Multiple Linear Regression Consumptiegoederen

*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: Tue, 16 Dec 2008 05:47:32 -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/16/t1229431721etbur1pdfou6zud.htm/, Retrieved Tue, 16 Dec 2008 13:48:52 +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/16/t1229431721etbur1pdfou6zud.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 «

98,5 0 97,0 0 103,3 0 99,6 0 100,1 0 102,9 0 95,9 0 94,5 0 107,4 0 116,0 0 102,8 0 99,8 0 109,6 0 103,0 0 111,6 0 106,3 0 97,9 0 108,8 0 103,9 0 101,2 0 122,9 0 123,9 0 111,7 0 120,9 0 99,6 0 103,3 0 119,4 0 106,5 0 101,9 0 124,6 0 106,5 0 107,8 0 127,4 0 120,1 0 118,5 0 127,7 0 107,7 0 104,5 0 118,8 0 110,3 0 109,6 0 119,1 0 96,5 0 106,7 0 126,3 0 116,2 0 118,8 0 115,2 0 110,0 0 111,4 0 129,6 0 108,1 0 117,8 0 122,9 0 100,6 0 111,8 0 127,0 0 128,6 0 124,8 0 118,5 0 114,7 0 112,6 0 128,7 0 111,0 0 115,8 0 126,0 0 111,1 1 113,2 1 120,1 1 130,6 1 124,0 1 119,4 1 116,7 1 116,5 1 119,6 1 126,5 1 111,3 1 123,5 1 114,2 1 103,7 1 129,5 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 111.703030303030 + 6.95696969696971X[t] + 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) 111.703030303030 1.164473 95.9258 0 0 X 6.95696969696971 2.705992 2.571 0.012019 0.00601 Multiple Linear Regression - Regression Statistics Multiple R 0.277863786296307 R-squared 0.0772082837349197 Adjusted R-squared 0.0655273759340959 F-TEST (value) 6.60978453485221 F-TEST (DF numerator) 1 F-TEST (DF denominator) 79 p-value 0.0120193831758937 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9.46022679762616 Sum Squared Residuals 7070.1753939394 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 98.5 111.703030303031 -13.2030303030308 2 97 111.703030303030 -14.7030303030303 3 103.3 111.703030303030 -8.4030303030303 4 99.6 111.703030303030 -12.1030303030303 5 100.1 111.703030303030 -11.6030303030303 6 102.9 111.703030303030 -8.80303030303029 7 95.9 111.703030303030 -15.8030303030303 8 94.5 111.703030303030 -17.2030303030303 9 107.4 111.703030303030 -4.30303030303029 10 116 111.703030303030 4.29696969696971 11 102.8 111.703030303030 -8.9030303030303 12 99.8 111.703030303030 -11.9030303030303 13 109.6 111.703030303030 -2.1030303030303 14 103 111.703030303030 -8.70303030303029 15 111.6 111.703030303030 -0.103030303030299 16 106.3 111.703030303030 -5.4030303030303 17 97.9 111.703030303030 -13.8030303030303 18 108.8 111.703030303030 -2.9030303030303 19 103.9 111.703030303030 -7.80303030303029 20 101.2 111.703030303030 -10.5030303030303 21 122.9 111.703030303030 11.1969696969697 22 123.9 111.703030303030 12.1969696969697 23 111.7 111.703030303030 -0.00303030303029095 24 120.9 111.703030303030 9.19696969696971 25 99.6 111.703030303030 -12.1030303030303 26 103.3 111.703030303030 -8.4030303030303 27 119.4 111.703030303030 7.69696969696971 28 106.5 111.703030303030 -5.20303030303029 29 101.9 111.703030303030 -9.80303030303029 30 124.6 111.703030303030 12.8969696969697 31 106.5 111.703030303030 -5.20303030303029 32 107.8 111.703030303030 -3.9030303030303 33 127.4 111.703030303030 15.6969696969697 34 120.1 111.703030303030 8.3969696969697 35 118.5 111.703030303030 6.7969696969697 36 127.7 111.703030303030 15.9969696969697 37 107.7 111.703030303030 -4.00303030303029 38 104.5 111.703030303030 -7.20303030303029 39 118.8 111.703030303030 7.0969696969697 40 110.3 111.703030303030 -1.40303030303030 41 109.6 111.703030303030 -2.1030303030303 42 119.1 111.703030303030 7.3969696969697 43 96.5 111.703030303030 -15.2030303030303 44 106.7 111.703030303030 -5.00303030303029 45 126.3 111.703030303030 14.5969696969697 46 116.2 111.703030303030 4.49696969696971 47 118.8 111.703030303030 7.0969696969697 48 115.2 111.703030303030 3.49696969696971 49 110 111.703030303030 -1.70303030303029 50 111.4 111.703030303030 -0.303030303030288 51 129.6 111.703030303030 17.8969696969697 52 108.1 111.703030303030 -3.6030303030303 53 117.8 111.703030303030 6.0969696969697 54 122.9 111.703030303030 11.1969696969697 55 100.6 111.703030303030 -11.1030303030303 56 111.8 111.703030303030 0.0969696969697036 57 127 111.703030303030 15.2969696969697 58 128.6 111.703030303030 16.8969696969697 59 124.8 111.703030303030 13.0969696969697 60 118.5 111.703030303030 6.7969696969697 61 114.7 111.703030303030 2.99696969696971 62 112.6 111.703030303030 0.896969696969701 63 128.7 111.703030303030 16.9969696969697 64 111 111.703030303030 -0.703030303030294 65 115.8 111.703030303030 4.09696969696970 66 126 111.703030303030 14.2969696969697 67 111.1 118.66 -7.56 68 113.2 118.66 -5.46 69 120.1 118.66 1.44000000000000 70 130.6 118.66 11.94 71 124 118.66 5.34 72 119.4 118.66 0.740000000000008 73 116.7 118.66 -1.96000000000000 74 116.5 118.66 -2.16 75 119.6 118.66 0.939999999999996 76 126.5 118.66 7.84 77 111.3 118.66 -7.36 78 123.5 118.66 4.84 79 114.2 118.66 -4.4600 80 103.7 118.66 -14.96 81 129.5 118.66 10.84 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0325121330515689 0.0650242661031378 0.967487866948431 6 0.0146455492178116 0.0292910984356232 0.985354450782188 7 0.0110459854735330 0.0220919709470660 0.988954014526467 8 0.011221382831346 0.022442765662692 0.988778617168654 9 0.026659333353057 0.053318666706114 0.973340666646943 10 0.204996894643103 0.409993789286206 0.795003105356897 11 0.142436260935907 0.284872521871813 0.857563739064093 12 0.102495148418430 0.204990296836859 0.89750485158157 13 0.104772844961543 0.209545689923085 0.895227155038457 14 0.0722485364325986 0.144497072865197 0.927751463567401 15 0.0864625619389822 0.172925123877964 0.913537438061018 16 0.0633701837194538 0.126740367438908 0.936629816280546 17 0.0616300625772794 0.123260125154559 0.93836993742272 18 0.0531233900995353 0.106246780199071 0.946876609900465 19 0.0383912764596840 0.0767825529193681 0.961608723540316 20 0.0308537211807165 0.061707442361433 0.969146278819284 21 0.190386950811432 0.380773901622864 0.809613049188568 22 0.432965219553632 0.865930439107265 0.567034780446368 23 0.397408263722097 0.794816527444194 0.602591736277903 24 0.503999118236819 0.992001763526362 0.496000881763181 25 0.525703648079519 0.948592703840963 0.474296351920482 26 0.503176040933222 0.993647918133556 0.496823959066778 27 0.558722096194084 0.882555807611833 0.441277903805916 28 0.517092590462801 0.965814819074397 0.482907409537199 29 0.525332462639131 0.949335074721739 0.474667537360869 30 0.673886001536421 0.652227996927158 0.326113998463579 31 0.643400205495425 0.713199589009151 0.356599794504575 32 0.607348198123962 0.785303603752077 0.392651801876038 33 0.774824772761169 0.450350454477663 0.225175227238831 34 0.784816616508049 0.430366766983902 0.215183383491951 35 0.775057932766598 0.449884134466803 0.224942067233402 36 0.867904110153983 0.264191779692034 0.132095889846017 37 0.84701166723133 0.305976665537340 0.152988332768670 38 0.847109874060016 0.305780251879968 0.152890125939984 39 0.833110158139317 0.333779683721367 0.166889841860683 40 0.801799113686505 0.396401772626989 0.198200886313495 41 0.77119882839582 0.457602343208359 0.228801171604180 42 0.751924090390014 0.496151819219973 0.248075909609986 43 0.881187745597588 0.237624508804824 0.118812254402412 44 0.883457790876887 0.233084418246225 0.116542209123113 45 0.915034679580539 0.169930640838923 0.0849653204194614 46 0.893814517372924 0.212370965254152 0.106185482627076 47 0.87455902191301 0.25088195617398 0.12544097808699 48 0.84459015706482 0.310819685870361 0.155409842935180 49 0.826521836688782 0.346956326622436 0.173478163311218 50 0.8020658729728 0.395868254054399 0.197934127027199 51 0.872896138834498 0.254207722331003 0.127103861165502 52 0.872892380921678 0.254215238156645 0.127107619078322 53 0.84218000460831 0.315639990783379 0.157819995391690 54 0.83012243035794 0.339755139284119 0.169877569642059 55 0.928509257880529 0.142981484238942 0.071490742119471 56 0.924421970077739 0.151156059844522 0.0755780299222611 57 0.931097249757148 0.137805500485703 0.0689027502428516 58 0.948052815413892 0.103894369172215 0.0519471845861077 59 0.945515466996946 0.108969066006107 0.0544845330030537 60 0.923309356530263 0.153381286939475 0.0766906434697373 61 0.896770792931094 0.206458414137812 0.103229207068906 62 0.879134437664128 0.241731124671744 0.120865562335872 63 0.903001955724654 0.193996088550692 0.0969980442753461 64 0.8940035214147 0.211992957170601 0.105996478585300 65 0.878953473657657 0.242093052684685 0.121046526342343 66 0.843610145090806 0.312779709818388 0.156389854909194 67 0.826321217778651 0.347357564442697 0.173678782221349 68 0.789677629520911 0.420644740958177 0.210322370479089 69 0.714059549392214 0.571880901215573 0.285940450607786 70 0.773287840726302 0.453424318547397 0.226712159273698 71 0.718417773032102 0.563164453935796 0.281582226967898 72 0.613792910441413 0.772414179117174 0.386207089558587 73 0.494501929135602 0.989003858271204 0.505498070864398 74 0.369639463517985 0.73927892703597 0.630360536482015 75 0.247583397712736 0.495166795425473 0.752416602287264 76 0.210416662185173 0.420833324370346 0.789583337814827 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 3 0.0416666666666667 OK 10% type I error level 7 0.0972222222222222 OK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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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