Trailing-Edge
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PDP-10 Archives
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decuslib10-08
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43,50504/transf.pri
There are 2 other files named transf.pri in the archive. Click here to see a list.
"MODEL" y = a * d + b * x + c * z;
"INPUT" n, m, n * [ z, d, x, y, r ],
o, < (o+1) * o : 2 > * [ correl element ],
s, s * ( t, t * [ estimate ],
t, < (t+1) * t : 2 > * [ covar element ],
n, n * ( 6 * [ residual element ] ) );
"OPTIONS" 1, 2, 3, 5(1), 7;
Transformed data matrix
=======================
obs.no. a b c dep.var.
1 1.000 25.000 1.398 0.790
2 1.000 50.000 1.699 0.984
3 1.000 80.000 1.903 1.058
4 1.000 130.000 2.114 1.163
5 1.000 180.000 2.255 1.209
Control information
===================
transformed variable
denoted by parameter mean standard deviation minimum maximum
a 1.000000 0.000000 1.000000 1.000000
b 93.000000 62.409935 25.000000 180.000000
c 1.873843 0.339506 1.397940 2.255273
dep.var. 1.040800 0.165565 0.790000 1.209000
Number of observations : 5
Correlation matrix of the variables
===================================
a b c dep.var.
a 1.000000
b * 1.000000
c * 0.962417 1.000000
dep.var. * 0.929750 0.993099 1.000000
Multiple correlation coefficient 0.997712 (adjusted 0.995418)
================================
Proportion of variation explained 0.995429 (adjusted 0.990858)
=================================
Standard deviation of the error term 0.015831
====================================
Regression parameters
=====================
right tail
parameter estimate standard deviation F - ratio probability
a -0.0899819314 0.1198580224 0.563607 0.531119
b -0.0009361326 0.0004670057 4.018189 0.182887
c 0.6499168512 0.0858477522 57.313607 0.017004
Correlation matrix of the estimates
===================================
a b c
a 1.000000
b 0.929333 1.000000
c -0.993392 -0.962417 1.000000
Analysis of variance
====================
source of right tail
variation df sum of squares mean square F - ratio probability
---------------------------------------------------------------------------------------------------------------
total 5 5.525970
---------------------------------------------------------------------------------------------------------------
mean 1 5.416323 5.416323 21612.954083 0.000000
regression 2 0.109146 0.054573 217.763834 0.004571
residual 2 0.000501 0.000251
---------------------------------------------------------------------------------------------------------------
regression null hypothesis : b = c = 0
Residual analysis
=================
standardized studentized
obs.no. observation fitted value standard deviation residual residual residual
1 0.790000 0.795160 0.015180 -0.005160 -0.515329 -1.148688
2 0.984000 0.967401 0.009923 0.016599 1.657926 1.345799
3 1.058000 1.071978 0.011074 -0.013978 -1.396083 -1.235555
4 1.163000 1.162208 0.009381 0.000792 0.079079 0.062090
5 1.209000 1.207254 0.014570 0.001746 0.174407 0.282096
sum of residuals : -0.000000
Upper bound for the right tail probability of the largest absolute studentized residual (no. 2) : 0.994142
Control information - submodel 1
===================
transformed variable
denoted by parameter mean standard deviation minimum maximum
c omitted
a 1.000000 0.000000 1.000000 1.000000
b 93.000000 62.409935 25.000000 180.000000
dep.var. 1.040800 0.165565 0.790000 1.209000
Number of observations : 5
Multiple correlation coefficient 0.929750 (adjusted 0.905122)
================================
Proportion of variation explained 0.864435 (adjusted 0.819246)
=================================
Standard deviation of the error term 0.070390
====================================
Regression parameters
=====================
right tail
parameter estimate standard deviation F - ratio probability
a 0.8114159135 0.0611679599 175.970168 0.000926
b 0.0024664955 0.0005639337 19.129543 0.022114
Correlation matrix of the estimates
===================================
a b
a 1.000000
b -0.857407 1.000000
Analysis of variance
====================
source of right tail
variation df sum of squares mean square F - ratio probability
---------------------------------------------------------------------------------------------------------------
total 5 5.525970
---------------------------------------------------------------------------------------------------------------
mean 1 5.416323 5.416323 1093.153257 0.000061
regression 1 0.094782 0.094782 19.129543 0.022114
residual 3 0.014864 0.004955
---------------------------------------------------------------------------------------------------------------
reduction 1 0.014363 0.014363 57.313607 0.017004
---------------------------------------------------------------------------------------------------------------
regression null hypothesis : b = 0 (in the reduced model)
reduction null hypothesis : c = 0 (in the original model)
Residual analysis
=================
standardized studentized
obs.no. observation fitted value standard deviation residual residual residual
1 0.790000 0.873078 0.049613 -0.083078 -1.523703 -1.663802
2 0.984000 0.934741 0.039736 0.049259 0.903443 0.847813
3 1.058000 1.008736 0.032322 0.049264 0.903537 0.787846
4 1.163000 1.132060 0.037767 0.030940 0.567451 0.520864
5 1.209000 1.255385 0.058293 -0.046385 -0.850729 -1.175640
sum of residuals : 0.000000
Upper bound for the right tail probability of the largest absolute studentized residual (no. 1) : 0.197018
End of job : 1
"MODEL" available = beta0 + beta1 * inorganic + beta2 * organic;
"INPUT" k, l, k * [ constant, inorganic, organic, available ],
l, < (l+1) * l : 2 > * [ correl element ],
r, r * ( u, u * [ estimate ],
u, < (u+1) * u : 2 > * [ covar element ],
k, k * ( 6 * [ residual element ] ) );
"OPTIONS" Transformed data matrix, Correlation matrix, Residual analysis;
Transformed data matrix
=======================
obs.no. beta0 beta1 beta2 dep.var.
1 1.000 0.400 53.000 64.000
2 1.000 0.400 23.000 60.000
3 1.000 3.100 19.000 71.000
4 1.000 0.600 34.000 61.000
5 1.000 4.700 24.000 54.000
6 1.000 1.700 65.000 77.000
7 1.000 9.400 44.000 81.000
8 1.000 10.100 31.000 93.000
9 1.000 11.600 29.000 93.000
10 1.000 12.600 58.000 51.000
11 1.000 10.900 37.000 76.000
12 1.000 23.100 46.000 96.000
13 1.000 23.100 50.000 77.000
14 1.000 21.600 44.000 93.000
15 1.000 23.100 56.000 95.000
16 1.000 1.900 36.000 54.000
17 1.000 26.800 58.000 168.000
18 1.000 29.900 51.000 99.000
Control information
===================
transformed variable
denoted by parameter mean standard deviation minimum maximum
beta0 1.000000 0.000000 1.000000 1.000000
beta1 11.944444 10.154583 0.400000 29.900000
beta2 42.111111 13.624756 19.000000 65.000000
dep.var. 81.277778 26.996308 51.000000 168.000000
Number of observations : 18
Correlation matrix of the variables
===================================
beta0 beta1 beta2 dep.var.
beta0 1.000000
beta1 * 1.000000
beta2 * 0.461567 1.000000
dep.var. * 0.693403 0.354466 1.000000
Multiple correlation coefficient 0.694487 (adjusted 0.642875)
================================
Proportion of variation explained 0.482313 (adjusted 0.413288)
=================================
Standard deviation of the error term 20.678399
====================================
Regression parameters
=====================
right tail
parameter estimate standard deviation F - ratio probability
beta0 56.2510240854 16.3107373404 11.893610 0.003581
beta1 1.7897741162 0.5567434145 10.334424 0.005787
beta2 0.0866492500 0.4149429933 0.043607 0.837396
Correlation matrix of the estimates
===================================
beta0 beta1 beta2
beta0 1.000000
beta1 0.086771 1.000000
beta2 -0.883117 -0.461567 1.000000
Analysis of variance
====================
source of right tail
variation df sum of squares mean square F - ratio probability
---------------------------------------------------------------------------------------------------------------
total 18 131299.000000
---------------------------------------------------------------------------------------------------------------
mean 1 118909.388889 118909.388889 278.088058 0.000000
regression 2 5975.668532 2987.834266 6.987514 0.007170
residual 15 6413.942579 427.596172
---------------------------------------------------------------------------------------------------------------
regression null hypothesis : beta1 = beta2 = 0
Residual analysis
=================
standardized studentized
obs.no. observation fitted value standard deviation residual residual residual
1 64.000000 61.559344 10.596613 2.440656 0.129295 0.137448
2 60.000000 58.959866 8.994436 1.040134 0.055101 0.055862
3 71.000000 63.445660 9.817069 7.554340 0.400194 0.415085
4 61.000000 60.270963 7.439813 0.729037 0.038621 0.037786
5 54.000000 66.742544 8.277594 -12.742544 -0.675041 -0.672453
6 77.000000 64.925841 14.017687 12.074159 0.639633 0.794248
7 81.000000 76.887468 5.234633 4.112532 0.217863 0.205577
8 93.000000 77.013869 6.457231 15.986131 0.846871 0.813778
9 93.000000 79.525232 7.240620 13.474768 0.713830 0.695677
10 51.000000 83.827834 8.070605 -32.827834 -1.739066 -1.724294
11 76.000000 78.965584 5.239553 -2.965584 -0.157103 -0.148253
12 96.000000 101.580672 7.461991 -5.580672 -0.295638 -0.289377
13 77.000000 101.927269 7.367271 -24.927269 -1.320530 -1.290133
14 93.000000 98.722712 7.026946 -5.722712 -0.303163 -0.294260
15 95.000000 102.447164 7.905720 -7.447164 -0.394516 -0.389751
16 54.000000 62.770968 6.954672 -8.770968 -0.464645 -0.450398
17 168.000000 109.242627 9.235282 58.757373 3.112692 3.175816
18 99.000000 114.184382 10.161448 -15.184382 -0.804398 -0.843133
sum of residuals : 0.000000
Upper bound for the right tail probability of the largest absolute studentized residual (no. 17) : 0.001810
End of job : 2
"MODEL" surface = alfa + beta * rate + gamma * wheel + delta * visco;
"INPUT" n, m, n * [ const, rate, wheel, visco, surface ],
s, s * ( t, t * [ estimate ],
n, n * ( 6 * [ residual element ] ) );
"OPTIONS" Transformed data matrix, Correlation matrix, Residual analysis;
Transformed data matrix
=======================
obs.no. alfa beta gamma delta dep.var.
1 1.000 4.051 8.575 2.226 3.235
2 1.000 2.765 8.594 2.235 3.453
3 1.000 2.777 9.024 2.235 3.246
4 1.000 4.440 9.287 2.244 2.856
5 1.000 2.263 8.434 2.283 3.643
6 1.000 4.440 9.333 2.254 2.901
7 1.000 4.406 8.666 2.254 3.277
8 1.000 4.406 8.987 2.303 2.960
9 1.000 3.199 9.210 2.244 3.105
10 1.000 3.199 8.795 2.254 3.273
11 1.000 2.765 9.071 2.263 3.250
12 1.000 3.199 8.389 2.263 3.472
13 1.000 3.182 8.936 2.244 3.223
14 1.000 2.293 8.476 2.244 3.681
15 1.000 4.075 8.039 2.244 3.572
16 1.000 3.189 9.138 2.254 3.157
17 1.000 4.075 8.949 2.323 3.096
18 1.000 4.075 8.575 2.313 3.277
19 1.000 2.293 8.648 2.323 3.681
20 1.000 2.777 8.732 2.283 3.450
21 1.000 2.777 8.949 2.283 3.292
22 1.000 4.075 9.230 2.303 2.896
23 1.000 4.440 8.476 2.283 3.346
24 1.000 3.199 8.795 2.283 3.307
25 1.000 2.777 9.024 2.283 3.250
26 1.000 4.075 8.949 2.283 3.140
27 1.000 3.199 9.105 0.489 3.153
28 1.000 4.075 9.220 0.480 2.896
29 1.000 3.199 8.575 0.399 3.431
30 1.000 2.777 8.987 0.472 3.246
31 1.000 2.293 8.896 0.489 3.367
32 1.000 4.440 8.764 1.115 3.091
33 1.000 4.075 8.987 1.076 2.934
34 1.000 4.440 9.180 0.612 2.885
35 1.000 3.199 8.748 0.663 3.346
Control information
===================
transformed variable
denoted by parameter mean standard deviation minimum maximum
alfa 1.000000 0.000000 1.000000 1.000000
beta 3.454469 0.748055 2.263364 4.439656
gamma 8.849891 0.298180 8.039157 9.332558
delta 1.851332 0.732791 0.398986 2.322788
dep.var. 3.239746 0.228501 2.856470 3.681351
Number of observations : 35
Correlation matrix of the variables
===================================
alfa beta gamma delta dep.var.
alfa 1.000000
beta * 1.000000
gamma * 0.181207 1.000000
delta * 0.002140 -0.184788 1.000000
dep.var. * -0.680447 -0.811063 0.197582 1.000000
Multiple correlation coefficient 0.978154 (adjusted 0.976014)
================================
Proportion of variation explained 0.956785 (adjusted 0.952603)
=================================
Standard deviation of the error term 0.049746
====================================
Regression parameters
=====================
right tail
parameter estimate standard deviation F - ratio probability
alfa 8.5161547705 0.2628811987 1049.465191 0.000000
beta -0.1692742083 0.0116047180 212.770983 0.000000
gamma -0.5346926136 0.0296232726 325.793413 0.000000
delta 0.0217758632 0.0118544683 3.374323 0.075824
Correlation matrix of the estimates
===================================
alfa beta gamma delta
alfa 1.000000
beta 0.034862 1.000000
gamma -0.984808 -0.184785 1.000000
delta -0.265643 -0.036859 0.188293 1.000000
Analysis of variance
====================
source of right tail
variation df sum of squares mean square F - ratio probability
---------------------------------------------------------------------------------------------------------------
total 35 369.133526
---------------------------------------------------------------------------------------------------------------
mean 1 367.358289 367.358289 148444.886282 0.000000
regression 3 1.698522 0.566174 228.783777 0.000000
residual 31 0.076716 0.002475
---------------------------------------------------------------------------------------------------------------
regression null hypothesis : beta = gamma = delta = 0
Residual analysis
=================
standardized studentized
obs.no. observation fitted value standard deviation residual residual residual
1 3.234749 3.293605 0.014471 -0.058856 -1.257138 -1.236599
2 3.453157 3.501612 0.013422 -0.048455 -1.034982 -1.011563
3 3.246491 3.269608 0.014359 -0.023117 -0.493769 -0.485355
4 2.856470 2.847656 0.018843 0.008814 0.188267 0.191448
5 3.642836 3.673238 0.018930 -0.030403 -0.649389 -0.660875
6 2.901422 2.823664 0.019745 0.077758 1.660861 1.702966
7 3.277145 3.185918 0.015933 0.091227 1.948567 1.935824
8 2.960105 3.015032 0.015004 -0.054926 -1.173202 -1.158055
9 3.104587 3.098805 0.015705 0.005782 0.123491 0.122484
10 3.273364 3.321185 0.010062 -0.047821 -1.021423 -0.981573
11 3.250374 3.247224 0.015359 0.003151 0.067294 0.066585
12 3.471966 3.538192 0.015827 -0.066226 -1.414548 -1.404229
13 3.222868 3.248424 0.010836 -0.025556 -0.545868 -0.526370
14 3.681351 3.644690 0.018075 0.036661 0.783071 0.791030
15 3.572346 3.576834 0.027207 -0.004488 -0.095864 -0.107766
16 3.157000 3.139466 0.014204 0.017534 0.374526 0.367787
17 3.095578 3.092069 0.012560 0.003508 0.074932 0.072882
18 3.277145 3.291563 0.014848 -0.014419 -0.307973 -0.303683
19 3.681351 3.554511 0.016812 0.126840 2.709233 2.709112
20 3.449988 3.426623 0.012525 0.023364 0.499047 0.485299
21 3.292126 3.310771 0.013578 -0.018645 -0.398240 -0.389586
22 2.895912 2.941291 0.016367 -0.045379 -0.969282 -0.965996
23 3.346389 3.282092 0.019265 0.064297 1.373353 1.401884
24 3.306887 3.321816 0.010222 -0.014929 -0.318878 -0.306648
25 3.250374 3.270650 0.014597 -0.020276 -0.433078 -0.426348
26 3.139833 3.091198 0.012345 0.048634 1.038804 1.009211
27 3.152736 3.116926 0.018870 0.035810 0.764892 0.778000
28 2.895912 2.906863 0.020483 -0.010951 -0.233917 -0.241572
29 3.430756 3.398086 0.021955 0.032670 0.697814 0.731865
30 3.246491 3.250894 0.019844 -0.004403 -0.094054 -0.096529
31 3.367296 3.382300 0.022304 -0.015004 -0.320487 -0.337434
32 3.091042 3.102835 0.017624 -0.011793 -0.251893 -0.253504
33 2.933857 3.044480 0.014266 -0.110623 -2.362858 -2.321236
34 2.884801 2.869558 0.020728 0.015243 0.325573 0.337059
35 3.346389 3.311411 0.017225 0.034978 0.747120 0.749494
sum of residuals : 0.000000
Upper bound for the right tail probability of the largest absolute studentized residual (no. 19) : 0.166067
End of job : 3