Consumptiegoederen met seizoenale en lange termijn trend

*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 05:45:53 -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/t122943160544qsydouvh0m9on.htm/, Retrieved Tue, 16 Dec 2008 13:46:55 +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/t122943160544qsydouvh0m9on.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] = + 101.931799517928 -4.85601080442307X[t] + 0.291678530898587t + 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) 101.931799517928 1.986631 51.3089 0 0 X -4.85601080442307 3.09841 -1.5673 0.121102 0.060551 t 0.291678530898587 0.051477 5.6662 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.58846223881155 R-squared 0.346287806507102 Adjusted R-squared 0.329525955391900 F-TEST (value) 20.6592818494271 F-TEST (DF numerator) 2 F-TEST (DF denominator) 78 p-value 6.3106899927945e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 8.01326014010615 Sum Squared Residuals 5008.5623696951 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 98.5 102.223478048827 -3.72347804882674 2 97 102.515156579725 -5.51515657972479 3 103.3 102.806835110623 0.493164889376648 4 99.6 103.098513641522 -3.49851364152198 5 100.1 103.390192172421 -3.29019217242057 6 102.9 103.681870703319 -0.78187070331914 7 95.9 103.973549234218 -8.07354923421773 8 94.5 104.265227765116 -9.76522776511632 9 107.4 104.556906296015 2.8430937039851 10 116 104.848584826913 11.1514151730865 11 102.8 105.140263357812 -2.34026335781209 12 99.8 105.431941888711 -5.63194188871067 13 109.6 105.723620419609 3.87637958039074 14 103 106.015298950508 -3.01529895050784 15 111.6 106.306977481406 5.29302251859356 16 106.3 106.598656012305 -0.298656012305021 17 97.9 106.890334543204 -8.9903345432036 18 108.8 107.182013074102 1.61798692589780 19 103.9 107.473691605001 -3.57369160500077 20 101.2 107.765370135899 -6.56537013589937 21 122.9 108.057048666798 14.8429513332021 22 123.9 108.348727197697 15.5512728023035 23 111.7 108.640405728595 3.05959427140487 24 120.9 108.932084259494 11.9679157405063 25 99.6 109.223762790392 -9.6237627903923 26 103.3 109.515441321291 -6.21544132129089 27 119.4 109.807119852189 9.59288014781053 28 106.5 110.098798383088 -3.59879838308806 29 101.9 110.390476913987 -8.49047691398665 30 124.6 110.682155444885 13.9178445551148 31 106.5 110.973833975784 -4.47383397578383 32 107.8 111.265512506682 -3.46551250668242 33 127.4 111.557191037581 15.842808962419 34 120.1 111.848869568480 8.2511304315204 35 118.5 112.140548099378 6.35945190062182 36 127.7 112.432226630277 15.2677733697232 37 107.7 112.723905161175 -5.02390516117535 38 104.5 113.015583692074 -8.51558369207394 39 118.8 113.307262222973 5.49273777702747 40 110.3 113.598940753871 -3.29894075387111 41 109.6 113.890619284770 -4.29061928476971 42 119.1 114.182297815668 4.91770218433171 43 96.5 114.473976346567 -17.9739763465669 44 106.7 114.765654877465 -8.06565487746546 45 126.3 115.057333408364 11.2426665916359 46 116.2 115.349011939263 0.850988060737367 47 118.8 115.640690470161 3.15930952983877 48 115.2 115.932369001060 -0.732369001059807 49 110 116.224047531958 -6.2240475319584 50 111.4 116.515726062857 -5.11572606285698 51 129.6 116.807404593756 12.7925954062444 52 108.1 117.099083124654 -8.99908312465416 53 117.8 117.390761655553 0.40923834444725 54 122.9 117.682440186451 5.21755981354867 55 100.6 117.97411871735 -17.3741187173499 56 111.8 118.265797248249 -6.46579724824851 57 127 118.557475779147 8.4425242208529 58 128.6 118.849154310046 9.75084568995431 59 124.8 119.140832840944 5.65916715905573 60 118.5 119.432511371843 -0.932511371842858 61 114.7 119.724189902741 -5.02418990274144 62 112.6 120.01586843364 -7.41586843364004 63 128.7 120.307546964539 8.39245303546137 64 111 120.599225495437 -9.5992254954372 65 115.8 120.890904026336 -5.09090402633579 66 126 121.182582557234 4.81741744276562 67 111.1 116.61825028371 -5.51825028370989 68 113.2 116.909928814608 -3.70992881460847 69 120.1 117.201607345507 2.89839265449293 70 130.6 117.493285876406 13.1067141235943 71 124 117.784964407304 6.21503559269576 72 119.4 118.076642938203 1.32335706179718 73 116.7 118.368321469101 -1.66832146910141 74 116.5 118.66 -2.16 75 119.6 118.951678530899 0.648321469101408 76 126.5 119.243357061797 7.25664293820283 77 111.3 119.535035592696 -8.23503559269576 78 123.5 119.826714123594 3.67328587640565 79 114.2 120.118392654493 -5.91839265449293 80 103.7 120.410071185392 -16.7100711853915 81 129.5 120.70174971629 8.79825028370989 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.0375373049266229 0.0750746098532458 0.962462695073377 7 0.0473802937831305 0.094760587566261 0.95261970621687 8 0.0331725143286285 0.066345028657257 0.966827485671371 9 0.0722702972786004 0.144540594557201 0.9277297027214 10 0.221021236343253 0.442042472686506 0.778978763656747 11 0.161825833882035 0.323651667764071 0.838174166117965 12 0.142981656218831 0.285963312437663 0.857018343781169 13 0.101231555935937 0.202463111871874 0.898768444064063 14 0.0722686462631369 0.144537292526274 0.927731353736863 15 0.05230300640684 0.10460601281368 0.94769699359316 16 0.0324290534662856 0.0648581069325712 0.967570946533714 17 0.0562353960569182 0.112470792113836 0.943764603943082 18 0.0356354141903459 0.0712708283806918 0.964364585809654 19 0.0255520682576133 0.0511041365152266 0.974447931742387 20 0.0241331223736804 0.0482662447473609 0.97586687762632 21 0.0915474233308437 0.183094846661687 0.908452576669156 22 0.167487101419107 0.334974202838215 0.832512898580893 23 0.124091769783544 0.248183539567089 0.875908230216456 24 0.124019250160013 0.248038500320026 0.875980749839987 25 0.237849310997312 0.475698621994625 0.762150689002688 26 0.264137216895736 0.528274433791472 0.735862783104264 27 0.244744704453099 0.489489408906199 0.7552552955469 28 0.231990713536596 0.463981427073193 0.768009286463404 29 0.289416278601801 0.578832557203601 0.7105837213982 30 0.348384578032824 0.696769156065649 0.651615421967175 31 0.339984992523215 0.67996998504643 0.660015007476785 32 0.314920506937148 0.629841013874296 0.685079493062852 33 0.417329378744683 0.834658757489366 0.582670621255317 34 0.381105149800257 0.762210299600515 0.618894850199743 35 0.333619826451688 0.667239652903376 0.666380173548312 36 0.434182249117823 0.868364498235647 0.565817750882177 37 0.445647715588055 0.89129543117611 0.554352284411945 38 0.506490522389211 0.987018955221577 0.493509477610789 39 0.461717865552815 0.92343573110563 0.538282134447185 40 0.428966770011586 0.857933540023172 0.571033229988414 41 0.402258112496703 0.804516224993405 0.597741887503297 42 0.357772886238560 0.715545772477119 0.64222711376144 43 0.649523633242866 0.700952733514269 0.350476366757134 44 0.671715470204842 0.656569059590316 0.328284529795158 45 0.69941518189992 0.601169636200162 0.300584818100081 46 0.639425369838028 0.721149260323945 0.360574630161972 47 0.581412632244284 0.837174735511431 0.418587367755716 48 0.51790998159828 0.96418003680344 0.48209001840172 49 0.499836406537527 0.999672813075054 0.500163593462473 50 0.470657047450688 0.941314094901377 0.529342952549312 51 0.550563358990119 0.898873282019763 0.449436641009881 52 0.574520117269601 0.850959765460799 0.425479882730399 53 0.505284879614913 0.989430240770175 0.494715120385087 54 0.459723262039665 0.91944652407933 0.540276737960335 55 0.733152068916721 0.533695862166558 0.266847931083279 56 0.744265025267436 0.511469949465128 0.255734974732564 57 0.724238458470277 0.551523083059445 0.275761541529723 58 0.738700392482436 0.522599215035128 0.261299607517564 59 0.710204023425095 0.579591953149809 0.289795976574905 60 0.641147662423014 0.717704675153972 0.358852337576986 61 0.586090863871277 0.827818272257447 0.413909136128723 62 0.568536099334663 0.862927801330674 0.431463900665337 63 0.594694780102841 0.810610439794318 0.405305219897159 64 0.602636307490694 0.794727385018611 0.397363692509306 65 0.580027502456746 0.839944995086509 0.419972497543254 66 0.495190081234278 0.990380162468555 0.504809918765722 67 0.495365229893334 0.990730459786669 0.504634770106666 68 0.503682473380663 0.992635053238674 0.496317526619337 69 0.427071004390288 0.854142008780576 0.572928995609712 70 0.447077090718705 0.894154181437409 0.552922909281295 71 0.368336757103713 0.736673514207425 0.631663242896287 72 0.265336958943389 0.530673917886779 0.734663041056611 73 0.177454454585692 0.354908909171383 0.822545545414308 74 0.109386023481817 0.218772046963634 0.890613976518183 75 0.0549091504932851 0.109818300986570 0.945090849506715 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 1 0.0142857142857143 OK 10% type I error level 7 0.1 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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