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SPSS Textbook Examples
Applied Regression Analysis by John Fox
Chapter 12: Diagnosing nonlinearity, nonconstant error variance, and nonnormality

page 297 Figure 12.1 The distribution of the studentized residuals from Ornstein's interlocking-directorate regression. A normal quanitle comparison plot is shown in (a). The 95% confidence envelope is based on the standard errors of the order statistics for an independent normal sample. A nonparametric density estimate is shown in (b).

NOTE: The dummy variables need to be calculated before the graphs can be made.

NOTE: We do not know how to do quantile-normal or density plots using SPSS.

page 298 Table 12.1 Regression of number of interlocking directorate and executive positions maintained by 248 dominant Canadian corporations on corporate assets, sector, and nation of control. The baseline category for sector is heavy manufacturing; for nation of control, Canada.

GET FILE='D:/ornstein.sav'.

compute sec1 = 0.
if sector = "AGR" sec1 = 1.
compute sec2 = 0.
if sector = "BNK" sec2 = 1.
compute sec3 = 0.
if sector = "CON" sec3 = 1.
compute sec4 = 0.
if sector = "FIN" sec4 = 1.
compute sec5 = 0.
if sector = "HLD" sec5 = 1.
compute sec6 = 0.
if sector = "MAN" sec6 = 1.
compute sec7 = 0.
if sector = "MER" sec7 = 1.
compute sec8 = 0.
if sector = "MIN" sec8 = 1.
compute sec9 = 0.
if sector = "TRN" sec9 = 1.
compute sec10 = 0.
if sector = "WOD" sec10 = 1.
compute nat1 = 0.
if nation = "CAN" nat1 = 1.
compute nat2 = 0.
if nation = "OTH" nat2 = 1.
compute nat3 = 0.
if nation = "UK" nat3 = 1.
compute nat4 = 0.
if nation = "US" nat4 = 1.
compute asset1 = sqrt(assets).
execute.

regression 
 /dep=intrlcks 
 /method=enter asset1 nat2 nat3 nat4 sec1 sec2 sec3 sec4 sec5 sec7 sec8 sec9 sec10 
 /save pre sre.
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1(a) . Enter
a All requested variables entered.
b Dependent Variable: Number interlocking director and executive positions

Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .809(a) .655 .635 9.712
a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Dependent Variable: Number interlocking director and executive positions

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 41816.553 13 3216.658 34.105 .000(a)
Residual 22069.834 234 94.316

Total 63886.387 247


a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Dependent Variable: Number interlocking director and executive positions



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 4.190 1.846
2.270 .024
ASSET1 .252 .019 .849 13.594 .000
NAT2 -1.159 2.664 -.019 -.435 .664
NAT3 -4.444 2.649 -.070 -1.677 .095
NAT4 -8.089 1.481 -.245 -5.462 .000
SEC1 -1.200 2.040 -.029 -.588 .557
SEC2 -14.376 5.577 -.158 -2.578 .011
SEC3 -5.126 4.699 -.045 -1.091 .276
SEC4 -5.699 2.926 -.101 -1.948 .053
SEC5 -2.430 4.014 -.025 -.605 .545
SEC7 -.867 2.634 -.015 -.329 .742
SEC8 .342 2.012 .009 .170 .865
SEC9 -.381 2.820 -.006 -.135 .893
SEC10 5.151 2.682 .085 1.921 .056
a Dependent Variable: Number interlocking director and executive positions

Casewise Diagnostics(a)
Case Number Std. Residual Number interlocking director and executive positions
9 3.478 77
10 -3.007 6
63 3.000 55
65 3.000 44
a Dependent Variable: Number interlocking director and executive positions

Residuals Statistics(a)

Minimum Maximum Mean Std. Deviation N
Predicted Value -1.57 86.57 13.58 13.011 248
Std. Predicted Value -1.165 5.610 .000 1.000 248
Standard Error of Predicted Value 1.502 4.848 2.195 .714 248
Adjusted Predicted Value -2.08 86.47 13.59 12.910 248
Residual -29.21 33.78 .00 9.453 248
Std. Residual -3.007 3.478 .000 .973 248
Stud. Residual -3.104 3.632 -.001 1.006 248
Deleted Residual -31.11 36.82 -.01 10.113 248
Stud. Deleted Residual -3.163 3.731 .001 1.015 248
Mahal. Distance 4.909 60.566 12.948 10.386 248
Cook's Distance .000 .119 .005 .013 248
Centered Leverage Value .020 .245 .052 .042 248
a Dependent Variable: Number interlocking director and executive positions
compute inter1 = sqrt(intrlcks + 1).
execute.

regression 
 /dep=inter1 
 /method=enter asset1 nat2 nat3 nat4 sec1 sec2 sec3 sec4 sec5 sec7 sec8 sec9 sec10.
 
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1(a) . Enter
a All requested variables entered.
b Dependent Variable: INTER1

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .762(a) .580 .557 1.21458
a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 477.297 13 36.715 24.888 .000(a)
Residual 345.200 234 1.475

Total 822.496 247


a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Dependent Variable: INTER1



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 2.329 .231
10.089 .000
ASSET1 2.601E-02 .002 .773 11.229 .000
NAT2 -.114 .333 -.016 -.342 .733
NAT3 -.527 .331 -.073 -1.589 .113
NAT4 -1.105 .185 -.296 -5.966 .000
SEC1 -5.672E-02 .255 -.012 -.222 .824
SEC2 -2.251 .697 -.218 -3.227 .001
SEC3 -.740 .588 -.057 -1.259 .209
SEC4 -8.804E-02 .366 -.014 -.241 .810
SEC5 -.245 .502 -.022 -.489 .626
SEC7 .148 .329 .022 .449 .654
SEC8 .356 .252 .081 1.416 .158
SEC9 .354 .353 .050 1.004 .316
SEC10 .786 .335 .115 2.343 .020
a Dependent Variable: INTER1

page 299 Figure 12.2 The distribution of the studentized residuals from Orstein's interlocking-directorate regression, after transforming the dependent variable. A normal quantile comparison plot is shown in (a), a nonparametric density estimate in (b).

NOTE: We do not know how to do quantile-normal or density plots using SPSS.

page 303 Figure 12.3 Detecting nonconstant spread in Ornstein's interlocking-directorate regression. (a) A plot of studentized residuals versus fitted values. (b) A plot of log spread (log absolute studentized residuals) versus log level (log fitted values). The least-squares line is shown on the plot.

Panel (a)

GRAPH
  /SCATTERPLOT(BIVAR)=pre_1 WITH sre_1.

Scatter of sre_1 pre_1

Panel (b)

compute abssre = abs(sre_1).
compute logsre = lg10(abssre).
compute logpre = lg10(pre_1 + 2).
execute.

IGRAPH
 /X1 = VAR(logpre)
 /Y = VAR(logsre)
 /FITLINE METHOD = REGRESSION LINEAR LINE = TOTAL MEFFECT
 /SCATTER COINCIDENT = NONE.
  Interactive Graph

page 304 Figure 12.4 Plot of log spread versus log level for Ornstein's interlocking-directorate regression, after transforming the dependent variable. The least-squares line is shown on the plot.

regression 
 /dep=inter1 
 /method=enter asset1 nat2 nat3 nat4 sec1 sec2 sec3 sec4 sec5 sec7 sec8 sec9 sec10 
 /save pre sre.
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1(a) . Enter
a All requested variables entered.
b Dependent Variable: INTER1

Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .762(a) .580 .557 1.21458
a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Dependent Variable: INTER1

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 477.297 13 36.715 24.888 .000(a)
Residual 345.200 234 1.475

Total 822.496 247


a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Dependent Variable: INTER1



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 2.329 .231
10.089 .000
ASSET1 2.601E-02 .002 .773 11.229 .000
NAT2 -.114 .333 -.016 -.342 .733
NAT3 -.527 .331 -.073 -1.589 .113
NAT4 -1.105 .185 -.296 -5.966 .000
SEC1 -5.672E-02 .255 -.012 -.222 .824
SEC2 -2.251 .697 -.218 -3.227 .001
SEC3 -.740 .588 -.057 -1.259 .209
SEC4 -8.804E-02 .366 -.014 -.241 .810
SEC5 -.245 .502 -.022 -.489 .626
SEC7 .148 .329 .022 .449 .654
SEC8 .356 .252 .081 1.416 .158
SEC9 .354 .353 .050 1.004 .316
SEC10 .786 .335 .115 2.343 .020
a Dependent Variable: INTER1

Residuals Statistics(a)

Minimum Maximum Mean Std. Deviation N
Predicted Value 1.4829 10.0740 3.3562 1.39010 248
Std. Predicted Value -1.348 4.833 .000 1.000 248
Standard Error of Predicted Value .18780 .60637 .27446 .08935 248
Adjusted Predicted Value 1.5058 10.2327 3.3578 1.38810 248
Residual -3.2470 2.9809 .0000 1.18219 248
Std. Residual -2.673 2.454 .000 .973 248
Stud. Residual -2.775 2.487 -.001 1.001 248
Deleted Residual -3.4995 3.0611 -.0016 1.25220 248
Stud. Deleted Residual -2.816 2.515 .000 1.005 248
Mahal. Distance 4.909 60.566 12.948 10.386 248
Cook's Distance .000 .051 .004 .007 248
Centered Leverage Value .020 .245 .052 .042 248
a Dependent Variable: INTER1
compute logfit2 = lg10(pre_2).
compute abs2 = abs(sre_2).
compute logres2 = lg10(abs2).
execute.
regression 
 /dep=logres2 
 /method=enter logfit2. 
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 LOGFIT2(a) . Enter
a All requested variables entered.
b Dependent Variable: LOGRES2

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .089(a) .008 .004 .48514
a Predictors: (Constant), LOGFIT2

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression .466 1 .466 1.980 .161(a)
Residual 57.898 246 .235

Total 58.364 247


a Predictors: (Constant), LOGFIT2
b Dependent Variable: LOGRES2



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) -.408 .098
-4.161 .000
LOGFIT2 .265 .189 .089 1.407 .161
a Dependent Variable: LOGRES2
IGRAPH
 /X1 = VAR(logfit2)
 /Y = VAR(logres2)
 /FITLINE METHOD = REGRESSION LINEAR LINE = TOTAL MEFFECT
 /SCATTER COINCIDENT = NONE.
  Interactive Graph

page 312 Figure 12.6 Partial-residual plots for the regression of occupational prestige on (a) education, (b) income, (c) percentage of women. The data are for 102 Canadian occupations in 1971. The least-squares line and a nonparamentric-regression smooth are shown on each plot.

GET FILE='D:\prestige.sav'.

regression 
 /dep=prestige 
 /method=enter percwomn educat income 
 /save res.

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 Average income, dollars, % of incumbents who were women, Average education, years(a) . Enter
a All requested variables entered.
b Dependent Variable: Pineo-Porter prestige score occ.

Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .893(a) .798 .792 7.84647
a Predictors: (Constant), Average income, dollars, % of incumbents who were women, Average education, years
b Dependent Variable: Pineo-Porter prestige score occ.

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 23861.856 3 7953.952 129.192 .000(a)
Residual 6033.570 98 61.567

Total 29895.426 101


a Predictors: (Constant), Average income, dollars, % of incumbents who were women, Average education, years
b Dependent Variable: Pineo-Porter prestige score occ.



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) -6.794 3.239
-2.098 .039
% of incumbents who were women -8.905E-03 .030 -.016 -.293 .770
Average education, years 4.187 .389 .664 10.771 .000
Average income, dollars 1.314E-03 .000 .324 4.729 .000
a Dependent Variable: Pineo-Porter prestige score occ.

Residuals Statistics(a)

Minimum Maximum Mean Std. Deviation N
Predicted Value 22.8487 93.1739 46.8333 15.37062 102
Residual -19.8246 17.5045 .0000 7.72906 102
Std. Predicted Value -1.560 3.015 .000 1.000 102
Std. Residual -2.527 2.231 .000 .985 102
a Dependent Variable: Pineo-Porter prestige score occ.

NOTE: We need to look at the output and get the coefficient for the variable education to complete the necessary calculation.

Panel (a)

compute resedu = res_1 + (4.187*educat).

IGRAPH
 /X1 = VAR(educat)
 /Y = VAR(resedu)
 /FITLINE METHOD = REGRESSION LINEAR LINE = TOTAL MEFFECT
 /SCATTER COINCIDENT = NONE.

  Interactive Graph

Panel (b)

compute resedu2 = res_1 + (.0013*income).

IGRAPH
 /X1 = VAR(income)
 /Y = VAR(resedu2)
 /FITLINE METHOD = REGRESSION LINEAR LINE = TOTAL MEFFECT
 /SCATTER COINCIDENT = NONE.
  Interactive Graph 

Panel (c)

compute resedu3 = res_1 + (-.0089*percwomn).

IGRAPH
 /X1 = VAR(percwomn)
 /Y = VAR(resedu3)
 /FITLINE METHOD = REGRESSION LINEAR LINE = TOTAL MEFFECT
 /SCATTER COINCIDENT = NONE.
  Interactive Graph 

page 313 Formula in the middle of the page.

NOTE: The criteria=tolerance option needs to used in this example or else SPSS will throw out the variable edu2 because of low tolerance.

GET FILE='D:\prestige.sav'.

compute loginc = lg10(income)/lg10(2).
compute edu2 = educat*educat.
compute edu3 = educat**3.
compute w2 = percwomn*percwomn.
execute.
regression 
 /criteria=tolerance(0.0000001) 
 /dep=prestige /method=enter loginc percwomn w2 educat edu2 edu3 
 /save res.
Warnings
For models with dependent variable Pineo-Porter prestige score occ., the following variables have low tolerances: EDU2

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 EDU3, W2, LOGINC, % of incumbents who were women, Average education, years, EDU2(a) . Enter
a All requested variables entered.
b Dependent Variable: Pineo-Porter prestige score occ.

Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .925(a) .856 .847 6.72135
a Predictors: (Constant), EDU3, W2, LOGINC, % of incumbents who were women, Average education, years, EDU2
b Dependent Variable: Pineo-Porter prestige score occ.

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 25603.648 6 4267.275 94.458 .000(a)
Residual 4291.778 95 45.177

Total 29895.426 101


a Predictors: (Constant), EDU3, W2, LOGINC, % of incumbents who were women, Average education, years, EDU2
b Dependent Variable: Pineo-Porter prestige score occ.



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 20.838 56.900
.366 .715
LOGINC 8.783 1.273 .436 6.901 .000
% of incumbents who were women -.179 .085 -.331 -2.108 .038
W2 2.500E-03 .001 .414 2.704 .008
Average education, years -29.920 15.252 -4.745 -1.962 .053
EDU2 2.916 1.414 10.414 2.062 .042
EDU3 -8.068E-02 .042 -5.106 -1.911 .059
a Dependent Variable: Pineo-Porter prestige score occ.

Residuals Statistics(a)

Minimum Maximum Mean Std. Deviation N
Predicted Value 16.3538 84.9538 46.8333 15.92173 102
Residual -13.9603 17.0921 .0000 6.51865 102
Std. Predicted Value -1.914 2.394 .000 1.000 102
Std. Residual -2.077 2.543 .000 .970 102
a Dependent Variable: Pineo-Porter prestige score occ.

page 314 Figure 12.7 The partial relationship between prestige and education, holding income and percentage of women at their average levels. The curve shows the cubic fit for education. The points are partial residuals, obtained by adding the least-squares residuals to the education fit.

DESCRIPTIVES
  VARIABLES=income percwomn
  /STATISTICS=MEAN STDDEV MIN MAX.
Descriptive Statistics

N Minimum Maximum Mean Std. Deviation
Average income, dollars 102 611 25879 6797.90 4245.922
% of incumbents who were women 102 .00 97.51 28.9790 31.72493
Valid N (listwise) 102



compute pm = 20.8+8.78*lg10(6797.902)/lg10(2)-0.179*28.979+0.0025*28.979*28.979-29.9*educat+2.91*edu2-.0807*edu3.
compute pmr = res_1 + pm.
execute.

NOTE: In order to get the cubic fit line on the plot, you need to use the code below, double-click on the resulting graph, select chart from the menu at the top, select options, click on fit line total, select fit options, select cubic, click on continue and OK. We do not know how to add the cubic fit line using code.

GRAPH
  /SCATTERPLOT(BIVAR)=educat WITH pmr.

Scatter of pmr educat

page 319 Table 12.2 Analysis of variance for vocabulary test scores, showing the incremental F-test for nonlinearity of the relationship between vocabulary and education.

GET FILE='D:\vocab.sav'.

regression 
 /dep=vocab 
 /method=enter educ.
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 Years of Education(a) . Enter
a All requested variables entered.
b Dependent Variable: Vocabulary Test Score

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .498(a) .248 .247 1.920
a Predictors: (Constant), Years of Education

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 1175.111 1 1175.111 318.917 .000(a)
Residual 3559.414 966 3.685

Total 4734.525 967


a Predictors: (Constant), Years of Education
b Dependent Variable: Vocabulary Test Score



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 1.135 .276
4.111 .000
Years of Education .374 .021 .498 17.858 .000
a Dependent Variable: Vocabulary Test Score

NOTE: The do-loop below is needed in order to create the dummy variables.

DO REPEAT A=e1 e2 e3 e4 e5 e6 e7 e8 e9 e10 e11 e12 e13 e14 e15 e16 e17 e18 e19 e20 
/B=1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20.
COMPUTE A=(educ=B).
END REPEAT.

regression 
 /dep=vocab 
 /method=enter e1 e2 e3 e4 e5 e6 e7 e8 e9 e10 e11 e12 e13 e14 e15 e16 e17 e18 e19 e20.
 
Warnings
For models with dependent variable Vocabulary Test Score, the following variables are constants or have missing correlations: E2. They will be deleted from the analysis.

Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 E20, E1, E3, E5, E4, E6, E7, E19, E17, E9, E18, E10, E8, E15, E11, E13, E16, E14, E12(a) . Enter
a All requested variables entered.
b Dependent Variable: Vocabulary Test Score

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .516(a) .266 .252 1.914
a Predictors: (Constant), E20, E1, E3, E5, E4, E6, E7, E19, E17, E9, E18, E10, E8, E15, E11, E13, E16, E14, E12

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 1261.694 19 66.405 18.127 .000(a)
Residual 3472.831 948 3.663

Total 4734.525 967


a Predictors: (Constant), E20, E1, E3, E5, E4, E6, E7, E19, E17, E9, E18, E10, E8, E15, E11, E13, E16, E14, E12
b Dependent Variable: Vocabulary Test Score



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 5.000 1.914
2.612 .009
E1 -4.000 2.707 -.058 -1.478 .140
E3 -3.000 2.344 -.062 -1.280 .201
E4 -2.833 2.067 -.101 -1.371 .171
E5 -2.500 2.140 -.073 -1.168 .243
E6 -1.714 2.046 -.066 -.838 .402
E7 -.818 1.999 -.039 -.409 .682
E8 -.682 1.936 -.064 -.352 .725
E9 -.765 1.942 -.064 -.394 .694
E10 -.561 1.937 -.051 -.290 .772
E11 -.328 1.930 -.035 -.170 .865
E12 .774 1.917 .164 .404 .687
E13 1.308 1.926 .161 .679 .497
E14 1.158 1.922 .173 .603 .547
E15 1.673 1.933 .166 .866 .387
E16 2.361 1.924 .321 1.227 .220
E17 2.419 1.945 .193 1.244 .214
E18 2.541 1.940 .220 1.310 .191
E19 3.692 1.986 .192 1.859 .063
E20 3.187 1.973 .184 1.616 .107
a Dependent Variable: Vocabulary Test Score

NOTE: The nonlinear effect needs to be calculated by hand.

Sum of Squares for nonlinear effect: 1261.69399-1175.11129 = 86.5827 F-value: (86.57/18)/(3472.8/948) = 1.3128753 p-value: 17255134

page 324 Figure 12.8 Box-Cox transformations for Ornstein's interlocking-directorate regression. The maximized log likelihood is plotted against the transformation parameter lamda. The intersection of the line near the top of the graph with the log likelihood curve marks off the 95% confidence interval for lamda. The maximum of the log likelihood corresponds to the MLE of lamda.

GET FILE='D:\ornstein.sav'.

compute asset1 = sqrt(assets).
compute dep1 = (intrlcks + 1).
compute logy = ln(dep1).
compute c1 = mean(logy).
compute c = exp(c1).

NOTE: The mean of c = 8.2501268.

compute cv = dep1*(ln(dep1/8.2501268)-1).
execute.

compute sec1 = 0.
if sector = "AGR" sec1 = 1.
compute sec2 = 0.
if sector = "BNK" sec2 = 1.
compute sec3 = 0.
if sector = "CON" sec3 = 1.
compute sec4 = 0.
if sector = "FIN" sec4 = 1.
compute sec5 = 0.
if sector = "HLD" sec5 = 1.
compute sec6 = 0.
if sector = "MAN" sec6 = 1.
compute sec7 = 0.
if sector = "MER" sec7 = 1.
compute sec8 = 0.
if sector = "MIN" sec8 = 1.
compute sec9 = 0.
if sector = "TRN" sec9 = 1.
compute sec10 = 0.
if sector = "WOD" sec10 = 1.
compute nat1 = 0.
if nation = "CAN" nat1 = 1.
compute nat2 = 0.
if nation = "OTH" nat2 = 1.
compute nat3 = 0.
if nation = "UK" nat3 = 1.
compute nat4 = 0.
if nation = "US" nat4 = 1.
compute asset1 = sqrt(assets).
execute.

regression 
 /dep=dep1 
 /method=enter asset1 nat2 nat3 nat4 sec1 sec2 sec3 sec4 sec5 sec7 sec8 sec9 sec10
 /method=test(cv).
 
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1(a) . Enter
2 CV . Test
a All requested variables entered.
b Dependent Variable: DEP1

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .809(a) .655 .635 9.71162
2 .928(b) .862 .853 6.15872
a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1, CV

ANOVA(d)
Model Sum of Squares df Mean Square F Sig. R Square Change
1 Regression 41816.553 13 3216.658 34.105 .000(a)
Residual 22069.834 234 94.316


Total 63886.387 247



2 Subset Tests CV 13232.181 1 13232.181 348.859 .000(b) .207
Regression 55048.734 14 3932.052 103.666 .000(c)
Residual 8837.653 233 37.930


Total 63886.387 247



a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Tested against the full model.
c Predictors in the Full Model: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1, CV.
d Dependent Variable: DEP1



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 5.190 1.846
2.812 .005
ASSET1 .252 .019 .849 13.594 .000
NAT2 -1.159 2.664 -.019 -.435 .664
NAT3 -4.444 2.649 -.070 -1.677 .095
NAT4 -8.089 1.481 -.245 -5.462 .000
SEC1 -1.200 2.040 -.029 -.588 .557
SEC2 -14.376 5.577 -.158 -2.578 .011
SEC3 -5.126 4.699 -.045 -1.091 .276
SEC4 -5.699 2.926 -.101 -1.948 .053
SEC5 -2.430 4.014 -.025 -.605 .545
SEC7 -.867 2.634 -.015 -.329 .742
SEC8 .342 2.012 .009 .170 .865
SEC9 -.381 2.820 -.006 -.135 .893
SEC10 5.151 2.682 .085 1.921 .056
2 (Constant) 11.416 1.217
9.379 .000
ASSET1 6.991E-02 .015 .236 4.582 .000
NAT2 -.137 1.690 -.002 -.081 .935
NAT3 -2.353 1.684 -.037 -1.397 .164
NAT4 -5.037 .953 -.153 -5.283 .000
SEC1 -2.703E-02 1.295 -.001 -.021 .983
SEC2 -12.679 3.538 -.140 -3.584 .000
SEC3 -3.911 2.980 -.034 -1.312 .191
SEC4 3.476 1.919 .062 1.811 .071
SEC5 -.635 2.547 -.007 -.249 .803
SEC7 1.511 1.675 .026 .902 .368
SEC8 3.029 1.284 .078 2.359 .019
SEC9 3.208 1.798 .052 1.784 .076
SEC10 3.875 1.702 .064 2.277 .024
CV .585 .031 .752 18.678 .000
a Dependent Variable: DEP1





Excluded Variables(b)

Beta In t Sig. Partial Correlation Collinearity Statistics
Model Tolerance
1 CV .752(a) 18.678 .000 .774 .366
a Predictors in the Model: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Dependent Variable: DEP1

page 324 Figure 12.9 Constructed-variable plot for the Box-Cox . transformation of Ornstein's interlocking- directorate regression. The least-squares line is shown on the plot.

regression 
 /dep=dep1 
 /method=enter asset1 nat2 nat3 nat4 sec1 sec2 sec3 sec4 sec5 sec7 sec8 sec9 sec10 
 /save res.
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1(a) . Enter
a All requested variables entered.
b Dependent Variable: DEP1

Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .809(a) .655 .635 9.71162
a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Dependent Variable: DEP1

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 41816.553 13 3216.658 34.105 .000(a)
Residual 22069.834 234 94.316

Total 63886.387 247


a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Dependent Variable: DEP1



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 5.190 1.846
2.812 .005
ASSET1 .252 .019 .849 13.594 .000
NAT2 -1.159 2.664 -.019 -.435 .664
NAT3 -4.444 2.649 -.070 -1.677 .095
NAT4 -8.089 1.481 -.245 -5.462 .000
SEC1 -1.200 2.040 -.029 -.588 .557
SEC2 -14.376 5.577 -.158 -2.578 .011
SEC3 -5.126 4.699 -.045 -1.091 .276
SEC4 -5.699 2.926 -.101 -1.948 .053
SEC5 -2.430 4.014 -.025 -.605 .545
SEC7 -.867 2.634 -.015 -.329 .742
SEC8 .342 2.012 .009 .170 .865
SEC9 -.381 2.820 -.006 -.135 .893
SEC10 5.151 2.682 .085 1.921 .056
a Dependent Variable: DEP1

Casewise Diagnostics(a)
Case Number Std. Residual DEP1
9 3.478 78.00
10 -3.007 7.00
63 3.000 56.00
65 3.000 45.00
a Dependent Variable: DEP1

Residuals Statistics(a)

Minimum Maximum Mean Std. Deviation N
Predicted Value -.5737 87.5717 14.5806 13.01145 248
Residual -29.2061 33.7793 .0000 9.45260 248
Std. Predicted Value -1.165 5.610 .000 1.000 248
Std. Residual -3.007 3.478 .000 .973 248
a Dependent Variable: DEP1
regression 
 /dep=cv 
 /method=enter asset1 nat2 nat3 nat4 sec1 sec2 sec3 sec4 sec5 sec7 sec8 sec9 sec10 
 /save res. 
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1(a) . Enter
a All requested variables entered.
b Dependent Variable: CV

Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .796(a) .634 .614 12.85360
a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Dependent Variable: CV

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 67034.519 13 5156.501 31.211 .000(a)
Residual 38660.348 234 165.215

Total 105694.866 247


a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Dependent Variable: CV



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) -10.642 2.443
-4.355 .000
ASSET1 .311 .025 .815 12.682 .000
NAT2 -1.746 3.526 -.022 -.495 .621
NAT3 -3.575 3.506 -.044 -1.019 .309
NAT4 -5.217 1.960 -.123 -2.662 .008
SEC1 -2.004 2.701 -.038 -.742 .459
SEC2 -2.901 7.381 -.025 -.393 .695
SEC3 -2.075 6.219 -.014 -.334 .739
SEC4 -15.683 3.872 -.216 -4.050 .000
SEC5 -3.068 5.313 -.025 -.577 .564
SEC7 -4.064 3.487 -.054 -1.166 .245
SEC8 -4.592 2.663 -.092 -1.724 .086
SEC9 -6.136 3.732 -.077 -1.644 .102
SEC10 2.181 3.550 .028 .614 .540
a Dependent Variable: CV

Casewise Diagnostics(a)
Case Number Std. Residual CV
2 5.441 169.77
3 3.585 137.15
4 -3.036 38.30
6 3.365 79.68
8 -3.277 -4.71
9 5.084 97.23
63 3.058 51.25
a Dependent Variable: CV

Residuals Statistics(a)

Minimum Maximum Mean Std. Deviation N
Predicted Value -18.0029 105.9243 .5393 16.47406 248
Residual -42.1238 69.9304 .0000 12.51078 248
Std. Predicted Value -1.126 6.397 .000 1.000 248
Std. Residual -3.277 5.441 .000 .973 248
a Dependent Variable: CV
 
regression 
 /dep=res_1 
 /method=enter res_2.
 
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 Unstandardized Residual(a) . Enter
a All requested variables entered.
b Dependent Variable: Unstandardized Residual

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .774(a) .600 .598 5.99378170
a Predictors: (Constant), Unstandardized Residual

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 13232.181 1 13232.181 368.324 .000(a)
Residual 8837.653 246 35.925

Total 22069.834 247


a Predictors: (Constant), Unstandardized Residual
b Dependent Variable: Unstandardized Residual



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 1.110E-14 .381
.000 1.000
Unstandardized Residual .585 .030 .774 19.192 .000
a Dependent Variable: Unstandardized Residual
IGRAPH
 /X1 = VAR(res_2)
 /Y = VAR(res_1)
 /FITLINE METHOD = REGRESSION LINEAR LINE = TOTAL
 /SCATTER COINCIDENT = NONE.
  Interactive Graph 

page 325 at the bottom.

GET FILE='D:\prestige.sav'.

compute w2 = percwomn**2.
compute linc = income*ln(income).
compute ledu = educat*ln(educat).
execute.
regression 
 /dep=prestige 
 /method=enter percwomn w2 educat income.
 
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 Average income, dollars, W2, Average education, years, % of incumbents who were women(a) . Enter
a All requested variables entered.
b Dependent Variable: Pineo-Porter prestige score occ.

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .896(a) .802 .794 7.80353
a Predictors: (Constant), Average income, dollars, W2, Average education, years, % of incumbents who were women

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 23988.605 4 5997.151 98.483 .000(a)
Residual 5906.821 97 60.895

Total 29895.426 101


a Predictors: (Constant), Average income, dollars, W2, Average education, years, % of incumbents who were women
b Dependent Variable: Pineo-Porter prestige score occ.



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) -6.177 3.250
-1.901 .060
% of incumbents who were women -.145 .099 -.268 -1.464 .146
W2 1.538E-03 .001 .255 1.443 .152
Average education, years 4.260 .390 .676 10.926 .000
Average income, dollars 1.272E-03 .000 .314 4.579 .000
a Dependent Variable: Pineo-Porter prestige score occ.
compute b1 = 4.260.
compute b2 = .00127.
regression 
 /dep=prestige 
 /method=enter percwomn w2 educat income ledu linc.
 
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 LINC, W2, Average education, years, % of incumbents who were women, Average income, dollars, LEDU(a) . Enter
a All requested variables entered.
b Dependent Variable: Pineo-Porter prestige score occ.

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .923(a) .853 .843 6.81050
a Predictors: (Constant), LINC, W2, Average education, years, % of incumbents who were women, Average income, dollars, LEDU

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 25489.052 6 4248.175 91.589 .000(a)
Residual 4406.374 95 46.383

Total 29895.426 101


a Predictors: (Constant), LINC, W2, Average education, years, % of incumbents who were women, Average income, dollars, LEDU
b Dependent Variable: Pineo-Porter prestige score occ.



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 32.844 23.493
1.398 .165
% of incumbents who were women -.125 .088 -.230 -1.413 .161
W2 2.294E-03 .001 .380 2.418 .018
Average education, years -14.442 7.445 -2.290 -1.940 .055
Average income, dollars 2.648E-02 .005 6.536 5.537 .000
LEDU 5.298 2.202 2.854 2.406 .018
LINC -2.430E-03 .000 -6.113 -5.301 .000
a Dependent Variable: Pineo-Porter prestige score occ.
compute d1 = 5.298.
compute d2 = -.0024298.

NOTE: 1 + d1/b1 = 2.2435437. NOTE: 1 + d2/b2 = -.91030351.

page 326 Figure 12.10 Constructed-variable plots for the Box-Tidwell transformation of (a) income and (b) education in the regression of occupational prestige on income, education, and percentage of women.

NOTE: To get the regression lines in the constructed variable plots, you need to double-click on the plot, go to chart, options and check on fit line total. Close the chart editor and the change will take effect.

NOTE:  You cannot specify which partial plots you want:  with SPSS code, it is all or nothing.  However, only the last chart for each
regression is desired.  Hence, only on those charts were the regression lines added.

Panel (a)

regression 
 /dep=prestige 
 /method=enter percwomn w2 educat income linc 
 /partialplot all.
 
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 LINC, W2, Average education, years, % of incumbents who were women, Average income, dollars(a) . Enter
a All requested variables entered.
b Dependent Variable: Pineo-Porter prestige score occ.

Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .918(a) .844 .835 6.97822
a Predictors: (Constant), LINC, W2, Average education, years, % of incumbents who were women, Average income, dollars
b Dependent Variable: Pineo-Porter prestige score occ.

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 25220.648 5 5044.130 103.585 .000(a)
Residual 4674.778 96 48.696

Total 29895.426 101


a Predictors: (Constant), LINC, W2, Average education, years, % of incumbents who were women, Average income, dollars
b Dependent Variable: Pineo-Porter prestige score occ.



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) -22.722 4.389
-5.177 .000
% of incumbents who were women -9.200E-02 .089 -.170 -1.030 .306
W2 1.875E-03 .001 .311 1.962 .053
Average education, years 3.444 .385 .546 8.957 .000
Average income, dollars 2.586E-02 .005 6.381 5.283 .000
LINC -2.357E-03 .000 -5.931 -5.030 .000
a Dependent Variable: Pineo-Porter prestige score occ.

Residuals Statistics(a)

Minimum Maximum Mean Std. Deviation N
Predicted Value 18.8346 81.2629 46.8333 15.80220 102
Residual -14.2740 19.7955 .0000 6.80330 102
Std. Predicted Value -1.772 2.179 .000 1.000 102
Std. Residual -2.046 2.837 .000 .975 102
a Dependent Variable: Pineo-Porter prestige score occ.

Prestige by percwomn partial regression plot
Prestige by w2 partial regression plot
Prestige by educat partial regression plot
Prestige by income partial regression plot
Prestige by linc partial regression plot

Panel (b)

regression 
 /dep=prestige 
 /method=enter percwomn w2 educat income ledu 
 /partialplot all.
 
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 LEDU, W2, Average income, dollars, % of incumbents who were women, Average education, years(a) . Enter
a All requested variables entered.
b Dependent Variable: Pineo-Porter prestige score occ.

Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .899(a) .809 .799 7.71221
a Predictors: (Constant), LEDU, W2, Average income, dollars, % of incumbents who were women, Average education, years
b Dependent Variable: Pineo-Porter prestige score occ.

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 24185.523 5 4837.105 81.326 .000(a)
Residual 5709.903 96 59.478

Total 29895.426 101


a Predictors: (Constant), LEDU, W2, Average income, dollars, % of incumbents who were women, Average education, years
b Dependent Variable: Pineo-Porter prestige score occ.



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 41.750 26.535
1.573 .119
% of incumbents who were women -.175 .099 -.322 -1.757 .082
W2 1.887E-03 .001 .312 1.762 .081
Average education, years -11.005 8.399 -1.745 -1.310 .193
Average income, dollars 1.161E-03 .000 .287 4.130 .000
LEDU 4.528 2.488 2.439 1.820 .072
a Dependent Variable: Pineo-Porter prestige score occ.

Residuals Statistics(a)

Minimum Maximum Mean Std. Deviation N
Predicted Value 25.9025 94.0397 46.8333 15.47452 102
Residual -19.6240 17.3239 .0000 7.51889 102
Std. Predicted Value -1.353 3.051 .000 1.000 102
Std. Residual -2.545 2.246 .000 .975 102
a Dependent Variable: Pineo-Porter prestige score occ.

Prestige by percwomn partial regression plot
Prestige by w2 partial regression plot
Prestige by educat partial regression plot
Prestige by income partial regression plot
Prestige by ledu partial regression plot


page 328 in the middle of the page.

GET FILE='D:\Apps\Christinesstuff\ARA\ornstein.sav'.

compute sec1 = 0.
if sector = "AGR" sec1 = 1.
compute sec2 = 0.
if sector = "BNK" sec2 = 1.
compute sec3 = 0.
if sector = "CON" sec3 = 1.
compute sec4 = 0.
if sector = "FIN" sec4 = 1.
compute sec5 = 0.
if sector = "HLD" sec5 = 1.
compute sec6 = 0.
if sector = "MAN" sec6 = 1.
compute sec7 = 0.
if sector = "MER" sec7 = 1.
compute sec8 = 0.
if sector = "MIN" sec8 = 1.
compute sec9 = 0.
if sector = "TRN" sec9 = 1.
compute sec10 = 0.
if sector = "WOD" sec10 = 1.
compute nat1 = 0.
if nation = "CAN" nat1 = 1.
compute nat2 = 0.
if nation = "OTH" nat2 = 1.
compute nat3 = 0.
if nation = "UK" nat3 = 1.
compute nat4 = 0.
if nation = "US" nat4 = 1.
compute asset1 = sqrt(assets).
compute y = intrlcks + 1.
execute.

regression 
 /dep=y 
 /method=enter asset1 nat2 nat3 nat4 sec1 sec2 sec3 sec4 sec5 sec7 sec8 sec9 sec10 
 /save res pre.
 
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1(a) . Enter
a All requested variables entered.
b Dependent Variable: Y

Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .809(a) .655 .635 9.71162
a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Dependent Variable: Y

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 41816.553 13 3216.658 34.105 .000(a)
Residual 22069.834 234 94.316

Total 63886.387 247


a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Dependent Variable: Y



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) 5.190 1.846
2.812 .005
ASSET1 .252 .019 .849 13.594 .000
NAT2 -1.159 2.664 -.019 -.435 .664
NAT3 -4.444 2.649 -.070 -1.677 .095
NAT4 -8.089 1.481 -.245 -5.462 .000
SEC1 -1.200 2.040 -.029 -.588 .557
SEC2 -14.376 5.577 -.158 -2.578 .011
SEC3 -5.126 4.699 -.045 -1.091 .276
SEC4 -5.699 2.926 -.101 -1.948 .053
SEC5 -2.430 4.014 -.025 -.605 .545
SEC7 -.867 2.634 -.015 -.329 .742
SEC8 .342 2.012 .009 .170 .865
SEC9 -.381 2.820 -.006 -.135 .893
SEC10 5.151 2.682 .085 1.921 .056
a Dependent Variable: Y

Casewise Diagnostics(a)
Case Number Std. Residual Y
9 3.478 78.00
10 -3.007 7.00
63 3.000 56.00
65 3.000 45.00
a Dependent Variable: Y

Residuals Statistics(a)

Minimum Maximum Mean Std. Deviation N
Predicted Value -.5737 87.5717 14.5806 13.01145 248
Residual -29.2061 33.7793 .0000 9.45260 248
Std. Predicted Value -1.165 5.610 .000 1.000 248
Std. Residual -3.007 3.478 .000 .973 248
a Dependent Variable: Y
compute res2 = res_1**2.
execute.

DESCRIPTIVES
  VARIABLES=res2
  /STATISTICS=MEAN.
 
Descriptive Statistics

N Mean
RES2 248 88.9913
Valid N (listwise) 248

NOTE: The mean of res2 is 88.913.

compute m = 88.913.
compute u = res2/m.
execute.

regression 
 /dep=u 
 /method=enter pre_1.
 
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 Unstandardized Predicted Value(a) . Enter
a All requested variables entered.
b Dependent Variable: U

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .396(a) .157 .153 1.79848
a Predictors: (Constant), Unstandardized Predicted Value

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 147.909 1 147.909 45.728 .000(a)
Residual 795.695 246 3.235

Total 943.604 247


a Predictors: (Constant), Unstandardized Predicted Value
b Dependent Variable: U



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) .134 .172
.779 .437
Unstandardized Predicted Value 5.947E-02 .009 .396 6.762 .000
a Dependent Variable: U

NOTE: The model sums of squares divided by 2 is 147.6488/2 = 73.8244.

DESCRIPTIVES
  VARIABLES=pre_1
  /STATISTICS=MEAN.
 
Descriptive Statistics

N Mean
Unstandardized Predicted Value 248 14.5806452
Valid N (listwise) 248

NOTE: The mean of pre_1 is 14.58. 1 - .5*.0594*14.58 = .566974.

regression 
 /dep=u 
 /method=enter asset1 nat2 nat3 nat4 sec1 sec2 sec3 sec4 sec5 sec7 sec8 sec9 sec10 
 /save res pre.
 
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1(a) . Enter
a All requested variables entered.
b Dependent Variable: U

Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .428(a) .183 .138 1.81484
a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Dependent Variable: U

ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
1 Regression 172.894 13 13.300 4.038 .000(a)
Residual 770.710 234 3.294

Total 943.604 247


a Predictors: (Constant), SEC10, NAT2, SEC5, SEC2, SEC4, NAT3, SEC9, SEC3, SEC7, SEC1, NAT4, SEC8, ASSET1
b Dependent Variable: U



Coefficients(a)

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. Error Beta
1 (Constant) .579 .345
1.678 .095
ASSET1 1.809E-02 .003 .502 5.227 .000
NAT2 -.149 .498 -.020 -.300 .764
NAT3 -.915 .495 -.119 -1.849 .066
NAT4 -.782 .277 -.195 -2.825 .005
SEC1 4.828E-03 .381 .001 .013 .990
SEC2 -2.689 1.042 -.244 -2.581 .010
SEC3 -.779 .878 -.056 -.887 .376
SEC4 -.991 .547 -.144 -1.812 .071
SEC5 -.505 .750 -.043 -.673 .501
SEC7 -.290 .492 -.041 -.590 .556
SEC8 8.885E-02 .376 .019 .236 .813
SEC9 -.477 .527 -.063 -.905 .366
SEC10 .540 .501 .074 1.078 .282
a Dependent Variable: U

Casewise Diagnostics(a)
Case Number Std. Residual U
9 5.455 12.83
10 3.814 9.59
23 3.688 9.43
63 3.991 9.55
65 4.840 9.55
83 3.687 8.79
86 3.552 7.99
143 3.816 8.14
a Dependent Variable: U

Residuals Statistics(a)

Minimum Maximum Mean Std. Deviation N
Predicted Value -.8232 4.8419 1.0009 .83665 248
Residual -4.8399 9.8995 .0000 1.76643 248
Std. Predicted Value -2.180 4.591 .000 1.000 248
Std. Residual -2.667 5.455 .000 .973 248
a Dependent Variable: U


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