"MODEL" y = a * d + b * x + c * z; "INPUT" n, m, n * [ z, d, x, y, r ], o, < (o+1) * o : 2 > * [ correl element ], s, s * ( t, t * [ estimate ], t, < (t+1) * t : 2 > * [ covar element ], n, n * ( 6 * [ residual element ] ) ); "OPTIONS" 1, 2, 3, 5(1), 7; Transformed data matrix ======================= obs.no. a b c dep.var. 1 1.000 25.000 1.398 0.790 2 1.000 50.000 1.699 0.984 3 1.000 80.000 1.903 1.058 4 1.000 130.000 2.114 1.163 5 1.000 180.000 2.255 1.209 Control information =================== transformed variable denoted by parameter mean standard deviation minimum maximum a 1.000000 0.000000 1.000000 1.000000 b 93.000000 62.409935 25.000000 180.000000 c 1.873843 0.339506 1.397940 2.255273 dep.var. 1.040800 0.165565 0.790000 1.209000 Number of observations : 5 Correlation matrix of the variables =================================== a b c dep.var. a 1.000000 b * 1.000000 c * 0.962417 1.000000 dep.var. * 0.929750 0.993099 1.000000 Multiple correlation coefficient 0.997712 (adjusted 0.995418) ================================ Proportion of variation explained 0.995429 (adjusted 0.990858) ================================= Standard deviation of the error term 0.015831 ==================================== Regression parameters ===================== right tail parameter estimate standard deviation F - ratio probability a -0.0899819314 0.1198580224 0.563607 0.531119 b -0.0009361326 0.0004670057 4.018189 0.182887 c 0.6499168512 0.0858477522 57.313607 0.017004 Correlation matrix of the estimates =================================== a b c a 1.000000 b 0.929333 1.000000 c -0.993392 -0.962417 1.000000 Analysis of variance ==================== source of right tail variation df sum of squares mean square F - ratio probability --------------------------------------------------------------------------------------------------------------- total 5 5.525970 --------------------------------------------------------------------------------------------------------------- mean 1 5.416323 5.416323 21612.954083 0.000000 regression 2 0.109146 0.054573 217.763834 0.004571 residual 2 0.000501 0.000251 --------------------------------------------------------------------------------------------------------------- regression null hypothesis : b = c = 0 Residual analysis ================= standardized studentized obs.no. observation fitted value standard deviation residual residual residual 1 0.790000 0.795160 0.015180 -0.005160 -0.515329 -1.148688 2 0.984000 0.967401 0.009923 0.016599 1.657926 1.345799 3 1.058000 1.071978 0.011074 -0.013978 -1.396083 -1.235555 4 1.163000 1.162208 0.009381 0.000792 0.079079 0.062090 5 1.209000 1.207254 0.014570 0.001746 0.174407 0.282096 sum of residuals : -0.000000 Upper bound for the right tail probability of the largest absolute studentized residual (no. 2) : 0.994142 Control information - submodel 1 =================== transformed variable denoted by parameter mean standard deviation minimum maximum c omitted a 1.000000 0.000000 1.000000 1.000000 b 93.000000 62.409935 25.000000 180.000000 dep.var. 1.040800 0.165565 0.790000 1.209000 Number of observations : 5 Multiple correlation coefficient 0.929750 (adjusted 0.905122) ================================ Proportion of variation explained 0.864435 (adjusted 0.819246) ================================= Standard deviation of the error term 0.070390 ==================================== Regression parameters ===================== right tail parameter estimate standard deviation F - ratio probability a 0.8114159135 0.0611679599 175.970168 0.000926 b 0.0024664955 0.0005639337 19.129543 0.022114 Correlation matrix of the estimates =================================== a b a 1.000000 b -0.857407 1.000000 Analysis of variance ==================== source of right tail variation df sum of squares mean square F - ratio probability --------------------------------------------------------------------------------------------------------------- total 5 5.525970 --------------------------------------------------------------------------------------------------------------- mean 1 5.416323 5.416323 1093.153257 0.000061 regression 1 0.094782 0.094782 19.129543 0.022114 residual 3 0.014864 0.004955 --------------------------------------------------------------------------------------------------------------- reduction 1 0.014363 0.014363 57.313607 0.017004 --------------------------------------------------------------------------------------------------------------- regression null hypothesis : b = 0 (in the reduced model) reduction null hypothesis : c = 0 (in the original model) Residual analysis ================= standardized studentized obs.no. observation fitted value standard deviation residual residual residual 1 0.790000 0.873078 0.049613 -0.083078 -1.523703 -1.663802 2 0.984000 0.934741 0.039736 0.049259 0.903443 0.847813 3 1.058000 1.008736 0.032322 0.049264 0.903537 0.787846 4 1.163000 1.132060 0.037767 0.030940 0.567451 0.520864 5 1.209000 1.255385 0.058293 -0.046385 -0.850729 -1.175640 sum of residuals : -0.000000 Upper bound for the right tail probability of the largest absolute studentized residual (no. 1) : 0.197018 End of job : 1 "MODEL" available = beta0 + beta1 * inorganic + beta2 * organic; "INPUT" k, l, k * [ constant, inorganic, organic, available ], l, < (l+1) * l : 2 > * [ correl element ], r, r * ( u, u * [ estimate ], u, < (u+1) * u : 2 > * [ covar element ], k, k * ( 6 * [ residual element ] ) ); "OPTIONS" Transformed data matrix, Correlation matrix, Residual analysis; Transformed data matrix ======================= obs.no. beta0 beta1 beta2 dep.var. 1 1.000 0.400 53.000 64.000 2 1.000 0.400 23.000 60.000 3 1.000 3.100 19.000 71.000 4 1.000 0.600 34.000 61.000 5 1.000 4.700 24.000 54.000 6 1.000 1.700 65.000 77.000 7 1.000 9.400 44.000 81.000 8 1.000 10.100 31.000 93.000 9 1.000 11.600 29.000 93.000 10 1.000 12.600 58.000 51.000 11 1.000 10.900 37.000 76.000 12 1.000 23.100 46.000 96.000 13 1.000 23.100 50.000 77.000 14 1.000 21.600 44.000 93.000 15 1.000 23.100 56.000 95.000 16 1.000 1.900 36.000 54.000 17 1.000 26.800 58.000 168.000 18 1.000 29.900 51.000 99.000 Control information =================== transformed variable denoted by parameter mean standard deviation minimum maximum beta0 1.000000 0.000000 1.000000 1.000000 beta1 11.944444 10.154583 0.400000 29.900000 beta2 42.111111 13.624756 19.000000 65.000000 dep.var. 81.277778 26.996308 51.000000 168.000000 Number of observations : 18 Correlation matrix of the variables =================================== beta0 beta1 beta2 dep.var. beta0 1.000000 beta1 * 1.000000 beta2 * 0.461567 1.000000 dep.var. * 0.693403 0.354466 1.000000 Multiple correlation coefficient 0.694487 (adjusted 0.642875) ================================ Proportion of variation explained 0.482313 (adjusted 0.413288) ================================= Standard deviation of the error term 20.678399 ==================================== Regression parameters ===================== right tail parameter estimate standard deviation F - ratio probability beta0 56.2510240854 16.3107373404 11.893610 0.003581 beta1 1.7897741162 0.5567434145 10.334424 0.005787 beta2 0.0866492500 0.4149429933 0.043607 0.837396 Correlation matrix of the estimates =================================== beta0 beta1 beta2 beta0 1.000000 beta1 0.086771 1.000000 beta2 -0.883117 -0.461567 1.000000 Analysis of variance ==================== source of right tail variation df sum of squares mean square F - ratio probability --------------------------------------------------------------------------------------------------------------- total 18 131299.000000 --------------------------------------------------------------------------------------------------------------- mean 1 118909.388889 118909.388889 278.088058 0.000000 regression 2 5975.668532 2987.834266 6.987514 0.007170 residual 15 6413.942579 427.596172 --------------------------------------------------------------------------------------------------------------- regression null hypothesis : beta1 = beta2 = 0 Residual analysis ================= standardized studentized obs.no. observation fitted value standard deviation residual residual residual 1 64.000000 61.559344 10.596613 2.440656 0.129295 0.137448 2 60.000000 58.959866 8.994436 1.040134 0.055101 0.055862 3 71.000000 63.445660 9.817069 7.554340 0.400194 0.415085 4 61.000000 60.270963 7.439813 0.729037 0.038621 0.037786 5 54.000000 66.742544 8.277594 -12.742544 -0.675041 -0.672453 6 77.000000 64.925841 14.017687 12.074159 0.639633 0.794248 7 81.000000 76.887468 5.234633 4.112532 0.217863 0.205577 8 93.000000 77.013869 6.457231 15.986131 0.846871 0.813778 9 93.000000 79.525232 7.240620 13.474768 0.713830 0.695677 10 51.000000 83.827834 8.070605 -32.827834 -1.739066 -1.724294 11 76.000000 78.965584 5.239553 -2.965584 -0.157103 -0.148253 12 96.000000 101.580672 7.461991 -5.580672 -0.295638 -0.289377 13 77.000000 101.927269 7.367271 -24.927269 -1.320530 -1.290133 14 93.000000 98.722712 7.026946 -5.722712 -0.303163 -0.294260 15 95.000000 102.447164 7.905720 -7.447164 -0.394516 -0.389751 16 54.000000 62.770968 6.954672 -8.770968 -0.464645 -0.450398 17 168.000000 109.242627 9.235282 58.757373 3.112692 3.175816 18 99.000000 114.184382 10.161448 -15.184382 -0.804398 -0.843133 sum of residuals : 0.000000 Upper bound for the right tail probability of the largest absolute studentized residual (no. 17) : 0.001810 End of job : 2 "MODEL" surface = alfa + beta * rate + gamma * wheel + delta * visco; "INPUT" n, m, n * [ const, rate, wheel, visco, surface ], s, s * ( t, t * [ estimate ], n, n * ( 6 * [ residual element ] ) ); "OPTIONS" Transformed data matrix, Correlation matrix, Residual analysis; Transformed data matrix ======================= obs.no. alfa beta gamma delta dep.var. 1 1.000 4.051 8.575 2.226 3.235 2 1.000 2.765 8.594 2.235 3.453 3 1.000 2.777 9.024 2.235 3.246 4 1.000 4.440 9.287 2.244 2.856 5 1.000 2.263 8.434 2.283 3.643 6 1.000 4.440 9.333 2.254 2.901 7 1.000 4.406 8.666 2.254 3.277 8 1.000 4.406 8.987 2.303 2.960 9 1.000 3.199 9.210 2.244 3.105 10 1.000 3.199 8.795 2.254 3.273 11 1.000 2.765 9.071 2.263 3.250 12 1.000 3.199 8.389 2.263 3.472 13 1.000 3.182 8.936 2.244 3.223 14 1.000 2.293 8.476 2.244 3.681 15 1.000 4.075 8.039 2.244 3.572 16 1.000 3.189 9.138 2.254 3.157 17 1.000 4.075 8.949 2.323 3.096 18 1.000 4.075 8.575 2.313 3.277 19 1.000 2.293 8.648 2.323 3.681 20 1.000 2.777 8.732 2.283 3.450 21 1.000 2.777 8.949 2.283 3.292 22 1.000 4.075 9.230 2.303 2.896 23 1.000 4.440 8.476 2.283 3.346 24 1.000 3.199 8.795 2.283 3.307 25 1.000 2.777 9.024 2.283 3.250 26 1.000 4.075 8.949 2.283 3.140 27 1.000 3.199 9.105 0.489 3.153 28 1.000 4.075 9.220 0.480 2.896 29 1.000 3.199 8.575 0.399 3.431 30 1.000 2.777 8.987 0.472 3.246 31 1.000 2.293 8.896 0.489 3.367 32 1.000 4.440 8.764 1.115 3.091 33 1.000 4.075 8.987 1.076 2.934 34 1.000 4.440 9.180 0.612 2.885 35 1.000 3.199 8.748 0.663 3.346 Control information =================== transformed variable denoted by parameter mean standard deviation minimum maximum alfa 1.000000 0.000000 1.000000 1.000000 beta 3.454469 0.748055 2.263364 4.439656 gamma 8.849891 0.298180 8.039157 9.332558 delta 1.851332 0.732791 0.398986 2.322788 dep.var. 3.239746 0.228501 2.856470 3.681351 Number of observations : 35 Correlation matrix of the variables =================================== alfa beta gamma delta dep.var. alfa 1.000000 beta * 1.000000 gamma * 0.181207 1.000000 delta * 0.002140 -0.184788 1.000000 dep.var. * -0.680447 -0.811063 0.197582 1.000000 Multiple correlation coefficient 0.978154 (adjusted 0.976014) ================================ Proportion of variation explained 0.956785 (adjusted 0.952603) ================================= Standard deviation of the error term 0.049746 ==================================== Regression parameters ===================== right tail parameter estimate standard deviation F - ratio probability alfa 8.5161547705 0.2628811987 1049.465191 0.000000 beta -0.1692742083 0.0116047180 212.770983 0.000000 gamma -0.5346926136 0.0296232726 325.793413 0.000000 delta 0.0217758632 0.0118544683 3.374323 0.075824 Correlation matrix of the estimates =================================== alfa beta gamma delta alfa 1.000000 beta 0.034862 1.000000 gamma -0.984808 -0.184785 1.000000 delta -0.265643 -0.036859 0.188293 1.000000 Analysis of variance ==================== source of right tail variation df sum of squares mean square F - ratio probability --------------------------------------------------------------------------------------------------------------- total 35 369.133526 --------------------------------------------------------------------------------------------------------------- mean 1 367.358289 367.358289 148444.886282 0.000000 regression 3 1.698522 0.566174 228.783777 0.000000 residual 31 0.076716 0.002475 --------------------------------------------------------------------------------------------------------------- regression null hypothesis : beta = gamma = delta = 0 Residual analysis ================= standardized studentized obs.no. observation fitted value standard deviation residual residual residual 1 3.234749 3.293605 0.014471 -0.058856 -1.257138 -1.236599 2 3.453157 3.501612 0.013422 -0.048455 -1.034982 -1.011563 3 3.246491 3.269608 0.014359 -0.023117 -0.493769 -0.485355 4 2.856470 2.847656 0.018843 0.008814 0.188267 0.191448 5 3.642836 3.673238 0.018930 -0.030403 -0.649389 -0.660875 6 2.901422 2.823664 0.019745 0.077758 1.660861 1.702966 7 3.277145 3.185918 0.015933 0.091227 1.948567 1.935824 8 2.960105 3.015032 0.015004 -0.054926 -1.173202 -1.158055 9 3.104587 3.098805 0.015705 0.005782 0.123491 0.122484 10 3.273364 3.321185 0.010062 -0.047821 -1.021423 -0.981573 11 3.250374 3.247224 0.015359 0.003151 0.067294 0.066585 12 3.471966 3.538192 0.015827 -0.066226 -1.414548 -1.404229 13 3.222868 3.248424 0.010836 -0.025556 -0.545868 -0.526370 14 3.681351 3.644690 0.018075 0.036661 0.783071 0.791030 15 3.572346 3.576834 0.027207 -0.004488 -0.095864 -0.107766 16 3.157000 3.139466 0.014204 0.017534 0.374526 0.367787 17 3.095578 3.092069 0.012560 0.003508 0.074932 0.072882 18 3.277145 3.291563 0.014848 -0.014419 -0.307973 -0.303683 19 3.681351 3.554511 0.016812 0.126840 2.709233 2.709112 20 3.449988 3.426623 0.012525 0.023364 0.499047 0.485299 21 3.292126 3.310771 0.013578 -0.018645 -0.398240 -0.389586 22 2.895912 2.941291 0.016367 -0.045379 -0.969282 -0.965996 23 3.346389 3.282092 0.019265 0.064297 1.373353 1.401884 24 3.306887 3.321816 0.010222 -0.014929 -0.318878 -0.306648 25 3.250374 3.270650 0.014597 -0.020276 -0.433078 -0.426348 26 3.139833 3.091198 0.012345 0.048634 1.038804 1.009211 27 3.152736 3.116926 0.018870 0.035810 0.764892 0.778000 28 2.895912 2.906863 0.020483 -0.010951 -0.233917 -0.241572 29 3.430756 3.398086 0.021955 0.032670 0.697814 0.731865 30 3.246491 3.250894 0.019844 -0.004403 -0.094054 -0.096529 31 3.367296 3.382300 0.022304 -0.015004 -0.320487 -0.337434 32 3.091042 3.102835 0.017624 -0.011793 -0.251893 -0.253504 33 2.933857 3.044480 0.014266 -0.110623 -2.362858 -2.321236 34 2.884801 2.869558 0.020728 0.015243 0.325573 0.337059 35 3.346389 3.311411 0.017225 0.034978 0.747120 0.749494 sum of residuals : 0.000000 Upper bound for the right tail probability of the largest absolute studentized residual (no. 19) : 0.166067 End of job : 3