*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: Mon, 23 Nov 2009 12:51:18 -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/23/t1259005977kju3s2zolg44k93.htm/, Retrieved Mon, 23 Nov 2009 20:53:09 +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/23/t1259005977kju3s2zolg44k93.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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Dataseries X:

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5560 543 3922 594 3759 611 4138 613 4634 611 3996 594 4308 595 4143 591 4429 589 5219 584 4929 573 5755 567 5592 569 4163 621 4962 629 5208 628 4755 612 4491 595 5732 597 5731 593 5040 590 6102 580 4904 574 5369 573 5578 573 4619 620 4731 626 5011 620 5299 588 4146 566 4625 557 4736 561 4219 549 5116 532 4205 526 4121 511 5103 499 4300 555 4578 565 3809 542 5526 527 4247 510 3830 514 4394 517 4826 508 4409 493 4569 490 4106 469 4794 478 3914 528 3793 534 4405 518 4022 506 4100 502 4788 516 3163 528 3585 533 3903 536 4178 537 3863 524 4187 536

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 Y[t] = + 2496.36706778836 + 4.58362496986466X[t] + 435.089337369235M1[t] -787.43485719658M2[t] -641.815711028509M3[t] -444.1745904089M4[t] -32.8815449881847M5[t] -605.788499567469M6[t] -148.483978610345M7[t] -374.062732659247M8[t] -360.706286901016M9[t] + 217.334833718593M10[t] -144.841820547284M11[t] -7.70522088479975t + 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) 2496.36706778836 1889.462205 1.3212 0.192829 0.096415 X 4.58362496986466 3.16446 1.4485 0.154125 0.077062 M1 435.089337369235 332.043635 1.3103 0.196449 0.098225 M2 -787.43485719658 368.108342 -2.1391 0.037649 0.018825 M3 -641.815711028509 380.55418 -1.6865 0.098322 0.049161 M4 -444.1745904089 371.634999 -1.1952 0.238009 0.119004 M5 -32.8815449881847 358.190362 -0.0918 0.927248 0.463624 M6 -605.788499567469 349.678934 -1.7324 0.089758 0.044879 M7 -148.483978610345 351.348796 -0.4226 0.674506 0.337253 M8 -374.062732659247 353.407878 -1.0584 0.295262 0.147631 M9 -360.706286901016 351.751102 -1.0255 0.310396 0.155198 M10 217.334833718593 348.321461 0.6239 0.535678 0.267839 M11 -144.841820547284 347.195312 -0.4172 0.678449 0.339224 t -7.70522088479975 6.74511 -1.1423 0.259098 0.129549 Multiple Linear Regression - Regression Statistics Multiple R 0.645003110142727 R-squared 0.41602901209379 Adjusted R-squared 0.25450512182186 F-TEST (value) 2.57565002547544 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 0.00894250554016951 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 546.868450688732 Sum Squared Residuals 14056059.8108586 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5560 5412.65954290931 147.340457090689 2 3922 4416.19500092179 -494.195000921786 3 3759 4632.03055069276 -873.030550692758 4 4138 4831.1337003673 -693.133700367297 5 4634 5225.55427496348 -591.554274963483 6 3996 4567.0204750117 -571.020475011699 7 4308 5021.20340005389 -713.203400053889 8 4143 4769.58492524073 -626.584925240728 9 4429 4766.06890017443 -337.06890017443 10 5219 5313.48667505992 -94.486675059916 11 4929 4893.18492524073 35.815074759272 12 5755 5002.81977508402 752.180224915976 13 5592 5439.37114150819 152.628858491811 14 4163 4447.49022449054 -284.490224490536 15 4962 4622.07314953272 339.926850467275 16 5208 4807.42542429767 400.574575702331 17 4755 5137.67524931575 -382.675249315750 18 4491 4479.14144936397 11.8585506360330 19 5732 4937.90799937602 794.09200062398 20 5731 4686.28952456286 1044.71047543714 21 5040 4678.1898745267 361.810125473302 22 6102 5202.68952456286 899.31047543714 23 4904 4805.305899593 98.6941004070043 24 5369 4937.85887428562 431.141125714385 25 5578 5365.24299077005 212.757009229950 26 4619 4350.44394890307 268.556051096926 27 4731 4515.85962400553 215.140375994466 28 5011 4678.29377392115 332.706226078845 29 5299 4935.2055994214 363.794400578598 30 4146 4253.75367462029 -107.753674620295 31 4625 4662.10034996384 -37.1003499638377 32 4736 4447.15087490959 288.849125090406 33 4219 4397.79860014465 -178.798600144650 34 5116 4890.21287539176 225.787124608240 35 4205 4492.82925042189 -287.829250421895 36 4121 4561.21147553641 -440.211475536409 37 5103 4933.59209238247 169.407907617531 38 4300 3960.04567524427 339.954324755726 39 4578 4143.79585022619 434.204149773808 40 3809 4228.30837565411 -419.308375654114 41 5526 4563.14182564206 962.85817435794 42 4247 3904.60802569028 342.391974309724 43 3830 4372.54182564206 -542.54182564206 44 4394 4153.00872561795 240.991274382048 45 4826 4117.4073257626 708.592674237398 46 4409 4618.98885094944 -209.988850949441 47 4569 4235.35610088917 333.64389911083 48 4106 4276.2365761845 -170.236576184496 49 4794 4744.87331739771 49.1266826022865 50 3914 3743.82515044033 170.174849559669 51 3793 3909.24082554279 -116.240825542791 52 4405 4025.83872575977 379.161274240235 53 4022 4374.42305065731 -352.423050657305 54 4100 3775.47637531376 324.523624686237 55 4788 4289.24642496419 498.753575035807 56 3163 4110.96594966887 -947.965949668866 57 3585 4139.53529939162 -554.535299391621 58 3903 4723.62207403602 -820.622074036024 59 4178 4358.32382385521 -180.323823855212 60 3863 4435.87329890946 -572.873298909455 61 4187 4918.26091503227 -731.260915032267 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.623188027781222 0.753623944437556 0.376811972218778 18 0.472261109683414 0.944522219366827 0.527738890316586 19 0.522477575480575 0.95504484903885 0.477522424519425 20 0.57547450356266 0.84905099287468 0.42452549643734 21 0.47798443023025 0.95596886046050 0.52201556976975 22 0.432191046340928 0.864382092681856 0.567808953659072 23 0.499651788538859 0.999303577077719 0.500348211461141 24 0.65391275952312 0.69217448095376 0.34608724047688 25 0.676920233090121 0.646159533819759 0.323079766909879 26 0.58176999109967 0.83646001780066 0.41823000890033 27 0.508746805013221 0.982506389973558 0.491253194986779 28 0.477300707127024 0.954601414254048 0.522699292872976 29 0.389545454573231 0.779090909146462 0.610454545426769 30 0.379533889941108 0.759067779882216 0.620466110058892 31 0.338916401010236 0.677832802020471 0.661083598989764 32 0.303634310526449 0.607268621052898 0.696365689473551 33 0.259534971746702 0.519069943493403 0.740465028253299 34 0.220396177789027 0.440792355578055 0.779603822210972 35 0.207394111828766 0.414788223657532 0.792605888171234 36 0.212974516735624 0.425949033471247 0.787025483264376 37 0.148630063087396 0.297260126174791 0.851369936912604 38 0.115346662110117 0.230693324220234 0.884653337889883 39 0.0935954595416284 0.187190919083257 0.906404540458372 40 0.119066148501201 0.238132297002402 0.880933851498799 41 0.301990011059850 0.603980022119701 0.69800998894015 42 0.211896866616686 0.423793733233373 0.788103133383314 43 0.974390130754177 0.0512197384916456 0.0256098692458228 44 0.927371817890468 0.145256364219064 0.0726281821095319 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 1 0.0357142857142857 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/105u7c1259005873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/105u7c1259005873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/1el5q1259005873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/1el5q1259005873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/2qxgu1259005873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/2qxgu1259005873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/3o38s1259005873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/3o38s1259005873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/4p0g61259005873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/4p0g61259005873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/5mjuz1259005873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/5mjuz1259005873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/6prwa1259005873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/6prwa1259005873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/7qluo1259005873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/7qluo1259005873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/82hp31259005873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/82hp31259005873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/9q7021259005873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259005977kju3s2zolg44k93/9q7021259005873.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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