"MODEL" Y = A3 * X3 + A2 * X2 + A1 * X1; "INPUT" 5 * [Y, X1, X2, X3]; "OPTIONS" 1, 2, 5, 8; Transformed data matrix ======================= obs.no. A3 A2 A1 dep.var. 1 4.000 1.000 2.000 8.000 2 1.000 2.000 -1.000 10.000 3 4.000 -3.000 1.000 9.000 4 2.000 1.000 2.000 6.000 5 6.000 4.000 1.000 12.000 Control information =================== transformed variable denoted by parameter mean standard deviation minimum maximum A3 3.400000 1.949359 1.000000 6.000000 A2 1.000000 2.549510 -3.000000 4.000000 A1 1.000000 1.224745 -1.000000 2.000000 dep.var. 9.000000 2.236068 6.000000 12.000000 Number of observations : 5 There is no constant independent variable in the transformed (sub)model (message) Correlation matrix of the variables =================================== A3 A2 A1 dep.var. A3 1.000000 A2 0.150908 1.000000 A1 0.418854 -0.160128 1.000000 dep.var. 0.516185 0.438529 -0.547723 1.000000 Multiple correlation coefficient 0.936662 (adjusted 0.832670) ================================ Proportion of variation explained 0.877336 (adjusted 0.693339) ================================= Standard deviation of the error term 5.105507 ==================================== Regression parameters ===================== right tail parameter estimate standard deviation F - ratio probability A3 2.5446171560 0.9982125895 6.498286 0.125553 A2 0.2665515256 1.0423373169 0.065395 0.822061 A1 -1.3851468048 2.3646149361 0.343140 0.617320 Correlation matrix of the estimates =================================== A3 A2 A1 A3 1.000000 A2 -0.452873 1.000000 A1 -0.751471 0.244757 1.000000 Analysis of variance ==================== source of right tail variation df sum of squares mean square F - ratio probability --------------------------------------------------------------------------------------------------------------- total 5 425.000000 --------------------------------------------------------------------------------------------------------------- regression 3 372.867588 124.289196 4.768212 0.178233 residual 2 52.132412 26.066206 --------------------------------------------------------------------------------------------------------------- regression null hypothesis : A3 = A2 = A1 = 0 Control information - submodel 1 =================== transformed variable denoted by parameter mean standard deviation minimum maximum A1 omitted A3 3.400000 1.949359 1.000000 6.000000 A2 1.000000 2.549510 -3.000000 4.000000 dep.var. 9.000000 2.236068 6.000000 12.000000 Number of observations : 5 There is no constant independent variable in the transformed (sub)model (message) Multiple correlation coefficient 0.925359 (adjusted 0.872057) ================================ Proportion of variation explained 0.856290 (adjusted 0.760483) ================================= Standard deviation of the error term 4.512086 ==================================== Regression parameters ===================== right tail parameter estimate standard deviation F - ratio probability A3 2.1052066559 0.5820385900 13.082354 0.036325 A2 0.4159957059 0.8931663715 0.216927 0.673122 Analysis of variance ==================== source of right tail variation df sum of squares mean square F - ratio probability --------------------------------------------------------------------------------------------------------------- total 5 425.000000 --------------------------------------------------------------------------------------------------------------- regression 2 363.923242 181.961621 8.937686 0.054479 residual 3 61.076758 20.358919 --------------------------------------------------------------------------------------------------------------- reduction 1 8.944346 8.944346 0.343140 0.617320 --------------------------------------------------------------------------------------------------------------- regression null hypothesis : A3 = A2 = 0 (in the reduced model) reduction null hypothesis : A1 = 0 (in the original model) Control information - submodel 2 =================== transformed variable denoted by parameter mean standard deviation minimum maximum A2 omitted A1 omitted A3 3.400000 1.949359 1.000000 6.000000 dep.var. 9.000000 2.236068 6.000000 12.000000 Number of observations : 5 There is no constant independent variable in the transformed (sub)model (message) Multiple correlation coefficient 0.919727 (adjusted 0.898539) ================================ Proportion of variation explained 0.845898 (adjusted 0.807373) ================================= Standard deviation of the error term 4.046392 ==================================== Regression parameters ===================== right tail parameter estimate standard deviation F - ratio probability A3 2.2191780822 0.4735943538 21.956913 0.009407 Analysis of variance ==================== source of right tail variation df sum of squares mean square F - ratio probability --------------------------------------------------------------------------------------------------------------- total 5 425.000000 --------------------------------------------------------------------------------------------------------------- regression 1 359.506849 359.506849 21.956913 0.009407 residual 4 65.493151 16.373288 --------------------------------------------------------------------------------------------------------------- reduction 2 13.360738 6.680369 0.256285 0.795998 --------------------------------------------------------------------------------------------------------------- regression null hypothesis : A3 = 0 (in the reduced model) reduction null hypothesis : A2 = A1 = 0 (in the original model) End of job : 1 "MODEL" Y - 4 * X1 = B2 * (X1 + X2) + B3 * X3; "INPUT" 5 * [Y, X1, X2, X3]; "OPTIONS" 1, 2, 9; Transformed data matrix ======================= obs.no. B2 B3 dep.var. 1 3.000 4.000 0.000 2 1.000 1.000 14.000 3 -2.000 4.000 5.000 4 3.000 2.000 -2.000 5 5.000 6.000 8.000 Control information =================== transformed variable denoted by parameter mean standard deviation minimum maximum B2 2.000000 2.645751 -2.000000 5.000000 B3 3.400000 1.949359 1.000000 6.000000 dep.var. 5.000000 6.403124 -2.000000 14.000000 Number of observations : 5 There is no constant independent variable in the transformed (sub)model (message) Correlation matrix of the variables =================================== B2 B3 dep.var. B2 1.000000 B3 0.339310 1.000000 dep.var. -0.177084 -0.140202 1.000000 Proportion of variation explained 0.293057 (adjusted -0.178239) ================================= Standard deviation of the error term 8.252406 ==================================== Regression parameters ===================== right tail parameter estimate standard deviation F - ratio probability B2 -0.2325836533 1.6513864104 0.019836 0.896920 B3 1.1991223258 1.3390842284 0.801883 0.436516 Correlation matrix of the estimates =================================== B2 B3 B2 1.000000 B3 -0.692631 1.000000 Analysis of variance ==================== source of right tail variation df sum of squares mean square F - ratio probability --------------------------------------------------------------------------------------------------------------- total 5 289.000000 --------------------------------------------------------------------------------------------------------------- regression 2 84.693363 42.346681 0.621811 0.594397 residual 3 204.306637 68.102212 --------------------------------------------------------------------------------------------------------------- reduction 1 152.174225 152.174225 5.837989 0.136963 --------------------------------------------------------------------------------------------------------------- regression null hypothesis : B2 = B3 = 0 End of job : 2