Trailing-Edge
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PDP-10 Archives
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decus_20tap5_198111
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decus/20-0149/searle.pri
There are 2 other files named searle.pri in the archive. Click here to see a list.
"MODEL" Income = Alpha1 * Years of schooling + Alpha2 * Age + Constant ;
"INPUT" 4.999999999999999999 * [ Man, Income, Years of schooling, Age ] ;
"OPTIONS" 1, 2, 3, 5(1,2) ;
Transformed data matrix
=======================
obs.no. Alpha1 Alpha2 Constant dep.var.
1 6.000 28.000 1.000 10.000
2 12.000 40.000 1.000 20.000
3 10.000 32.000 1.000 17.000
4 8.000 36.000 1.000 12.000
5 9.000 34.000 1.000 11.000
Control information
===================
transformed variable
denoted by parameter mean standard deviation minimum maximum
Alpha1 9.000000 2.236068 6.000000 12.000000
Alpha2 34.000000 4.472136 28.000000 40.000000
Constant 1.000000 0.000000 1.000000 1.000000
dep.var. 14.000000 4.301163 10.000000 20.000000
Number of observations : 5
Correlation matrix of the variables
===================================
Alpha1 Alpha2 Constant dep.var.
Alpha1 1.000000
Alpha2 0.800000 1.000000
Constant * * 1.000000
dep.var. 0.909782 0.649844 * 1.000000
Multiple correlation coefficient 0.919018 (adjusted 0.830174)
================================
Proportion of variation explained 0.844595 (adjusted 0.689189)
=================================
Standard deviation of the error term 2.397916
====================================
Regression parameters
=====================
right tail
parameter estimate standard deviation F - ratio probability
Alpha1 2.0833333333 0.8936504412 5.434783 0.145018
Alpha2 -0.2083333333 0.4468252206 0.217391 0.686888
Constant 2.3333333333 10.0566451221 0.053833 0.838102
Correlation matrix of the estimates
===================================
Alpha1 Alpha2 Constant
Alpha1 1.000000
Alpha2 -0.800000 1.000000
Constant 0.408764 -0.870845 1.000000
Analysis of variance
====================
source of right tail
variation df sum of squares mean square F - ratio probability
---------------------------------------------------------------------------------------------------------------
total 5 1054.000000
---------------------------------------------------------------------------------------------------------------
mean 1 980.000000 980.000000 170.434783 0.005816
regression 2 62.500000 31.250000 5.434783 0.155405
residual 2 11.500000 5.750000
---------------------------------------------------------------------------------------------------------------
regression null hypothesis : Alpha1 = Alpha2 = 0
Residual analysis
=================
standardized studentized
obs.no. observation fitted value standard deviation residual residual residual
1 10.000000 9.000000 2.006240 1.000000 0.659380 0.761387
2 20.000000 19.000000 2.006240 1.000000 0.659380 0.761387
3 17.000000 16.500000 2.006240 0.500000 0.329690 0.380693
4 12.000000 11.500000 2.006240 0.500000 0.329690 0.380693
5 11.000000 14.000000 1.072381 -3.000000 -1.978141 -1.398757
sum of residuals : 0.000000
Upper bound for the right tail probability of the largest absolute studentized residual (no. 5) : 0.471040
Control information - submodel 1
===================
transformed variable
denoted by parameter mean standard deviation minimum maximum
Constant omitted
Alpha1 9.000000 2.236068 6.000000 12.000000
Alpha2 34.000000 4.472136 28.000000 40.000000
dep.var. 14.000000 4.301163 10.000000 20.000000
Number of observations : 5
There is no constant independent variable in the transformed (sub)model (message)
Multiple correlation coefficient 0.994382 (adjusted 0.990619)
================================
Proportion of variation explained 0.988796 (adjusted 0.981326)
=================================
Standard deviation of the error term 1.984065
====================================
Regression parameters
=====================
right tail
parameter estimate standard deviation F - ratio probability
Alpha1 1.9985786481 0.6748219549 8.771302 0.059466
Alpha2 -0.1180511687 0.1817334456 0.421960 0.562263
Correlation matrix of the estimates
===================================
Alpha1 Alpha2
Alpha1 1.000000
Alpha2 -0.989778 1.000000
Analysis of variance
====================
source of right tail
variation df sum of squares mean square F - ratio probability
---------------------------------------------------------------------------------------------------------------
total 5 1054.000000
---------------------------------------------------------------------------------------------------------------
regression 2 1042.190461 521.095231 132.374829 0.001186
residual 3 11.809539 3.936513
---------------------------------------------------------------------------------------------------------------
reduction 1 0.309539 0.309539 0.053833 0.838102
---------------------------------------------------------------------------------------------------------------
regression null hypothesis : Alpha1 = Alpha2 = 0 (in the reduced model)
reduction null hypothesis : Constant = 0 (in the original model)
Residual analysis
=================
standardized studentized
obs.no. observation fitted value standard deviation residual residual residual
1 10.000000 8.686039 1.225558 1.313961 0.854970 0.842123
2 20.000000 19.260897 1.374745 0.739103 0.480921 0.516642
3 17.000000 16.208149 1.293188 0.791851 0.515243 0.526245
4 12.000000 11.738787 1.424930 0.261213 0.169966 0.189201
5 11.000000 13.973468 0.882242 -2.973468 -1.934781 -1.673193
sum of residuals : 0.132660
Upper bound for the right tail probability of the largest absolute studentized residual (no. 5) : 0.169908
Control information - submodel 2
===================
transformed variable
denoted by parameter mean standard deviation minimum maximum
Alpha2 omitted
Constant omitted
Alpha1 9.000000 2.236068 6.000000 12.000000
dep.var. 14.000000 4.301163 10.000000 20.000000
Number of observations : 5
There is no constant independent variable in the transformed (sub)model (message)
Multiple correlation coefficient 0.993589 (adjusted 0.991980)
================================
Proportion of variation explained 0.987220 (adjusted 0.984024)
=================================
Standard deviation of the error term 1.835115
====================================
Regression parameters
=====================
right tail
parameter estimate standard deviation F - ratio probability
Alpha1 1.5647058824 0.0890161526 308.978166 0.000062
Correlation matrix of the estimates
===================================
Alpha1
Alpha1 1.000000
Analysis of variance
====================
source of right tail
variation df sum of squares mean square F - ratio probability
---------------------------------------------------------------------------------------------------------------
total 5 1054.000000
---------------------------------------------------------------------------------------------------------------
regression 1 1040.529412 1040.529412 308.978166 0.000062
residual 4 13.470588 3.367647
---------------------------------------------------------------------------------------------------------------
reduction 2 1.970588 0.985294 0.171355 0.853712
---------------------------------------------------------------------------------------------------------------
regression null hypothesis : Alpha1 = 0 (in the reduced model)
reduction null hypothesis : Alpha2 = Constant = 0 (in the original model)
Residual analysis
=================
standardized studentized
obs.no. observation fitted value standard deviation residual residual residual
1 10.000000 9.388235 0.534097 0.611765 0.372714 0.348450
2 20.000000 18.776471 1.068194 1.223529 0.745429 0.819960
3 17.000000 15.647059 0.890162 1.352941 0.824272 0.843079
4 12.000000 12.517647 0.712129 -0.517647 -0.315374 -0.306063
5 11.000000 14.082353 0.801145 -3.082353 -1.877907 -1.866957
sum of residuals : -0.411765
Upper bound for the right tail probability of the largest absolute studentized residual (no. 5) : 0.101945
End of job : 1