Paper Multiple Regression T2

*Unverified author*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Wed, 24 Dec 2008 06:50:10 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/24/t1230126789ezntyi5g7kefpag.htm/, Retrieved Wed, 24 Dec 2008 14:53:19 +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/2008/Dec/24/t1230126789ezntyi5g7kefpag.htm/}, year = {2008}, } @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 = {2008}, 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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103,3 107,9 101 94,6 94,2 92,3 107,1 102,6 103,1 104,1 92,7 87 109,3 113,9 103,3 100,8 97,4 98,9 110,8 103,5 99,8 104,9 95,2 85,7 110 113,7 101,1 103,6 96,2 98,3 119,7 109,4 103,5 118,2 98,7 96,8 121,8 124 119,6 122,5 109,7 111,6 131,2 124,4 116,9 131,8 107,4 111 134 126,2 131,2 130,1 123,1 126,3 148,6 130,1 142,3 154,4 121,6 124,8 143,6 146,9 144,6 137,1 134,7 130,8 153,5 137,6 146,5 156,7 137,6 131,4 147,4 158,5 151,5 142,5 131,3 133,4 136,9 143,2 136,4 145,9 138,8 122,9

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 Bewerkende_industrie[t] = + 75.479761904762 + 23.2561259920636M1[t] + 25.6679067460318M2[t] + 19.4368303571428M3[t] + 15.7343253968254M4[t] + 8.67467757936508M5[t] + 8.70074404761906M6[t] + 24.6125248015873M7[t] + 15.7814484126984M8[t] + 14.7646577380953M9[t] + 23.7192956349206M10[t] + 5.31679067460318M11[t] + 0.688219246031746t + 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) 75.479761904762 2.812542 26.8368 0 0 M1 23.2561259920636 3.459689 6.722 0 0 M2 25.6679067460318 3.457083 7.4247 0 0 M3 19.4368303571428 3.454724 5.6262 0 0 M4 15.7343253968254 3.452611 4.5572 2.1e-05 1.1e-05 M5 8.67467757936508 3.450746 2.5139 0.014211 0.007106 M6 8.70074404761906 3.449129 2.5226 0.013892 0.006946 M7 24.6125248015873 3.44776 7.1387 0 0 M8 15.7814484126984 3.44664 4.5788 1.9e-05 1e-05 M9 14.7646577380953 3.445768 4.2849 5.6e-05 2.8e-05 M10 23.7192956349206 3.445145 6.8848 0 0 M11 5.31679067460318 3.444771 1.5434 0.12717 0.063585 t 0.688219246031746 0.029297 23.4909 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.949403561963653 R-squared 0.901367123469272 Adjusted R-squared 0.884696778140135 F-TEST (value) 54.070093070827 F-TEST (DF numerator) 12 F-TEST (DF denominator) 71 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.44434379787771 Sum Squared Residuals 2948.59925595239 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 103.3 99.4241071428567 3.87589285714327 2 107.9 102.524107142857 5.37589285714286 3 101 96.9812500000001 4.01874999999989 4 94.6 93.9669642857143 0.633035714285726 5 94.2 87.5955357142857 6.6044642857143 6 92.3 88.3098214285714 3.99017857142856 7 107.1 104.909821428571 2.19017857142855 8 102.6 96.7669642857143 5.8330357142857 9 103.1 96.4383928571428 6.66160714285715 10 104.1 106.08125 -1.98125000000005 11 92.7 88.3669642857143 4.33303571428572 12 87 83.7383928571429 3.26160714285713 13 109.3 107.682738095238 1.61726190476183 14 113.9 110.782738095238 3.11726190476189 15 103.3 105.239880952381 -1.93988095238094 16 100.8 102.225595238095 -1.42559523809525 17 97.4 95.8541666666667 1.54583333333333 18 98.9 96.5684523809524 2.33154761904761 19 110.8 113.168452380952 -2.36845238095239 20 103.5 105.025595238095 -1.52559523809525 21 99.8 104.697023809524 -4.89702380952383 22 104.9 114.339880952381 -9.43988095238096 23 95.2 96.6255952380952 -1.42559523809524 24 85.7 91.9970238095238 -6.29702380952381 25 110 115.941369047619 -5.94136904761912 26 113.7 119.041369047619 -5.34136904761906 27 101.1 113.498511904762 -12.3985119047619 28 103.6 110.484226190476 -6.8842261904762 29 96.2 104.112797619048 -7.91279761904763 30 98.3 104.827083333333 -6.52708333333335 31 119.7 121.427083333333 -1.72708333333333 32 109.4 113.284226190476 -3.88422619047619 33 103.5 112.955654761905 -9.45565476190477 34 118.2 122.598511904762 -4.39851190476191 35 98.7 104.884226190476 -6.18422619047619 36 96.8 100.255654761905 -3.45565476190477 37 121.8 124.2 -2.40000000000007 38 124 127.3 -3.30000000000001 39 119.6 121.757142857143 -2.15714285714285 40 122.5 118.742857142857 3.75714285714286 41 109.7 112.371428571429 -2.67142857142858 42 111.6 113.085714285714 -1.48571428571430 43 131.2 129.685714285714 1.51428571428571 44 124.4 121.542857142857 2.85714285714286 45 116.9 121.214285714286 -4.31428571428571 46 131.8 130.857142857143 0.942857142857157 47 107.4 113.142857142857 -5.74285714285714 48 111 108.514285714286 2.48571428571428 49 134 132.458630952381 1.54136904761898 50 126.2 135.558630952381 -9.35863095238095 51 131.2 130.015773809524 1.1842261904762 52 130.1 127.001488095238 3.09851190476191 53 123.1 120.630059523810 2.46994047619046 54 126.3 121.344345238095 4.95565476190476 55 148.6 137.944345238095 10.6556547619048 56 130.1 129.801488095238 0.298511904761902 57 142.3 129.472916666667 12.8270833333334 58 154.4 139.115773809524 15.2842261904762 59 121.6 121.401488095238 0.198511904761902 60 124.8 116.772916666667 8.02708333333334 61 143.6 140.717261904762 2.88273809523803 62 146.9 143.817261904762 3.0827380952381 63 144.6 138.274404761905 6.32559523809526 64 137.1 135.260119047619 1.83988095238096 65 134.7 128.888690476190 5.81130952380952 66 130.8 129.602976190476 1.19702380952384 67 153.5 146.202976190476 7.29702380952382 68 137.6 138.060119047619 -0.460119047619040 69 146.5 137.731547619048 8.76845238095238 70 156.7 147.374404761905 9.32559523809524 71 137.6 129.660119047619 7.93988095238095 72 131.4 125.031547619048 6.3684523809524 73 147.4 148.975892857143 -1.57589285714291 74 158.5 152.075892857143 6.42410714285715 75 151.5 146.533035714286 4.96696428571432 76 142.5 143.51875 -1.01874999999999 77 131.3 137.147321428571 -5.84732142857141 78 133.4 137.861607142857 -4.46160714285713 79 136.9 154.461607142857 -17.5616071428571 80 143.2 146.31875 -3.11875 81 136.4 145.990178571429 -9.59017857142856 82 145.9 155.633035714286 -9.7330357142857 83 138.8 137.91875 0.881250000000013 84 122.9 133.290178571429 -10.3901785714286 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0105247791084532 0.0210495582169064 0.989475220891547 17 0.00276082362483344 0.00552164724966688 0.997239176375167 18 0.000741044850868127 0.00148208970173625 0.999258955149132 19 0.000158141619711302 0.000316283239422605 0.999841858380289 20 0.000170552400347407 0.000341104800694814 0.999829447599653 21 0.00126919757354393 0.00253839514708787 0.998730802426456 22 0.000565527749931905 0.00113105549986381 0.999434472250068 23 0.000171028228610221 0.000342056457220442 0.99982897177139 24 0.000121982794485703 0.000243965588971407 0.999878017205514 25 3.67468016580961e-05 7.34936033161921e-05 0.999963253198342 26 1.13823226302880e-05 2.27646452605759e-05 0.99998861767737 27 1.82508193296501e-05 3.65016386593001e-05 0.99998174918067 28 9.18659193965268e-06 1.83731838793054e-05 0.99999081340806 29 5.19754627772188e-06 1.03950925554438e-05 0.999994802453722 30 1.71351594730862e-06 3.42703189461724e-06 0.999998286484053 31 8.67128546842385e-06 1.73425709368477e-05 0.999991328714532 32 3.87980190056814e-06 7.75960380113628e-06 0.9999961201981 33 2.32962775626846e-06 4.65925551253691e-06 0.999997670372244 34 3.17709484388431e-05 6.35418968776862e-05 0.999968229051561 35 1.59841952233015e-05 3.19683904466029e-05 0.999984015804777 36 2.01415295323012e-05 4.02830590646024e-05 0.999979858470468 37 4.33214603274093e-05 8.66429206548186e-05 0.999956678539673 38 3.51821014126516e-05 7.03642028253032e-05 0.999964817898587 39 0.000166139445679948 0.000332278891359896 0.99983386055432 40 0.00148939828654709 0.00297879657309417 0.998510601713453 41 0.00108789290038108 0.00217578580076216 0.998912107099619 42 0.000843389595340073 0.00168677919068015 0.99915661040466 43 0.00095072282949695 0.0019014456589939 0.999049277170503 44 0.00100357810047184 0.00200715620094368 0.998996421899528 45 0.00115346648277394 0.00230693296554788 0.998846533517226 46 0.00281144430979309 0.00562288861958618 0.997188555690207 47 0.00466442937123581 0.00932885874247163 0.995335570628764 48 0.00612580833642518 0.0122516166728504 0.993874191663575 49 0.00579915802939322 0.0115983160587864 0.994200841970607 50 0.0332489892366782 0.0664979784733563 0.966751010763322 51 0.0716482812767082 0.143296562553416 0.928351718723292 52 0.080016491554823 0.160032983109646 0.919983508445177 53 0.0854018057366868 0.170803611473374 0.914598194263313 54 0.0833029262718461 0.166605852543692 0.916697073728154 55 0.117817968342807 0.235635936685614 0.882182031657193 56 0.116622155770696 0.233244311541392 0.883377844229304 57 0.179838064720684 0.359676129441368 0.820161935279316 58 0.277718638170293 0.555437276340586 0.722281361829707 59 0.472905596073235 0.94581119214647 0.527094403926765 60 0.425539532948817 0.851079065897633 0.574460467051183 61 0.358525802466266 0.717051604932533 0.641474197533734 62 0.47027459476579 0.94054918953158 0.52972540523421 63 0.516801752751002 0.966396494497996 0.483198247248998 64 0.54326918476736 0.91346163046528 0.45673081523264 65 0.430126126976769 0.860252253953539 0.569873873023231 66 0.415280946580352 0.830561893160705 0.584719053419648 67 0.509736407945724 0.980527184108552 0.490263592054276 68 0.700145076214644 0.599709847570712 0.299854923785356 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 31 0.584905660377358 NOK 5% type I error level 34 0.641509433962264 NOK 10% type I error level 35 0.660377358490566 NOK

Charts produced by software:

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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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