WS 7 Model 1

*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 11:02:51 -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/t1258740264oavm88irgyoyho7.htm/, Retrieved Fri, 20 Nov 2009 19:04:36 +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/t1258740264oavm88irgyoyho7.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 1

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] = + 267912.577777778 -6253.24444444445dummy_variable[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) 267912.577777778 2671.648691 100.2799 0 0 dummy_variable -6253.24444444445 5343.297382 -1.1703 0.246668 0.123334 Multiple Linear Regression - Regression Statistics Multiple R 0.151884696481867 R-squared 0.0230689610253889 Adjusted R-squared 0.0062253224223785 F-TEST (value) 1.36959487015268 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.246667802057425 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 17921.964256143 Sum Squared Residuals 18629414562.3111 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 286602 267912.577777777 18689.4222222226 2 283042 267912.577777778 15129.4222222222 3 276687 267912.577777778 8774.42222222221 4 277915 267912.577777778 10002.4222222222 5 277128 267912.577777778 9215.42222222221 6 277103 267912.577777778 9190.42222222221 7 275037 267912.577777778 7124.42222222221 8 270150 267912.577777778 2237.42222222221 9 267140 267912.577777778 -772.577777777788 10 264993 267912.577777778 -2919.57777777779 11 287259 267912.577777778 19346.4222222222 12 291186 267912.577777778 23273.4222222222 13 292300 267912.577777778 24387.4222222222 14 288186 267912.577777778 20273.4222222222 15 281477 267912.577777778 13564.4222222222 16 282656 267912.577777778 14743.4222222222 17 280190 267912.577777778 12277.4222222222 18 280408 267912.577777778 12495.4222222222 19 276836 267912.577777778 8923.42222222221 20 275216 267912.577777778 7303.42222222221 21 274352 267912.577777778 6439.42222222221 22 271311 267912.577777778 3398.42222222221 23 289802 267912.577777778 21889.4222222222 24 290726 267912.577777778 22813.4222222222 25 292300 267912.577777778 24387.4222222222 26 278506 267912.577777778 10593.4222222222 27 269826 267912.577777778 1913.42222222221 28 265861 267912.577777778 -2051.57777777779 29 269034 267912.577777778 1121.42222222221 30 264176 267912.577777778 -3736.57777777779 31 255198 267912.577777778 -12714.5777777778 32 253353 267912.577777778 -14559.5777777778 33 246057 267912.577777778 -21855.5777777778 34 235372 267912.577777778 -32540.5777777778 35 258556 267912.577777778 -9356.57777777779 36 260993 267912.577777778 -6919.57777777779 37 254663 267912.577777778 -13249.5777777778 38 250643 267912.577777778 -17269.5777777778 39 243422 267912.577777778 -24490.5777777778 40 247105 267912.577777778 -20807.5777777778 41 248541 267912.577777778 -19371.5777777778 42 245039 267912.577777778 -22873.5777777778 43 237080 267912.577777778 -30832.5777777778 44 237085 267912.577777778 -30827.5777777778 45 225554 267912.577777778 -42358.5777777778 46 226839 261659.333333333 -34820.3333333333 47 247934 261659.333333333 -13725.3333333333 48 248333 261659.333333333 -13326.3333333333 49 246969 261659.333333333 -14690.3333333333 50 245098 261659.333333333 -16561.3333333333 51 246263 261659.333333333 -15396.3333333333 52 255765 261659.333333333 -5894.33333333333 53 264319 261659.333333333 2659.66666666667 54 268347 261659.333333333 6687.66666666667 55 273046 261659.333333333 11386.6666666667 56 273963 261659.333333333 12303.6666666667 57 267430 261659.333333333 5770.66666666667 58 271993 261659.333333333 10333.6666666667 59 292710 261659.333333333 31050.6666666667 60 295881 261659.333333333 34221.6666666667 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0272895128985364 0.0545790257970728 0.972710487101464 6 0.00740811672593806 0.0148162334518761 0.992591883274062 7 0.00253702284269948 0.00507404568539896 0.9974629771573 8 0.00231443668251552 0.00462887336503104 0.997685563317484 9 0.00265726836806633 0.00531453673613267 0.997342731631934 10 0.00295950215893888 0.00591900431787775 0.99704049784106 11 0.00313045295485732 0.00626090590971464 0.996869547045143 12 0.00500917893603 0.01001835787206 0.99499082106397 13 0.00717621196404911 0.0143524239280982 0.99282378803595 14 0.0056642111865225 0.011328422373045 0.994335788813478 15 0.00293032134310244 0.00586064268620489 0.997069678656898 16 0.00157566451334914 0.00315132902669828 0.99842433548665 17 0.000783087320820566 0.00156617464164113 0.99921691267918 18 0.000391401452460286 0.000782802904920572 0.99960859854754 19 0.000195314520854545 0.00039062904170909 0.999804685479145 20 0.000102378861873731 0.000204757723747462 0.999897621138126 21 5.56853980804253e-05 0.000111370796160851 0.99994431460192 22 3.73313706641159e-05 7.46627413282319e-05 0.999962668629336 23 6.69654652638743e-05 0.000133930930527749 0.999933034534736 24 0.000158168276335494 0.000316336552670989 0.999841831723665 25 0.000601868763757595 0.00120373752751519 0.999398131236242 26 0.000595351383861799 0.00119070276772360 0.999404648616138 27 0.0007425658088238 0.0014851316176476 0.999257434191176 28 0.00121565640400221 0.00243131280800442 0.998784343595998 29 0.00160413678016420 0.00320827356032840 0.998395863219836 30 0.00275199744036401 0.00550399488072801 0.997248002559636 31 0.00852247017103958 0.0170449403420792 0.99147752982896 32 0.0201196901533702 0.0402393803067403 0.97988030984663 33 0.0561846865497263 0.112369373099453 0.943815313450274 34 0.19011332851186 0.38022665702372 0.80988667148814 35 0.188939007175251 0.377878014350501 0.81106099282475 36 0.191836420240463 0.383672840480926 0.808163579759537 37 0.198224596559515 0.396449193119030 0.801775403440485 38 0.209277569801611 0.418555139603222 0.79072243019839 39 0.237676426205206 0.475352852410413 0.762323573794794 40 0.242400978913519 0.484801957827038 0.757599021086481 41 0.242743154096922 0.485486308193844 0.757256845903078 42 0.247689667908432 0.495379335816863 0.752310332091568 43 0.265417898132187 0.530835796264373 0.734582101867813 44 0.276107726324625 0.55221545264925 0.723892273675375 45 0.31450472382757 0.62900944765514 0.68549527617243 46 0.483717751234171 0.967435502468342 0.516282248765829 47 0.46926497981226 0.93852995962452 0.53073502018774 48 0.454676669192868 0.909353338385735 0.545323330807132 49 0.470918231705178 0.941836463410357 0.529081768294822 50 0.5547705752002 0.8904588495996 0.4452294247998 51 0.699423295134779 0.601153409730442 0.300576704865221 52 0.750414457147422 0.499171085705155 0.249585542852578 53 0.718067475589597 0.563865048820807 0.281932524410403 54 0.645079126420355 0.70984174715929 0.354920873579645 55 0.5136393629401 0.9727212741198 0.4863606370599 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 21 0.411764705882353 NOK 5% type I error level 27 0.529411764705882 NOK 10% type I error level 28 0.549019607843137 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/10j5ol1258740167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/10j5ol1258740167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/12ark1258740167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/12ark1258740167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/2uliy1258740167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/2uliy1258740167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/326351258740167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/326351258740167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/4fzbm1258740167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/4fzbm1258740167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/5v38q1258740167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/5v38q1258740167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/69unh1258740167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/69unh1258740167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/7l5id1258740167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/7l5id1258740167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/8pswr1258740167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/8pswr1258740167.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/993x21258740167.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740264oavm88irgyoyho7/993x21258740167.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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