Mutiple regression paper

*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: Wed, 16 Dec 2009 03:49:26 -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/16/t1260960653ix11c0h06at4jno.htm/, Retrieved Wed, 16 Dec 2009 11:51:05 +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/16/t1260960653ix11c0h06at4jno.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:

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User-defined keywords:

Dataseries X:

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101.5 0 99.2 0 107.8 0 92.3 0 99.2 0 101.6 0 87 0 71.4 0 104.7 0 115.1 0 102.5 0 75.3 0 96.7 1 94.6 1 98.6 1 99.5 1 92 1 93.6 1 89.3 1 66.9 1 108.8 1 113.2 1 105.5 1 77.8 1 102.1 1 97 1 95.5 1 99.3 1 86.4 1 92.4 1 85.7 1 61.9 1 104.9 1 107.9 1 95.6 1 79.8 1 94.8 1 93.7 1 108.1 1 96.9 1 88.8 1 106.7 1 86.8 1 69.8 1 110.9 1 105.4 1 99.2 1 84.4 1 87.2 1 91.9 1 97.9 1 94.5 1 85 1 100.3 1 78.7 1 65.8 1 104.8 1 96 1 103.3 1 82.9 1 91.4 1 94.5 1 109.3 1 92.1 1 99.3 1 109.6 1 87.5 1 73.1 1 110.7 1 111.6 1 110.7 1 84 1 101.6 1 102.1 1 113.9 1 99 1 100.4 1 109.5 1 93 1 76.8 1 105.3 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 96.4666666666666 -1.47826086956518X[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) 96.4666666666666 3.509054 27.4908 0 0 X -1.47826086956518 3.801964 -0.3888 0.698459 0.349229 Multiple Linear Regression - Regression Statistics Multiple R 0.0437033195659165 R-squared 0.00190998014108062 Adjusted R-squared -0.0107240707432097 F-TEST (value) 0.151177176550365 F-TEST (DF numerator) 1 F-TEST (DF denominator) 79 p-value 0.698458914502653 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.1557182414647 Sum Squared Residuals 11673.1573913043 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.5 96.4666666666671 5.0333333333329 2 99.2 96.4666666666666 2.73333333333336 3 107.8 96.4666666666666 11.3333333333334 4 92.3 96.4666666666666 -4.16666666666663 5 99.2 96.4666666666666 2.73333333333337 6 101.6 96.4666666666666 5.13333333333336 7 87 96.4666666666666 -9.46666666666663 8 71.4 96.4666666666666 -25.0666666666666 9 104.7 96.4666666666666 8.23333333333337 10 115.1 96.4666666666666 18.6333333333334 11 102.5 96.4666666666666 6.03333333333337 12 75.3 96.4666666666666 -21.1666666666666 13 96.7 94.9884057971014 1.71159420289855 14 94.6 94.9884057971014 -0.388405797101454 15 98.6 94.9884057971014 3.61159420289855 16 99.5 94.9884057971014 4.51159420289855 17 92 94.9884057971014 -2.98840579710145 18 93.6 94.9884057971014 -1.38840579710145 19 89.3 94.9884057971014 -5.68840579710145 20 66.9 94.9884057971014 -28.0884057971014 21 108.8 94.9884057971014 13.8115942028985 22 113.2 94.9884057971014 18.2115942028986 23 105.5 94.9884057971014 10.5115942028986 24 77.8 94.9884057971014 -17.1884057971015 25 102.1 94.9884057971014 7.11159420289855 26 97 94.9884057971014 2.01159420289855 27 95.5 94.9884057971014 0.511594202898552 28 99.3 94.9884057971014 4.31159420289855 29 86.4 94.9884057971014 -8.58840579710144 30 92.4 94.9884057971014 -2.58840579710144 31 85.7 94.9884057971014 -9.28840579710145 32 61.9 94.9884057971014 -33.0884057971015 33 104.9 94.9884057971014 9.91159420289856 34 107.9 94.9884057971014 12.9115942028986 35 95.6 94.9884057971014 0.611594202898546 36 79.8 94.9884057971014 -15.1884057971015 37 94.8 94.9884057971014 -0.188405797101451 38 93.7 94.9884057971014 -1.28840579710145 39 108.1 94.9884057971014 13.1115942028985 40 96.9 94.9884057971014 1.91159420289856 41 88.8 94.9884057971014 -6.18840579710145 42 106.7 94.9884057971014 11.7115942028986 43 86.8 94.9884057971014 -8.18840579710145 44 69.8 94.9884057971014 -25.1884057971015 45 110.9 94.9884057971014 15.9115942028986 46 105.4 94.9884057971014 10.4115942028986 47 99.2 94.9884057971014 4.21159420289855 48 84.4 94.9884057971014 -10.5884057971014 49 87.2 94.9884057971014 -7.78840579710145 50 91.9 94.9884057971014 -3.08840579710144 51 97.9 94.9884057971014 2.91159420289856 52 94.5 94.9884057971014 -0.488405797101448 53 85 94.9884057971014 -9.98840579710145 54 100.3 94.9884057971014 5.31159420289855 55 78.7 94.9884057971014 -16.2884057971014 56 65.8 94.9884057971014 -29.1884057971015 57 104.8 94.9884057971014 9.81159420289855 58 96 94.9884057971014 1.01159420289855 59 103.3 94.9884057971014 8.31159420289855 60 82.9 94.9884057971014 -12.0884057971014 61 91.4 94.9884057971014 -3.58840579710144 62 94.5 94.9884057971014 -0.488405797101448 63 109.3 94.9884057971014 14.3115942028985 64 92.1 94.9884057971014 -2.88840579710145 65 99.3 94.9884057971014 4.31159420289855 66 109.6 94.9884057971014 14.6115942028985 67 87.5 94.9884057971014 -7.48840579710145 68 73.1 94.9884057971014 -21.8884057971015 69 110.7 94.9884057971014 15.7115942028986 70 111.6 94.9884057971014 16.6115942028985 71 110.7 94.9884057971014 15.7115942028986 72 84 94.9884057971014 -10.9884057971014 73 101.6 94.9884057971014 6.61159420289855 74 102.1 94.9884057971014 7.11159420289855 75 113.9 94.9884057971014 18.9115942028986 76 99 94.9884057971014 4.01159420289855 77 100.4 94.9884057971014 5.41159420289856 78 109.5 94.9884057971014 14.5115942028986 79 93 94.9884057971014 -1.98840579710145 80 76.8 94.9884057971014 -18.1884057971015 81 105.3 94.9884057971014 10.3115942028985 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.150718083017066 0.301436166034131 0.849281916982934 6 0.0634165118539387 0.126833023707877 0.936583488146061 7 0.120284345146306 0.240568690292612 0.879715654853694 8 0.573597082807296 0.852805834385409 0.426402917192704 9 0.514012300614302 0.971975398771397 0.485987699385698 10 0.636965441338143 0.726069117323714 0.363034558661857 11 0.573888179685533 0.852223640628934 0.426111820314467 12 0.720651383432616 0.558697233134768 0.279348616567384 13 0.636400512357327 0.727198975285346 0.363599487642673 14 0.548278626299363 0.903442747401274 0.451721373700637 15 0.462904563056324 0.925809126112649 0.537095436943676 16 0.382058975993984 0.764117951987968 0.617941024006016 17 0.313981865028635 0.627963730057269 0.686018134971365 18 0.245901027269537 0.491802054539075 0.754098972730463 19 0.200190697752787 0.400381395505575 0.799809302247213 20 0.473316480585761 0.946632961171523 0.526683519414239 21 0.520960145972494 0.958079708055012 0.479039854027506 22 0.611272895974614 0.777454208050772 0.388727104025386 23 0.585179954727692 0.829640090544615 0.414820045272308 24 0.650131163709447 0.699737672581105 0.349868836290553 25 0.604233783584312 0.791532432831377 0.395766216415688 26 0.536330825786853 0.927338348426294 0.463669174213147 27 0.466491321855059 0.932982643710117 0.533508678144941 28 0.405197983180482 0.810395966360965 0.594802016819518 29 0.372926123280848 0.745852246561695 0.627073876719152 30 0.312820924442544 0.625641848885088 0.687179075557456 31 0.286599961483115 0.57319992296623 0.713400038516885 32 0.665824540031966 0.668350919936069 0.334175459968034 33 0.650183568001447 0.699632863997106 0.349816431998553 34 0.659724698488222 0.680550603023557 0.340275301511778 35 0.598104674131121 0.803790651737758 0.401895325868879 36 0.62522016658319 0.749559666833619 0.374779833416809 37 0.562340536878133 0.875318926243734 0.437659463121867 38 0.498294738381363 0.996589476762725 0.501705261618637 39 0.509351572768868 0.981296854462263 0.490648427231132 40 0.446490538410498 0.892981076820996 0.553509461589502 41 0.398111815075711 0.796223630151421 0.60188818492429 42 0.392471414961648 0.784942829923296 0.607528585038352 43 0.357272619898988 0.714545239797977 0.642727380101012 44 0.558586798600142 0.882826402799715 0.441413201399858 45 0.600418812017567 0.799162375964867 0.399581187982433 46 0.581144670339919 0.837710659320162 0.418855329660081 47 0.523177142156260 0.953645715687479 0.476822857843740 48 0.506916386188097 0.986167227623805 0.493083613811903 49 0.468851014925228 0.937702029850457 0.531148985074772 50 0.408453389450606 0.816906778901213 0.591546610549394 51 0.347735915522847 0.695471831045694 0.652264084477153 52 0.28827824946527 0.57655649893054 0.71172175053473 53 0.271835069765992 0.543670139531983 0.728164930234008 54 0.225837036779740 0.451674073559481 0.77416296322026 55 0.273045564209073 0.546091128418145 0.726954435790927 56 0.627867542127792 0.744264915744417 0.372132457872208 57 0.590363913747091 0.819272172505818 0.409636086252909 58 0.520921219545181 0.958157560909639 0.479078780454819 59 0.469088062542726 0.938176125085453 0.530911937457274 60 0.498797848409647 0.997595696819293 0.501202151590353 61 0.445518700039744 0.891037400079488 0.554481299960256 62 0.378946595631877 0.757893191263753 0.621053404368123 63 0.370739041235287 0.741478082470575 0.629260958764713 64 0.314838192543218 0.629676385086436 0.685161807456782 65 0.248561132340253 0.497122264680505 0.751438867659747 66 0.239909957111864 0.479819914223728 0.760090042888136 67 0.219260868129277 0.438521736258554 0.780739131870723 68 0.502932385242514 0.994135229514972 0.497067614757486 69 0.484423840107071 0.968847680214142 0.515576159892929 70 0.485686854519487 0.971373709038975 0.514313145480513 71 0.482864395821316 0.965728791642633 0.517135604178684 72 0.538865111700845 0.92226977659831 0.461134888299155 73 0.42253399812393 0.84506799624786 0.57746600187607 74 0.307866238204365 0.615732476408729 0.692133761795635 75 0.361021480169440 0.722042960338881 0.63897851983056 76 0.226154950052559 0.452309900105118 0.773845049947441 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 0 0 OK 10% type I error level 0 0 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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