werkloosheid - Oceanië

*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, 21 Dec 2008 02:47:03 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/21/t12298529213d2agaoomfjsdj2.htm/, Retrieved Sun, 21 Dec 2008 10:48:41 +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/2008/Dec/21/t12298529213d2agaoomfjsdj2.htm/}, year = {2008}, } @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 = {2008}, 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:

» Textbox « » Textfile « » CSV «

180144 40,6 173666 63,6 165688 66,8 161570 71,5 156145 99,4 153730 78,2 182698 57,2 200765 86,5 176512 66,1 166618 75 158644 55 159585 66,8 163095 41,4 159044 53,3 155511 71,4 153745 68,2 150569 84,1 150605 94 179612 91,4 194690 79,9 189917 40,7 184128 60,3 175335 49,1 179566 42 181140 54,3 177876 39,3 175041 47,8 169292 74,5 166070 78,8 166972 81,4 206348 66 215706 88,8 202108 54,4 195411 75,8 193111 51,6 195198 53 198770 62,7 194163 52,3 190420 30,5 189733 49,9 186029 53,8 191531 65,3 232571 62,7 243477 55,4 227247 66,2 217859 67,2 208679 42,4 213188 56,3 216234 44,9 213586 30 209465 54,4 204045 47,8 200237 63,6 203666 72,5 241476 82,2 260307 67,9 243324 67,8 244460 65,6 233575 78,1 237217 41,6 235243 64,3 230354 55,9 227184 78,3 221678 69,8 217142 59,3 219452 103,6 256446 109,7 265845 76,3 248624 81,8 241114 99,6 229245 100,6 231805 79,9 219277 49,3 219313 62,7 212610 101,3 214771 101,2 211142 83,3 211457 127,8 240048 103,7 240636 91,5 230580 95,1 208795 109 197922 132,6 194596 79,5

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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation werkloosheid[t] = + 171222.029233325 -316.729605880591`Oceanië`[t] + 6054.0529953463M1[t] + 1308.00721670290M2[t] -77.0047002834496M3[t] -2650.94603994353M4[t] -5824.58634515159M5[t] -865.201090459052M6[t] + 30390.0223464562M7[t] + 39905.3600429454M8[t] + 20789.6507051609M9[t] + 14838.7123772535M10[t] + 3021.61600273844M11[t] + 1027.80408687881t + 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) 171222.029233325 6714.866745 25.4989 0 0 `Oceanië` -316.729605880591 86.114804 -3.678 0.000458 0.000229 M1 6054.0529953463 6795.384513 0.8909 0.376031 0.188016 M2 1308.00721670290 6792.540118 0.1926 0.847857 0.423929 M3 -77.0047002834496 6800.003636 -0.0113 0.990997 0.495498 M4 -2650.94603994353 6840.479396 -0.3875 0.699534 0.349767 M5 -5824.58634515159 6918.863163 -0.8418 0.402744 0.201372 M6 -865.201090459052 7268.027035 -0.119 0.905583 0.452791 M7 30390.0223464562 7054.998997 4.3076 5.3e-05 2.6e-05 M8 39905.3600429454 6959.106864 5.7343 0 0 M9 20789.6507051609 6792.674736 3.0606 0.003131 0.001566 M10 14838.7123772535 6961.410282 2.1316 0.036555 0.018277 M11 3021.61600273844 6846.198436 0.4414 0.660315 0.330158 t 1027.80408687881 63.667117 16.1434 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.919194102983335 R-squared 0.844917798959339 Adjusted R-squared 0.816116818766073 F-TEST (value) 29.3364251247567 F-TEST (DF numerator) 13 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12629.2986150177 Sum Squared Residuals 11164942845.5102 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 180144 165444.664316798 14699.3356832019 2 173666 154441.641689780 19224.3583102202 3 165688 153070.899120854 12617.1008791456 4 161570 150036.132720434 11533.8672795656 5 156145 139053.540498037 17091.4595019634 6 153730 151755.397484276 1974.60251572352 7 182698 190689.746731563 -7991.74673156298 8 200765 191952.711062630 8812.2889373703 9 176512 180326.089771688 -3814.08977168806 10 166618 172584.062038322 -5966.06203832218 11 158644 168129.361868298 -9485.36186829777 12 159585 162398.140603047 -2813.14060304718 13 163095 177524.929674639 -14429.9296746393 14 159044 170037.605672896 -10993.6056728957 15 155511 163947.591976349 -8436.59197634943 16 153745 163414.989462386 -9669.98946238606 17 150569 156233.152510555 -5664.15251055543 18 150605 159084.718753909 -8479.71875390893 19 179612 192191.243252993 -12579.2432529925 20 194690 206376.775503987 -11686.7755039874 21 189917 200704.670803601 -10787.6708036008 22 184128 189573.636287313 -5445.63628731262 23 175335 182331.715585539 -6996.71558553902 24 179566 182586.683871432 -3020.6838714316 25 181140 185772.766801325 -4632.76680132544 26 177876 186805.469197770 -8929.46919776973 27 175041 183756.059717677 -8715.05971767717 28 169292 173753.241987884 -4461.24198788412 29 166070 170245.468464268 -4175.46846426833 30 166972 175409.16083055 -8437.16083055014 31 206348 212569.824284905 -6221.82428490532 32 215706 215891.531054196 -185.531054195876 33 202108 208699.124245583 -6591.12424558252 34 195411 196997.976438709 -1586.97643870922 35 193111 193873.540613383 -762.540613383318 36 195198 191436.307249291 3761.69275070912 37 198770 195445.887154474 3324.11284552574 38 194163 195021.633363868 -858.633363867807 39 190420 201569.130941957 -11149.1309419572 40 189733 193878.439335092 -4145.43933509242 41 186029 190497.357653829 -4468.35765382887 42 191531 192842.156527773 -1311.15652777342 43 232571 225948.681026857 6622.31897314298 44 243477 238803.948933153 4673.05106684662 45 227247 217295.363938737 9951.63606126269 46 217859 212055.500091828 5803.49990817193 47 208679 209121.102030031 -442.102030030528 48 213188 202724.748592431 10463.2514075693 49 216234 213417.323181695 2816.67681830545 50 213586 214418.352617551 -832.352617550763 51 209465 206332.942403957 3132.05759604319 52 204045 206877.220549987 -2832.22054998744 53 200237 199727.056558745 509.943441255147 54 203666 202895.352407979 770.647592021066 55 241476 232106.102754731 9369.89724526872 56 260307 247178.477902192 13128.5220978082 57 243324 229122.245611874 14201.7543881259 58 244460 224895.916503783 19564.0834962172 59 233575 210147.504142639 23427.4958573608 60 237217 219714.322841421 17502.6771585789 61 235243 219606.417870157 15636.5821298432 62 230354 218548.704867789 11805.2951322108 63 227184 211096.753865956 16087.2461340436 64 221678 212242.81826316 9435.1817368398 65 217142 213422.642906577 3719.35709342285 66 219452 205378.710707638 14073.2892923617 67 256446 235729.687635561 20716.3123644392 68 265845 256851.598255341 8993.40174465942 69 248624 237021.680172092 11602.3198279084 70 241114 226460.758946388 14653.2410536115 71 229245 215354.737052872 13890.2629471283 72 231805 219917.227978740 11887.7720212597 73 219277 236691.011000911 -17414.0110009115 74 219313 228728.592590347 -9415.59259034696 75 212610 216145.621973249 -3535.62197324861 76 214771 214631.157681055 139.842318944598 77 211142 218154.781407989 -7012.78140798875 78 211457 210047.503287874 1409.49671212622 79 240048 249963.71431339 -9915.7143133901 80 240636 264370.957288501 -23734.9572885013 81 230580 245142.825456426 -14562.8254564255 82 208795 235817.149693657 -27022.1496936567 83 197922 217553.038707239 -19631.0387072385 84 194596 232377.568863638 -37781.5688636383 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0279393974372567 0.0558787948745134 0.972060602562743 18 0.0174004012850225 0.0348008025700451 0.982599598714977 19 0.00481148625341641 0.00962297250683282 0.995188513746584 20 0.00163907453077871 0.00327814906155742 0.998360925469221 21 0.0272224992610859 0.0544449985221718 0.972777500738914 22 0.0602233689877562 0.120446737975512 0.939776631012244 23 0.0830755311555476 0.166151062311095 0.916924468844452 24 0.0762957362706195 0.152591472541239 0.92370426372938 25 0.0758540667978005 0.151708133595601 0.9241459332022 26 0.0498920460415071 0.0997840920830141 0.950107953958493 27 0.0337885682356044 0.0675771364712087 0.966211431764396 28 0.0255955741022070 0.0511911482044141 0.974404425897793 29 0.0154054996931230 0.0308109993862461 0.984594500306877 30 0.0121260470751758 0.0242520941503516 0.987873952924824 31 0.0183507559983312 0.0367015119966623 0.981649244001669 32 0.0159101259466158 0.0318202518932316 0.984089874053384 33 0.0153240897261080 0.0306481794522161 0.984675910273892 34 0.0148219476475862 0.0296438952951723 0.985178052352414 35 0.0172777379591701 0.0345554759183402 0.98272226204083 36 0.0164871365746113 0.0329742731492227 0.983512863425389 37 0.0131840976087569 0.0263681952175137 0.986815902391243 38 0.0108154247055063 0.0216308494110127 0.989184575294494 39 0.00965254508863068 0.0193050901772614 0.99034745491137 40 0.00879665663586 0.01759331327172 0.99120334336414 41 0.00802556962670085 0.0160511392534017 0.9919744303733 42 0.00959374587760221 0.0191874917552044 0.990406254122398 43 0.0186553522413188 0.0373107044826377 0.981344647758681 44 0.0185811756031608 0.0371623512063215 0.98141882439684 45 0.0274003483482936 0.0548006966965872 0.972599651651706 46 0.0315698833708759 0.0631397667417519 0.968430116629124 47 0.0293236602375894 0.0586473204751787 0.97067633976241 48 0.0315581773940748 0.0631163547881497 0.968441822605925 49 0.0327732706377883 0.0655465412755767 0.967226729362212 50 0.0330483429236176 0.0660966858472352 0.966951657076382 51 0.0351138558432100 0.0702277116864201 0.96488614415679 52 0.0519296163903055 0.103859232780611 0.948070383609695 53 0.130536807570454 0.261073615140907 0.869463192429546 54 0.245628894661628 0.491257789323257 0.754371105338372 55 0.500283292577968 0.999433414844065 0.499716707422032 56 0.593865367759027 0.812269264481945 0.406134632240973 57 0.819559736745277 0.360880526509445 0.180440263254723 58 0.802068837057312 0.395862325885377 0.197931162942688 59 0.763425353649756 0.473149292700488 0.236574646350244 60 0.701867273296067 0.596265453407865 0.298132726703933 61 0.657373912266978 0.685252175466043 0.342626087733022 62 0.63524281756716 0.729514364865679 0.364757182432840 63 0.526628762338092 0.946742475323816 0.473371237661908 64 0.461246035914199 0.922492071828398 0.538753964085801 65 0.509130249321852 0.981739501356297 0.490869750678148 66 0.682875793583306 0.634248412833388 0.317124206416694 67 0.697700042039087 0.604599915921827 0.302299957960913 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0392156862745098 NOK 5% type I error level 19 0.372549019607843 NOK 10% type I error level 31 0.607843137254902 NOK

Charts produced by software:

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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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