Trailing-Edge
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decus_20tap5_198111
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decus/20-0149/mulreg.pri
There are 2 other files named mulreg.pri in the archive. Click here to see a list.
***********************************
* Example 1 originates from: *
* reference [4], page 472, 479 *
***********************************
"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);
Transformed data matrix
=======================
obs.no. c a b dep.var. repeats
1 1.398 1.000 25.000 0.790 10.000
2 1.699 1.000 50.000 0.984 10.000
3 1.903 1.000 80.000 1.058 10.000
4 2.114 1.000 130.000 1.163 10.000
5 2.255 1.000 180.000 1.209 10.000
Control information
===================
transformed variable
denoted by parameter mean standard deviation minimum maximum
c 1.873843 0.306746 1.397940 2.255273
a 1.000000 0.000000 1.000000 1.000000
b 93.000000 56.387870 25.000000 180.000000
dep.var. 1.040800 0.163655 0.670000 1.330000
Number of observations : 5
Correlation matrix of the variables
===================================
c a b dep.var.
c 1.000000
a * 1.000000
b 0.962417 * 1.000000
dep.var. 0.907742 * 0.849838 1.000000
Multiple correlation coefficient 0.911959 (adjusted 0.908023)
================================
Proportion of variation explained 0.831669 (adjusted 0.824506)
=================================
Standard deviation of the error term 0.068558
====================================
Regression parameters
=====================
right tail
parameter estimate standard deviation F - ratio probability
c 0.6499168512 0.1175695440 30.558070 0.000001
a -0.0899819314 0.1641470240 0.300500 0.586163
b -0.0009361326 0.0006395700 2.142390 0.149935
Correlation matrix of the estimates
===================================
c a b
c 1.000000
a -0.993392 1.000000
b -0.962417 0.929333 1.000000
Analysis of variance
====================
source of right tail
variation df sum of squares mean square F - ratio probability
---------------------------------------------------------------------------------------------------------------
total 50 55.475600
---------------------------------------------------------------------------------------------------------------
mean 1 54.163232 54.163232 11523.444701 0.000000
regression 2 1.091456 0.545728 116.105776 0.000000
residual 47 0.220912 0.004700
---------------------------------------------------------------------------------------------------------------
lack of fit 2 0.005012 0.002506 0.522336 0.596686
pure error 45 0.215900 0.004798
---------------------------------------------------------------------------------------------------------------
regression null hypothesis : c = b = 0
Residual analysis
=================
standardized studentized
obs.no. observation fitted value standard deviation residual residual residual
1 0.790000 0.795160 0.020789 -0.005160 -0.118992 -0.078976
2 0.984000 0.967401 0.013590 0.016599 0.382824 0.247021
3 1.058000 1.071978 0.015165 -0.013978 -0.322363 -0.209059
4 1.163000 1.162208 0.012847 0.000792 0.018260 0.011757
5 1.209000 1.207254 0.019954 0.001746 0.040272 0.026622
sum of residuals : 0.000000
Upper bound for the right tail probability of the largest absolute studentized residual (no. 2) : 1.000000
Control information - submodel 1
===================
transformed variable
denoted by parameter mean standard deviation minimum maximum
b omitted
c 1.873843 0.306746 1.397940 2.255273
a 1.000000 0.000000 1.000000 1.000000
dep.var. 1.040800 0.163655 0.670000 1.330000
Number of observations : 5
Multiple correlation coefficient 0.907742 (adjusted 0.905720)
================================
Proportion of variation explained 0.823996 (adjusted 0.820329)
=================================
Standard deviation of the error term 0.069370
====================================
Regression parameters
=====================
right tail
parameter estimate standard deviation F - ratio probability
c 0.4842988398 0.0323066319 224.720913 0.000000
a 0.1332999205 0.0613273100 4.724458 0.034701
Correlation matrix of the estimates
===================================
c a
c 1.000000
a -0.987122 1.000000
Analysis of variance
====================
source of right tail
variation df sum of squares mean square F - ratio probability
---------------------------------------------------------------------------------------------------------------
total 50 55.475600
---------------------------------------------------------------------------------------------------------------
mean 1 54.163232 54.163232 11255.564569 0.000000
regression 1 1.081386 1.081386 224.720852 0.000000
residual 48 0.230982 0.004812
---------------------------------------------------------------------------------------------------------------
lack of fit 3 0.015082 0.005027 1.047838 0.380681
pure error 45 0.215900 0.004798
---------------------------------------------------------------------------------------------------------------
reduction 1 0.010070 0.010070 2.142390 0.149935
---------------------------------------------------------------------------------------------------------------
regression null hypothesis : c = 0 (in the reduced model)
reduction null hypothesis : b = 0 (in the original model)
Residual analysis
=================
standardized studentized
obs.no. observation fitted value standard deviation residual residual residual
1 0.790000 0.810321 0.018238 -0.020321 -0.378175 -0.303615
2 0.984000 0.956109 0.011321 0.027891 0.519060 0.407526
3 1.058000 1.054964 0.009856 0.003036 0.056497 0.044211
4 1.163000 1.157080 0.012506 0.005920 0.110169 0.086758
5 1.209000 1.225526 0.015751 -0.016526 -0.307551 -0.244617
sum of residuals : -0.000000
Upper bound for the right tail probability of the largest absolute studentized residual (no. 2) : 1.000000
Control information - submodel 2
===================
transformed variable
denoted by parameter mean standard deviation minimum maximum
a omitted
b omitted
c 1.873843 0.306746 1.397940 2.255273
dep.var. 1.040800 0.163655 0.670000 1.330000
Number of observations : 5
There is no constant independent variable in the transformed (sub)model (message)
Multiple correlation coefficient 0.997711 (adjusted 0.997664)
================================
Proportion of variation explained 0.995427 (adjusted 0.995333)
=================================
Standard deviation of the error term 0.071958
====================================
Regression parameters
=====================
right tail
parameter estimate standard deviation F - ratio probability
c 0.5536156656 0.0053607978 10664.926934 0.000000
Correlation matrix of the estimates
===================================
c
c 1.000000
Analysis of variance
====================
source of right tail
variation df sum of squares mean square F - ratio probability
---------------------------------------------------------------------------------------------------------------
total 50 55.475600
---------------------------------------------------------------------------------------------------------------
regression 1 55.221883 55.221883 10664.926934 0.000000
residual 49 0.253717 0.005178
---------------------------------------------------------------------------------------------------------------
lack of fit 4 0.037817 0.009454 1.970525 0.115263
pure error 45 0.215900 0.004798
---------------------------------------------------------------------------------------------------------------
reduction 2 0.032804 0.016402 3.489644 0.038633
---------------------------------------------------------------------------------------------------------------
regression null hypothesis : c = 0 (in the reduced model)
reduction null hypothesis : a = b = 0 (in the original model)
Residual analysis
=================
standardized studentized
obs.no. observation fitted value standard deviation residual residual residual
1 0.790000 0.773921 0.007494 0.016079 0.249818 0.224666
2 0.984000 0.940576 0.009108 0.043424 0.674690 0.608353
3 1.058000 1.053580 0.010202 0.004420 0.068669 0.062046
4 1.163000 1.170312 0.011332 -0.007312 -0.113612 -0.102902
5 1.209000 1.248554 0.012090 -0.039554 -0.614569 -0.557614
sum of residuals : 0.170553
Upper bound for the right tail probability of the largest absolute studentized residual (no. 2) : 1.000000
End of job : 1
***********************************
* Example 2 originates from: *
* reference [9], page 475, ff. *
***********************************
"Model" available = beta0 + beta1 * inorganic + beta2 * organic;
"Input" 18 * [soil sample, available, inorganic, organic];
"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
***********************************
* Example 3 originates from: *
* reference [3], page 228, 339 *
***********************************
"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);
Transformed data matrix
=======================
obs.no. Lnalpha 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
Lnalpha 1.000000 0.000000 1.000000 1.000000
beta -3.454469 0.748055 -4.439656 -2.263364
gamma 8.849891 0.298180 8.039157 9.332558
delta -1.778466 0.899585 -2.322788 0.662688
dep.var. 3.239746 0.228501 2.856470 3.681351
Number of observations : 35
Multiple correlation coefficient 0.977342 (adjusted 0.975121)
================================
Proportion of variation explained 0.955197 (adjusted 0.950861)
=================================
Standard deviation of the error term 0.050652
====================================
Regression parameters
=====================
right tail
parameter estimate standard deviation F - ratio probability
Lnalpha 8.5495323331 0.2660238985 1032.864643 0.000000
beta 0.1684244052 0.0118081196 203.445721 0.000000
gamma -0.5371370141 0.0300960773 318.530024 0.000000
delta -0.0144134670 0.0098170458 2.155635 0.152122
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 143182.007167 0.000000
regression 3 1.695702 0.565234 220.306257 0.000000
residual 31 0.079536 0.002566
---------------------------------------------------------------------------------------------------------------
regression null hypothesis : beta = gamma = delta = 0
Residual analysis
=================
standardized studentized
obs.no. observation fitted value standard deviation residual residual residual
1 3.234749 3.293078 0.014784 -0.058329 -1.223587 -1.203971
2 3.453157 3.499877 0.013574 -0.046720 -0.980073 -0.957386
3 3.246491 3.266833 0.014411 -0.020342 -0.426727 -0.418915
4 2.856470 2.845581 0.019237 0.010889 0.228428 0.232392
5 3.642836 3.671117 0.019169 -0.028281 -0.593273 -0.603205
6 2.901422 2.821409 0.020142 0.080013 1.678467 1.721617
7 3.277145 3.185264 0.016296 0.091881 1.927422 1.915802
8 2.960105 3.013233 0.015317 -0.053128 -1.114483 -1.100383
9 3.104587 3.095864 0.015813 0.008723 0.182980 0.181266
10 3.273364 3.319189 0.010115 -0.045825 -0.961298 -0.923297
11 3.250374 3.244114 0.015389 0.006261 0.131334 0.129733
12 3.471966 3.537118 0.016090 -0.065151 -1.366704 -1.356500
13 3.222868 3.246139 0.010877 -0.023271 -0.488175 -0.470406
14 3.681351 3.642772 0.018313 0.038579 0.809285 0.816897
15 3.572346 3.577499 0.027704 -0.005154 -0.108113 -0.121538
16 3.157000 3.136624 0.014274 0.020376 0.427441 0.419268
17 3.095578 3.089933 0.012748 0.005644 0.118401 0.115136
18 3.277145 3.290415 0.015136 -0.013270 -0.278377 -0.274531
19 3.681351 3.551596 0.016891 0.129755 2.721926 2.717208
20 3.449988 3.424209 0.012559 0.025778 0.540765 0.525330
21 3.292126 3.307827 0.013565 -0.015701 -0.329363 -0.321724
22 2.895912 2.938617 0.016603 -0.042705 -0.895839 -0.892395
23 3.346389 3.281716 0.019666 0.064673 1.356676 1.385494
24 3.306887 3.319607 0.010241 -0.012720 -0.266842 -0.256427
25 3.250374 3.267523 0.014593 -0.017148 -0.359731 -0.353541
26 3.139833 3.089357 0.012571 0.050476 1.058853 1.028699
27 3.152736 3.127162 0.016604 0.025574 0.536468 0.534411
28 2.895912 2.917634 0.018272 -0.021722 -0.455674 -0.459805
29 3.430756 3.410283 0.019104 0.020473 0.429475 0.436419
30 3.246491 3.261192 0.017683 -0.014701 -0.308384 -0.309713
31 3.367296 3.392279 0.020625 -0.024983 -0.524074 -0.540015
32 3.091042 3.110356 0.016548 -0.019313 -0.405145 -0.403428
33 2.933857 3.051431 0.013053 -0.117574 -2.466405 -2.402326
34 2.884801 2.862104 0.027070 0.022697 0.476118 0.530148
35 3.346389 3.302140 0.026253 0.044249 0.928227 1.021480
sum of residuals : 0.000000
Upper bound for the right tail probability of the largest absolute studentized residual (no. 19) : 0.161110
Control information - submodel 1
===================
transformed variable
denoted by parameter mean standard deviation minimum maximum
delta omitted
Lnalpha 1.000000 0.000000 1.000000 1.000000
beta -3.454469 0.748055 -4.439656 -2.263364
gamma 8.849891 0.298180 8.039157 9.332558
dep.var. 3.239746 0.228501 2.856470 3.681351
Number of observations : 35
Multiple correlation coefficient 0.975747 (adjusted 0.974211)
================================
Proportion of variation explained 0.952082 (adjusted 0.949087)
=================================
Standard deviation of the error term 0.051559
====================================
Regression parameters
=====================
right tail
parameter estimate standard deviation F - ratio probability
Lnalpha 8.6444323107 0.2626702491 1083.056676 0.000000
beta 0.1684884827 0.0120193633 196.506767 0.000000
gamma -0.5449387793 0.0301534149 326.604694 0.000000
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 138191.417269 0.000000
regression 2 1.690171 0.845086 317.901017 0.000000
residual 32 0.085067 0.002658
---------------------------------------------------------------------------------------------------------------
reduction 1 0.005531 0.005531 2.155635 0.152122
---------------------------------------------------------------------------------------------------------------
regression null hypothesis : beta = gamma = 0 (in the reduced model)
reduction null hypothesis : delta = 0 (in the original model)
Residual analysis
=================
standardized studentized
obs.no. observation fitted value standard deviation residual residual residual
1 3.234749 3.288736 0.014744 -0.053986 -1.095063 -1.092714
2 3.453157 3.495338 0.013453 -0.042181 -0.855592 -0.847461
3 3.246491 3.258939 0.013610 -0.012448 -0.252494 -0.250309
4 2.856470 2.835391 0.018263 0.021079 0.427577 0.437186
5 3.642836 3.667170 0.019319 -0.024335 -0.493612 -0.509070
6 2.901422 2.810729 0.019119 0.090693 1.839619 1.894046
7 3.277145 3.179790 0.016148 0.097355 1.974757 1.988259
8 2.960105 3.004546 0.014381 -0.044441 -0.901445 -0.897568
9 3.104587 3.086354 0.014683 0.018233 0.369839 0.368910
10 3.273364 3.312784 0.009289 -0.039420 -0.799600 -0.777283
11 3.250374 3.235443 0.014465 0.014931 0.302866 0.301712
12 3.471966 3.533738 0.016210 -0.061771 -1.252973 -1.262069
13 3.222868 3.238771 0.009822 -0.015903 -0.322584 -0.314204
14 3.681351 3.639046 0.018461 0.042305 0.858113 0.878776
15 3.572346 3.577070 0.028198 -0.004725 -0.095835 -0.109457
16 3.157000 3.127544 0.013095 0.029456 0.597494 0.590683
17 3.095578 3.081275 0.011505 0.014303 0.290113 0.284576
18 3.277145 3.284817 0.014911 -0.007672 -0.155626 -0.155449
19 3.681351 3.545399 0.016648 0.135953 2.757671 2.786075
20 3.449988 3.417901 0.012012 0.032087 0.650846 0.639938
21 3.292126 3.299829 0.012646 -0.007702 -0.156232 -0.154093
22 2.895912 2.928056 0.015231 -0.032144 -0.652013 -0.652569
23 3.346389 3.277298 0.019783 0.069091 1.401447 1.451105
24 3.306887 3.312784 0.009289 -0.005897 -0.119624 -0.116286
25 3.250374 3.258939 0.013610 -0.008564 -0.173721 -0.172218
26 3.139833 3.081275 0.011505 0.058558 1.187784 1.165114
27 3.152736 3.143769 0.012373 0.008967 0.181893 0.179159
28 2.895912 2.933425 0.015036 -0.037513 -0.760916 -0.760637
29 3.430756 3.432323 0.012027 -0.001567 -0.031791 -0.031260
30 3.246491 3.279000 0.013097 -0.032509 -0.659420 -0.651909
31 3.367296 3.410576 0.016728 -0.043280 -0.877901 -0.887443
32 3.091042 3.120529 0.015296 -0.029487 -0.598106 -0.598860
33 2.933857 3.060447 0.011724 -0.126590 -2.567757 -2.521296
34 2.884801 2.893928 0.016507 -0.009128 -0.185143 -0.186866
35 3.346389 3.338135 0.009558 0.008254 0.167433 0.162920
sum of residuals : 0.000000
Upper bound for the right tail probability of the largest absolute studentized residual (no. 19) : 0.125816
End of job : 3
**************************************
* Example 4 originates from: *
* reference [1], page 88, 93, ff. *
**************************************
"Model" y = alfa0 + alfa1 * x;
"Input" 5 * ([x], n, n * [y]);
"Option" Transformed data matrix, Print input data;
"Data"
1.000 4.000 1.100 0.700 1.800 0.400 3.000 5.000 3.000 1.400 4.900
4.400 4.500 5.000 3.000 7.300 8.200 6.200 10.000 4.000 12.000 13.100
12.600 13.200 15.000 4.000 18.700 19.700 17.400 17.100
Transformed data matrix
=======================
obs.no. alfa0 alfa1 dep.var. repeats
1 1.000 1.000 1.000 4.000
2 1.000 3.000 3.640 5.000
3 1.000 5.000 7.233 3.000
4 1.000 10.000 12.725 4.000
5 1.000 15.000 18.225 4.000
Control information
===================
transformed variable
denoted by parameter mean standard deviation minimum maximum
alfa0 1.000000 0.000000 1.000000 1.000000
alfa1 6.700000 5.262579 1.000000 15.000000
dep.var. 8.385000 6.545571 0.400000 19.700000
Number of observations : 5
Multiple correlation coefficient 0.987051 (adjusted 0.986326)
================================
Proportion of variation explained 0.974269 (adjusted 0.972840)
=================================
Standard deviation of the error term 1.078736
====================================
Regression parameters
=====================
right tail
parameter estimate standard deviation F - ratio probability
alfa0 0.1594830832 0.3968072487 0.161536 0.692478
alfa1 1.2276890919 0.0470261690 681.549798 0.000000
Analysis of variance
====================
source of right tail
variation df sum of squares mean square F - ratio probability
---------------------------------------------------------------------------------------------------------------
total 20 2220.210000
---------------------------------------------------------------------------------------------------------------
mean 1 1406.164500 1406.164500 1208.387113 0.000000
regression 1 793.099430 793.099430 681.549798 0.000000
residual 18 20.946070 1.163671
---------------------------------------------------------------------------------------------------------------
lack of fit 3 4.252403 1.417468 1.273658 0.319196
pure error 15 16.693667 1.112911
---------------------------------------------------------------------------------------------------------------
regression null hypothesis : alfa1 = 0
End of job : 4
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* Marten van Gelderen *
* Mathematisch Centrum *
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