*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 06:47:03 -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/t1258725200rynw96fqp629189.htm/, Retrieved Fri, 20 Nov 2009 14:53:32 +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/t1258725200rynw96fqp629189.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:

shwws7

Dataseries X:

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6539 2605 6699 2682 6962 2755 6981 2760 7024 2735 6940 2659 6774 2654 6671 2670 6965 2785 6969 2845 6822 2723 6878 2746 6691 2767 6837 2940 7018 2977 7167 2993 7076 2892 7171 2824 7093 2771 6971 2686 7142 2738 7047 2723 6999 2731 6650 2632 6475 2606 6437 2605 6639 2646 6422 2627 6272 2535 6232 2456 6003 2404 5673 2319 6050 2519 5977 2504 5796 2382 5752 2394 5609 2381 5839 2501 6069 2532 6006 2515 5809 2429 5797 2389 5502 2261 5568 2272 5864 2439 5764 2373 5615 2327 5615 2364 5681 2388 5915 2553 6334 2663 6494 2694 6620 2679 6578 2611 6495 2580 6538 2627 6737 2732 6651 2707 6530 2633 6563 2683

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 Voeding-Mannen[t] = -680.485941116593 + 2.72524569486599`Landbouw-Mannen`[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) -680.485941116593 366.662006 -1.8559 0.068551 0.034276 `Landbouw-Mannen` 2.72524569486599 0.140154 19.4446 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.931128936563007 R-squared 0.867001096504955 Adjusted R-squared 0.864708011961937 F-TEST (value) 378.093820895005 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 188.217027668222 Sum Squared Residuals 2054687.67124708 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6539 6418.77909400931 120.220905990687 2 6699 6628.62301251398 70.3769874860152 3 6962 6827.5659482392 134.434051760797 4 6981 6841.19217671353 139.807823286467 5 7024 6773.06103434188 250.938965658117 6 6940 6565.94236153207 374.057638467932 7 6774 6552.31613305774 221.683866942262 8 6671 6595.9200641756 75.0799358244062 9 6965 6909.32331908518 55.6766809148177 10 6969 7072.83806077714 -103.838060777141 11 6822 6740.35808600349 81.641913996509 12 6878 6803.03873698541 74.9612630145912 13 6691 6860.2688965776 -169.268896577594 14 6837 7331.73640178941 -494.73640178941 15 7018 7432.57049249945 -414.570492499452 16 7167 7476.17442361731 -309.174423617308 17 7076 7200.92460843584 -124.924608435843 18 7171 7015.60790118496 155.392098815044 19 7093 6871.16987935706 221.830120642942 20 6971 6639.52399529345 331.476004706550 21 7142 6781.23677142648 360.763228573519 22 7047 6740.35808600349 306.641913996509 23 6999 6762.16005156242 236.839948437581 24 6650 6492.36072777069 157.639272229314 25 6475 6421.50433970417 53.4956602958294 26 6437 6418.7790940093 18.2209059906953 27 6639 6530.51416749881 108.485832501190 28 6422 6478.73449929636 -56.7344992963563 29 6272 6228.01189536869 43.9881046313144 30 6232 6012.71748547427 219.282514525727 31 6003 5871.00470934124 131.995290658759 32 5673 5639.35882527763 33.6411747223675 33 6050 6184.40796425083 -134.407964250830 34 5977 6143.52927882784 -166.52927882784 35 5796 5811.04930405419 -15.0493040541896 36 5752 5843.75225239258 -91.7522523925815 37 5609 5808.32405835932 -199.324058359324 38 5839 6135.35354174324 -296.353541743242 39 6069 6219.83615828409 -150.836158284088 40 6006 6173.50698147137 -167.506981471366 41 5809 5939.13585171289 -130.135851712891 42 5797 5830.12602391825 -33.1260239182515 43 5502 5481.2945749754 20.7054250245948 44 5568 5511.27227761893 56.727722381069 45 5864 5966.38830866155 -102.388308661551 46 5764 5786.5220928004 -22.5220928003957 47 5615 5661.16079083656 -46.1607908365604 48 5615 5761.9948815466 -146.994881546602 49 5681 5827.40077822339 -146.400778223386 50 5915 6277.06631787627 -362.066317876273 51 6334 6576.84334431153 -242.843344311532 52 6494 6661.32596085238 -167.325960852377 53 6620 6620.44727542939 -0.447275429387657 54 6578 6435.1305681785 142.869431821499 55 6495 6350.64795163765 144.352048362345 56 6538 6478.73449929636 59.2655007036436 57 6737 6764.88529725728 -27.8852972572850 58 6651 6696.75415488564 -45.7541548856353 59 6530 6495.08597346555 34.9140265344477 60 6563 6631.34825820885 -68.3482582088516 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0628999590440095 0.125799918088019 0.93710004095599 6 0.230305108629044 0.460610217258089 0.769694891370956 7 0.135506143219711 0.271012286439422 0.864493856780289 8 0.102306117370197 0.204612234740394 0.897693882629803 9 0.0683293693746747 0.136658738749349 0.931670630625325 10 0.0705151934800804 0.141030386960161 0.92948480651992 11 0.0407251026391445 0.081450205278289 0.959274897360855 12 0.0218295342528045 0.043659068505609 0.978170465747195 13 0.051508424607804 0.103016849215608 0.948491575392196 14 0.135344911688740 0.270689823377481 0.86465508831126 15 0.144551033044048 0.289102066088095 0.855448966955952 16 0.185182365732348 0.370364731464696 0.814817634267652 17 0.175839177788649 0.351678355577297 0.824160822211351 18 0.244607034399061 0.489214068798122 0.755392965600939 19 0.282124477224012 0.564248954448023 0.717875522775989 20 0.338822807570688 0.677645615141375 0.661177192429312 21 0.546476251654459 0.907047496691081 0.453523748345541 22 0.652308533281114 0.695382933437771 0.347691466718885 23 0.696613422444616 0.606773155110768 0.303386577555384 24 0.734198051529046 0.531603896941909 0.265801948470954 25 0.808004766462004 0.383990467075992 0.191995233537996 26 0.852862030645593 0.294275938708813 0.147137969354407 27 0.852294075074966 0.295411849850069 0.147705924925034 28 0.878744159721962 0.242511680556075 0.121255840278038 29 0.901160991791156 0.197678016417688 0.0988390082088439 30 0.94454439886344 0.110911202273121 0.0554556011365605 31 0.96598279302537 0.0680344139492611 0.0340172069746305 32 0.97804685721996 0.0439062855600803 0.0219531427800402 33 0.980819645455291 0.0383607090894176 0.0191803545447088 34 0.984274567725745 0.0314508645485097 0.0157254322742548 35 0.98050559306138 0.0389888138772397 0.0194944069386199 36 0.976122862657654 0.0477542746846918 0.0238771373423459 37 0.979904770663912 0.0401904586721769 0.0200952293360884 38 0.991595908838778 0.0168081823224436 0.00840409116122178 39 0.988957069402686 0.0220858611946273 0.0110429305973136 40 0.986719937521943 0.0265601249561136 0.0132800624780568 41 0.981301615584333 0.0373967688313340 0.0186983844156670 42 0.96873776751615 0.0625244649677012 0.0312622324838506 43 0.952432826146645 0.09513434770671 0.047567173853355 44 0.941607640056503 0.116784719886993 0.0583923599434966 45 0.911769549251577 0.176460901496845 0.0882304507484225 46 0.873506709175368 0.252986581649265 0.126493290824632 47 0.826723755437437 0.346552489125126 0.173276244562563 48 0.761408104221401 0.477183791557199 0.238591895778599 49 0.68612921848411 0.62774156303178 0.31387078151589 50 0.976658403347915 0.0466831933041691 0.0233415966520846 51 0.998358214797964 0.00328357040407267 0.00164178520203633 52 0.999719370570747 0.000561258858505697 0.000280629429252849 53 0.998513030575421 0.00297393884915722 0.00148696942457861 54 0.996828305563862 0.00634338887227504 0.00317169443613752 55 0.98967111035173 0.0206577792965402 0.0103288896482701 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.0784313725490196 NOK 5% type I error level 17 0.333333333333333 NOK 10% type I error level 21 0.411764705882353 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/10tbfz1258724818.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/10tbfz1258724818.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/1gb101258724818.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/1gb101258724818.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/2kb5n1258724818.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/2kb5n1258724818.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/3m6q91258724818.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/3m6q91258724818.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/4dk7k1258724818.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/4dk7k1258724818.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/5bvw41258724818.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/5bvw41258724818.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/6igtm1258724818.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/6igtm1258724818.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/7z1no1258724818.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/7z1no1258724818.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/8je9a1258724818.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/8je9a1258724818.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/9m4jf1258724818.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725200rynw96fqp629189/9m4jf1258724818.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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