Trailing-Edge
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PDP-10 Archives
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decus_20tap5_198111
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decus/20-0149/mulreg.inp
There are 2 other files named mulreg.inp in the archive. Click here to see a list.
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* Example 1 originates from: *
* reference [4], page 472, 479 *
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"Model" y = c * Log (x) + a + b * x;
"Input" 5 * ([x], 10 * [y]);
"Options" Transformed data matrix, Correlation matrix,
Residual analysis, Process submodels (1, 2);
"Data" 25 0.67 0.70 0.75 0.76 0.78 0.80 0.83 0.84 0.88 0.89
50 0.88 0.92 0.93 0.96 0.98 1.00 1.01 1.03 1.06 1.07
80 0.96 0.98 0.99 1.03 1.05 1.06 1.08 1.11 1.15 1.17
130 1.07 1.09 1.11 1.13 1.14 1.14 1.19 1.22 1.25 1.29
180 1.10 1.13 1.17 1.19 1.20 1.21 1.23 1.25 1.28 1.33
"Run"
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* Example 2 originates from: *
* reference [9], page 475, ff. *
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"Model" available = beta0 + beta1 * inorganic + beta2 * organic;
"Input" 18 * [soil sample, available, inorganic, organic];
"Options" Transformed data matrix, Correlation matrix, Residual analysis;
"Data" 1 64 0.4 53
2 60 0.4 23
3 71 3.1 19
4 61 0.6 34
5 54 4.7 24
6 77 1.7 65
7 81 9.4 44
8 93 10.1 31
9 93 11.6 29
10 51 12.6 58
11 76 10.9 37
12 96 23.1 46
13 77 23.1 50
14 93 21.6 44
15 95 23.1 56
16 54 1.9 36
17 168 26.8 58
18 99 29.9 51
"Run"
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* Example 3 originates from: *
* reference [3], page 228, 339 *
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"Model" Ln (Mean surface volume) = Lnalpha + beta * Ln (Feed rate)
+ gamma * Ln (Wheel velocity) + delta * Ln (Feed viscosity);
"Input" 35 * [Run number, Feed rate, Wheel velocity,
Feed viscosity, Mean surface volume];
"Options" Transformed data matrix, Residual analysis, Process submodels (1);
"Data" 1 0.0174 5300 0.108 25.4
2 0.0630 5400 0.107 31.6
3 0.0622 8300 0.107 25.7
4 0.0118 10800 0.106 17.4
5 0.1040 4600 0.102 38.2
6 0.0118 11300 0.105 18.2
7 0.0122 5800 0.105 26.5
8 0.0122 8000 0.100 19.3
9 0.0408 10000 0.106 22.3
10 0.0408 6600 0.105 26.4
11 0.0630 8700 0.104 25.8
12 0.0408 4400 0.104 32.2
13 0.0415 7600 0.106 25.1
14 0.1010 4800 0.106 39.7
15 0.0170 3100 0.106 35.6
16 0.0412 9300 0.105 23.5
17 0.0170 7700 0.098 22.1
18 0.0170 5300 0.099 26.5
19 0.1010 5700 0.098 39.7
20 0.0622 6200 0.102 31.5
21 0.0622 7700 0.102 26.9
22 0.0170 10200 0.100 18.1
23 0.0118 4800 0.102 28.4
24 0.0408 6600 0.102 27.3
25 0.0622 8300 0.102 25.8
26 0.0170 7700 0.102 23.1
27 0.0408 9000 0.613 23.4
28 0.0170 10100 0.619 18.1
29 0.0408 5300 0.671 30.9
30 0.0622 8000 0.624 25.7
31 0.1010 7300 0.613 29.0
32 0.0118 6400 0.328 22.0
33 0.0170 8000 0.341 18.8
34 0.0118 9700 1.845 17.9
35 0.0408 6300 1.940 28.4
"Run"
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* Example 4 originates from: *
* reference [1], page 88, 93, ff. *
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"Model" y = alfa0 + alfa1 * x;
"Input" 5 * ([x], n, n * [y]);
"Option" Transformed data matrix, Print input data;
"Data" 1 4 1.1 0.7 1.8 0.4
3 5 3.0 1.4 4.9 4.4 4.5
5 3 7.3 8.2 6.2
10 4 12.0 13.1 12.6 13.2
15 4 18.7 19.7 17.4 17.1
"Run"
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* Marten van Gelderen *
* Mathematisch Centrum *
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"Exit"