*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, 20 Nov 2009 09:45:13 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p.htm/, Retrieved Fri, 20 Nov 2009 17:46:24 +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/2009/Nov/20/t1258735572j9lq8dinp73713p.htm/}, year = {2009}, } @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 = {2009}, 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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111.4 0 87.4 0 96.8 0 114.1 0 110.3 0 103.9 0 101.6 0 94.6 0 95.9 0 104.7 0 102.8 0 98.1 0 113.9 0 80.9 0 95.7 0 113.2 0 105.9 0 108.8 0 102.3 0 99 0 100.7 0 115.5 0 100.7 0 109.9 0 114.6 0 85.4 0 100.5 0 114.8 0 116.5 0 112.9 0 102 0 106 0 105.3 0 118.8 0 106.1 0 109.3 0 117.2 0 92.5 0 104.2 0 112.5 0 122.4 0 113.3 0 100 0 110.7 0 112.8 0 109.8 0 117.3 0 109.1 0 115.9 0 96 0 99.8 0 116.8 1 115.7 1 99.4 1 94.3 1 91 1 93.2 1 103.1 1 94.1 1 91.8 1 102.7 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 10 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 98.85059602649 -14.9166390728477X[t] + 9.55899144591611M1[t] -16.0242356512141M2[t] -5.28014486754967M3[t] + 12.3672737306843M4[t] + 12.0313645143488M5[t] + 5.31545529801325M6[t] -2.52045391832229M7[t] -2.51636313465784M8[t] -1.41227235099337M9[t] + 7.17181843267108M10[t] + 0.775909216335541M11[t] + 0.215909216335541t + 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) 98.85059602649 2.308625 42.818 0 0 X -14.9166390728477 1.978489 -7.5394 0 0 M1 9.55899144591611 2.613236 3.6579 0.000641 0.000321 M2 -16.0242356512141 2.745086 -5.8374 0 0 M3 -5.28014486754967 2.742979 -1.925 0.060298 0.030149 M4 12.3672737306843 2.743548 4.5078 4.3e-05 2.2e-05 M5 12.0313645143488 2.738862 4.3928 6.3e-05 3.2e-05 M6 5.31545529801325 2.734794 1.9436 0.05794 0.02897 M7 -2.52045391832229 2.731347 -0.9228 0.360831 0.180415 M8 -2.51636313465784 2.728524 -0.9222 0.361112 0.180556 M9 -1.41227235099337 2.726326 -0.518 0.606879 0.30344 M10 7.17181843267108 2.724755 2.6321 0.011445 0.005722 M11 0.775909216335541 2.723812 0.2849 0.777002 0.388501 t 0.215909216335541 0.041385 5.2171 4e-06 2e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.911311400399914 R-squared 0.830488468498853 Adjusted R-squared 0.783602300211301 F-TEST (value) 17.712867116918 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 7.07212066686225e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.30622765600411 Sum Squared Residuals 871.549041390728 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 111.4 108.625496688742 2.77450331125823 2 87.4 83.258178807947 4.14182119205302 3 96.8 94.218178807947 2.58182119205298 4 114.1 112.081506622517 2.01849337748342 5 110.3 111.961506622517 -1.66150662251656 6 103.9 105.461506622517 -1.56150662251655 7 101.6 97.8415066225165 3.75849337748346 8 94.6 98.0615066225166 -3.46150662251656 9 95.9 99.3815066225166 -3.48150662251655 10 104.7 108.181506622517 -3.48150662251655 11 102.8 102.001506622517 0.798493377483445 12 98.1 101.441506622517 -3.34150662251656 13 113.9 111.216407284768 2.6835927152318 14 80.9 85.8490894039735 -4.94908940397352 15 95.7 96.8090894039735 -1.10908940397351 16 113.2 114.672417218543 -1.47241721854304 17 105.9 114.552417218543 -8.65241721854304 18 108.8 108.052417218543 0.747582781456949 19 102.3 100.432417218543 1.86758278145695 20 99 100.652417218543 -1.65241721854304 21 100.7 101.972417218543 -1.27241721854305 22 115.5 110.772417218543 4.72758278145695 23 100.7 104.592417218543 -3.89241721854304 24 109.9 104.032417218543 5.86758278145696 25 114.6 113.807317880795 0.792682119205299 26 85.4 88.44 -3.04000000000001 27 100.5 99.4 1.10000000000000 28 114.8 117.263327814570 -2.46332781456953 29 116.5 117.143327814570 -0.643327814569542 30 112.9 110.643327814570 2.25667218543047 31 102 103.023327814570 -1.02332781456954 32 106 103.243327814570 2.75667218543047 33 105.3 104.563327814570 0.736672185430459 34 118.8 113.363327814570 5.43667218543046 35 106.1 107.183327814570 -1.08332781456954 36 109.3 106.623327814570 2.67667218543046 37 117.2 116.398228476821 0.801771523178817 38 92.5 91.0309105960265 1.46908940397350 39 104.2 101.990910596026 2.20908940397351 40 112.5 119.854238410596 -7.35423841059602 41 122.4 119.734238410596 2.66576158940398 42 113.3 113.234238410596 0.0657615894039687 43 100 105.614238410596 -5.61423841059603 44 110.7 105.834238410596 4.86576158940398 45 112.8 107.154238410596 5.64576158940397 46 109.8 115.954238410596 -6.15423841059603 47 117.3 109.774238410596 7.52576158940397 48 109.1 109.214238410596 -0.114238410596029 49 115.9 118.989139072848 -3.08913907284767 50 96 93.621821192053 2.37817880794700 51 99.8 104.581821192053 -4.78182119205298 52 116.8 107.528509933775 9.27149006622517 53 115.7 107.408509933775 8.29149006622516 54 99.4 100.908509933775 -1.50850993377483 55 94.3 93.2885099337748 1.01149006622516 56 91 93.5085099337748 -2.50850993377483 57 93.2 94.8285099337748 -1.62850993377483 58 103.1 103.628509933775 -0.528509933774839 59 94.1 97.4485099337748 -3.34850993377484 60 91.8 96.8885099337748 -5.08850993377484 61 102.7 106.663410596026 -3.96341059602648 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.253278761677422 0.506557523354845 0.746721238322578 18 0.298159054606797 0.596318109213594 0.701840945393203 19 0.18210115042017 0.36420230084034 0.81789884957983 20 0.162175029512201 0.324350059024402 0.837824970487799 21 0.140275498665304 0.280550997330608 0.859724501334696 22 0.281099139455318 0.562198278910636 0.718900860544682 23 0.242114361928597 0.484228723857194 0.757885638071403 24 0.375554373022973 0.751108746045947 0.624445626977027 25 0.280854085376917 0.561708170753834 0.719145914623083 26 0.233311476238726 0.466622952477453 0.766688523761273 27 0.163883873527759 0.327767747055519 0.83611612647224 28 0.123389280249298 0.246778560498595 0.876610719750702 29 0.155473287151311 0.310946574302621 0.84452671284869 30 0.113796345102078 0.227592690204156 0.886203654897922 31 0.0831409789683891 0.166281957936778 0.916859021031611 32 0.0743333003678874 0.148666600735775 0.925666699632113 33 0.0573738475173432 0.114747695034686 0.942626152482657 34 0.0542565987997916 0.108513197599583 0.945743401200208 35 0.0438346530073836 0.0876693060147673 0.956165346992616 36 0.0254263718734956 0.0508527437469911 0.974573628126504 37 0.0143043350008936 0.0286086700017871 0.985695664999106 38 0.0098359173218155 0.019671834643631 0.990164082678185 39 0.00465505544254089 0.00931011088508178 0.99534494455746 40 0.081651434605367 0.163302869210734 0.918348565394633 41 0.116169748568398 0.232339497136796 0.883830251431602 42 0.0661980568780347 0.132396113756069 0.933801943121965 43 0.177083641042444 0.354167282084888 0.822916358957556 44 0.126005658545489 0.252011317090978 0.87399434145451 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0357142857142857 NOK 5% type I error level 3 0.107142857142857 NOK 10% type I error level 5 0.178571428571429 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/101x1b1258735502.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/101x1b1258735502.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/1sstz1258735502.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/1sstz1258735502.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/2m3331258735502.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/2m3331258735502.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/3k9vv1258735502.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/3k9vv1258735502.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/4xw041258735502.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/4xw041258735502.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/5sp801258735502.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/5sp801258735502.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/6ngb61258735502.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/6ngb61258735502.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/7knt41258735502.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/7knt41258735502.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/8v9by1258735502.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/8v9by1258735502.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/9hc891258735502.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735572j9lq8dinp73713p/9hc891258735502.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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