WS 7 Model 2

*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 11:37:24 -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/t1258742295l1p80n1oha15tte.htm/, Retrieved Fri, 20 Nov 2009 19:38:27 +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/t1258742295l1p80n1oha15tte.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 2

Dataseries X:

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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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation nwwmb[t] = + 280315.607407407 -7229.51851851852dummy_variable[t] -4302.90370370365M1[t] -9774.70370370372M2[t] -15334.7037037037M3[t] -13009.3037037037M4[t] -11027.3037037037M5[t] -11855.1037037037M6[t] -15430.3037037037M7[t] -16916.3037037037M8[t] -22763.1037037037M9[t] -23322.2M10[t] -2171.60000000001M11[t] + 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) 280315.607407407 8458.084243 33.1417 0 0 dummy_variable -7229.51851851852 5553.004354 -1.3019 0.199292 0.099646 M1 -4302.90370370365 11595.0135 -0.3711 0.712231 0.356115 M2 -9774.70370370372 11595.0135 -0.843 0.403493 0.201747 M3 -15334.7037037037 11595.0135 -1.3225 0.192393 0.096196 M4 -13009.3037037037 11595.0135 -1.122 0.267573 0.133786 M5 -11027.3037037037 11595.0135 -0.951 0.34645 0.173225 M6 -11855.1037037037 11595.0135 -1.0224 0.311811 0.155905 M7 -15430.3037037037 11595.0135 -1.3308 0.189685 0.094842 M8 -16916.3037037037 11595.0135 -1.4589 0.151236 0.075618 M9 -22763.1037037037 11595.0135 -1.9632 0.055558 0.027779 M10 -23322.2 11541.702811 -2.0207 0.04903 0.024515 M11 -2171.60000000001 11541.702811 -0.1882 0.851567 0.425784 Multiple Linear Regression - Regression Statistics Multiple R 0.423309059985496 R-squared 0.179190560265804 Adjusted R-squared -0.0303778073259051 F-TEST (value) 0.855045836950505 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.595817608029634 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 18249.0344800242 Sum Squared Residuals 15652281194.2963 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 286602 276012.703703703 10589.2962962966 2 283042 270540.903703704 12501.0962962963 3 276687 264980.903703704 11706.0962962963 4 277915 267306.303703704 10608.6962962963 5 277128 269288.303703704 7839.69629629632 6 277103 268460.503703704 8642.4962962963 7 275037 264885.303703704 10151.6962962963 8 270150 263399.303703704 6750.69629629628 9 267140 257552.503703704 9587.49629629633 10 264993 256993.407407407 7999.5925925926 11 287259 278144.007407407 9114.99259259259 12 291186 280315.607407407 10870.3925925926 13 292300 276012.703703704 16287.2962962962 14 288186 270540.903703704 17645.0962962963 15 281477 264980.903703704 16496.0962962963 16 282656 267306.303703704 15349.6962962963 17 280190 269288.303703704 10901.6962962963 18 280408 268460.503703704 11947.4962962963 19 276836 264885.303703704 11950.6962962963 20 275216 263399.303703704 11816.6962962963 21 274352 257552.503703704 16799.4962962963 22 271311 256993.407407407 14317.5925925926 23 289802 278144.007407407 11657.9925925926 24 290726 280315.607407407 10410.3925925926 25 292300 276012.703703704 16287.2962962962 26 278506 270540.903703704 7965.0962962963 27 269826 264980.903703704 4845.09629629629 28 265861 267306.303703704 -1445.30370370371 29 269034 269288.303703704 -254.303703703708 30 264176 268460.503703704 -4284.5037037037 31 255198 264885.303703704 -9687.3037037037 32 253353 263399.303703704 -10046.3037037037 33 246057 257552.503703704 -11495.5037037037 34 235372 256993.407407407 -21621.4074074074 35 258556 278144.007407407 -19588.0074074074 36 260993 280315.607407407 -19322.6074074074 37 254663 276012.703703704 -21349.7037037038 38 250643 270540.903703704 -19897.9037037037 39 243422 264980.903703704 -21558.9037037037 40 247105 267306.303703704 -20201.3037037037 41 248541 269288.303703704 -20747.3037037037 42 245039 268460.503703704 -23421.5037037037 43 237080 264885.303703704 -27805.3037037037 44 237085 263399.303703704 -26314.3037037037 45 225554 257552.503703704 -31998.5037037037 46 226839 249763.888888889 -22924.8888888889 47 247934 270914.488888889 -22980.4888888889 48 248333 273086.088888889 -24753.0888888889 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 2260.21481481482 54 268347 261230.985185185 7116.01481481482 55 273046 257655.785185185 15390.2148148148 56 273963 256169.785185185 17793.2148148148 57 267430 250322.985185185 17107.0148148148 58 271993 249763.888888889 22229.1111111111 59 292710 270914.488888889 21795.5111111111 60 295881 273086.088888889 22794.9111111111 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0132003402450938 0.0264006804901877 0.986799659754906 17 0.00276282108519264 0.00552564217038527 0.997237178914807 18 0.000590551806344612 0.00118110361268922 0.999409448193655 19 0.000107098133618631 0.000214196267237261 0.999892901866381 20 3.19451423826744e-05 6.38902847653488e-05 0.999968054857617 21 1.85074315587043e-05 3.70148631174086e-05 0.999981492568441 22 8.629697499727e-06 1.7259394999454e-05 0.9999913703025 23 2.22681911124678e-06 4.45363822249355e-06 0.999997773180889 24 5.21299249819169e-07 1.04259849963834e-06 0.99999947870075 25 3.11468622378155e-07 6.22937244756309e-07 0.999999688531378 26 4.89464989846093e-07 9.78929979692187e-07 0.99999951053501 27 1.33583296998494e-06 2.67166593996987e-06 0.99999866416703 28 1.11248061855685e-05 2.22496123711369e-05 0.999988875193814 29 1.27486690731494e-05 2.54973381462989e-05 0.999987251330927 30 3.48197090339416e-05 6.96394180678833e-05 0.999965180290966 31 0.000207785856561576 0.000415571713123152 0.999792214143438 32 0.000452976179492956 0.000905952358985912 0.999547023820507 33 0.00150246490898493 0.00300492981796986 0.998497535091015 34 0.00699095335079604 0.0139819067015921 0.993009046649204 35 0.0129584871556606 0.0259169743113213 0.98704151284434 36 0.018499950966882 0.036999901933764 0.981500049033118 37 0.0452392181342164 0.0904784362684329 0.954760781865784 38 0.0740904318614539 0.148180863722908 0.925909568138546 39 0.094111675883865 0.18822335176773 0.905888324116135 40 0.0943915340138384 0.188783068027677 0.905608465986162 41 0.0814956129703632 0.162991225940726 0.918504387029637 42 0.0641440560726251 0.128288112145250 0.935855943927375 43 0.0444148739175566 0.0888297478351133 0.955585126082443 44 0.0252870438385458 0.0505740876770916 0.974712956161454 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 17 0.586206896551724 NOK 5% type I error level 21 0.724137931034483 NOK 10% type I error level 24 0.827586206896552 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/108bvo1258742239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/108bvo1258742239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/1hje01258742239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/1hje01258742239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/2l78t1258742239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/2l78t1258742239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/3vo3s1258742239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/3vo3s1258742239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/4j86l1258742239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/4j86l1258742239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/5u8co1258742239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/5u8co1258742239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/6vwoe1258742239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/6vwoe1258742239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/72vv71258742239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/72vv71258742239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/8pvzj1258742239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/8pvzj1258742239.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/9n0ki1258742239.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742295l1p80n1oha15tte/9n0ki1258742239.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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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