*********************************** * Example 1 originates from: * * reference [4], page 472, 479 * *********************************** "Model" y = c * Log (x) + a + b * x; "Input" 5 * ([x], 10 * [y]); "Options" Transformed data matrix, Correlation matrix, Residual analysis, Process submodels (1, 2); Transformed data matrix ======================= obs.no. c a b dep.var. repeats 1 1.398 1.000 25.000 0.790 10.000 2 1.699 1.000 50.000 0.984 10.000 3 1.903 1.000 80.000 1.058 10.000 4 2.114 1.000 130.000 1.163 10.000 5 2.255 1.000 180.000 1.209 10.000 Control information =================== transformed variable denoted by parameter mean standard deviation minimum maximum c 1.873843 0.306746 1.397940 2.255273 a 1.000000 0.000000 1.000000 1.000000 b 93.000000 56.387870 25.000000 180.000000 dep.var. 1.040800 0.163655 0.670000 1.330000 Number of observations : 5 Correlation matrix of the variables =================================== c a b dep.var. c 1.000000 a * 1.000000 b 0.962417 * 1.000000 dep.var. 0.907742 * 0.849838 1.000000 Multiple correlation coefficient 0.911959 (adjusted 0.908023) ================================ Proportion of variation explained 0.831669 (adjusted 0.824506) ================================= Standard deviation of the error term 0.068558 ==================================== Regression parameters ===================== right tail parameter estimate standard deviation F - ratio probability c 0.6499168512 0.1175695440 30.558070 0.000001 a -0.0899819314 0.1641470240 0.300500 0.586163 b -0.0009361326 0.0006395700 2.142390 0.149935 Correlation matrix of the estimates =================================== c a b c 1.000000 a -0.993392 1.000000 b -0.962417 0.929333 1.000000 Analysis of variance ==================== source of right tail variation df sum of squares mean square F - ratio probability --------------------------------------------------------------------------------------------------------------- total 50 55.475600 --------------------------------------------------------------------------------------------------------------- mean 1 54.163232 54.163232 11523.444701 0.000000 regression 2 1.091456 0.545728 116.105776 0.000000 residual 47 0.220912 0.004700 --------------------------------------------------------------------------------------------------------------- lack of fit 2 0.005012 0.002506 0.522336 0.596686 pure error 45 0.215900 0.004798 --------------------------------------------------------------------------------------------------------------- regression null hypothesis : c = b = 0 Residual analysis ================= standardized studentized obs.no. observation fitted value standard deviation residual residual residual 1 0.790000 0.795160 0.020789 -0.005160 -0.118992 -0.078976 2 0.984000 0.967401 0.013590 0.016599 0.382824 0.247021 3 1.058000 1.071978 0.015165 -0.013978 -0.322363 -0.209059 4 1.163000 1.162208 0.012847 0.000792 0.018260 0.011757 5 1.209000 1.207254 0.019954 0.001746 0.040272 0.026622 sum of residuals : 0.000000 Upper bound for the right tail probability of the largest absolute studentized residual (no. 2) : 1.000000 Control information - submodel 1 =================== transformed variable denoted by parameter mean standard deviation minimum maximum b omitted c 1.873843 0.306746 1.397940 2.255273 a 1.000000 0.000000 1.000000 1.000000 dep.var. 1.040800 0.163655 0.670000 1.330000 Number of observations : 5 Multiple correlation coefficient 0.907742 (adjusted 0.905720) ================================ Proportion of variation explained 0.823996 (adjusted 0.820329) ================================= Standard deviation of the error term 0.069370 ==================================== Regression parameters ===================== right tail parameter estimate standard deviation F - ratio probability c 0.4842988398 0.0323066319 224.720913 0.000000 a 0.1332999205 0.0613273100 4.724458 0.034701 Correlation matrix of the estimates =================================== c a c 1.000000 a -0.987122 1.000000 Analysis of variance ==================== source of right tail variation df sum of squares mean square F - ratio probability --------------------------------------------------------------------------------------------------------------- total 50 55.475600 --------------------------------------------------------------------------------------------------------------- mean 1 54.163232 54.163232 11255.564569 0.000000 regression 1 1.081386 1.081386 224.720852 0.000000 residual 48 0.230982 0.004812 --------------------------------------------------------------------------------------------------------------- lack of fit 3 0.015082 0.005027 1.047838 0.380681 pure error 45 0.215900 0.004798 --------------------------------------------------------------------------------------------------------------- reduction 1 0.010070 0.010070 2.142390 0.149935 --------------------------------------------------------------------------------------------------------------- regression null hypothesis : c = 0 (in the reduced model) reduction null hypothesis : b = 0 (in the original model) Residual analysis ================= standardized studentized obs.no. observation fitted value standard deviation residual residual residual 1 0.790000 0.810321 0.018238 -0.020321 -0.378175 -0.303615 2 0.984000 0.956109 0.011321 0.027891 0.519060 0.407526 3 1.058000 1.054964 0.009856 0.003036 0.056497 0.044211 4 1.163000 1.157080 0.012506 0.005920 0.110169 0.086758 5 1.209000 1.225526 0.015751 -0.016526 -0.307551 -0.244617 sum of residuals : -0.000000 Upper bound for the right tail probability of the largest absolute studentized residual (no. 2) : 1.000000 Control information - submodel 2 =================== transformed variable denoted by parameter mean standard deviation minimum maximum a omitted b omitted c 1.873843 0.306746 1.397940 2.255273 dep.var. 1.040800 0.163655 0.670000 1.330000 Number of observations : 5 There is no constant independent variable in the transformed (sub)model (message) Multiple correlation coefficient 0.997711 (adjusted 0.997664) ================================ Proportion of variation explained 0.995427 (adjusted 0.995333) ================================= Standard deviation of the error term 0.071958 ==================================== Regression parameters ===================== right tail parameter estimate standard deviation F - ratio probability c 0.5536156656 0.0053607978 10664.926934 0.000000 Correlation matrix of the estimates =================================== c c 1.000000 Analysis of variance ==================== source of right tail variation df sum of squares mean square F - ratio probability --------------------------------------------------------------------------------------------------------------- total 50 55.475600 --------------------------------------------------------------------------------------------------------------- regression 1 55.221883 55.221883 10664.926934 0.000000 residual 49 0.253717 0.005178 --------------------------------------------------------------------------------------------------------------- lack of fit 4 0.037817 0.009454 1.970525 0.115263 pure error 45 0.215900 0.004798 --------------------------------------------------------------------------------------------------------------- reduction 2 0.032804 0.016402 3.489644 0.038633 --------------------------------------------------------------------------------------------------------------- regression null hypothesis : c = 0 (in the reduced model) reduction null hypothesis : a = b = 0 (in the original model) Residual analysis ================= standardized studentized obs.no. observation fitted value standard deviation residual residual residual 1 0.790000 0.773921 0.007494 0.016079 0.249818 0.224666 2 0.984000 0.940576 0.009108 0.043424 0.674690 0.608353 3 1.058000 1.053580 0.010202 0.004420 0.068669 0.062046 4 1.163000 1.170312 0.011332 -0.007312 -0.113612 -0.102902 5 1.209000 1.248554 0.012090 -0.039554 -0.614569 -0.557614 sum of residuals : 0.170553 Upper bound for the right tail probability of the largest absolute studentized residual (no. 2) : 1.000000 End of job : 1 *********************************** * Example 2 originates from: * * reference [9], page 475, ff. * *********************************** "Model" available = beta0 + beta1 * inorganic + beta2 * organic; "Input" 18 * [soil sample, available, inorganic, organic]; "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 *********************************** * Example 3 originates from: * * reference [3], page 228, 339 * *********************************** "Model" Ln (Mean surface volume) = Lnalpha + beta * Ln (Feed rate) + gamma * Ln (Wheel velocity) + delta * Ln (Feed viscosity); "Input" 35 * [Run number, Feed rate, Wheel velocity, Feed viscosity, Mean surface volume]; "Options" Transformed data matrix, Residual analysis, Process submodels (1); Transformed data matrix ======================= obs.no. Lnalpha 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 Lnalpha 1.000000 0.000000 1.000000 1.000000 beta -3.454469 0.748055 -4.439656 -2.263364 gamma 8.849891 0.298180 8.039157 9.332558 delta -1.778466 0.899585 -2.322788 0.662688 dep.var. 3.239746 0.228501 2.856470 3.681351 Number of observations : 35 Multiple correlation coefficient 0.977342 (adjusted 0.975121) ================================ Proportion of variation explained 0.955197 (adjusted 0.950861) ================================= Standard deviation of the error term 0.050652 ==================================== Regression parameters ===================== right tail parameter estimate standard deviation F - ratio probability Lnalpha 8.5495323331 0.2660238985 1032.864643 0.000000 beta 0.1684244052 0.0118081196 203.445721 0.000000 gamma -0.5371370141 0.0300960773 318.530024 0.000000 delta -0.0144134670 0.0098170458 2.155635 0.152122 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 143182.007167 0.000000 regression 3 1.695702 0.565234 220.306257 0.000000 residual 31 0.079536 0.002566 --------------------------------------------------------------------------------------------------------------- regression null hypothesis : beta = gamma = delta = 0 Residual analysis ================= standardized studentized obs.no. observation fitted value standard deviation residual residual residual 1 3.234749 3.293078 0.014784 -0.058329 -1.223587 -1.203971 2 3.453157 3.499877 0.013574 -0.046720 -0.980073 -0.957386 3 3.246491 3.266833 0.014411 -0.020342 -0.426727 -0.418915 4 2.856470 2.845581 0.019237 0.010889 0.228428 0.232392 5 3.642836 3.671117 0.019169 -0.028281 -0.593273 -0.603205 6 2.901422 2.821409 0.020142 0.080013 1.678467 1.721617 7 3.277145 3.185264 0.016296 0.091881 1.927422 1.915802 8 2.960105 3.013233 0.015317 -0.053128 -1.114483 -1.100383 9 3.104587 3.095864 0.015813 0.008723 0.182980 0.181266 10 3.273364 3.319189 0.010115 -0.045825 -0.961298 -0.923297 11 3.250374 3.244114 0.015389 0.006261 0.131334 0.129733 12 3.471966 3.537118 0.016090 -0.065151 -1.366704 -1.356500 13 3.222868 3.246139 0.010877 -0.023271 -0.488175 -0.470406 14 3.681351 3.642772 0.018313 0.038579 0.809285 0.816897 15 3.572346 3.577499 0.027704 -0.005154 -0.108113 -0.121538 16 3.157000 3.136624 0.014274 0.020376 0.427441 0.419268 17 3.095578 3.089933 0.012748 0.005644 0.118401 0.115136 18 3.277145 3.290415 0.015136 -0.013270 -0.278377 -0.274531 19 3.681351 3.551596 0.016891 0.129755 2.721926 2.717208 20 3.449988 3.424209 0.012559 0.025778 0.540765 0.525330 21 3.292126 3.307827 0.013565 -0.015701 -0.329363 -0.321724 22 2.895912 2.938617 0.016603 -0.042705 -0.895839 -0.892395 23 3.346389 3.281716 0.019666 0.064673 1.356676 1.385494 24 3.306887 3.319607 0.010241 -0.012720 -0.266842 -0.256427 25 3.250374 3.267523 0.014593 -0.017148 -0.359731 -0.353541 26 3.139833 3.089357 0.012571 0.050476 1.058853 1.028699 27 3.152736 3.127162 0.016604 0.025574 0.536468 0.534411 28 2.895912 2.917634 0.018272 -0.021722 -0.455674 -0.459805 29 3.430756 3.410283 0.019104 0.020473 0.429475 0.436419 30 3.246491 3.261192 0.017683 -0.014701 -0.308384 -0.309713 31 3.367296 3.392279 0.020625 -0.024983 -0.524074 -0.540015 32 3.091042 3.110356 0.016548 -0.019313 -0.405145 -0.403428 33 2.933857 3.051431 0.013053 -0.117574 -2.466405 -2.402326 34 2.884801 2.862104 0.027070 0.022697 0.476118 0.530148 35 3.346389 3.302140 0.026253 0.044249 0.928227 1.021480 sum of residuals : 0.000000 Upper bound for the right tail probability of the largest absolute studentized residual (no. 19) : 0.161110 Control information - submodel 1 =================== transformed variable denoted by parameter mean standard deviation minimum maximum delta omitted Lnalpha 1.000000 0.000000 1.000000 1.000000 beta -3.454469 0.748055 -4.439656 -2.263364 gamma 8.849891 0.298180 8.039157 9.332558 dep.var. 3.239746 0.228501 2.856470 3.681351 Number of observations : 35 Multiple correlation coefficient 0.975747 (adjusted 0.974211) ================================ Proportion of variation explained 0.952082 (adjusted 0.949087) ================================= Standard deviation of the error term 0.051559 ==================================== Regression parameters ===================== right tail parameter estimate standard deviation F - ratio probability Lnalpha 8.6444323107 0.2626702491 1083.056676 0.000000 beta 0.1684884827 0.0120193633 196.506767 0.000000 gamma -0.5449387793 0.0301534149 326.604694 0.000000 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 138191.417269 0.000000 regression 2 1.690171 0.845086 317.901017 0.000000 residual 32 0.085067 0.002658 --------------------------------------------------------------------------------------------------------------- reduction 1 0.005531 0.005531 2.155635 0.152122 --------------------------------------------------------------------------------------------------------------- regression null hypothesis : beta = gamma = 0 (in the reduced model) reduction null hypothesis : delta = 0 (in the original model) Residual analysis ================= standardized studentized obs.no. observation fitted value standard deviation residual residual residual 1 3.234749 3.288736 0.014744 -0.053986 -1.095063 -1.092714 2 3.453157 3.495338 0.013453 -0.042181 -0.855592 -0.847461 3 3.246491 3.258939 0.013610 -0.012448 -0.252494 -0.250309 4 2.856470 2.835391 0.018263 0.021079 0.427577 0.437186 5 3.642836 3.667170 0.019319 -0.024335 -0.493612 -0.509070 6 2.901422 2.810729 0.019119 0.090693 1.839619 1.894046 7 3.277145 3.179790 0.016148 0.097355 1.974757 1.988259 8 2.960105 3.004546 0.014381 -0.044441 -0.901445 -0.897568 9 3.104587 3.086354 0.014683 0.018233 0.369839 0.368910 10 3.273364 3.312784 0.009289 -0.039420 -0.799600 -0.777283 11 3.250374 3.235443 0.014465 0.014931 0.302866 0.301712 12 3.471966 3.533738 0.016210 -0.061771 -1.252973 -1.262069 13 3.222868 3.238771 0.009822 -0.015903 -0.322584 -0.314204 14 3.681351 3.639046 0.018461 0.042305 0.858113 0.878776 15 3.572346 3.577070 0.028198 -0.004725 -0.095835 -0.109457 16 3.157000 3.127544 0.013095 0.029456 0.597494 0.590683 17 3.095578 3.081275 0.011505 0.014303 0.290113 0.284576 18 3.277145 3.284817 0.014911 -0.007672 -0.155626 -0.155449 19 3.681351 3.545399 0.016648 0.135953 2.757671 2.786075 20 3.449988 3.417901 0.012012 0.032087 0.650846 0.639938 21 3.292126 3.299829 0.012646 -0.007702 -0.156232 -0.154093 22 2.895912 2.928056 0.015231 -0.032144 -0.652013 -0.652569 23 3.346389 3.277298 0.019783 0.069091 1.401447 1.451105 24 3.306887 3.312784 0.009289 -0.005897 -0.119624 -0.116286 25 3.250374 3.258939 0.013610 -0.008564 -0.173721 -0.172218 26 3.139833 3.081275 0.011505 0.058558 1.187784 1.165114 27 3.152736 3.143769 0.012373 0.008967 0.181893 0.179159 28 2.895912 2.933425 0.015036 -0.037513 -0.760916 -0.760637 29 3.430756 3.432323 0.012027 -0.001567 -0.031791 -0.031260 30 3.246491 3.279000 0.013097 -0.032509 -0.659420 -0.651909 31 3.367296 3.410576 0.016728 -0.043280 -0.877901 -0.887443 32 3.091042 3.120529 0.015296 -0.029487 -0.598106 -0.598860 33 2.933857 3.060447 0.011724 -0.126590 -2.567757 -2.521296 34 2.884801 2.893928 0.016507 -0.009128 -0.185143 -0.186866 35 3.346389 3.338135 0.009558 0.008254 0.167433 0.162920 sum of residuals : 0.000000 Upper bound for the right tail probability of the largest absolute studentized residual (no. 19) : 0.125816 End of job : 3 ************************************** * Example 4 originates from: * * reference [1], page 88, 93, ff. * ************************************** "Model" y = alfa0 + alfa1 * x; "Input" 5 * ([x], n, n * [y]); "Option" Transformed data matrix, Print input data; "Data" 1.000 4.000 1.100 0.700 1.800 0.400 3.000 5.000 3.000 1.400 4.900 4.400 4.500 5.000 3.000 7.300 8.200 6.200 10.000 4.000 12.000 13.100 12.600 13.200 15.000 4.000 18.700 19.700 17.400 17.100 Transformed data matrix ======================= obs.no. alfa0 alfa1 dep.var. repeats 1 1.000 1.000 1.000 4.000 2 1.000 3.000 3.640 5.000 3 1.000 5.000 7.233 3.000 4 1.000 10.000 12.725 4.000 5 1.000 15.000 18.225 4.000 Control information =================== transformed variable denoted by parameter mean standard deviation minimum maximum alfa0 1.000000 0.000000 1.000000 1.000000 alfa1 6.700000 5.262579 1.000000 15.000000 dep.var. 8.385000 6.545571 0.400000 19.700000 Number of observations : 5 Multiple correlation coefficient 0.987051 (adjusted 0.986326) ================================ Proportion of variation explained 0.974269 (adjusted 0.972840) ================================= Standard deviation of the error term 1.078736 ==================================== Regression parameters ===================== right tail parameter estimate standard deviation F - ratio probability alfa0 0.1594830832 0.3968072487 0.161536 0.692478 alfa1 1.2276890919 0.0470261690 681.549798 0.000000 Analysis of variance ==================== source of right tail variation df sum of squares mean square F - ratio probability --------------------------------------------------------------------------------------------------------------- total 20 2220.210000 --------------------------------------------------------------------------------------------------------------- mean 1 1406.164500 1406.164500 1208.387113 0.000000 regression 1 793.099430 793.099430 681.549798 0.000000 residual 18 20.946070 1.163671 --------------------------------------------------------------------------------------------------------------- lack of fit 3 4.252403 1.417468 1.273658 0.319196 pure error 15 16.693667 1.112911 --------------------------------------------------------------------------------------------------------------- regression null hypothesis : alfa1 = 0 End of job : 4 ************************** * Marten van Gelderen * * Mathematisch Centrum * **************************