Paper Regressie analyse

*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, 19 Dec 2009 00:59:01 -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/Dec/19/t1261209818nlcpt9tcdrirn3d.htm/, Retrieved Sat, 19 Dec 2009 09:03:50 +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/Dec/19/t1261209818nlcpt9tcdrirn3d.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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84 0 78 0 74 0 75 0 79 0 79 0 82 0 88 0 81 0 69 1 62 1 62 1 68 1 57 1 67 1 72 0 75 0 81 0 80 0 79 0 81 0 83 0 84 0 90 0 84 0 90 0 92 0 93 0 85 0 93 0 94 0 94 0 102 0 96 0 96 0 92 0 90 0 84 0 86 0 70 0 67 1 60 1 62 1 61 1 54 1 50 1 45 1 34 1 37 1 44 1 34 1 37 1 31 1 31 1 28 1 31 1 33 1 36 1 39 1 42 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 Consumentenvertrouwen[t] = + 84.8823529411764 -37.1515837104072Dummy[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) 84.8823529411764 1.87876 45.18 0 0 Dummy -37.1515837104072 2.854041 -13.0172 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.863131319876742 R-squared 0.744995675352167 Adjusted R-squared 0.740599049065136 F-TEST (value) 169.447123024676 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 10.9549585658261 Sum Squared Residuals 6960.64479638009 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 84 84.8823529411765 -0.88235294117653 2 78 84.8823529411765 -6.88235294117655 3 74 84.8823529411765 -10.8823529411765 4 75 84.8823529411765 -9.88235294117647 5 79 84.8823529411765 -5.88235294117647 6 79 84.8823529411765 -5.88235294117647 7 82 84.8823529411765 -2.88235294117647 8 88 84.8823529411765 3.11764705882353 9 81 84.8823529411765 -3.88235294117647 10 69 47.7307692307692 21.2692307692308 11 62 47.7307692307692 14.2692307692308 12 62 47.7307692307692 14.2692307692308 13 68 47.7307692307692 20.2692307692308 14 57 47.7307692307692 9.26923076923077 15 67 47.7307692307692 19.2692307692308 16 72 84.8823529411765 -12.8823529411765 17 75 84.8823529411765 -9.88235294117647 18 81 84.8823529411765 -3.88235294117647 19 80 84.8823529411765 -4.88235294117647 20 79 84.8823529411765 -5.88235294117647 21 81 84.8823529411765 -3.88235294117647 22 83 84.8823529411765 -1.88235294117647 23 84 84.8823529411765 -0.882352941176466 24 90 84.8823529411765 5.11764705882353 25 84 84.8823529411765 -0.882352941176466 26 90 84.8823529411765 5.11764705882353 27 92 84.8823529411765 7.11764705882353 28 93 84.8823529411765 8.11764705882353 29 85 84.8823529411765 0.117647058823534 30 93 84.8823529411765 8.11764705882353 31 94 84.8823529411765 9.11764705882353 32 94 84.8823529411765 9.11764705882353 33 102 84.8823529411765 17.1176470588235 34 96 84.8823529411765 11.1176470588235 35 96 84.8823529411765 11.1176470588235 36 92 84.8823529411765 7.11764705882353 37 90 84.8823529411765 5.11764705882353 38 84 84.8823529411765 -0.882352941176466 39 86 84.8823529411765 1.11764705882353 40 70 84.8823529411765 -14.8823529411765 41 67 47.7307692307692 19.2692307692308 42 60 47.7307692307692 12.2692307692308 43 62 47.7307692307692 14.2692307692308 44 61 47.7307692307692 13.2692307692308 45 54 47.7307692307692 6.26923076923077 46 50 47.7307692307692 2.26923076923077 47 45 47.7307692307692 -2.73076923076923 48 34 47.7307692307692 -13.7307692307692 49 37 47.7307692307692 -10.7307692307692 50 44 47.7307692307692 -3.73076923076923 51 34 47.7307692307692 -13.7307692307692 52 37 47.7307692307692 -10.7307692307692 53 31 47.7307692307692 -16.7307692307692 54 31 47.7307692307692 -16.7307692307692 55 28 47.7307692307692 -19.7307692307692 56 31 47.7307692307692 -16.7307692307692 57 33 47.7307692307692 -14.7307692307692 58 36 47.7307692307692 -11.7307692307692 59 39 47.7307692307692 -8.73076923076923 60 42 47.7307692307692 -5.73076923076923 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0808038469457202 0.161607693891440 0.91919615305428 6 0.0269315450500590 0.0538630901001179 0.973068454949941 7 0.0120394408495623 0.0240788816991246 0.987960559150438 8 0.0223315219656241 0.0446630439312482 0.977668478034376 9 0.0087643230743747 0.0175286461487494 0.991235676925625 10 0.00404123603580790 0.00808247207161579 0.995958763964192 11 0.00292478537966667 0.00584957075933334 0.997075214620333 12 0.00154524413395477 0.00309048826790954 0.998454755866045 13 0.00112066517126445 0.00224133034252890 0.998879334828735 14 0.00163865398829926 0.00327730797659853 0.9983613460117 15 0.00149957160922871 0.00299914321845743 0.998500428390771 16 0.00245584435269680 0.00491168870539361 0.997544155647303 17 0.00179650311572764 0.00359300623145527 0.998203496884272 18 0.00092737378372585 0.0018547475674517 0.999072626216274 19 0.000456307042312799 0.000912614084625598 0.999543692957687 20 0.000227050868430197 0.000454101736860394 0.99977294913157 21 0.000113941218781313 0.000227882437562625 0.999886058781219 22 6.72738559719893e-05 0.000134547711943979 0.999932726144028 23 4.43567446317935e-05 8.87134892635869e-05 0.999955643255368 24 0.000124392629258548 0.000248785258517095 0.999875607370741 25 7.27654935084346e-05 0.000145530987016869 0.999927234506492 26 0.000124310909900682 0.000248621819801363 0.9998756890901 27 0.000267174776586487 0.000534349553172975 0.999732825223413 28 0.000523727785664136 0.00104745557132827 0.999476272214336 29 0.000296440142223765 0.000592880284447529 0.999703559857776 30 0.000438649344462605 0.00087729868892521 0.999561350655537 31 0.000655726919332424 0.00131145383866485 0.999344273080668 32 0.000832975576131995 0.00166595115226399 0.999167024423868 33 0.00487469376368874 0.00974938752737749 0.995125306236311 34 0.0063030573939403 0.0126061147878806 0.99369694260606 35 0.00808467724894304 0.0161693544978861 0.991915322751057 36 0.00679749671182172 0.0135949934236434 0.993202503288178 37 0.00516885765804109 0.0103377153160822 0.994831142341959 38 0.00311997131995365 0.0062399426399073 0.996880028680046 39 0.00245695311415261 0.00491390622830522 0.997543046885847 40 0.00340737301566633 0.00681474603133266 0.996592626984334 41 0.0123792258350601 0.0247584516701202 0.98762077416494 42 0.0252398761539341 0.0504797523078682 0.974760123846066 43 0.087038922290705 0.17407784458141 0.912961077709295 44 0.342995158158753 0.685990316317505 0.657004841841247 45 0.64957685912162 0.700846281756759 0.350423140878379 46 0.872209834281575 0.255580331436850 0.127790165718425 47 0.945589372415255 0.108821255169490 0.0544106275847452 48 0.95568228542530 0.0886354291494021 0.0443177145747011 49 0.94793375161696 0.104132496766081 0.0520662483830406 50 0.975645517625003 0.0487089647499937 0.0243544823749969 51 0.96248314391862 0.0750337121627612 0.0375168560813806 52 0.941336289705797 0.117327420588407 0.0586637102942035 53 0.912968634386182 0.174062731227636 0.0870313656138179 54 0.86528961044928 0.269420779101438 0.134710389550719 55 0.875254265323877 0.249491469352245 0.124745734676123 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 27 0.529411764705882 NOK 5% type I error level 36 0.705882352941177 NOK 10% type I error level 40 0.784313725490196 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/10l7dh1261209536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/10l7dh1261209536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/1cqc21261209536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/1cqc21261209536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/2884s1261209536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/2884s1261209536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/32yz81261209536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/32yz81261209536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/4jubm1261209536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/4jubm1261209536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/54ff01261209536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/54ff01261209536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/67vqk1261209536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/67vqk1261209536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/7wj411261209536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/7wj411261209536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/8l56b1261209536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/8l56b1261209536.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/9lpdw1261209536.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/19/t1261209818nlcpt9tcdrirn3d/9lpdw1261209536.ps (open in new window)

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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