*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: Wed, 18 Nov 2009 10:09:25 -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/18/t1258564626y2biqqty6hyk7ke.htm/, Retrieved Wed, 18 Nov 2009 18:17:18 +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/18/t1258564626y2biqqty6hyk7ke.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:

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Dataseries X:

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8.9 1.4 8.8 1.2 8.3 1 7.5 1.7 7.2 2.4 7.4 2 8.8 2.1 9.3 2 9.3 1.8 8.7 2.7 8.2 2.3 8.3 1.9 8.5 2 8.6 2.3 8.5 2.8 8.2 2.4 8.1 2.3 7.9 2.7 8.6 2.7 8.7 2.9 8.7 3 8.5 2.2 8.4 2.3 8.5 2.8 8.7 2.8 8.7 2.8 8.6 2.2 8.5 2.6 8.3 2.8 8 2.5 8.2 2.4 8.1 2.3 8.1 1.9 8 1.7 7.9 2 7.9 2.1 8 1.7 8 1.8 7.9 1.8 8 1.8 7.7 1.3 7.2 1.3 7.5 1.3 7.3 1.2 7 1.4 7 2.2 7 2.9 7.2 3.1 7.3 3.5 7.1 3.6 6.8 4.4 6.4 4.1 6.1 5.1 6.5 5.8 7.7 5.9 7.9 5.4 7.5 5.5 6.9 4.8 6.6 3.2 6.9 2.7

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 werkloosheid[t] = + 8.88296297277623 -0.0140292732825159inflatie[t] -0.0308076438884565t + 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) 8.88296297277623 0.167938 52.8944 0 0 inflatie -0.0140292732825159 0.068795 -0.2039 0.839136 0.419568 t -0.0308076438884565 0.004597 -6.7017 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.73636290216635 R-squared 0.54223032368685 Adjusted R-squared 0.526168229781126 F-TEST (value) 33.7583833633047 F-TEST (DF numerator) 2 F-TEST (DF denominator) 57 p-value 2.13029593965075e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.511654878348778 Sum Squared Residuals 14.9220707286718 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.9 8.83251434629228 0.0674856537077226 2 8.8 8.8045125570603 -0.00451255706029862 3 8.3 8.77651076782835 -0.476510767828345 4 7.5 8.73588263264213 -1.23588263264213 5 7.2 8.69525449745591 -1.49525449745591 6 7.4 8.67005856288046 -1.27005856288046 7 8.8 8.63784799166375 0.162152008336249 8 9.3 8.60844327510355 0.691556724896453 9 9.3 8.5804414858716 0.719558514128407 10 8.7 8.53700749602887 0.162992503971126 11 8.2 8.51181156145342 -0.311811561453423 12 8.3 8.48661562687797 -0.186615626877972 13 8.5 8.45440505566127 0.0455949443387354 14 8.6 8.41938862978805 0.180611370211946 15 8.5 8.38156634925834 0.118433650741661 16 8.2 8.35637041468289 -0.156370414682889 17 8.1 8.32696569812268 -0.226965698122684 18 7.9 8.29054634492122 -0.390546344921221 19 8.6 8.25973870103276 0.340261298967235 20 8.7 8.2261252024878 0.473874797512195 21 8.7 8.1939146312711 0.506085368728903 22 8.5 8.17433040600865 0.325669593991347 23 8.4 8.14211983479195 0.257880165208056 24 8.5 8.10429755426223 0.39570244573777 25 8.7 8.07348991037377 0.626510089626225 26 8.7 8.04268226648532 0.657317733514682 27 8.6 8.02029218656637 0.579707813433629 28 8.5 7.9838728333649 0.516127166635092 29 8.3 7.95025933481995 0.349740665180053 30 8 7.92366047291625 0.0763395270837537 31 8.2 7.89425575635604 0.305744243643958 32 8.1 7.86485103979584 0.235148960204163 33 8.1 7.83965510522039 0.260344894779613 34 8 7.81165331598843 0.188346684011567 35 7.9 7.77663689011522 0.123363109884778 36 7.9 7.74442631889851 0.155573681101487 37 8 7.71923038432306 0.280769615676936 38 8 7.68701981310636 0.312980186893644 39 7.9 7.6562121692179 0.243787830782101 40 8 7.62540452532944 0.374595474670557 41 7.7 7.60161151808224 0.098388481917756 42 7.2 7.57080387419379 -0.370803874193788 43 7.5 7.53999623030533 -0.0399962303053312 44 7.3 7.51059151374513 -0.210591513745127 45 7 7.47697801520017 -0.476978015200167 46 7 7.4349469526857 -0.434946952685697 47 7 7.39431881749948 -0.39431881749948 48 7.2 7.36070531895452 -0.160705318954520 49 7.3 7.32428596575306 -0.0242859657530572 50 7.1 7.29207539453635 -0.192075394536349 51 6.8 7.25004433202188 -0.45004433202188 52 6.4 7.22344547011818 -0.823445470118178 53 6.1 7.1786085529472 -1.07860855294721 54 6.5 7.13798041776099 -0.637980417760988 55 7.7 7.10576984654428 0.594230153455721 56 7.9 7.08197683929708 0.818023160702919 57 7.5 7.04976626808037 0.450233731919627 58 6.9 7.02877911548968 -0.128779115489677 59 6.6 7.02041830885325 -0.420418308853247 60 6.9 6.99662530160605 -0.0966253016060481 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.332309836605121 0.664619673210242 0.667690163394879 7 0.989472226206846 0.0210555475863075 0.0105277737931538 8 0.997392323533058 0.00521535293388309 0.00260767646694155 9 0.995209867119807 0.0095802657603866 0.0047901328801933 10 0.992853473282038 0.0142930534359248 0.00714652671796238 11 0.995732979282546 0.00853404143490875 0.00426702071745438 12 0.99768555189925 0.00462889620150193 0.00231444810075097 13 0.996429416620366 0.00714116675926826 0.00357058337963413 14 0.993389709200488 0.0132205815990248 0.0066102907995124 15 0.98896652513653 0.0220669497269384 0.0110334748634692 16 0.989460009777674 0.0210799804446530 0.0105399902223265 17 0.99229393597184 0.0154121280563215 0.00770606402816077 18 0.996699289112933 0.00660142177413332 0.00330071088706666 19 0.994842153728483 0.0103156925430347 0.00515784627151734 20 0.992235928672093 0.0155281426558145 0.00776407132790727 21 0.987988065415175 0.0240238691696490 0.0120119345848245 22 0.982578549966736 0.0348429000665282 0.0174214500332641 23 0.976195491394032 0.0476090172119358 0.0238045086059679 24 0.963859586803748 0.0722808263925048 0.0361404131962524 25 0.946794823252412 0.106410353495176 0.0532051767475879 26 0.924966017667194 0.150067964665613 0.0750339823328063 27 0.900038748967948 0.199922502064104 0.0999612510320519 28 0.866934864317057 0.266130271365886 0.133065135682943 29 0.829882029548065 0.340235940903870 0.170117970451935 30 0.82515136818986 0.349697263620280 0.174848631810140 31 0.78392907105448 0.43214185789104 0.21607092894552 32 0.740963950463474 0.518072099073051 0.259036049536526 33 0.688407897366369 0.623184205267262 0.311592102633631 34 0.630422551861365 0.73915489627727 0.369577448138635 35 0.571069661974462 0.857860676051076 0.428930338025538 36 0.505001789192672 0.989996421614656 0.494998210807328 37 0.441556296013005 0.88311259202601 0.558443703986995 38 0.392994679955304 0.785989359910608 0.607005320044696 39 0.351299853949564 0.702599707899128 0.648700146050436 40 0.36295428440533 0.72590856881066 0.63704571559467 41 0.344982254031659 0.689964508063318 0.655017745968341 42 0.312992585796739 0.625985171593479 0.68700741420326 43 0.28501702128419 0.57003404256838 0.71498297871581 44 0.251452554716664 0.502905109433328 0.748547445283336 45 0.219079484874540 0.438158969749079 0.78092051512546 46 0.201414267720149 0.402828535440299 0.79858573227985 47 0.187036777956486 0.374073555912972 0.812963222043514 48 0.169796157336045 0.33959231467209 0.830203842663955 49 0.198956884204240 0.397913768408479 0.80104311579576 50 0.301497157008336 0.602994314016673 0.698502842991664 51 0.350636299698282 0.701272599396565 0.649363700301718 52 0.456375544650265 0.91275108930053 0.543624455349735 53 0.361217041183297 0.722434082366593 0.638782958816703 54 0.73464686174508 0.53070627650984 0.26535313825492 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 6 0.122448979591837 NOK 5% type I error level 17 0.346938775510204 NOK 10% type I error level 18 0.36734693877551 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/107tm61258564161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/107tm61258564161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/1xpmc1258564161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/1xpmc1258564161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/2slbl1258564161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/2slbl1258564161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/3v4ry1258564161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/3v4ry1258564161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/49brp1258564161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/49brp1258564161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/57g6b1258564161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/57g6b1258564161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/6aek41258564161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/6aek41258564161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/7ibmk1258564161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/7ibmk1258564161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/81spn1258564161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/81spn1258564161.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/98uay1258564161.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564626y2biqqty6hyk7ke/98uay1258564161.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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