+
+
+AT_SETUP([LINEAR REGRESSION vif])
+AT_DATA([regression-vif.sps], [dnl
+SET FORMAT=F10.3.
+
+data list notable list /competence_x1 motivation_x2 performance_y.
+begin data
+32 34 36
+35 37 39
+38 45 49
+31 41 41
+36 40 38
+32 38 36
+33 39 37
+31 40 41
+30 37 40
+35 37 43
+31 34 36
+34 32 35
+31 42 34
+25 36 40
+35 42 40
+36 41 44
+30 38 32
+34 41 41
+34 41 44
+22 27 26
+27 26 33
+30 30 35
+30 35 37
+37 39 44
+29 35 36
+31 35 29
+31 45 41
+29 30 32
+29 35 36
+31 37 37
+36 45 42
+32 44 39
+27 26 31
+33 39 35
+20 25 28
+30 36 39
+27 37 39
+25 39 36
+32 38 38
+32 38 35
+end data.
+
+regression /variables=competence_x1 motivation_x2
+ /statistics=defaults tol
+ /dependent=performance_y
+ .
+])
+
+
+AT_CHECK([pspp -O format=csv regression-vif.sps], [0], [dnl
+Table: Model Summary (performance_y)
+R,R Square,Adjusted R Square,Std. Error of the Estimate
+.785,.616,.595,2.980
+
+Table: ANOVA (performance_y)
+,Sum of Squares,df,Mean Square,F,Sig.
+Regression,526.494,2,263.247,29.641,.000
+Residual,328.606,37,8.881,,
+Total,855.100,39,,,
+
+Table: Coefficients (performance_y)
+,Unstandardized Coefficients,,Standardized Coefficients,t,Sig.,Collinearity Statistics,
+,B,Std. Error,Beta,,,Tolerance,VIF
+(Constant),7.220,4.020,.000,1.796,.080,,
+competence_x1,.432,.166,.358,2.609,.013,.552,1.812
+motivation_x2,.453,.125,.499,3.636,.001,.552,1.812
+])
+
+AT_CLEANUP