ws 7 2

*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 09:48:09 -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/t1258735738su7q1cf94qfrguc.htm/, Retrieved Fri, 20 Nov 2009 17:49:10 +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/t1258735738su7q1cf94qfrguc.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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29.837 0 29.571 0 30.167 0 30.524 0 30.996 0 31.033 0 31.198 0 30.937 0 31.649 0 33.115 0 34.106 0 33.926 0 33.382 0 32.851 0 32.948 0 36.112 0 36.113 0 35.210 0 35.193 0 34.383 0 35.349 0 37.058 0 38.076 0 36.630 0 36.045 0 35.638 0 35.114 0 35.465 0 35.254 0 35.299 0 35.916 0 36.683 0 37.288 0 38.536 0 38.977 0 36.407 0 34.955 0 34.951 0 32.680 0 34.791 0 34.178 0 35.213 0 34.871 0 35.299 0 35.443 0 37.108 0 36.419 0 34.471 0 33.868 0 34.385 0 33.643 1 34.627 1 32.919 1 35.500 1 36.110 1 37.086 1 37.711 1 40.427 1 39.884 1 38.512 1 38.767 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 saldo_zichtrek[t] = + 34.51236 + 2.32273090909091crisis[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) 34.51236 0.327383 105.4188 0 0 crisis 2.32273090909091 0.770948 3.0128 0.003809 0.001904 Multiple Linear Regression - Regression Statistics Multiple R 0.365151455024568 R-squared 0.133335585106559 Adjusted R-squared 0.118646357735484 F-TEST (value) 9.07709995483573 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.0038089721258604 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.31495081189860 Sum Squared Residuals 316.18083842909 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 29.837 34.5123599999999 -4.67535999999994 2 29.571 34.51236 -4.94136 3 30.167 34.51236 -4.34536 4 30.524 34.51236 -3.98836 5 30.996 34.51236 -3.51636 6 31.033 34.51236 -3.47936 7 31.198 34.51236 -3.31436 8 30.937 34.51236 -3.57536 9 31.649 34.51236 -2.86336 10 33.115 34.51236 -1.39736 11 34.106 34.51236 -0.406359999999999 12 33.926 34.51236 -0.586359999999999 13 33.382 34.51236 -1.13036000000000 14 32.851 34.51236 -1.66136000000000 15 32.948 34.51236 -1.56436 16 36.112 34.51236 1.59964 17 36.113 34.51236 1.60064 18 35.21 34.51236 0.69764 19 35.193 34.51236 0.680639999999997 20 34.383 34.51236 -0.129359999999998 21 35.349 34.51236 0.836639999999996 22 37.058 34.51236 2.54564 23 38.076 34.51236 3.56364 24 36.63 34.51236 2.11764 25 36.045 34.51236 1.53264 26 35.638 34.51236 1.12564000000000 27 35.114 34.51236 0.601639999999996 28 35.465 34.51236 0.952640000000003 29 35.254 34.51236 0.741639999999997 30 35.299 34.51236 0.786639999999999 31 35.916 34.51236 1.40364000000000 32 36.683 34.51236 2.17064 33 37.288 34.51236 2.77564000000000 34 38.536 34.51236 4.02364 35 38.977 34.51236 4.46464 36 36.407 34.51236 1.89464000000000 37 34.955 34.51236 0.442639999999997 38 34.951 34.51236 0.43864 39 32.68 34.51236 -1.83236 40 34.791 34.51236 0.278639999999996 41 34.178 34.51236 -0.334360000000004 42 35.213 34.51236 0.70064 43 34.871 34.51236 0.358640000000001 44 35.299 34.51236 0.786639999999999 45 35.443 34.51236 0.930639999999997 46 37.108 34.51236 2.59564000000000 47 36.419 34.51236 1.90664000000000 48 34.471 34.51236 -0.0413600000000043 49 33.868 34.51236 -0.644359999999999 50 34.385 34.51236 -0.127360000000003 51 33.643 36.8350909090909 -3.19209090909091 52 34.627 36.8350909090909 -2.20809090909091 53 32.919 36.8350909090909 -3.91609090909091 54 35.5 36.8350909090909 -1.33509090909091 55 36.11 36.8350909090909 -0.72509090909091 56 37.086 36.8350909090909 0.250909090909089 57 37.711 36.8350909090909 0.875909090909089 58 40.427 36.8350909090909 3.59190909090909 59 39.884 36.8350909090909 3.04890909090909 60 38.512 36.8350909090909 1.67690909090909 61 38.767 36.8350909090909 1.93190909090909 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0444514180904146 0.0889028361808292 0.955548581909585 6 0.0262134528231743 0.0524269056463486 0.973786547176826 7 0.0176989157193473 0.0353978314386947 0.982301084280653 8 0.0091577418377983 0.0183154836755966 0.990842258162202 9 0.0113300203266738 0.0226600406533476 0.988669979673326 10 0.0787689098987102 0.157537819797420 0.92123109010129 11 0.287849469216091 0.575698938432182 0.712150530783909 12 0.415816595286859 0.831633190573717 0.584183404713141 13 0.444830891370887 0.889661782741774 0.555169108629113 14 0.437354127072819 0.874708254145637 0.562645872927181 15 0.436457973985527 0.872915947971054 0.563542026014473 16 0.734640438933907 0.530719122132185 0.265359561066093 17 0.860160445903812 0.279679108192376 0.139839554096188 18 0.879216607515913 0.241566784968174 0.120783392484087 19 0.886703953004279 0.226592093991443 0.113296046995721 20 0.873435534307263 0.253128931385474 0.126564465692737 21 0.874882625026938 0.250234749946125 0.125117374973062 22 0.926159605670542 0.147680788658917 0.0738403943294585 23 0.973201455902888 0.0535970881942249 0.0267985440971125 24 0.975935223771255 0.0481295524574908 0.0240647762287454 25 0.972201234964531 0.055597530070938 0.027798765035469 26 0.96419353656095 0.0716129268781028 0.0358064634390514 27 0.951124146208242 0.0977517075835165 0.0488758537917582 28 0.935847397260447 0.128305205479105 0.0641526027395527 29 0.915035567694846 0.169928864610309 0.0849644323051545 30 0.889227010210709 0.221545979578582 0.110772989789291 31 0.864911053814098 0.270177892371803 0.135088946185901 32 0.854817442679886 0.290365114640229 0.145182557320114 33 0.863731718490316 0.272536563019368 0.136268281509684 34 0.921760049845808 0.156479900308384 0.0782399501541919 35 0.971203168603495 0.0575936627930105 0.0287968313965053 36 0.964808797076874 0.0703824058462523 0.0351912029231262 37 0.946507392606015 0.106985214787970 0.0534926073939852 38 0.921094353444643 0.157811293110715 0.0789056465553575 39 0.920090648834259 0.159818702331482 0.0799093511657408 40 0.885531542386359 0.228936915227283 0.114468457613641 41 0.84749551731093 0.30500896537814 0.15250448268907 42 0.793827854519345 0.41234429096131 0.206172145480655 43 0.729444767167321 0.541110465665357 0.270555232832679 44 0.655338533346338 0.689322933307324 0.344661466653662 45 0.574501101421803 0.850997797156394 0.425498898578197 46 0.564425653195201 0.871148693609598 0.435574346804799 47 0.531164530372495 0.93767093925501 0.468835469627505 48 0.436770353983095 0.87354070796619 0.563229646016905 49 0.345530142512706 0.691060285025412 0.654469857487294 50 0.257647146910681 0.515294293821361 0.74235285308932 51 0.300845667093492 0.601691334186984 0.699154332906508 52 0.301886592724949 0.603773185449898 0.698113407275051 53 0.650793002960837 0.698413994078326 0.349206997039163 54 0.72966473464844 0.540670530703121 0.270335265351561 55 0.80303690084981 0.393926198300381 0.196963099150190 56 0.809690198205877 0.380619603588247 0.190309801794123 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 4 0.0769230769230769 NOK 10% type I error level 12 0.230769230769231 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/10888h1258735685.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/10888h1258735685.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/1k5zb1258735685.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/1k5zb1258735685.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/2m2hg1258735685.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/2m2hg1258735685.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/31wr31258735685.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/31wr31258735685.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/4hci91258735685.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/4hci91258735685.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/5mdan1258735685.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/5mdan1258735685.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/6yv8u1258735685.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/6yv8u1258735685.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/7zs8p1258735685.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/7zs8p1258735685.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/88l571258735685.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/88l571258735685.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/9zj0n1258735685.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735738su7q1cf94qfrguc/9zj0n1258735685.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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