33.53
40.55
16.66
13.20
264.0
748.45
36.50
36.19
16.46
14.11
238.8
684.45
34.66
37.31
17.66
15.68
230.7
827.80
33.13
32.52
17.50
10.53
251.6
860.45
35.75
33.71
16.40
11.00
257.9
875.15
34.46
34.14
16.28
11.31
263.9
909.45
34.60
34.85
16.06
11.96
266.5
905.55
35.38
35.89
15.93
12.58
229.1
756.00
35.85
33.53
16.60
10.66
239.3
769.35
35.68
33.79
16.41
10.85
258.0
793.50
35.35
34.72
16.17
11.41
257.6
801.65
35.04
35.22
15.92
11.91
267.3
819.65
34.07
36.50
16.04
12.85
267.0
808.55
32.20
37.60
16.19
13.58
259.6
774.95
34.32
37.89
16.62
14.21
240.4
711.85
31.08
37.71
17.37
15.56
227.2
694.85
35.73
37.00
18.12
15.83
196.0
638.10
34.11
36.76
18.53
16.41
278.7
774.55
34.79
34.62
15.54
13.10
272.3
757.90
35.77
35.40
15.70
13.63
267.4
753.35
36.44
35.96
16.45
14.51
254.5
704.70
37.82
36.26
17.62
15.38
224.7
666.80
35.07
36.34
18.12
16.10
181.5
568.55
35.26
35.90
19.05
16.73
227.5
653.10
35.56
31.84
16.51
10.58
253.6
704.05
35.73
33.16
16.02
11.28
263.0
709.60
36.46
33.83
15.89
11.91
265.8
726.90
36.26
34.89
15.83
12.65
263.8
697.15
37.20
36.27
16.71
14.06
b. Construct and interpret a plot of the residuals versus the predicted response.
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