📄 osr.res
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******OPTIMAL SUBSET REGRESSION******
MEANS OF X AND Y
132.35 162.77 116.09 78.7711541.70 61.63
1ORDER REGRESSION SET
X 1
REGRESSION EQUATION
Y= 63.876
+ -.017X 1
DEPARTURE SQUARE SUM = 478.0475
REGRESSION SQUARE SUM= 83.3028
RESIDUES SQUARE SUM= 394.7446
MULTIPLE CORRELATION COEFFICIENT= .42
CONTINGENCY TABLE
3 5 1
4 2 7
2 6 12
S1= 9.53 S2= 7.32 CSC= 16.85
MEAN--Y= 61.63 RMSE= 3.03
FITTING
62.13 58.42 62.74 60.38 61.07 59.20 60.95 63.01 61.60 63.13
62.47 59.90 61.79 61.80 61.21 60.50 63.08 61.21 61.85 63.15
63.62 63.28 63.11 56.81 61.09 63.66 61.77 62.42 59.92 60.07
61.72 61.60 60.58 61.26 62.19 61.80 61.84 62.57 62.18 63.13
61.97 62.77 61.06
FORECAST
62.21 63.01
2ORDER REGRESSION SET
X 1
X 3
REGRESSION EQUATION
Y= 58.080
+ -.015X 1
+ .047X 3
DEPARTURE SQUARE SUM = 478.0475
REGRESSION SQUARE SUM= 106.8814
RESIDUES SQUARE SUM= 371.1660
MULTIPLE CORRELATION COEFFICIENT= .47
CONTINGENCY TABLE
3 3 1
5 6 7
1 4 12
S1= 9.02 S2= 9.17 CSC= 18.19
MEAN--Y= 61.63 RMSE= 2.94
FITTING
62.48 59.50 61.36 59.54 60.15 60.37 61.94 63.25 62.41 62.36
62.45 59.55 60.77 61.21 60.45 62.01 64.97 60.74 61.77 63.37
64.26 63.49 62.39 56.82 61.06 64.76 61.70 62.26 59.38 59.75
60.71 61.08 59.95 61.02 62.21 62.68 61.05 62.16 62.77 63.64
62.59 62.57 61.08
FORECAST
61.14 61.83
3ORDER REGRESSION SET
X 1
X 2
X 3
REGRESSION EQUATION
Y= 69.912
+ -.012X 1
+ -.074X 2
+ .046X 3
DEPARTURE SQUARE SUM = 478.0475
REGRESSION SQUARE SUM= 126.7611
RESIDUES SQUARE SUM= 351.2863
MULTIPLE CORRELATION COEFFICIENT= .51
CONTINGENCY TABLE
3 5 2
5 5 5
1 3 13
S1= 11.30 S2= 10.61 CSC= 21.90
MEAN--Y= 61.63 RMSE= 2.86
FITTING
63.34 59.28 62.29 60.13 59.65 60.16 61.94 62.47 62.30 62.47
62.58 60.44 60.67 61.32 59.48 62.24 65.85 61.17 62.02 63.83
63.66 64.00 62.50 56.89 61.14 64.07 62.92 61.07 58.40 59.41
61.14 62.86 60.06 60.25 61.80 62.61 60.34 62.06 62.42 63.28
63.91 61.47 60.12
FORECAST
60.82 62.19
4ORDER REGRESSION SET
X 1
X 2
X 3
X 5
REGRESSION EQUATION
Y= 69.129
+ -.012X 1
+ -.060X 2
+ .047X 3
+ .000X 5
DEPARTURE SQUARE SUM = 478.0475
REGRESSION SQUARE SUM= 135.6364
RESIDUES SQUARE SUM= 342.4111
MULTIPLE CORRELATION COEFFICIENT= .53
CONTINGENCY TABLE
3 4 2
4 2 6
2 7 12
S1= 6.31 S2= 11.07 CSC= 17.38
MEAN--Y= 61.63 RMSE= 2.82
FITTING
64.82 59.38 61.99 59.42 59.97 59.86 62.63 62.51 62.16 62.51
62.54 61.02 60.91 61.43 58.93 61.72 65.88 61.62 61.13 63.30
64.27 63.22 62.50 56.72 61.01 63.74 62.52 60.99 58.35 59.68
61.46 62.85 60.36 60.78 61.79 62.58 60.30 62.24 63.06 63.70
63.53 61.08 59.52
FORECAST
60.96 62.52
5ORDER REGRESSION SET
X 1
X 2
X 3
X 4
X 5
REGRESSION EQUATION
Y= 69.375
+ -.012X 1
+ -.059X 2
+ .048X 3
+ -.004X 4
+ .000X 5
DEPARTURE SQUARE SUM = 478.0475
REGRESSION SQUARE SUM= 137.4395
RESIDUES SQUARE SUM= 340.6080
MULTIPLE CORRELATION COEFFICIENT= .54
CONTINGENCY TABLE
3 4 2
4 2 6
2 7 12
S1= 6.31 S2= 10.93 CSC= 17.24
MEAN--Y= 61.63 RMSE= 2.81
FITTING
64.96 59.35 62.16 59.56 60.26 59.94 62.44 62.12 61.75 62.23
62.44 61.11 61.08 61.63 59.12 61.94 66.09 61.59 60.94 63.19
64.29 63.25 62.57 56.72 61.15 64.03 62.74 61.20 58.23 59.35
61.18 62.61 60.23 60.86 61.93 62.83 60.54 62.47 63.06 63.48
63.27 60.74 59.35
FORECAST
61.04 62.74
**********************************************
OPTIMAL REGRESSION SET IS-- 3
X 1
X 2
X 3
THE FINAL RESULT OF FITTING AND FORECAST
RMSE= 2.86
NO. REG. OBS.
1 63.34 65.00
2 59.28 60.00
3 62.29 61.00
4 60.13 61.00
5 59.65 55.00
6 60.16 57.00
7 61.94 61.00
8 62.47 64.00
9 62.30 63.00
10 62.47 63.00
11 62.58 65.00
12 60.44 62.00
13 60.67 66.00
14 61.32 62.00
15 59.48 57.00
16 62.24 64.00
17 65.85 67.00
18 61.17 62.00
19 62.02 64.00
20 63.83 64.00
21 63.66 63.00
22 64.00 64.00
23 62.50 55.00
24 56.89 57.00
25 61.14 61.00
26 64.07 63.00
27 62.92 63.00
28 61.07 63.00
29 58.40 55.00
30 59.41 60.00
31 61.14 62.00
32 62.86 63.00
33 60.06 63.00
34 60.25 58.00
35 61.80 66.00
36 62.61 63.00
37 60.34 67.00
38 62.06 59.00
39 62.42 63.00
40 63.28 67.00
41 63.91 56.00
42 61.47 57.00
43 60.12 59.00
44 60.82 .00
45 62.19 .00
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