WS 7 Model 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: Fri, 20 Nov 2009 11:55:39 -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/20/t1258743397s9yvrlq1tedeumf.htm/, Retrieved Fri, 20 Nov 2009 19:56:49 +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/20/t1258743397s9yvrlq1tedeumf.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 3

Dataseries X:

» Textbox « » Textfile « » CSV «

286602 0 283042 0 276687 0 277915 0 277128 0 277103 0 275037 0 270150 0 267140 0 264993 0 287259 0 291186 0 292300 0 288186 0 281477 0 282656 0 280190 0 280408 0 276836 0 275216 0 274352 0 271311 0 289802 0 290726 0 292300 0 278506 0 269826 0 265861 0 269034 0 264176 0 255198 0 253353 0 246057 0 235372 0 258556 0 260993 0 254663 0 250643 0 243422 0 247105 0 248541 0 245039 0 237080 0 237085 0 225554 0 226839 1 247934 1 248333 1 246969 1 245098 1 246263 1 255765 1 264319 1 268347 1 273046 1 273963 1 267430 1 271993 1 292710 1 295881 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation nwwmb[t] = + 300922.883597884 + 18529.5767195767dummy_variable[t] -8596.08624338618M1[t] -13209.2497354498M2[t] -17910.6132275132M3[t] -14726.5767195767M4[t] -11885.9402116402M5[t] -11855.1037037037M6[t] -14571.6671957672M7[t] -15199.0306878307M8[t] -20187.1941798942M9[t] -25039.473015873M10[t] -3030.23650793651M11[t] -858.636507936508t + 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) 300922.883597884 7984.947807 37.6863 0 0 dummy_variable 18529.5767195767 6809.58637 2.7211 0.009155 0.004578 M1 -8596.08624338618 9443.315278 -0.9103 0.367419 0.183709 M2 -13209.2497354498 9429.494661 -1.4008 0.167971 0.083985 M3 -17910.6132275132 9418.731272 -1.9016 0.063498 0.031749 M4 -14726.5767195767 9411.0356 -1.5648 0.124479 0.06224 M5 -11885.9402116402 9406.415175 -1.2636 0.21274 0.10637 M6 -11855.1037037037 9404.874529 -1.2605 0.213836 0.106918 M7 -14571.6671957672 9406.415175 -1.5491 0.128206 0.064103 M8 -15199.0306878307 9411.0356 -1.615 0.113145 0.056573 M9 -20187.1941798942 9418.731272 -2.1433 0.03741 0.018705 M10 -25039.473015873 9367.823008 -2.6729 0.01037 0.005185 M11 -3030.23650793651 9363.181258 -0.3236 0.747683 0.373841 t -858.636507936508 170.239659 -5.0437 8e-06 4e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.686640127164347 R-squared 0.47147466423227 Adjusted R-squared 0.322108808471825 F-TEST (value) 3.15650897477149 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0.00197702536040545 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 14802.0422352870 Sum Squared Residuals 10078620899.4201 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 286602 291468.160846561 -4866.16084656058 2 283042 285996.360846561 -2954.36084656087 3 276687 280436.360846561 -3749.36084656086 4 277915 282761.760846561 -4846.76084656088 5 277128 284743.760846561 -7615.76084656085 6 277103 283915.960846561 -6812.96084656087 7 275037 280340.760846561 -5303.76084656087 8 270150 278854.760846561 -8704.76084656087 9 267140 273007.960846561 -5867.96084656083 10 264993 267297.045502645 -2304.04550264550 11 287259 288447.645502646 -1188.64550264552 12 291186 290619.245502645 566.754497354496 13 292300 281164.522751323 11135.4772486772 14 288186 275692.722751323 12493.2772486772 15 281477 270132.722751323 11344.2772486772 16 282656 272458.122751323 10197.8772486773 17 280190 274440.122751323 5749.87724867724 18 280408 273612.322751323 6795.67724867724 19 276836 270037.122751323 6798.87724867725 20 275216 268551.122751323 6664.87724867725 21 274352 262704.322751323 11647.6772486772 22 271311 256993.407407407 14317.5925925926 23 289802 278144.007407407 11657.9925925926 24 290726 280315.607407407 10410.3925925926 25 292300 270860.884656085 21439.1153439153 26 278506 265389.084656085 13116.9153439154 27 269826 259829.084656085 9996.91534391535 28 265861 262154.484656085 3706.51534391535 29 269034 264136.484656085 4897.51534391534 30 264176 263308.684656085 867.315343915351 31 255198 259733.484656085 -4535.48465608465 32 253353 258247.484656085 -4894.48465608464 33 246057 252400.684656085 -6343.68465608465 34 235372 246689.769312169 -11317.7693121693 35 258556 267840.369312169 -9284.3693121693 36 260993 270011.969312169 -9018.96931216931 37 254663 260557.246560847 -5894.24656084662 38 250643 255085.446560847 -4442.44656084654 39 243422 249525.446560847 -6103.44656084655 40 247105 251850.846560847 -4745.84656084654 41 248541 253832.846560847 -5291.84656084655 42 245039 253005.046560847 -7966.04656084655 43 237080 249429.846560847 -12349.8465608465 44 237085 247943.846560847 -10858.8465608466 45 225554 242097.046560847 -16543.0465608466 46 226839 254915.707936508 -28076.7079365079 47 247934 276066.307936508 -28132.3079365079 48 248333 278237.907936508 -29904.9079365079 49 246969 268783.185185185 -21814.1851851852 50 245098 263311.385185185 -18213.3851851852 51 246263 257751.385185185 -11488.3851851852 52 255765 260076.785185185 -4311.78518518518 53 264319 262058.785185185 2260.21481481481 54 268347 261230.985185185 7116.01481481481 55 273046 257655.785185185 15390.2148148148 56 273963 256169.785185185 17793.2148148148 57 267430 250322.985185185 17107.0148148148 58 271993 244612.06984127 27380.9301587302 59 292710 265762.66984127 26947.3301587302 60 295881 267934.26984127 27946.7301587302 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.000136108739726055 0.000272217479452110 0.999863891260274 18 9.8240077334868e-06 1.96480154669736e-05 0.999990175992266 19 2.41865338570884e-06 4.83730677141767e-06 0.999997581346614 20 1.50760129608247e-07 3.01520259216494e-07 0.99999984923987 21 4.27371871427268e-08 8.54743742854537e-08 0.999999957262813 22 4.61035879434699e-09 9.22071758869399e-09 0.99999999538964 23 6.35788730089558e-10 1.27157746017912e-09 0.999999999364211 24 8.76244940826283e-10 1.75248988165257e-09 0.999999999123755 25 4.98162206386414e-10 9.96324412772828e-10 0.999999999501838 26 2.42428256344013e-07 4.84856512688027e-07 0.999999757571744 27 2.88850392459384e-06 5.77700784918767e-06 0.999997111496075 28 3.18550778620203e-05 6.37101557240405e-05 0.999968144922138 29 4.17225184766949e-05 8.34450369533899e-05 0.999958277481523 30 0.000112814280269323 0.000225628560538645 0.99988718571973 31 0.000557302764205200 0.00111460552841040 0.999442697235795 32 0.00144299842322808 0.00288599684645616 0.998557001576772 33 0.0128520775144557 0.0257041550289113 0.987147922485544 34 0.0417405053008607 0.0834810106017214 0.95825949469914 35 0.0824288361115926 0.164857672223185 0.917571163888407 36 0.188163900789082 0.376327801578163 0.811836099210918 37 0.342850405167805 0.685700810335609 0.657149594832195 38 0.564078727279963 0.871842545440074 0.435921272720037 39 0.736120453741328 0.527759092517344 0.263879546258672 40 0.874836556606486 0.250326886787029 0.125163443393514 41 0.96026901317121 0.0794619736575782 0.0397309868287891 42 0.997814670622459 0.00437065875508251 0.00218532937754125 43 0.992758255068867 0.0144834898622669 0.00724174493113345 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 17 0.62962962962963 NOK 5% type I error level 19 0.703703703703704 NOK 10% type I error level 21 0.777777777777778 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/10ihq81258743334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/10ihq81258743334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/1fucy1258743334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/1fucy1258743334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/2rg8f1258743334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/2rg8f1258743334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/3ga5h1258743334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/3ga5h1258743334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/4ecv71258743334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/4ecv71258743334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/5ssrj1258743334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/5ssrj1258743334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/6tgo11258743334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/6tgo11258743334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/7vi7j1258743334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/7vi7j1258743334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/8qni21258743334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/8qni21258743334.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/9m5391258743334.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258743397s9yvrlq1tedeumf/9m5391258743334.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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