BBWS7-3

*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 13:31:52 -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/t1259008361oxxewtuazyke4bk.htm/, Retrieved Mon, 23 Nov 2009 21:32:53 +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/t1259008361oxxewtuazyke4bk.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:

lineaire trend

Dataseries X:

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3922 8.1 3759 7.7 4138 7.5 4634 7.6 3996 7.8 4308 7.8 4143 7.8 4429 7.5 5219 7.5 4929 7.1 5755 7.5 5592 7.5 4163 7.6 4962 7.7 5208 7.7 4755 7.9 4491 8.1 5732 8.2 5731 8.2 5040 8.2 6102 7.9 4904 7.3 5369 6.9 5578 6.6 4619 6.7 4731 6.9 5011 7 5299 7.1 4146 7.2 4625 7.1 4736 6.9 4219 7 5116 6.8 4205 6.4 4121 6.7 5103 6.6 4300 6.4 4578 6.3 3809 6.2 5526 6.5 4247 6.8 3830 6.8 4394 6.4 4826 6.1 4409 5.8 4569 6.1 4106 7.2 4794 7.3 3914 6.9 3793 6.1 4405 5.8 4022 6.2 4100 7.1 4788 7.7 3163 7.9 3585 7.7 3903 7.4 4178 7.5 3863 8 4187 8.1

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 Bouw[t] = + 6446.10011836946 -106.382518310276Wman[t] -1067.36064862766M1[t] -890.214420729451M2[t] -733.829941000222M3[t] -360.003055411703M4[t] -957.610267625952M5[t] -466.821633868461M6[t] -681.109503773026M7[t] -692.180324776207M8[t] -168.161747244211M9[t] -564.815519346008M10[t] -421.167430827846M11[t] -17.4227315602574t + 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) 6446.10011836946 1075.718021 5.9924 0 0 Wman -106.382518310276 132.146573 -0.805 0.424942 0.212471 M1 -1067.36064862766 359.628835 -2.968 0.004746 0.002373 M2 -890.214420729451 362.268806 -2.4573 0.017827 0.008914 M3 -733.829941000222 363.753797 -2.0174 0.049512 0.024756 M4 -360.003055411703 358.581873 -1.004 0.320648 0.160324 M5 -957.610267625952 356.157477 -2.6887 0.009956 0.004978 M6 -466.821633868461 356.696624 -1.3087 0.197125 0.098562 M7 -681.109503773026 355.899972 -1.9138 0.061884 0.030942 M8 -692.180324776207 355.223747 -1.9486 0.057461 0.028731 M9 -168.161747244211 355.935043 -0.4725 0.638839 0.31942 M10 -564.815519346008 358.334909 -1.5762 0.121828 0.060914 M11 -421.167430827846 354.848398 -1.1869 0.241363 0.120681 t -17.4227315602574 4.819443 -3.6151 0.000742 0.000371 Multiple Linear Regression - Regression Statistics Multiple R 0.611003276177982 R-squared 0.373325003500227 Adjusted R-squared 0.196221200141595 F-TEST (value) 2.10794458628453 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0.0320159092689808 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 561.003319836701 Sum Squared Residuals 14477337.3439188 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3922 4499.61833986833 -577.618339868325 2 3759 4701.89484353037 -942.89484353037 3 4138 4862.1330953614 -724.133095361397 4 4634 5207.89899755863 -573.89899755863 5 3996 4571.59255012207 -575.592550122068 6 4308 5044.9584523193 -736.958452319301 7 4143 4813.24785085448 -670.247850854479 8 4429 4816.66905378412 -387.669053784123 9 5219 5323.26489975586 -104.264899755863 10 4929 4951.74140341792 -22.7414034179176 11 5755 5035.41375305171 719.586246948289 12 5592 5439.1584523193 152.841547680700 13 4163 4343.73682030036 -180.736820300360 14 4962 4492.82206480728 469.177935192721 15 5208 4631.78381297625 576.216187023748 16 4755 4966.91146334246 -211.911463342458 17 4491 4330.60501590590 160.394984094104 18 5732 4793.3326662721 938.6673337279 19 5731 4561.62206480728 1169.37793519272 20 5040 4533.12851224384 506.871487756159 21 6102 5071.63911370866 1030.36088629134 22 4904 4721.39212103277 182.607878967227 23 5369 4890.17048531479 478.829514685211 24 5578 5325.82994007546 252.170059924540 25 4619 4230.40830805652 388.591691943481 26 4731 4368.85530073241 362.144699267589 27 5011 4497.17879707036 513.821202929645 28 5299 4842.94469926759 456.055300732411 29 4146 4217.27650366205 -71.2765036620552 30 4625 4701.28065769032 -76.2806576903159 31 4736 4490.84655988755 245.153440112451 32 4219 4451.71475549308 -232.714755493083 33 5116 4979.58710512688 136.412894873123 34 4205 4608.06360878893 -403.063608788932 35 4121 4702.37421025375 -581.374210253755 36 5103 5116.75716135237 -13.7571613523704 37 4300 4053.25028482651 246.749715173488 38 4578 4223.61203299549 354.387967004513 39 3809 4373.21203299549 -564.212032995487 40 5526 4697.70143153067 828.298568469335 41 4247 4050.75673226308 196.243267736924 42 3830 4524.12263446031 -694.122634460309 43 4394 4334.96504031960 59.0349596804023 44 4826 4338.38624324924 487.613756750759 45 4409 4876.89684471406 -467.896844714064 46 4569 4430.90558555893 138.094414441074 47 4106 4440.11017237553 -334.110172375528 48 4794 4833.21661981209 -39.2166198120883 49 3914 3790.98624694829 123.013753051715 50 3793 4035.81575793445 -242.815757934454 51 4405 4206.69226159651 198.307738403492 52 4022 4520.54340830066 -498.543408300659 53 4100 3809.76919804690 290.230801953095 54 4788 4219.30558925797 568.694410742028 55 3163 3966.31848413109 -803.318484131095 56 3585 3959.10143522971 -374.101435229711 57 3903 4497.61203669453 -594.612036694534 58 4178 4072.89728120145 105.102718798549 59 3863 4145.93137900422 -282.931379004218 60 4187 4539.03782644078 -352.037826440778 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.509330383506595 0.98133923298681 0.490669616493405 18 0.492421599358276 0.984843198716551 0.507578400641724 19 0.49857629001267 0.99715258002534 0.50142370998733 20 0.459684358441068 0.919368716882137 0.540315641558932 21 0.404790520728304 0.809581041456608 0.595209479271696 22 0.466034488856163 0.932068977712326 0.533965511143837 23 0.466096846086062 0.932193692172124 0.533903153913938 24 0.365536823875465 0.73107364775093 0.634463176124535 25 0.318644103629915 0.63728820725983 0.681355896370085 26 0.230932582230031 0.461865164460062 0.769067417769969 27 0.191542035113332 0.383084070226663 0.808457964886668 28 0.134456492112266 0.268912984224531 0.865543507887734 29 0.128441918945668 0.256883837891336 0.871558081054332 30 0.128243008769058 0.256486017538117 0.871756991230942 31 0.102666090666917 0.205332181333834 0.897333909333083 32 0.110696567802516 0.221393135605032 0.889303432197484 33 0.128036308522617 0.256072617045234 0.871963691477383 34 0.162904001739144 0.325808003478289 0.837095998260856 35 0.410498777757035 0.820997555514071 0.589501222242965 36 0.349971679916673 0.699943359833346 0.650028320083327 37 0.257710173866978 0.515420347733955 0.742289826133022 38 0.206482092652334 0.412964185304669 0.793517907347666 39 0.24776663458995 0.4955332691799 0.75223336541005 40 0.537454775466213 0.925090449067573 0.462545224533787 41 0.466647794782001 0.933295589564001 0.533352205217999 42 0.856966342201411 0.286067315597178 0.143033657798589 43 0.817625696988386 0.364748606023229 0.182374303011614 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:

http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/10vyrg1259008306.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/10vyrg1259008306.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/1ctfy1259008306.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/1ctfy1259008306.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/2997l1259008306.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/2997l1259008306.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/3qfp61259008306.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/3qfp61259008306.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/4y1up1259008306.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/4y1up1259008306.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/50znr1259008306.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/50znr1259008306.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/6yx6a1259008306.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/6yx6a1259008306.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/7uad11259008306.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/7uad11259008306.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/88fum1259008306.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/88fum1259008306.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/9qjmr1259008306.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1259008361oxxewtuazyke4bk/9qjmr1259008306.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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