Multiple Regression bouwvergunningen Waals gewest

*The author of this computation has been verified*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sat, 13 Dec 2008 07:26:45 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/13/t1229178622842mdlgeczul25d.htm/, Retrieved Sat, 13 Dec 2008 15:30:31 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Dec/13/t1229178622842mdlgeczul25d.htm/}, year = {2008}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

727 0 817 0 918 0 786 0 803 0 756 0 725 0 523 0 538 0 587 0 505 0 521 0 498 0 550 0 637 0 622 0 668 0 669 0 670 0 499 0 539 0 593 0 429 0 622 0 533 0 655 0 835 0 686 0 706 0 869 0 777 0 739 0 637 0 597 0 629 0 940 0 444 0 496 0 801 0 659 0 767 0 876 0 601 0 697 0 745 0 655 0 572 0 628 0 650 0 677 0 900 0 780 0 896 0 1092 0 823 0 735 0 770 0 915 0 645 0 566 0 707 0 785 0 762 0 712 0 714 0 823 0 609 0 620 0 619 0 638 0 483 0 535 0 617 0 698 0 804 0 824 0 878 0 1019 0 974 0 773 0 734 0 827 0 804 0 721 0 659 0 732 0 839 0 994 0 828 0 1039 0 1072 0 803 0 1035 0 922 0 834 0 1739 0 359 1 513 1 699 1 741 1 793 1 877 1 750 1 752 1 675 1 682 1 583 1 632 1 606 1 645 1 980 1 847 1 941 1 1066 1 936 1 880 1 808 1 741 1 780 1 675 1 782 1 795 1 873 1 727 1 998 1 768 1 714 1 782 1 578 1 664 1 560 1 516 1 752 1 597 1 716 1 691 1 752 1 718 1 737 1 621 1 472 1 719 1 497 1 536 1 653 1 605 1 637 1 743 1 719 1 653 1 675 1 590 1 527 1 534 1 463 1 542 1 568 1 501 1 678 1 774 1 665 1 742 1 715 1 638 1 656 1 606 1 498 1 587 1 677 1 547 1 871 1 731 1 752 1 862 1 619 1 700 1 667 1 667 1 650 1 547 1 637 1 655 1 703 1 886 1 896 1 831 1 741 1 833 1 750 1 779 1 655 1 739 1 845 1 795 1 1021 1 726 1 1045 1 915 1 852 1 772 1 729 1 755 1 691 1 729 1 702 1 702 1 894 1 765 1 753 1 876 1 781 1 776 1 606 1 775 1 663 1 649 1 821 1 771 1 635 1 1070 1 693 1 779 1

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation y[t] = + 633.392420024744 -156.736181071050x[t] -36.1916043164056M1[t] -21.7170907931429M2[t] + 117.389001677488M3[t] + 93.0214099375927M4[t] + 118.232765566119M5[t] + 167.654647510434M6[t] + 81.1449762433361M7[t] + 22.2159809946689M8[t] -15.0463475873316M9[t] + 15.4135460528901M10[t] -81.1265603068882M11[t] + 1.26232858200054t + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 633.392420024744 35.674009 17.755 0 0 x -156.736181071050 34.990025 -4.4795 1.2e-05 6e-06 M1 -36.1916043164056 43.584114 -0.8304 0.407273 0.203637 M2 -21.7170907931429 43.572195 -0.4984 0.618717 0.309359 M3 117.389001677488 43.561952 2.6948 0.00762 0.00381 M4 93.0214099375927 43.553388 2.1358 0.033864 0.016932 M5 118.232765566119 43.546502 2.7151 0.007182 0.003591 M6 167.654647510434 43.541296 3.8505 0.000157 7.8e-05 M7 81.1449762433361 44.138701 1.8384 0.067428 0.033714 M8 22.2159809946689 44.131239 0.5034 0.615211 0.307605 M9 -15.0463475873316 44.125436 -0.341 0.733455 0.366728 M10 15.4135460528901 44.121289 0.3493 0.727184 0.363592 M11 -81.1265603068882 44.118801 -1.8388 0.067367 0.033684 t 1.26232858200054 0.270513 4.6664 5e-06 3e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.547342569738143 R-squared 0.299583888647554 Adjusted R-squared 0.255807881688026 F-TEST (value) 6.84356361978212 F-TEST (DF numerator) 13 F-TEST (DF denominator) 208 p-value 6.32160990221564e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 132.353916414178 Sum Squared Residuals 3643652.31155564 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 727 598.463144290339 128.536855709661 2 817 614.199986395603 202.800013604397 3 918 754.568407448232 163.431592551768 4 786 731.463144290339 54.5368557096613 5 803 757.936828500864 45.0631714991363 6 756 808.62103902718 -52.6210390271794 7 725 723.373696342083 1.62630365791650 8 523 665.707029675417 -142.707029675417 9 538 629.707029675417 -91.7070296754168 10 587 661.429251897639 -74.4292518976394 11 505 566.151474119862 -61.1514741198616 12 521 648.540363008751 -127.540363008751 13 498 613.611087274345 -115.611087274345 14 550 629.347929379608 -79.3479293796079 15 637 769.71635043224 -132.71635043224 16 622 746.611087274345 -124.611087274345 17 668 773.084771484872 -105.084771484872 18 669 823.768982011188 -154.768982011188 19 670 738.52163932609 -68.5216393260901 20 499 680.854972659423 -181.854972659423 21 539 644.854972659423 -105.854972659423 22 593 676.577194881646 -83.5771948816456 23 429 581.299417103868 -152.299417103868 24 622 663.688305992757 -41.6883059927568 25 533 628.759030258352 -95.759030258352 26 655 644.495872363615 10.5041276363851 27 835 784.864293416247 50.1357065837534 28 686 761.759030258352 -75.7590302583519 29 706 788.232714468878 -82.2327144688782 30 869 838.916924995194 30.083075004806 31 777 753.669582310097 23.3304176899034 32 739 696.00291564343 42.9970843565702 33 637 660.00291564343 -23.0029156434299 34 597 691.725137865652 -94.7251378656521 35 629 596.447360087874 32.5526399121257 36 940 678.836248976763 261.163751023237 37 444 643.906973242358 -199.906973242358 38 496 659.643815347622 -163.643815347621 39 801 800.012236400253 0.987763599746929 40 659 776.906973242358 -117.906973242358 41 767 803.380657452885 -36.3806574528847 42 876 854.0648679792 21.9351320207995 43 601 768.817525294103 -167.817525294103 44 697 711.150858627436 -14.1508586274363 45 745 675.150858627436 69.8491413725636 46 655 706.873080849659 -51.8730808496586 47 572 611.595303071881 -39.5953030718808 48 628 693.98419196077 -65.9841919607698 49 650 659.054916226365 -9.05491622636499 50 677 674.791758331628 2.20824166837205 51 900 815.16017938426 84.8398206157404 52 780 792.054916226365 -12.0549162263649 53 896 818.528600436891 77.4713995631087 54 1092 869.212810963207 222.787189036793 55 823 783.96546827811 39.0345317218904 56 735 726.298801611443 8.70119838855715 57 770 690.298801611443 79.701198388557 58 915 722.021023833665 192.978976166335 59 645 626.743246055887 18.2567539441127 60 566 709.132134944776 -143.132134944776 61 707 674.202859210371 32.7971407896285 62 785 689.939701315634 95.0602986843656 63 762 830.308122368266 -68.3081223682661 64 712 807.202859210371 -95.2028592103714 65 714 833.676543420898 -119.676543420898 66 823 884.360753947214 -61.3607539472135 67 609 799.113411262116 -190.113411262116 68 620 741.446744595449 -121.446744595449 69 619 705.44674459545 -86.4467445954494 70 638 737.168966817672 -99.1689668176716 71 483 641.891189039894 -158.891189039894 72 535 724.280077928783 -189.280077928783 73 617 689.350802194378 -72.350802194378 74 698 705.087644299641 -7.08764429964094 75 804 845.456065352273 -41.4560653522726 76 824 822.350802194378 1.64919780562209 77 878 848.824486404904 29.1755135950957 78 1019 899.50869693122 119.49130306878 79 974 814.261354246123 159.738645753877 80 773 756.594687579456 16.4053124205441 81 734 720.594687579456 13.4053124205441 82 827 752.316909801678 74.6830901983219 83 804 657.0391320239 146.960867976100 84 721 739.428020912789 -18.4280209127893 85 659 704.498745178385 -45.4987451783845 86 732 720.235587283647 11.7644127163525 87 839 860.60400833628 -21.6040083362791 88 994 837.498745178384 156.501254821616 89 828 863.97242938891 -35.9724293889108 90 1039 914.656639915226 124.343360084774 91 1072 829.40929723013 242.590702769871 92 803 771.742630563462 31.2573694365377 93 1035 735.742630563462 299.257369436538 94 922 767.464852785685 154.535147214315 95 834 672.187075007907 161.812924992093 96 1739 754.575963896796 984.424036103204 97 359 562.910507091341 -203.910507091341 98 513 578.647349196604 -65.647349196604 99 699 719.015770249236 -20.0157702492356 100 741 695.910507091341 45.0894929086591 101 793 722.384191301867 70.6158086981327 102 877 773.068401828183 103.931598171817 103 750 687.821059143086 62.1789408569143 104 752 630.154392476419 121.845607523581 105 675 594.154392476419 80.845607523581 106 682 625.876614698641 56.1233853013589 107 583 530.598836920863 52.4011630791366 108 632 612.987725809752 19.0122741902477 109 606 578.058450075348 27.9415499246524 110 645 593.79529218061 51.2047078193895 111 980 734.163713233242 245.836286766758 112 847 711.058450075347 135.941549924653 113 941 737.532134285874 203.467865714126 114 1066 788.21634481219 277.783655187810 115 936 702.969002127092 233.030997872908 116 880 645.302335460425 234.697664539575 117 808 609.302335460426 198.697664539575 118 741 641.024557682648 99.9754423173523 119 780 545.74677990487 234.25322009513 120 675 628.135668793759 46.8643312062412 121 782 593.206393059354 188.793606940646 122 795 608.943235164617 186.056764835383 123 873 749.311656217249 123.688343782751 124 727 726.206393059354 0.793606940646066 125 998 752.68007726988 245.319922730120 126 768 803.364287796196 -35.3642877961960 127 714 718.116945111099 -4.11694511109868 128 782 660.450278444432 121.549721555568 129 578 624.450278444432 -46.450278444432 130 664 656.172500666654 7.82749933334584 131 560 560.894722888876 -0.894722888876403 132 516 643.283611777765 -127.283611777765 133 752 608.354336043361 143.645663956639 134 597 624.091178148623 -27.0911781486235 135 716 764.459599201255 -48.4595992012551 136 691 741.35433604336 -50.3543360433604 137 752 767.828020253887 -15.8280202538868 138 718 818.512230780203 -100.512230780203 139 737 733.264888095105 3.73511190489481 140 621 675.598221428438 -54.5982214284384 141 472 639.598221428438 -167.598221428438 142 719 671.32044365066 47.6795563493394 143 497 576.042665872883 -79.042665872883 144 536 658.431554761772 -122.431554761772 145 653 623.502279027367 29.4977209726329 146 605 639.23912113263 -34.23912113263 147 637 779.607542185262 -142.607542185262 148 743 756.502279027367 -13.5022790273670 149 719 782.975963237893 -63.9759632378933 150 653 833.660173764209 -180.660173764209 151 675 748.412831079112 -73.4128310791117 152 590 690.746164412445 -100.746164412445 153 527 654.746164412445 -127.746164412445 154 534 686.468386634667 -152.468386634667 155 463 591.19060885689 -128.190608856889 156 542 673.579497745778 -131.579497745778 157 568 638.650222011374 -70.6502220113736 158 501 654.387064116637 -153.387064116637 159 678 794.755485169268 -116.755485169268 160 774 771.650222011373 2.34977798862653 161 665 798.1239062219 -133.123906221900 162 742 848.808116748216 -106.808116748216 163 715 763.560774063118 -48.5607740631182 164 638 705.894107396451 -67.8941073964514 165 656 669.894107396452 -13.8941073964515 166 606 701.616329618674 -95.6163296186737 167 498 606.338551840896 -108.338551840896 168 587 688.727440729785 -101.727440729785 169 677 653.79816499538 23.2018350046199 170 547 669.535007100643 -122.535007100643 171 871 809.903428153275 61.0965718467253 172 731 786.79816499538 -55.7981649953799 173 752 813.271849205906 -61.2718492059063 174 862 863.956059732222 -1.95605973222207 175 619 778.708717047125 -159.708717047125 176 700 721.042050380458 -21.0420503804579 177 667 685.042050380458 -18.042050380458 178 667 716.76427260268 -49.7642726026802 179 650 621.486494824902 28.5135051750976 180 547 703.875383713791 -156.875383713791 181 637 668.946107979387 -31.9461079793866 182 655 684.68295008465 -29.6829500846495 183 703 825.051371137281 -122.051371137281 184 886 801.946107979387 84.0538920206135 185 896 828.419792189913 67.5802078100872 186 831 879.104002716229 -48.1040027162286 187 741 793.856660031131 -52.8566600311312 188 833 736.189993364464 96.8100066355355 189 750 700.189993364464 49.8100066355355 190 779 731.912215586687 47.0877844133133 191 655 636.634437808909 18.3655621910911 192 739 719.023326697798 19.9766733022021 193 845 684.094050963393 160.905949036607 194 795 699.830893068656 95.169106931344 195 1021 840.199314121288 180.800685878712 196 726 817.094050963393 -91.094050963393 197 1045 843.56773517392 201.432264826081 198 915 894.251945700235 20.7480542997649 199 852 809.004603015138 42.9953969848623 200 772 751.337936348471 20.6620636515291 201 729 715.337936348471 13.662063651529 202 755 747.060158570693 7.93984142930679 203 691 651.782380792915 39.2176192070846 204 729 734.171269681804 -5.17126968180438 205 702 699.2419939474 2.75800605260042 206 702 714.978836052663 -12.9788360526625 207 894 855.347257105294 38.6527428947058 208 765 832.2419939474 -67.2419939473995 209 753 858.715678157926 -105.715678157926 210 876 909.399888684242 -33.3998886842416 211 781 824.152545999144 -43.1525459991442 212 776 766.485879332477 9.51412066752254 213 606 730.485879332478 -124.485879332478 214 775 762.2081015547 12.7918984453003 215 663 666.930323776922 -3.93032377692193 216 649 749.319212665811 -100.319212665811 217 821 714.389936931406 106.610063068594 218 771 730.126779036669 40.873220963331 219 635 870.4952000893 -235.495200089301 220 1070 847.389936931406 222.610063068594 221 693 873.863621141932 -180.863621141932 222 779 924.547831668248 -145.547831668248

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice) par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab')

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Software written by Ed van Stee & Patrick Wessa

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