Model3

*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 15:56:26 -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/Nov/21/t12587581278cjlr067uouaqyt.htm/, Retrieved Sat, 21 Nov 2009 00:02:19 +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/21/t12587581278cjlr067uouaqyt.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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562 13.9 561 15.9 555 18.2 544 19.7 537 20.1 543 19.9 594 20 611 22.6 613 20.6 611 20.1 594 20.2 595 21.8 591 22 589 19.5 584 17.5 573 18.2 567 18.8 569 19.7 621 18.8 629 18.5 628 18.7 612 18.5 595 19.3 597 18.9 593 21.4 590 22.5 580 25 574 22.9 573 22.9 573 21.3 620 22.3 626 20.9 620 19.9 588 20.2 566 19.8 557 17.7 561 18.1 549 17.6 532 18.2 526 16 511 16.3 499 17.3 555 19 565 18.6 542 18 527 17.9 510 17.8 514 18.5 517 17.4 508 19 493 17.4 490 20.6 469 18.5 478 20 528 18.8 534 18.8 518 19.7 506 15.3 502 10.6 516 6.1 528 0.9

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 Y[t] = + 582.265639057551 + 1.75858938712907X[t] -3.13436250319707M1[t] -15.9053730581449M2[t] -25.5924034907366M3[t] -31.8332314091302M4[t] -40.0057953604147M5[t] -38.0224822175212M6[t] + 14.4773770150556M7[t] + 25.2475798231175M8[t] + 18.8729362634568M9[t] + 6.74241560961807M10[t] -5.59913577067612M11[t] -1.54606174677481t + 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) 582.265639057551 24.044391 24.2163 0 0 X 1.75858938712907 1.04319 1.6858 0.098467 0.049234 M1 -3.13436250319707 15.126943 -0.2072 0.836746 0.418373 M2 -15.9053730581449 15.88376 -1.0014 0.32178 0.16089 M3 -25.5924034907366 15.915677 -1.608 0.114533 0.057267 M4 -31.8332314091302 15.940638 -1.997 0.051636 0.025818 M5 -40.0057953604147 15.912614 -2.5141 0.015417 0.007708 M6 -38.0224822175212 15.9634 -2.3819 0.021326 0.010663 M7 14.4773770150556 15.991756 0.9053 0.369923 0.184961 M8 25.2475798231175 16.017304 1.5763 0.121672 0.060836 M9 18.8729362634568 15.930975 1.1847 0.242104 0.121052 M10 6.74241560961807 15.796348 0.4268 0.671449 0.335725 M11 -5.59913577067612 15.725664 -0.3561 0.723396 0.361698 t -1.54606174677481 0.200869 -7.6969 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.850876194482518 R-squared 0.723990298337052 Adjusted R-squared 0.647647189366449 F-TEST (value) 9.48337457170938 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 3.27576876735236e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 24.8221773982055 Sum Squared Residuals 28958.6030670352 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 562 602.029607288672 -40.0296072886724 2 561 591.229713761208 -30.2297137612081 3 555 584.041377172239 -29.0413771722385 4 544 578.892371587764 -34.8923715877638 5 537 569.877181644556 -32.877181644556 6 543 569.962715163249 -26.9627151632489 7 594 621.092371587764 -27.0923715877637 8 611 634.888845055586 -23.8888450555864 9 613 623.450960974893 -10.4509609748928 10 611 608.895083880715 2.10491611928523 11 594 595.183329692359 -1.18332969235863 12 595 602.050146735666 -7.05014673566647 13 591 597.72144036312 -6.72144036312042 14 589 579.007894593575 9.99210540642488 15 584 564.257623639951 19.7423763600495 16 573 557.701746545772 15.2982534542275 17 567 549.03827447999 17.9617255200094 18 569 551.058256324525 17.9417436754746 19 621 600.429323361911 20.5706766380888 20 629 609.12588760706 19.8741123929404 21 628 601.55690017805 26.4430998219501 22 612 587.528599900011 24.4714000999894 23 595 575.047858282645 19.9521417173552 24 597 578.397496551695 18.6025034483055 25 593 578.113545769545 14.8864542304547 26 590 565.730921793665 24.2690782063354 27 580 558.894303082121 21.1056969178792 28 574 547.414375703981 26.5856242960186 29 573 537.695750005922 35.3042499940779 30 573 535.319258382634 37.6807416173657 31 620 588.031645255565 31.9683547444348 32 626 594.793761174872 31.2062388251283 33 620 585.114466481307 34.8855335186929 34 588 571.965460896832 16.0345391031677 35 566 557.374412014912 8.62558798508829 36 557 557.734448325842 -0.734448325841977 37 561 553.757459830722 7.24254016927827 38 549 538.561092835435 10.4389071645655 39 532 528.383154288346 3.61684571165448 40 526 516.727367971493 9.27263202850682 41 511 507.536319089573 3.46368091042744 42 499 509.73215987282 -10.7321598728203 43 555 563.675559316742 -8.67555931674166 44 565 572.196264623177 -7.19626462317713 45 542 563.220405684464 -21.2204056844642 46 527 549.367964345138 -22.3679643451378 47 510 535.304492279356 -25.3044922793559 48 514 540.588578874248 -26.5885788742476 49 517 533.973706298434 -16.9737062984337 50 508 522.470377016118 -14.4703770161176 51 493 508.423541817345 -15.4235418173446 52 490 506.264138190989 -16.2641381909892 53 469 492.852474779959 -23.8524747799588 54 478 495.927610256771 -17.9276102567711 55 528 544.771100478018 -16.7711004780182 56 534 553.995241539305 -19.9952415393053 57 518 547.657266681286 -29.6572666812860 58 506 526.242890977305 -20.2428909773046 59 502 504.089907730729 -2.08990773072896 60 516 500.22932951255 15.7706704874505 61 528 486.404240449506 41.5957595504935 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 6.29250140119729e-05 0.000125850028023946 0.999937074985988 18 6.60327958204104e-05 0.000132065591640821 0.99993396720418 19 8.0168944908761e-06 1.60337889817522e-05 0.99999198310551 20 0.000249454799432816 0.000498909598865632 0.999750545200567 21 0.000758197502507031 0.00151639500501406 0.999241802497493 22 0.0190189651825651 0.0380379303651303 0.980981034817435 23 0.0441770430497985 0.088354086099597 0.955822956950201 24 0.0448755965427485 0.089751193085497 0.955124403457251 25 0.0568812560796435 0.113762512159287 0.943118743920357 26 0.0511367989144467 0.102273597828893 0.948863201085553 27 0.052512207118005 0.10502441423601 0.947487792881995 28 0.0341472890791498 0.0682945781582996 0.96585271092085 29 0.036318693806574 0.072637387613148 0.963681306193426 30 0.0388393257290556 0.0776786514581111 0.961160674270944 31 0.0472481993459507 0.0944963986919014 0.95275180065405 32 0.072513997550545 0.145027995101090 0.927486002449455 33 0.345238179383169 0.690476358766339 0.65476182061683 34 0.888513856423247 0.222972287153506 0.111486143576753 35 0.99392819931754 0.0121436013649206 0.00607180068246028 36 0.99728748080554 0.0054250383889217 0.00271251919446085 37 0.999075001592098 0.00184999681580371 0.000924998407901855 38 0.999103108520628 0.0017937829587431 0.00089689147937155 39 0.999714858093225 0.000570283813549708 0.000285141906774854 40 0.998982397377822 0.00203520524435637 0.00101760262217819 41 0.99958577928494 0.00082844143012013 0.000414220715060065 42 0.999831467894593 0.000337064210813316 0.000168532105406658 43 0.999086622986417 0.00182675402716551 0.000913377013582753 44 0.99898711560189 0.00202576879622052 0.00101288439811026 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 14 0.5 NOK 5% type I error level 16 0.571428571428571 NOK 10% type I error level 22 0.785714285714286 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/10yyeo1258757781.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/10yyeo1258757781.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/1xkpi1258757781.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/1xkpi1258757781.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/2i9g11258757781.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/2i9g11258757781.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/32zkn1258757781.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/32zkn1258757781.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/4ryyg1258757781.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/4ryyg1258757781.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/5hwzj1258757781.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/5hwzj1258757781.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/6a6e71258757781.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/6a6e71258757781.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/7re7u1258757781.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/7re7u1258757781.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/897881258757781.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/897881258757781.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/98fao1258757781.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12587581278cjlr067uouaqyt/98fao1258757781.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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