?? gt.res
字號(hào):
DIFFERENCE DATA-1:
-15.00 21.00 -12.00 27.00 0.00 -6.00 2.00 2.00 -12.00 -9.00
14.00 0.00 -16.00 -3.00 8.00 27.00 12.00 -39.00 6.00 4.00
-4.00 -8.00 -9.00 7.00 9.00 -11.00 6.00 15.00 -23.00 7.00
-2.00 -3.00 8.00 6.00 19.00 2.00 -19.00 15.00 13.00 -9.00
-9.00 -13.00 -9.00 14.00 10.00 -20.00 1.00 9.00 0.00 -5.00
9.00 0.00 12.00 9.00 -21.00 -19.00 11.00 13.00 -1.00 5.00
-8.00 2.00 -1.00 7.00 19.00 -11.00 -1.00 -17.00 19.00 8.00
-6.00 -10.00 9.00 -4.00 4.00 12.00 4.00 -10.00 0.00 3.00
-6.00 -1.00 15.00 -9.00 -14.00 3.00 -1.00 0.00 -7.00 11.00
9.00 4.00 -24.00 -1.00 -9.00 30.00 5.00 -7.00 -5.00 11.00
-1.00 1.00 -26.00 3.00 10.00 -2.00 -4.00 16.00 -3.00 -15.00
12.00 13.00 -26.00 3.00 -13.00 31.00 -7.00 9.00 6.00 6.00
-15.00 22.00 -21.00 -3.00 9.00 17.00 0.00 0.00
DIFFERENCE DATA-2:
36.00 -33.00 39.00 -27.00 -6.00 8.00 0.00 -14.00 3.00 23.00
-14.00 -16.00 13.00 11.00 19.00 -15.00 -51.00 45.00 -2.00 -8.00
-4.00 -1.00 16.00 2.00 -20.00 17.00 9.00 -38.00 30.00 -9.00
-1.00 11.00 -2.00 13.00 -17.00 -21.00 34.00 -2.00 -22.00 0.00
-4.00 4.00 23.00 -4.00 -30.00 21.00 8.00 -9.00 -5.00 14.00
-9.00 12.00 -3.00 -30.00 2.00 30.00 2.00 -14.00 6.00 -13.00
10.00 -3.00 8.00 12.00 -30.00 10.00 -16.00 36.00 -11.00 -14.00
-4.00 19.00 -13.00 8.00 8.00 -8.00 -14.00 10.00 3.00 -9.00
5.00 16.00 -24.00 -5.00 17.00 -4.00 1.00 -7.00 18.00 -2.00
-5.00 -28.00 23.00 -8.00 39.00 -25.00 -12.00 2.00 16.00 -12.00
2.00 -27.00 29.00 7.00 -12.00 -2.00 20.00 -19.00 -12.00 27.00
1.00 -39.00 29.00 -16.00 44.00 -38.00 16.00 -3.00 0.00 -21.00
37.00 -43.00 18.00 12.00 8.00 -17.00 0.00 0.00
NO. PERIOD CSC
1 0- 11 16.55
2 0- 12 16.09
3 0- 14 21.64
4 0- 15 13.73
5 0- 18 16.30
6 0- 19 14.94
7 0- 20 20.35
8 0- 21 27.64
9 0- 22 26.91
10 0- 23 28.21
11 0- 24 28.30
12 0- 25 28.01
13 0- 26 29.01
14 0- 27 30.29
15 0- 28 41.37
16 0- 29 38.33
17 0- 30 25.84
18 0- 31 31.67
19 0- 32 29.18
20 0- 33 21.94
21 0- 34 23.71
22 0- 35 31.58
23 0- 36 44.51
24 0- 37 32.84
25 0- 38 41.47
26 0- 39 41.05
27 0- 40 43.67
28 0- 41 47.00
29 0- 42 63.43
30 1- 11 13.65
31 1- 14 16.59
32 1- 16 14.47
33 1- 20 16.21
34 1- 24 19.73
35 1- 25 18.62
36 1- 26 20.66
37 1- 27 15.97
38 1- 28 25.78
39 1- 29 13.46
40 1- 30 16.91
41 1- 32 19.16
42 1- 34 21.32
43 1- 36 27.33
44 1- 37 15.73
45 1- 38 20.42
46 1- 39 20.62
47 1- 40 25.11
48 1- 42 27.16
49 2- 26 16.03
50 2- 28 13.71
51 2- 43 80.50
52 3- 3 80.20
53 3- 4 83.97
54 3- 5 79.19
55 3- 6 93.58
56 3- 7 93.98
57 3- 8 88.32
58 3- 9 87.67
59 3- 10 87.47
60 3- 11 83.43
61 3- 12 90.45
62 3- 13 88.15
63 3- 14 100.81
64 3- 15 90.39
65 3- 16 93.84
66 3- 17 85.37
67 3- 18 95.69
68 3- 19 94.66
69 3- 20 100.90
70 3- 21 104.71
71 3- 22 95.78
72 3- 23 102.01
73 3- 24 101.48
74 3- 25 103.77
75 3- 26 102.70
76 3- 27 101.36
77 3- 28 115.36
78 3- 29 111.30
79 3- 30 106.64
80 3- 31 112.80
81 3- 32 109.70
82 3- 33 98.47
83 3- 34 98.50
84 3- 35 108.13
85 3- 36 118.62
86 3- 37 111.78
87 3- 38 119.95
88 3- 39 112.80
89 3- 40 113.83
90 3- 41 111.41
91 3- 42 135.54
MAX-MIN-N0:
91 87 85 77 89 88 80 86 90 78
81 84 79 70 74 75 72 73 76 69
63 83 82 71 67 68 56 65 55 61
64 57 62 58 59 66 53 60 51 52
54 29 28 23 27 25 15 26 16 24
18 22 14 19 13 11 10 12 8 43
48 9 17 38 47 21 20 3 42 36
46 45 7 34 41 35 40 31 1 5
33 2 49 37 44 6 32 4 50 30
39
MAX-MIN-CSC:
135.54 119.95 118.62 115.36 113.83 112.80 112.80 111.78 111.41 111.30
109.70 108.13 106.64 104.71 103.77 102.70 102.01 101.48 101.36 100.90
100.81 98.50 98.47 95.78 95.69 94.66 93.98 93.84 93.58 90.45
90.39 88.32 88.15 87.67 87.47 85.37 83.97 83.43 80.50 80.20
79.19 63.43 47.00 44.51 43.67 41.47 41.37 41.05 38.33 32.84
31.67 31.58 30.29 29.18 29.01 28.30 28.21 28.01 27.64 27.33
27.16 26.91 25.84 25.78 25.11 23.71 21.94 21.64 21.32 20.66
20.62 20.42 20.35 19.73 19.16 18.62 16.91 16.59 16.55 16.30
16.21 16.09 16.03 15.97 15.73 14.94 14.47 13.73 13.71 13.65
13.46
mean= -6.15 -3.03 -5.07 -6.26 -10.51 -5.37 -4.75 -11.37 -4.38 -3.89
mean= -12.38
1ORDER REGRESSION SET
X 1
REGRESSION EQUATION
Y= -7.788
+ 0.747x 1
DEPARTURE SQUARE SUM =46508.2422
REGRESSION SQUARE SUM=34719.7539
RESIDUES SQUARE SUM=11788.4893
MULTIPLE CORRELATION COEFFICIENT= 0.8640
MEAN--Y= -12.38 RMSE= 9.60
FITTING
-36.19 -43.29 -36.19 -36.93 -35.19 -36.69 -33.20 -30.46 -30.21 -36.93
-39.43 -35.19 -25.48 -33.45 -40.67 -37.18 -24.48 -21.99 -30.21 -37.18
-34.94 -33.70 -34.44 -32.95 -29.96 -28.71 -39.43 -30.21 -21.24 -34.94
-34.94 -36.44 -30.46 -29.21 -22.49 -15.26 -15.76 -24.23 -14.26 -17.75
-20.99 -17.25 -18.50 -25.60 -18.50 -19.25 -17.50 -19.00 -15.51 -12.77
-12.52 -19.25 -21.74 -17.50 -7.79 -15.76 -22.98 -19.50 -6.79 -4.30
-12.52 -19.50 -17.25 -16.01 -16.76 -15.26 -12.27 -11.03 -21.74 -12.52
-3.55 -17.25 -17.25 -18.75 -12.77 -11.52 -4.80 2.43 1.93 -6.54
3.42 -0.07 -3.30 0.43 -0.81 -7.91 -0.81 -1.56 0.18 -1.31
2.18 4.92 5.17 -1.56 -4.05 0.18 9.90 1.93 -5.30 -1.81
10.90 13.39 5.17 -1.81 0.43 1.68 0.93 2.43 5.42 6.66
-4.05 5.17 14.13 0.43 0.43 -1.06 4.92 6.16 12.89 20.11
19.62 11.15 21.11 17.62 14.38 18.12 16.87 9.78
FORECAST
16.87 16.13 17.87
2ORDER REGRESSION SET
X 1
X 7
REGRESSION EQUATION
Y= -7.344
+ 0.465x 1
+ 0.458x 7
DEPARTURE SQUARE SUM =46508.2422
REGRESSION SQUARE SUM=35878.2383
RESIDUES SQUARE SUM=10630.0039
MULTIPLE CORRELATION COEFFICIENT= 0.8783
MEAN--Y= -12.38 RMSE= 9.11
FITTING
-42.43 -47.86 -39.41 -35.94 -31.19 -32.80 -36.02 -28.93 -26.25 -33.42
-41.15 -37.03 -31.32 -36.28 -39.07 -35.98 -26.35 -24.11 -31.98 -35.41
-35.61 -33.23 -35.76 -32.88 -33.08 -32.54 -38.52 -30.72 -23.99 -31.03
-33.09 -37.12 -34.40 -29.59 -21.47 -13.30 -14.30 -24.96 -13.37 -13.02
-18.02 -21.87 -21.16 -25.92 -21.50 -20.25 -18.25 -17.46 -14.60 -15.65
-14.57 -20.37 -20.31 -19.74 -11.74 -18.77 -23.49 -20.64 -10.66 -7.97
-11.60 -18.00 -19.70 -19.93 -16.36 -11.49 -5.97 -5.88 -17.93 -6.81
1.29 -10.22 -16.40 -15.84 -12.46 -11.69 -5.78 -0.37 1.04 -3.55
-0.09 -1.34 -4.96 -1.03 -3.87 -6.35 -3.99 -4.68 -2.91 -1.78
1.54 4.74 2.83 -4.45 -7.01 -0.34 9.65 8.35 3.16 -0.05
13.25 17.32 9.22 -1.31 1.58 2.01 1.54 4.19 6.97 9.46
3.48 6.47 12.97 2.84 4.44 1.45 7.12 5.83 9.79 14.98
16.73 12.60 20.29 16.06 10.95 12.27 15.53 15.04
FORECAST
23.13 21.98 17.68
3ORDER REGRESSION SET
X 1
X 7
X 8
REGRESSION EQUATION
Y= -4.872
+ 0.301x 1
+ 0.356x 7
+ 0.349x 8
DEPARTURE SQUARE SUM =46508.2422
REGRESSION SQUARE SUM=36464.8359
RESIDUES SQUARE SUM=10043.4053
MULTIPLE CORRELATION COEFFICIENT= 0.8855
MEAN--Y= -12.38 RMSE= 8.86
FITTING
-43.09 -45.24 -37.51 -35.97 -32.86 -32.42 -36.86 -29.92 -26.37 -32.35
-40.95 -35.39 -33.32 -38.71 -38.97 -35.28 -24.47 -20.32 -32.28 -38.68
-37.39 -35.65 -35.10 -32.87 -34.20 -33.18 -37.07 -30.37 -21.80 -33.14
-33.69 -37.74 -34.49 -29.46 -21.83 -15.38 -16.58 -25.45 -15.78 -13.48
-18.31 -22.05 -19.82 -24.61 -20.09 -17.57 -17.12 -19.18 -14.54 -17.14
-18.51 -21.15 -19.34 -14.87 -6.83 -18.16 -25.55 -21.98 -15.27 -10.94
-12.97 -18.31 -19.11 -19.51 -15.28 -7.54 -10.48 -9.46 -19.00 -9.48
-2.52 -8.48 -12.59 -12.50 -11.64 -9.66 -3.87 -1.48 -0.78 -2.08
-1.87 -0.90 -1.96 -0.18 -5.07 -3.71 -4.03 -6.69 -4.14 -1.57
5.09 9.96 1.95 -7.47 -7.62 -2.79 6.62 6.38 2.01 -0.07
9.10 13.46 11.92 -2.68 0.43 -0.39 0.94 4.28 8.05 10.59
6.35 6.64 12.44 7.43 7.45 4.81 10.30 7.54 11.73 16.65
17.09 11.78 19.65 15.08 9.19 11.22 15.42 19.99
FORECAST
28.31 20.96 13.17
4ORDER REGRESSION SET
X 1
X 6
X 7
X 8
REGRESSION EQUATION
Y= -4.043
+ 0.467x 1
+ -0.246x 6
+ 0.398x 7
+ 0.430x 8
DEPARTURE SQUARE SUM =46508.2422
REGRESSION SQUARE SUM=36806.0195
RESIDUES SQUARE SUM= 9702.2236
MULTIPLE CORRELATION COEFFICIENT= 0.8896
MEAN--Y= -12.38 RMSE= 8.71
FITTING
-43.95 -46.51 -37.71 -34.72 -31.11 -33.84 -36.78 -26.19 -22.94 -31.72
-41.54 -34.38 -32.18 -39.29 -41.76 -36.27 -21.86 -15.17 -30.90 -41.74
-39.39 -36.89 -35.73 -32.30 -33.95 -31.69 -38.83 -29.97 -19.32 -31.60
-35.54 -40.70 -33.87 -28.68 -21.05 -11.58 -13.80 -23.72 -15.07 -12.59
-17.78 -22.65 -18.66 -25.57 -22.22 -17.77 -14.91 -18.72 -13.40 -15.46
-17.32 -22.95 -20.60 -15.43 -3.86 -19.08 -27.80 -23.17 -15.40 -9.51
-12.88 -19.78 -19.55 -20.36 -14.78 -6.30 -10.27 -9.78 -18.84 -9.83
-0.56 -7.45 -12.78 -15.29 -12.53 -10.66 -1.17 -1.46 -0.49 -2.33
-1.82 0.38 -1.34 -1.92 -6.34 -3.60 -3.81 -7.97 -4.11 -1.44
5.48 12.29 1.55 -10.08 -10.91 -3.45 8.90 5.28 -0.66 -1.97
10.92 17.05 13.77 -3.76 -0.67 -1.89 -1.34 6.42 7.94 10.83
6.12 7.09 13.03 6.13 5.28 3.65 6.92 4.19 11.17 16.82
18.65 10.78 18.51 14.20 8.95 11.10 14.96 19.88
FORECAST
30.61 19.87 11.55
5ORDER REGRESSION SET
X 1
X 6
X 7
X 8
X10
REGRESSION EQUATION
Y= -4.284
+ 0.429x 1
+ -0.352x 6
+ 0.310x 7
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