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```

"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

```