Consumptiegoederen eenvoudig model

*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, 20 Dec 2008 07:23:33 -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/20/t1229783063huw06qujmf0crpb.htm/, Retrieved Sat, 20 Dec 2008 15:24:23 +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/20/t1229783063huw06qujmf0crpb.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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 111.703030303030 + 6.9569696969697X[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 6.9569696969697 2.705992 2.571 0.012019 0.00601 Multiple Linear Regression - Regression Statistics Multiple R 0.277863786296307 R-squared 0.0772082837349196 Adjusted R-squared 0.0655273759340957 F-TEST (value) 6.6097845348522 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 98.5 111.703030303030 -13.2030303030305 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 -8.8030303030303 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 4.2969696969697 11 102.8 111.703030303030 -8.9030303030303 12 99.8 111.703030303030 -11.9030303030303 13 109.6 111.703030303030 -2.10303030303031 14 103 111.703030303030 -8.7030303030303 15 111.6 111.703030303030 -0.103030303030305 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 -7.8030303030303 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 -0.00303030303029685 24 120.9 111.703030303030 9.1969696969697 25 99.6 111.703030303030 -12.1030303030303 26 103.3 111.703030303030 -8.4030303030303 27 119.4 111.703030303030 7.6969696969697 28 106.5 111.703030303030 -5.2030303030303 29 101.9 111.703030303030 -9.8030303030303 30 124.6 111.703030303030 12.8969696969697 31 106.5 111.703030303030 -5.2030303030303 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 -4.0030303030303 38 104.5 111.703030303030 -7.2030303030303 39 118.8 111.703030303030 7.0969696969697 40 110.3 111.703030303030 -1.40303030303030 41 109.6 111.703030303030 -2.10303030303031 42 119.1 111.703030303030 7.3969696969697 43 96.5 111.703030303030 -15.2030303030303 44 106.7 111.703030303030 -5.0030303030303 45 126.3 111.703030303030 14.5969696969697 46 116.2 111.703030303030 4.4969696969697 47 118.8 111.703030303030 7.0969696969697 48 115.2 111.703030303030 3.4969696969697 49 110 111.703030303030 -1.7030303030303 50 111.4 111.703030303030 -0.303030303030294 51 129.6 111.703030303030 17.8969696969697 52 108.1 111.703030303030 -3.60303030303031 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 0.0969696969696975 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 2.9969696969697 62 112.6 111.703030303030 0.896969696969695 63 128.7 111.703030303030 16.9969696969697 64 111 111.703030303030 -0.7030303030303 65 115.8 111.703030303030 4.0969696969697 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 0.740000000000007 73 116.7 118.66 -1.96000000000000 74 116.5 118.66 -2.16 75 119.6 118.66 0.939999999999997 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 -4.46000000000000 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 0.0325121330515691 0.0650242661031381 0.96748786694843 6 0.0146455492178116 0.0292910984356232 0.985354450782188 7 0.0110459854735330 0.0220919709470659 0.988954014526467 8 0.0112213828313460 0.0224427656626921 0.988778617168654 9 0.026659333353057 0.053318666706114 0.973340666646943 10 0.204996894643103 0.409993789286207 0.795003105356897 11 0.142436260935907 0.284872521871815 0.857563739064093 12 0.102495148418430 0.204990296836860 0.89750485158157 13 0.104772844961543 0.209545689923086 0.895227155038457 14 0.072248536432598 0.144497072865196 0.927751463567402 15 0.0864625619389827 0.172925123877965 0.913537438061017 16 0.0633701837194541 0.126740367438908 0.936629816280546 17 0.0616300625772794 0.123260125154559 0.93836993742272 18 0.0531233900995353 0.106246780199071 0.946876609900465 19 0.0383912764596846 0.0767825529193693 0.961608723540315 20 0.0308537211807166 0.0617074423614333 0.969146278819283 21 0.190386950811431 0.380773901622861 0.80961304918857 22 0.432965219553632 0.865930439107263 0.567034780446368 23 0.397408263722098 0.794816527444195 0.602591736277902 24 0.503999118236817 0.992001763526365 0.496000881763183 25 0.525703648079518 0.948592703840963 0.474296351920482 26 0.503176040933224 0.993647918133552 0.496823959066776 27 0.558722096194084 0.882555807611833 0.441277903805916 28 0.517092590462799 0.965814819074402 0.482907409537201 29 0.525332462639132 0.949335074721737 0.474667537360868 30 0.673886001536419 0.652227996927162 0.326113998463581 31 0.643400205495422 0.713199589009155 0.356599794504578 32 0.607348198123959 0.785303603752082 0.392651801876041 33 0.774824772761166 0.450350454477667 0.225175227238834 34 0.78481661650805 0.430366766983901 0.215183383491951 35 0.7750579327666 0.449884134466799 0.224942067233400 36 0.867904110153983 0.264191779692034 0.132095889846017 37 0.847011667231329 0.305976665537343 0.152988332768671 38 0.847109874060015 0.305780251879970 0.152890125939985 39 0.833110158139317 0.333779683721367 0.166889841860683 40 0.801799113686505 0.39640177262699 0.198200886313495 41 0.77119882839582 0.457602343208359 0.228801171604179 42 0.751924090390013 0.496151819219973 0.248075909609987 43 0.881187745597588 0.237624508804824 0.118812254402412 44 0.883457790876887 0.233084418246226 0.116542209123113 45 0.915034679580539 0.169930640838923 0.0849653204194615 46 0.893814517372924 0.212370965254152 0.106185482627076 47 0.87455902191301 0.250881956173979 0.125440978086990 48 0.84459015706482 0.310819685870359 0.155409842935179 49 0.826521836688782 0.346956326622436 0.173478163311218 50 0.802065872972797 0.395868254054405 0.197934127027203 51 0.872896138834498 0.254207722331004 0.127103861165502 52 0.872892380921678 0.254215238156644 0.127107619078322 53 0.842180004608309 0.315639990783382 0.157819995391691 54 0.83012243035794 0.33975513928412 0.16987756964206 55 0.92850925788053 0.142981484238941 0.0714907421194706 56 0.924421970077739 0.151156059844523 0.0755780299222615 57 0.931097249757148 0.137805500485704 0.0689027502428519 58 0.948052815413892 0.103894369172216 0.0519471845861081 59 0.945515466996946 0.108969066006109 0.0544845330030543 60 0.923309356530263 0.153381286939475 0.0766906434697373 61 0.896770792931094 0.206458414137811 0.103229207068906 62 0.879134437664129 0.241731124671742 0.120865562335871 63 0.903001955724654 0.193996088550692 0.0969980442753462 64 0.8940035214147 0.211992957170600 0.105996478585300 65 0.878953473657658 0.242093052684685 0.121046526342342 66 0.843610145090806 0.312779709818389 0.156389854909194 67 0.826321217778652 0.347357564442696 0.173678782221348 68 0.789677629520911 0.420644740958178 0.210322370479089 69 0.714059549392213 0.571880901215574 0.285940450607787 70 0.773287840726302 0.453424318547397 0.226712159273698 71 0.718417773032102 0.563164453935796 0.281582226967898 72 0.613792910441413 0.772414179117174 0.386207089558587 73 0.494501929135601 0.989003858271202 0.505498070864399 74 0.369639463517985 0.73927892703597 0.630360536482015 75 0.247583397712735 0.49516679542547 0.752416602287265 76 0.210416662185173 0.420833324370347 0.789583337814826 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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