WS 7 Model 5

*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: Sun, 22 Nov 2009 13:04:45 -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/22/t1258920374ftpfoxvn7bbu1bt.htm/, Retrieved Sun, 22 Nov 2009 21:06:26 +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/22/t1258920374ftpfoxvn7bbu1bt.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:

WS 7 Model 5

Dataseries X:

» Textbox « » Textfile « » CSV «

277128 0 277915 286602 277103 0 277128 283042 275037 0 277103 276687 270150 0 275037 277915 267140 0 270150 277128 264993 0 267140 277103 287259 0 264993 275037 291186 0 287259 270150 292300 0 291186 267140 288186 0 292300 264993 281477 0 288186 287259 282656 0 281477 291186 280190 0 282656 292300 280408 0 280190 288186 276836 0 280408 281477 275216 0 276836 282656 274352 0 275216 280190 271311 0 274352 280408 289802 0 271311 276836 290726 0 289802 275216 292300 0 290726 274352 278506 0 292300 271311 269826 0 278506 289802 265861 0 269826 290726 269034 0 265861 292300 264176 0 269034 278506 255198 0 264176 269826 253353 0 255198 265861 246057 0 253353 269034 235372 0 246057 264176 258556 0 235372 255198 260993 0 258556 253353 254663 0 260993 246057 250643 0 254663 235372 243422 0 250643 258556 247105 0 243422 260993 248541 0 247105 254663 245039 0 248541 250643 237080 0 245039 243422 237085 0 237080 247105 225554 0 237085 248541 226839 1 225554 245039 247934 1 226839 etc...

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 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation nwwmb[t] = + 18592.6614036799 + 5070.20661526654dummy_variable[t] + 1.09315352748084`y[t-1]`[t] -0.141678933809927`y[t-4] `[t] -1113.51362844413M1[t] -4788.11504224625M2[t] -8151.06975649466M3[t] -5304.29940467738M4[t] -9150.79794834342M5[t] -5800.60338064344M6[t] + 17130.3065854846M7[t] -3934.41364080719M8[t] -8074.2150653462M9[t] -13077.5188296226M10[t] -8827.75850794206M11[t] -95.0676676861505t + 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) 18592.6614036799 12851.143247 1.4468 0.155753 0.077876 dummy_variable 5070.20661526654 2020.757669 2.5091 0.016258 0.008129 `y[t-1]` 1.09315352748084 0.076589 14.273 0 0 `y[t-4] ` -0.141678933809927 0.091655 -1.5458 0.130032 0.065016 M1 -1113.51362844413 2361.601036 -0.4715 0.63984 0.31992 M2 -4788.11504224625 2525.289385 -1.8961 0.065191 0.032596 M3 -8151.06975649466 2721.215839 -2.9954 0.004688 0.002344 M4 -5304.29940467738 2510.866985 -2.1125 0.040931 0.020466 M5 -9150.79794834342 2418.510059 -3.7837 0.000507 0.000254 M6 -5800.60338064344 2358.64987 -2.4593 0.018342 0.009171 M7 17130.3065854846 2373.894636 7.2161 0 0 M8 -3934.41364080719 3023.927286 -1.3011 0.200671 0.100336 M9 -8074.2150653462 3649.0272 -2.2127 0.032689 0.016344 M10 -13077.5188296226 3826.149902 -3.4179 0.001463 0.000731 M11 -8827.75850794206 2525.452699 -3.4955 0.001173 0.000586 t -95.0676676861505 54.948861 -1.7301 0.09132 0.04566 Multiple Linear Regression - Regression Statistics Multiple R 0.986524164699137 R-squared 0.97322992753533 Adjusted R-squared 0.963191150361078 F-TEST (value) 96.9470594517807 F-TEST (DF numerator) 15 F-TEST (DF denominator) 40 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3478.32596296376 Sum Squared Residuals 483950060.185111 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 277128 280582.376909595 -3454.37690959532 2 277103 276456.773006343 646.226993656633 3 275037 273871.791410584 1165.20858941606 4 270150 274191.057176221 -4041.057176221 5 267140 265018.750996978 2121.24900302167 6 264993 264987.02775262 5.97224737992656 7 287259 285768.578104812 1490.42189518815 8 291186 289641.331603252 1544.66839674845 9 292300 290125.730004212 2174.26999578847 10 288186 286549.316272753 1636.68372724742 11 281477 283052.152174479 -1575.15217447895 12 282656 283894.502825794 -1238.50282579428 13 280190 283816.919206300 -3626.91920629965 14 280408 277934.400659738 2473.59934026233 15 276836 275665.209713725 1170.79028627528 16 275216 274345.128534732 870.871465267632 17 274352 268982.033859637 5369.96614036351 18 271311 271261.790104336 49.2098956636920 19 289802 291279.429677278 -1477.42967727796 20 290726 290562.663532720 163.336467279566 21 292300 287460.278898699 4839.72110130066 22 278506 284513.376756708 -6007.37675670765 23 269826 270969.324487552 -1143.32448755191 24 265861 270082.531374434 -4221.53137443373 25 269034 264316.593700025 4717.40629997492 26 264176 265969.819974208 -1793.81997420765 27 255198 258431.030901241 -3233.03090124131 28 253353 251930.158188206 1422.84181179422 29 246057 245522.176461673 534.823538327469 30 235372 241489.931485635 -6117.93148563475 31 258556 253917.421810689 4638.57818931071 32 260993 258362.702930707 2630.29706929339 33 254663 257825.538486029 -3162.53848602947 34 250643 247321.344632872 3321.65536712773 35 243422 243796.875704944 -374.875704944312 36 247105 244290.633361566 2814.36663843374 37 248541 248004.964158165 536.03584183524 38 245039 246374.612856055 -1335.61285605488 39 237080 240111.430401924 -3031.43040192388 40 237085 233640.920647613 3444.07935238698 41 225554 229501.369254947 -3947.36925494718 42 226839 225717.709071048 1121.29092895169 43 247934 251085.876286496 -3151.87628649625 44 248333 252985.453660058 -4652.45366005771 45 246969 250820.452611060 -3851.45261105966 46 245098 244048.962337668 1049.03766233249 47 246263 243169.647633025 3093.35236697518 48 255765 253119.332438206 2645.66756179425 49 264319 262491.146025915 1827.85397408481 50 268347 268337.393503656 9.6064963435664 51 273046 269117.537572526 3928.46242747385 52 273963 275659.735453228 -1696.73545322784 53 267430 271508.669426765 -4078.66942676547 54 271993 267051.541586361 4941.45841363944 55 292710 294209.694120725 -1499.69412072466 56 295881 295566.848273264 314.151726736299 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.135343432794227 0.270686865588453 0.864656567205773 20 0.110975922411538 0.221951844823075 0.889024077588462 21 0.0825155023692401 0.165031004738480 0.91748449763076 22 0.386627057180675 0.77325411436135 0.613372942819325 23 0.263281862975112 0.526563725950223 0.736718137024888 24 0.235362827671022 0.470725655342044 0.764637172328978 25 0.37579505156672 0.75159010313344 0.62420494843328 26 0.294975428542361 0.589950857084722 0.705024571457639 27 0.245351382569773 0.490702765139547 0.754648617430226 28 0.245186372332541 0.490372744665082 0.754813627667459 29 0.226305856352048 0.452611712704097 0.773694143647952 30 0.461959417757368 0.923918835514736 0.538040582242632 31 0.670496432393479 0.659007135213042 0.329503567606521 32 0.773409489222659 0.453181021554682 0.226590510777341 33 0.716165992785455 0.567668014429091 0.283834007214545 34 0.808794749983291 0.382410500033418 0.191205250016709 35 0.694522403401937 0.610955193196126 0.305477596598063 36 0.660456613446626 0.679086773106747 0.339543386553374 37 0.610924057351535 0.778151885296929 0.389075942648465 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/22/t1258920374ftpfoxvn7bbu1bt/109qu81258920277.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/109qu81258920277.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/14xtk1258920277.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/14xtk1258920277.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/2tp381258920277.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/2tp381258920277.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/37r0u1258920277.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/37r0u1258920277.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/461yg1258920277.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/461yg1258920277.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/5eqqm1258920277.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/5eqqm1258920277.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/6bbqf1258920277.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/6bbqf1258920277.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/7ipdx1258920277.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/7ipdx1258920277.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/8t7sn1258920277.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/8t7sn1258920277.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/91m031258920277.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258920374ftpfoxvn7bbu1bt/91m031258920277.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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