"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