Investeringsgoederen met monthly dummies

*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 05:55:08 -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/t1229172985kq3e7qkrrh6trwk.htm/, Retrieved Sat, 13 Dec 2008 13:56:34 +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/t1229172985kq3e7qkrrh6trwk.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:

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97.7 0 101.5 0 119.6 0 108.1 0 117.8 0 125.5 0 89.2 0 92.3 0 104.6 0 122.8 0 96.0 0 94.6 0 93.3 0 101.1 0 114.2 0 104.7 0 113.3 0 118.2 0 83.6 0 73.9 0 99.5 0 97.7 0 103.0 0 106.3 0 92.2 0 101.8 0 122.8 0 111.8 0 106.3 0 121.5 0 81.9 0 85.4 0 110.9 0 117.3 0 106.3 0 105.5 0 101.3 0 105.9 0 126.3 0 111.9 0 108.9 0 127.2 0 94.2 0 85.7 0 116.2 0 107.2 0 110.6 0 112.0 0 104.5 0 112.0 0 132.8 0 110.8 0 128.7 0 136.8 0 94.9 0 88.8 0 123.2 0 125.3 0 122.7 0 125.7 0 116.3 0 118.7 0 142.0 0 127.9 0 131.9 0 152.3 0 110.8 1 99.1 1 135.0 1 133.2 1 131.0 1 133.9 1 119.9 1 136.9 1 148.9 1 145.1 1 142.4 1 159.6 1 120.7 1 109.0 1 142.0 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 108.853592814371 + 24.8784431137725X[t] -8.80765611633877M1[t] -1.27908468776732M2[t] + 17.1066295979469M3[t] + 4.77805816937552M4[t] + 8.9209153122327M5[t] + 22.0352010265184M6[t] -19.4902908468777M7[t] -25.3617194183062M8[t] + 2.80970915312234M9[t] + 4.25000000000001M10[t] -1.39999999999999M11[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) 108.853592814371 3.615278 30.1093 0 0 X 24.8784431137725 2.546303 9.7704 0 0 M1 -8.80765611633877 4.893106 -1.8 0.076294 0.038147 M2 -1.27908468776732 4.893106 -0.2614 0.79457 0.397285 M3 17.1066295979469 4.893106 3.4961 0.000836 0.000418 M4 4.77805816937552 4.893106 0.9765 0.332284 0.166142 M5 8.9209153122327 4.893106 1.8232 0.072675 0.036337 M6 22.0352010265184 4.893106 4.5033 2.7e-05 1.3e-05 M7 -19.4902908468777 4.902112 -3.9759 0.000172 8.6e-05 M8 -25.3617194183062 4.902112 -5.1736 2e-06 1e-06 M9 2.80970915312234 4.902112 0.5732 0.568425 0.284212 M10 4.25000000000001 5.077427 0.837 0.405503 0.202752 M11 -1.39999999999999 5.077427 -0.2757 0.783591 0.391796 Multiple Linear Regression - Regression Statistics Multiple R 0.887443067897232 R-squared 0.78755519875885 Adjusted R-squared 0.750064939716295 F-TEST (value) 21.0069287028634 F-TEST (DF numerator) 12 F-TEST (DF denominator) 68 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 8.79436068323905 Sum Squared Residuals 5259.17302822926 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 97.7 100.045936698033 -2.34593669803255 2 101.5 107.574508126604 -6.07450812660396 3 119.6 125.960222412318 -6.3602224123182 4 108.1 113.631650983747 -5.5316509837468 5 117.8 117.774508126604 0.0254918733961180 6 125.5 130.888793840890 -5.3887938408896 7 89.2 89.3633019674936 -0.163301967493593 8 92.3 83.491873396065 8.80812660393496 9 104.6 111.663301967494 -7.06330196749358 10 122.8 113.103592814371 9.69640718562876 11 96 107.453592814371 -11.4535928143713 12 94.6 108.853592814371 -14.2535928143713 13 93.3 100.045936698033 -6.7459366980325 14 101.1 107.574508126604 -6.47450812660394 15 114.2 125.960222412318 -11.7602224123182 16 104.7 113.631650983747 -8.93165098374679 17 113.3 117.774508126604 -4.47450812660394 18 118.2 130.888793840890 -12.6887938408897 19 83.6 89.3633019674936 -5.76330196749359 20 73.9 83.491873396065 -9.5918733960650 21 99.5 111.663301967494 -12.1633019674936 22 97.7 113.103592814371 -15.4035928143713 23 103 107.453592814371 -4.45359281437126 24 106.3 108.853592814371 -2.55359281437126 25 92.2 100.045936698033 -7.8459366980325 26 101.8 107.574508126604 -5.77450812660393 27 122.8 125.960222412318 -3.16022241231823 28 111.8 113.631650983747 -1.83165098374680 29 106.3 117.774508126604 -11.4745081266040 30 121.5 130.888793840890 -9.38879384088967 31 81.9 89.3633019674936 -7.46330196749358 32 85.4 83.491873396065 1.90812660393499 33 110.9 111.663301967494 -0.763301967493584 34 117.3 113.103592814371 4.19640718562875 35 106.3 107.453592814371 -1.15359281437126 36 105.5 108.853592814371 -3.35359281437126 37 101.3 100.045936698033 1.25406330196750 38 105.9 107.574508126604 -1.67450812660392 39 126.3 125.960222412318 0.33977758768177 40 111.9 113.631650983747 -1.73165098374679 41 108.9 117.774508126604 -8.87450812660394 42 127.2 130.888793840890 -3.68879384088966 43 94.2 89.3633019674936 4.83669803250642 44 85.7 83.491873396065 2.20812660393499 45 116.2 111.663301967494 4.53669803250641 46 107.2 113.103592814371 -5.90359281437125 47 110.6 107.453592814371 3.14640718562874 48 112 108.853592814371 3.14640718562875 49 104.5 100.045936698033 4.4540633019675 50 112 107.574508126604 4.42549187339607 51 132.8 125.960222412318 6.83977758768178 52 110.8 113.631650983747 -2.83165098374679 53 128.7 117.774508126604 10.9254918733960 54 136.8 130.888793840890 5.91120615911035 55 94.9 89.3633019674936 5.53669803250643 56 88.8 83.491873396065 5.30812660393499 57 123.2 111.663301967494 11.5366980325064 58 125.3 113.103592814371 12.1964071856287 59 122.7 107.453592814371 15.2464071856288 60 125.7 108.853592814371 16.8464071856287 61 116.3 100.045936698033 16.2540633019675 62 118.7 107.574508126604 11.1254918733961 63 142 125.960222412318 16.0397775876818 64 127.9 113.631650983747 14.2683490162532 65 131.9 117.774508126604 14.1254918733961 66 152.3 130.888793840890 21.4112061591103 67 110.8 114.241745081266 -3.44174508126604 68 99.1 108.370316509837 -9.27031650983747 69 135 136.541745081266 -1.54174508126604 70 133.2 137.982035928144 -4.78203592814372 71 131 132.332035928144 -1.33203592814371 72 133.9 133.732035928144 0.167964071856299 73 119.9 124.924379811805 -5.02437981180494 74 136.9 132.452951240376 4.44704875962362 75 148.9 150.838665526091 -1.93866552609067 76 145.1 138.510094097519 6.58990590248075 77 142.4 142.652951240376 -0.252951240376392 78 159.6 155.767236954662 3.83276304533787 79 120.7 114.241745081266 6.45825491873396 80 109 108.370316509837 0.62968349016253 81 142 136.541745081266 5.45825491873396 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0591013589218678 0.118202717843736 0.940898641078132 17 0.029274314573707 0.058548629147414 0.970725685426293 18 0.0311731995508393 0.0623463991016787 0.96882680044916 19 0.0200546772179027 0.0401093544358055 0.979945322782097 20 0.158149410848668 0.316298821697335 0.841850589151332 21 0.126929901872757 0.253859803745515 0.873070098127243 22 0.515716758557871 0.968566482884258 0.484283241442129 23 0.460063951885567 0.920127903771135 0.539936048114433 24 0.456990433591258 0.913980867182516 0.543009566408742 25 0.407219504059145 0.81443900811829 0.592780495940855 26 0.346513224182915 0.693026448365831 0.653486775817085 27 0.309323552106847 0.618647104213694 0.690676447893153 28 0.263326100031246 0.526652200062492 0.736673899968754 29 0.312087731857358 0.624175463714717 0.687912268142642 30 0.322327513241449 0.644655026482899 0.677672486758551 31 0.318002464033226 0.636004928066451 0.681997535966774 32 0.251084725292943 0.502169450585887 0.748915274707057 33 0.256283138300359 0.512566276600719 0.743716861699641 34 0.223072565974137 0.446145131948274 0.776927434025863 35 0.210144200274358 0.420288400548717 0.789855799725642 36 0.20812813284254 0.41625626568508 0.79187186715746 37 0.190053745762731 0.380107491525461 0.80994625423727 38 0.179966925588555 0.359933851177109 0.820033074411445 39 0.180106677515616 0.360213355031232 0.819893322484384 40 0.168023707631076 0.336047415262151 0.831976292368924 41 0.273086302351758 0.546172604703515 0.726913697648242 42 0.391992954205408 0.783985908410816 0.608007045794592 43 0.384979613324960 0.769959226649919 0.615020386675041 44 0.318531665972703 0.637063331945405 0.681468334027297 45 0.350180960213817 0.700361920427633 0.649819039786183 46 0.430987487420382 0.861974974840764 0.569012512579618 47 0.456373649527413 0.912747299054827 0.543626350472587 48 0.520573428142107 0.958853143715785 0.479426571857892 49 0.514279658829122 0.971440682341756 0.485720341170878 50 0.54836814505998 0.90326370988004 0.45163185494002 51 0.566772639065489 0.866454721869022 0.433227360934511 52 0.840436211181095 0.31912757763781 0.159563788818905 53 0.859225477704368 0.281549044591264 0.140774522295632 54 0.949061064414845 0.101877871170310 0.0509389355851549 55 0.966217118733773 0.0675657625324532 0.0337828812662266 56 0.954149672954069 0.0917006540918625 0.0458503270459312 57 0.956253635076897 0.087492729846206 0.043746364923103 58 0.94059240263039 0.118815194739220 0.0594075973696102 59 0.927604722447509 0.144790555104982 0.072395277552491 60 0.912961125663617 0.174077748672767 0.0870388743363834 61 0.915205190739738 0.169589618520524 0.0847948092602622 62 0.898094776165685 0.203810447668629 0.101905223834315 63 0.854210871451135 0.291578257097731 0.145789128548865 64 0.814998457638663 0.370003084722674 0.185001542361337 65 0.690415597157773 0.619168805684455 0.309584402842227 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 0.02 OK 10% type I error level 6 0.12 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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