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: Thu, 19 Nov 2009 14:16:20 -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/19/t1258665473785sosjwpmbi5se.htm/, Retrieved Thu, 19 Nov 2009 22:18:04 +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/19/t1258665473785sosjwpmbi5se.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:

Dataseries X:

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4.3 96.2 4.1 96.8 3.9 109.9 3.8 88 3.7 91.1 3.7 106.4 4.1 68.6 4.1 100.1 3.8 108 3.7 106 3.5 108.6 3.6 91.5 4.1 99.2 3.8 98 3.7 96.6 3.6 102.8 3.3 96.9 3.4 110 3.7 70.5 3.7 101.9 3.4 109.6 3.3 107.8 3 113 3 93.8 3.3 108 3 102.8 2.9 116.3 2.8 89.2 2.5 106.7 2.6 112.1 2.8 74.2 2.7 108.8 2.4 111.5 2.2 118.8 2.1 118.9 2.1 97.6 2.3 116.4 2.1 107.9 2 121.2 1.9 97.9 1.7 113.4 1.8 117.6 2.1 79.6 2 115.9 1.8 115.7 1.7 129.1 1.6 123.3 1.6 96.7 1.8 121.2 1.7 118.2 1.7 102.1 1.5 125.4 1.5 116.7 1.5 121.3 1.8 85.3 1.8 114.2 1.7 124.4 1.7 131 1.8 118.3 2 99.6

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 unempl[t] = + 5.27350954525389 -0.0126416256269494proman[t] + 0.366770003948042M1[t] + 0.147528205533444M2[t] + 0.148660914596817M3[t] -0.0350531745152288M4[t] -0.116195958064706M5[t] + 0.0960089185315434M6[t] -0.0378519689375812M7[t] + 0.378004755217993M8[t] + 0.254054582521167M9[t] + 0.257968449222469M10[t] + 0.155666429147978M11[t] -0.0444982262546406t + 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) 5.27350954525389 0.534087 9.8739 0 0 proman -0.0126416256269494 0.006315 -2.0018 0.051227 0.025614 M1 0.366770003948042 0.186394 1.9677 0.055147 0.027573 M2 0.147528205533444 0.173759 0.849 0.400257 0.200128 M3 0.148660914596817 0.186955 0.7952 0.430598 0.215299 M4 -0.0350531745152288 0.161555 -0.217 0.829189 0.414594 M5 -0.116195958064706 0.170646 -0.6809 0.499338 0.249669 M6 0.0960089185315434 0.198414 0.4839 0.630765 0.315383 M7 -0.0378519689375812 0.191981 -0.1972 0.844567 0.422284 M8 0.378004755217993 0.17645 2.1423 0.037498 0.018749 M9 0.254054582521167 0.19509 1.3022 0.199316 0.099658 M10 0.257968449222469 0.213032 1.2109 0.232104 0.116052 M11 0.155666429147978 0.202344 0.7693 0.445637 0.222819 t -0.0444982262546406 0.003026 -14.7063 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.971427086516528 R-squared 0.94367058441799 Adjusted R-squared 0.92775140175351 F-TEST (value) 59.2788338639719 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.241960821749243 Sum Squared Residuals 2.69307180603216 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4.3 4.37965693763479 -0.0796569376347907 2 4.1 4.10833193758935 -0.00833193758935118 3 3.9 3.89936112468505 0.000638875314950401 4 3.8 3.94800041054855 -0.148000410548554 5 3.7 3.78317036130089 -0.0831703613008928 6 3.7 3.75746013955018 -0.057460139550176 7 4.1 4.0569544745251 0.0430455254749015 8 4.1 4.03010176517713 0.0698982348228741 9 3.8 3.76178452377276 0.0382154762272423 10 3.7 3.74648341547332 -0.0464834154733195 11 3.5 3.56681494251412 -0.0668149425141187 12 3.6 3.58282208533234 0.0171779146676650 13 4.1 3.80775334569822 0.292246654301775 14 3.8 3.55918327178133 0.240816728218674 15 3.7 3.53351603046779 0.166483969532212 16 3.6 3.22692563621402 0.373074363785985 17 3.3 3.1758702176089 0.124129782391101 18 3.4 3.17797157223747 0.222028427762529 19 3.7 3.49895667077821 0.201043329221793 20 3.7 3.47336812399293 0.226631876007071 21 3.4 3.20757920771395 0.192420792286047 22 3.3 3.18974977428912 0.110250225710876 23 3 2.97721307469985 0.022786925300146 24 3 3.01976763133466 -0.0197676313346639 25 3.3 3.16252832512538 0.137471674874616 26 3 2.96452475371628 0.035475246283718 27 2.9 2.7504972905612 0.149502709438802 28 2.8 2.86487302968484 -0.0648730296848397 29 2.5 2.51800357140911 -0.0180035714091081 30 2.6 2.61744544336519 -0.0174454433651901 31 2.8 2.91820394090281 -0.118203940902807 32 2.7 2.85216219211129 -0.152162192111291 33 2.4 2.64958140396706 -0.249581403967062 34 2.2 2.51671317733699 -0.316713177336993 35 2.1 2.36864876844517 -0.268648768445165 36 2.1 2.43775073889657 -0.337750738896569 37 2.3 2.52235995480332 -0.222359954803322 38 2.1 2.36607374796315 -0.266073747963153 39 2 2.15457460993346 -0.154574609933459 40 1.9 2.22091217167469 -0.320912171674693 41 1.7 1.89932596465286 -0.19932596465286 42 1.8 2.01393778736128 -0.213937787361281 43 2.1 2.31596044746159 -0.215960447461593 44 2 2.22842793510426 -0.228427935104264 45 1.8 2.06250786127819 -0.262507861278187 46 1.7 1.85252571832373 -0.152525718323727 47 1.6 1.7790469006309 -0.179046900630901 48 1.6 1.91514948690514 -0.315149486905136 49 1.8 1.92770143673828 -0.127701436738278 50 1.7 1.70188628894989 -0.00188628894988755 51 1.7 1.86205094435251 -0.162050944352505 52 1.5 1.33928875187790 0.160711248122102 53 1.5 1.32362988502824 0.17637011497176 54 1.5 1.43318505748588 0.0668149425141189 55 1.8 1.70992446633230 0.090075533667705 56 1.8 1.71593998361439 0.0840600163856097 57 1.7 1.41854700326804 0.281452996731960 58 1.7 1.29452791457684 0.405472085423163 59 1.8 1.30827631370996 0.491723686290039 60 2 1.34451005753130 0.655489942468704 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0236747283528351 0.0473494567056701 0.976325271647165 18 0.00569619103736995 0.0113923820747399 0.99430380896263 19 0.00314260325388835 0.00628520650777669 0.996857396746112 20 0.0015781762718727 0.0031563525437454 0.998421823728127 21 0.000751290883266664 0.00150258176653333 0.999248709116733 22 0.000311178535794732 0.000622357071589463 0.999688821464205 23 0.000307886877352475 0.000615773754704951 0.999692113122647 24 0.000758662736263702 0.00151732547252740 0.999241337263736 25 0.00490288175990124 0.00980576351980248 0.995097118240099 26 0.0151697409309748 0.0303394818619495 0.984830259069025 27 0.0249892238227263 0.0499784476454527 0.975010776177274 28 0.0406834781731418 0.0813669563462835 0.959316521826858 29 0.0532691388953546 0.106538277790709 0.946730861104645 30 0.0875458855125677 0.175091771025135 0.912454114487432 31 0.178507275930518 0.357014551861036 0.821492724069482 32 0.367234579219025 0.73446915843805 0.632765420780975 33 0.495433092053572 0.990866184107143 0.504566907946428 34 0.534715097976511 0.930569804046978 0.465284902023489 35 0.488356625493512 0.976713250987025 0.511643374506488 36 0.440450142908868 0.880900285817737 0.559549857091132 37 0.494320379543081 0.988640759086162 0.505679620456919 38 0.475259774719181 0.950519549438363 0.524740225280819 39 0.582387943488719 0.835224113022563 0.417612056511281 40 0.487244349558929 0.974488699117858 0.512755650441071 41 0.37697089576828 0.75394179153656 0.62302910423172 42 0.337718597117964 0.675437194235927 0.662281402882037 43 0.322981328999180 0.645962657998359 0.67701867100082 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 7 0.259259259259259 NOK 5% type I error level 11 0.407407407407407 NOK 10% type I error level 12 0.444444444444444 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/100ofd1258665376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/100ofd1258665376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/1oqon1258665376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/1oqon1258665376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/2fx8l1258665376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/2fx8l1258665376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/34iq31258665376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/34iq31258665376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/40aj91258665376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/40aj91258665376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/5qan61258665376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/5qan61258665376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/64hzb1258665376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/64hzb1258665376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/7fzvz1258665376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/7fzvz1258665376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/8lp361258665376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/8lp361258665376.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/9iws51258665376.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665473785sosjwpmbi5se/9iws51258665376.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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