*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: Fri, 24 Dec 2010 14:49:18 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/24/t1293202037vz4a473ham0vr4d.htm/, Retrieved Fri, 24 Dec 2010 15:47:28 +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/2010/Dec/24/t1293202037vz4a473ham0vr4d.htm/}, year = {2010}, } @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 = {2010}, 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:

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186448 17822 1942 16739 4872 1020 190530 22422 2547 17851 4905 1200 194207 18817 2033 17034 4971 1279 190855 22043 2049 18055 4971 1308 200779 19191 2007 18216 4930 1173 204428 23171 2660 18960 5001 1291 207617 19463 2063 17903 5059 1466 212071 22522 2113 18842 5085 1507 214239 20265 2145 18907 5111 1478 215883 24249 2866 19862 5190 1629 223484 20299 2163 18836 5076 1712 221529 25455 2157 19846 5134 1727 225247 21089 2201 19511 4804 1519 226699 26237 2838 20318 4579 1617 231406 21362 2142 19843 4526 1637 232324 26489 2253 20975 4550 1633 237192 21828 2258 20485 4566 1469 236727 27496 2979 21407 4588 1657 240698 21991 2288 20404 4564 1599 240688 27611 2431 21454 4723 1420 245283 22512 2393 21558 4553 1495 243556 28581 3244 22442 4556 1623 247826 23000 2476 21201 4542 1346 245798 28385 2490 21804 4234 1613 250479 23387 2547 22537 4341 1563 249216 30192 3461 22736 4269 2071 251896 24346 2549 21525 4217 1584 247616 30393 2496 22427 4207 1843 249994 24753 2532 23437 4267 1598 246552 31 etc...

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 11 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Nettoschuld[t] = + 298294.469041642 -1.95030151290886Fiscale_en_parafiscale_ontvangsten[t] + 0.73016016571353`Niet-fiscale_en_niet-parafiscale_ontvangsten`[t] -4.03551319728063Lopende_uitgaven_exclusief_rentelasten[t] + 4.00579626869047Rentelasten[t] + 0.738416486158941Kapitaaluitgaven[t] -11369.5789100911Q1[t] + 1758.11058024843Q2[t] -14910.9267657763Q3[t] + 2519.67782924212t + 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) 298294.469041642 45829.530616 6.5088 0 0 Fiscale_en_parafiscale_ontvangsten -1.95030151290886 0.960864 -2.0297 0.046418 0.023209 `Niet-fiscale_en_niet-parafiscale_ontvangsten` 0.73016016571353 2.270274 0.3216 0.748759 0.374379 Lopende_uitgaven_exclusief_rentelasten -4.03551319728063 0.935233 -4.315 5.5e-05 2.7e-05 Rentelasten 4.00579626869047 7.531516 0.5319 0.596601 0.298301 Kapitaaluitgaven 0.738416486158941 1.324364 0.5576 0.579029 0.289515 Q1 -11369.5789100911 7243.332733 -1.5697 0.121276 0.060638 Q2 1758.11058024843 3439.814033 0.5111 0.610982 0.305491 Q3 -14910.9267657763 7090.795068 -2.1029 0.039296 0.019648 t 2519.67782924212 437.122785 5.7642 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.861464592776989 R-squared 0.742121244608423 Adjusted R-squared 0.706955959782299 F-TEST (value) 21.1038030340964 F-TEST (DF numerator) 9 F-TEST (DF denominator) 66 p-value 3.33066907387547e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9859.0427309608 Sum Squared Residuals 6415247755.68012 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 186448 208823.234267209 -22375.2342672094 2 190530 211718.577096666 -21188.5770966655 3 194207 207844.483947061 -13637.4839470611 4 190855 214896.253527762 -24041.253527762 5 200779 210664.304137359 -9885.30413735928 6 204428 216395.388885442 -11967.3888854415 7 207617 213668.938277781 -6051.93827778149 8 212071 221515.157439769 -9444.15743976935 9 214239 216910.880265923 -2671.88026592262 10 215883 221888.735528788 -6005.73552878836 11 223484 218674.828725630 4809.17127437049 12 221529 222212.841860719 -683.841860718597 13 225247 221786.477755821 3460.52224417879 14 226699 223773.206417490 2925.79358250970 15 231406 220342.705196993 11063.2948030074 16 232324 223380.146218904 8943.8537810962 17 237192 225544.645193788 11647.3548062119 18 236727 227170.355667098 9556.6443329019 19 240698 227161.517774597 13536.4822254026 20 240688 230003.296969320 10684.7030306803 21 245283 230024.939714763 15258.0602852367 22 243556 230996.434486162 12559.5655138384 23 247826 231918.394069053 15907.6059309471 24 245798 235386.804752465 10411.1952475353 25 250479 233759.797965415 16719.2020345849 26 249216 236086.360998478 13129.6390015217 27 251896 236991.654302206 14904.3456977944 28 247616 239101.246163163 8514.75383683748 29 249994 237260.898788844 12733.1012111557 30 246552 240340.294264083 6211.70573591669 31 248771 242982.386028709 5788.61397129056 32 247551 243312.387843767 4238.61215623313 33 249745 243795.128967564 5949.87103243581 34 245742 244211.253017490 1530.74698251036 35 249019 246650.26509291 2368.73490708996 36 245841 245179.961921947 661.038078053122 37 248771 245396.14571178 3374.85428822007 38 244723 248295.994016682 -3572.9940166815 39 246878 253083.675074333 -6205.67507433331 40 246014 249913.896772608 -3899.89677260799 41 248496 252352.763666955 -3856.76366695481 42 244351 250399.090535314 -6048.09053531363 43 248016 254881.26920228 -6865.26920227976 44 246509 250370.048849400 -3861.04884939954 45 249426 252720.140499244 -3294.1404992438 46 247840 248645.745920033 -805.745920033213 47 251035 251959.481361750 -924.481361749696 48 250161 251728.777814715 -1567.77781471523 49 254278 255563.891302993 -1285.89130299264 50 250801 249325.68545299 1475.31454701018 51 253985 256002.933440916 -2017.93344091640 52 249174 248037.855676653 1136.14432334709 53 251287 253947.961753821 -2660.9617538212 54 247947 251702.272309612 -3755.27230961240 55 249992 259005.241509730 -9013.24150973048 56 243805 252649.244224262 -8844.24422426233 57 255812 262353.338161668 -6541.33816166754 58 250417 248578.605610102 1838.39438989784 59 253033 257430.555567759 -4397.55556775866 60 248705 253558.733106178 -4853.73310617796 61 253950 257565.673511725 -3615.67351172509 62 251484 255770.446380281 -4286.4463802807 63 251093 259985.783108545 -8892.78310854484 64 245996 251503.147938196 -5507.14793819644 65 252721 261760.816592643 -9039.81659264323 66 248019 253684.635832678 -5665.63583267816 67 250464 262783.255799995 -12319.2557999952 68 245571 252929.597241125 -7358.5972411249 69 252690 260916.210230392 -8226.21023039204 70 250183 249830.105732772 352.894267227925 71 253639 259571.680491802 -5932.68049180247 72 254436 248197.503697946 6238.49630205385 73 265280 260969.751512092 4310.24848790783 74 268705 254989.81184784 13715.1881521603 75 270643 262762.951027949 7880.04897205093 76 271480 252247.097981102 19232.9020188979 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 13 0.132549874205991 0.265099748411982 0.867450125794009 14 0.0814339766354018 0.162867953270804 0.918566023364598 15 0.0498805227846876 0.0997610455693752 0.950119477215312 16 0.0641691240198255 0.128338248039651 0.935830875980175 17 0.0383636820509473 0.0767273641018947 0.961636317949053 18 0.0335898804395 0.067179760879 0.9664101195605 19 0.0376016597464303 0.0752033194928607 0.96239834025357 20 0.0414740402605266 0.0829480805210532 0.958525959739473 21 0.0351710305357613 0.0703420610715226 0.964828969464239 22 0.0420299158014762 0.0840598316029523 0.957970084198524 23 0.0400083447661942 0.0800166895323883 0.959991655233806 24 0.0635877866844505 0.127175573368901 0.936412213315549 25 0.106223407650356 0.212446815300712 0.893776592349644 26 0.0797818858228004 0.159563771645601 0.9202181141772 27 0.157750781694862 0.315501563389723 0.842249218305138 28 0.708347605606698 0.583304788786604 0.291652394393302 29 0.992323922241038 0.0153521555179241 0.00767607775896203 30 0.996369431860674 0.00726113627865281 0.00363056813932641 31 0.99931369098619 0.00137261802762209 0.000686309013811044 32 0.99970202963678 0.00059594072643854 0.00029797036321927 33 0.999981093835514 3.78123289709924e-05 1.89061644854962e-05 34 0.999992556053692 1.48878926150279e-05 7.44394630751396e-06 35 0.99999704339589 5.91320821968562e-06 2.95660410984281e-06 36 0.99999952973987 9.40520261433662e-07 4.70260130716831e-07 37 0.99999984343799 3.13124021191439e-07 1.56562010595719e-07 38 0.999999917573843 1.64852314140184e-07 8.2426157070092e-08 39 0.99999990287863 1.94242741137966e-07 9.71213705689832e-08 40 0.99999990782866 1.84342679378059e-07 9.21713396890297e-08 41 0.999999960316943 7.93661146713693e-08 3.96830573356847e-08 42 0.999999919912304 1.60175391929061e-07 8.00876959645304e-08 43 0.999999848841434 3.02317132184845e-07 1.51158566092423e-07 44 0.999999575300565 8.49398869843723e-07 4.24699434921862e-07 45 0.999999762815416 4.74369168423505e-07 2.37184584211753e-07 46 0.999999496775016 1.00644996880398e-06 5.0322498440199e-07 47 0.99999875782997 2.48434006125817e-06 1.24217003062908e-06 48 0.999996525806712 6.948386575673e-06 3.4741932878365e-06 49 0.99999992954392 1.40912160476381e-07 7.04560802381907e-08 50 0.999999814210625 3.71578750447092e-07 1.85789375223546e-07 51 0.999999417183593 1.16563281372596e-06 5.8281640686298e-07 52 0.99999786219909 4.27560181867091e-06 2.13780090933545e-06 53 0.999999808104857 3.8379028621007e-07 1.91895143105035e-07 54 0.999999391027484 1.2179450329388e-06 6.089725164694e-07 55 0.999999945866565 1.08266870649372e-07 5.4133435324686e-08 56 0.999999648990994 7.02018011171852e-07 3.51009005585926e-07 57 0.999998590260182 2.81947963542467e-06 1.40973981771234e-06 58 0.9999967133974 6.57320520149258e-06 3.28660260074629e-06 59 0.999978572287802 4.28554243963222e-05 2.14277121981611e-05 60 0.999914624315326 0.000170751369348923 8.53756846744616e-05 61 0.999508576095022 0.000982847809955332 0.000491423904977666 62 0.997650253394567 0.00469949321086592 0.00234974660543296 63 0.99642720537261 0.00714558925478153 0.00357279462739076 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 34 0.666666666666667 NOK 5% type I error level 35 0.686274509803922 NOK 10% type I error level 43 0.843137254901961 NOK

Charts produced by software:

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Parameters (Session):

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

Parameters (R input):

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

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

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test 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)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$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') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$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() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() 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') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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