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: Sun, 20 Dec 2009 03:57:13 -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/Dec/20/t12613067278prnpeifnrl3fyd.htm/, Retrieved Sun, 20 Dec 2009 11:58:59 +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/Dec/20/t12613067278prnpeifnrl3fyd.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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100.0 100.0 95.3 100.6 90.7 114.2 88.4 91.5 86.0 94.7 86.0 110.6 95.3 71.3 95.3 104.1 88.4 112.3 86.0 110.2 81.4 112.9 83.7 95.1 95.3 103.1 88.4 101.9 86.0 100.4 83.7 106.9 76.7 100.7 79.1 114.3 86.0 73.3 86.0 105.9 79.1 113.9 76.7 112.1 69.8 117.5 69.8 97.5 76.7 112.3 69.8 106.9 67.4 120.9 65.1 92.7 58.1 110.9 60.5 116.5 65.1 77.1 62.8 113.1 55.8 115.9 51.2 123.5 48.8 123.6 48.8 101.5 53.5 121.0 48.8 112.2 46.5 126.0 44.2 101.8 39.5 117.9 41.9 122.2 48.8 82.7 46.5 120.5 41.9 120.3 39.5 134.2 37.2 128.2 37.2 100.5 41.9 126.0 39.5 122.9 39.5 106.1 34.9 130.4 34.9 121.3 34.9 126.1 41.9 88.7 41.9 118.7 39.5 129.3 39.5 136.2 41.9 123.0 46.5 103.5

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 Werkloosheid[t] = + 122.578682792808 -0.282440508201765Productie[t] + 8.53269735149619M1[t] + 3.43605920340464M2[t] + 3.43543322256755M3[t] -0.79249080882931M4[t] -2.72395608114270M5[t] + 2.24731688263167M6[t] -0.883745028590917M7[t] + 8.78854064022755M8[t] + 5.9237896997247M9[t] + 5.98224706118411M10[t] + 3.63537681441102M11[t] -1.03449887127078t + 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) 122.578682792808 12.40093 9.8846 0 0 Productie -0.282440508201765 0.141067 -2.0022 0.051183 0.025591 M1 8.53269735149619 4.332484 1.9695 0.054939 0.027469 M2 3.43605920340464 4.040241 0.8505 0.399475 0.199738 M3 3.43543322256755 4.343856 0.7909 0.433078 0.216539 M4 -0.79249080882931 3.756521 -0.211 0.833847 0.416924 M5 -2.72395608114270 3.966113 -0.6868 0.495652 0.247826 M6 2.24731688263167 4.608881 0.4876 0.628145 0.314073 M7 -0.883745028590917 4.461179 -0.1981 0.843842 0.421921 M8 8.78854064022755 4.101621 2.1427 0.037462 0.018731 M9 5.9237896997247 4.533997 1.3065 0.197868 0.098934 M10 5.98224706118411 4.951828 1.2081 0.233188 0.116594 M11 3.63537681441102 4.703997 0.7728 0.443578 0.221789 t -1.03449887127078 0.070302 -14.7151 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.971426418637605 R-squared 0.943669286827083 Adjusted R-squared 0.927749737452129 F-TEST (value) 59.2773868531544 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.62474753413341 Sum Squared Residuals 1455.33810184603 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 101.832830452856 -1.83283045285648 2 95.3 95.5322291285732 -0.232229128573165 3 90.7 90.6559133649214 0.0440866350786144 4 88.4 91.8048899984338 -3.40488999843381 5 86 87.935116228604 -1.93511622860400 6 86 87.3810862406995 -1.38108624069954 7 95.3 94.3154374305355 0.984562569464472 8 95.3 93.6891755590654 1.61082444093464 9 88.4 87.4739135800373 0.926086419962743 10 86 87.0909971374496 -1.09099713744960 11 81.4 82.947038647261 -1.54703864726096 12 83.7 83.3046040075706 0.395395992429427 13 95.3 88.5432784221819 6.75672157781813 14 88.4 82.7510700126617 5.64892998733833 15 86 82.1396059228564 3.86039407714355 16 83.7 75.0413197168774 8.65868028312265 17 76.7 73.8264867241441 2.87351327585588 18 79.1 73.9220699051037 5.17793009489627 19 86 81.3365699588827 4.66343004111732 20 86 80.7667961890529 5.23320381094713 21 79.1 74.6080223116651 4.49197768833486 22 76.7 74.140373716617 2.55962628338305 23 69.8 69.2338258542836 0.566174145716451 24 69.8 70.212760332637 -0.412760332637047 25 76.7 73.5308392914764 3.16916070852364 26 69.8 68.9248810164035 0.87511898359645 27 67.4 63.935589049471 3.46441095052902 28 65.1 66.6379884780931 -1.53798847809310 29 58.1 58.5316070852368 -0.431607085236823 30 60.5 60.8867143318105 -0.386714331810541 31 65.1 67.8493095724667 -2.74930957246669 32 62.8 66.3192380747509 -3.51923807475087 33 55.8 61.6291548400123 -5.8291548400123 34 51.2 58.5065654678675 -7.30656546786754 35 48.8 55.0969522990035 -6.29695229900349 36 48.8 56.6690118445807 -7.86901184458068 37 53.5 58.6596204148717 -5.15962041487171 38 48.8 55.0139598676849 -6.2139598676849 39 46.5 50.0811560023927 -3.58115600239269 40 44.2 51.6537933982077 -7.45379339820773 41 39.5 44.1405370725752 -4.64053707257518 42 41.9 46.8628169798112 -4.96281697981119 43 48.8 53.8536562712875 -5.0536562712875 44 46.5 51.8151918588085 -5.31519185880851 45 41.9 47.9724301486752 -6.07243014867524 46 39.5 43.0704655748594 -3.57046557485937 47 37.2 41.3837395060261 -4.18373950602606 48 37.2 44.5374658975332 -7.33746589753315 49 41.9 44.8334314186136 -2.93343141861359 50 39.5 39.5778599746767 -0.0778599746767222 51 39.5 43.2877356603585 -3.78773566035850 52 34.9 31.162008408388 3.737991591612 53 34.9 30.7662528894399 4.13374711056012 54 34.9 33.347312542575 1.55268745742499 55 41.9 39.7450267668276 2.15497323317240 56 41.9 39.9095983183224 1.99040168167761 57 39.5 33.0164791196101 6.48352088038994 58 39.5 30.0915981032065 9.40840189679346 59 41.9 30.4384436934259 11.4615563065741 60 46.5 31.2761579176786 15.2238420823214 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0225939755667750 0.0451879511335499 0.977406024433225 18 0.00528927355010481 0.0105785471002096 0.994710726449895 19 0.00295188955602626 0.00590377911205251 0.997048110443974 20 0.00148356296112436 0.00296712592224872 0.998516437038876 21 0.000706830307742383 0.00141366061548477 0.999293169692258 22 0.000291744693991266 0.000583489387982532 0.99970825530601 23 0.000288759339290264 0.000577518678580528 0.99971124066071 24 0.000710136553411108 0.00142027310682222 0.999289863446589 25 0.00475837053817052 0.00951674107634104 0.99524162946183 26 0.0147525736211144 0.0295051472422289 0.985247426378886 27 0.0244674943428643 0.0489349886857286 0.975532505657136 28 0.0403010167088986 0.0806020334177972 0.959698983291101 29 0.0527062857576043 0.105412571515209 0.947293714242396 30 0.086549809116371 0.173099618232742 0.913450190883629 31 0.176487414791029 0.352974829582059 0.82351258520897 32 0.363647145206618 0.727294290413236 0.636352854793382 33 0.493483456328786 0.986966912657571 0.506516543671214 34 0.53312302277294 0.93375395445412 0.46687697722706 35 0.487478068819239 0.974956137638478 0.512521931180761 36 0.440327768163693 0.880655536327386 0.559672231836307 37 0.493463531920419 0.986927063840837 0.506536468079581 38 0.475226404430385 0.95045280886077 0.524773595569615 39 0.586243221366837 0.827513557266326 0.413756778633163 40 0.491971250204012 0.983942500408023 0.508028749795988 41 0.381044928958924 0.762089857917847 0.618955071041076 42 0.34219501301204 0.68439002602408 0.65780498698796 43 0.322834767689665 0.64566953537933 0.677165232310335 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/Dec/20/t12613067278prnpeifnrl3fyd/10ttt11261306627.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/10ttt11261306627.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/1yhyk1261306627.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/1yhyk1261306627.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/2rcyv1261306627.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/2rcyv1261306627.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/3vgig1261306627.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/3vgig1261306627.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/4va8x1261306627.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/4va8x1261306627.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/5lms11261306627.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/5lms11261306627.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/6pssi1261306627.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/6pssi1261306627.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/75r741261306627.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/75r741261306627.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/8j13v1261306627.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/8j13v1261306627.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/94hvf1261306627.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t12613067278prnpeifnrl3fyd/94hvf1261306627.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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