Werkloosheid- Azië

*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, 17 Dec 2008 07:07:42 -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/17/t1229522980z22yx4ojgxw8h1d.htm/, Retrieved Wed, 17 Dec 2008 15:10:03 +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/17/t1229522980z22yx4ojgxw8h1d.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 1235,8 173666 1147,1 165688 1376,9 161570 1157,7 156145 1506 153730 1271,3 182698 1240,2 200765 1408,3 176512 1334,6 166618 1601,2 158644 1566,4 159585 1297,5 163095 1487,6 159044 1320,9 155511 1514 153745 1290,9 150569 1392,5 150605 1288,2 179612 1304,4 194690 1297,8 189917 1211 184128 1454 175335 1405,7 179566 1160,8 181140 1492,1 177876 1263 175041 1376,3 169292 1368,6 166070 1427,6 166972 1339,8 206348 1248,3 215706 1309,8 202108 1424 195411 1590,5 193111 1423,1 195198 1355,3 198770 1515 194163 1385,6 190420 1430 189733 1494,2 186029 1580,9 191531 1369,8 232571 1407,5 243477 1388,3 227247 1478,5 217859 1630,4 208679 1413,5 213188 1493,8 216234 1641,3 213586 1465 209465 1725,1 204045 1628,4 200237 1679,8 203666 1876 241476 1669,4 260307 1712,4 243324 1768,8 244460 1820,5 233575 1776,2 237217 1693,7 235243 1799,1 230354 1917,5 227184 1887,2 221678 1787,8 217142 1803,8 219452 2196,4 256446 1759,5 265845 2002,6 248624 2056,8 241114 1851,1 229245 1984,3 231805 1725,3 219277 2096,6 219313 1792,2 212610 2029,9 214771 1785,3 211142 2026,5 211457 1930,8 240048 1845,5 240636 1943,1 230580 2066,8 208795 2354,4 197922 2190,7 194596 1929,6

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 5 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation werkloosheid[t] = + 98514.9949379193 + 64.8271222961237`Azië`[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) 98514.9949379193 14554.952269 6.7685 0 0 `Azië` 64.8271222961237 9.005445 7.1987 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.622285856514731 R-squared 0.387239687218273 Adjusted R-squared 0.379767000477032 F-TEST (value) 51.8206771710579 F-TEST (DF numerator) 1 F-TEST (DF denominator) 82 p-value 2.63510990805571e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 23194.5345792005 Sum Squared Residuals 44114887616.3496 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 180144 178628.352671469 1515.64732853101 2 173666 172878.186923803 787.813076197305 3 165688 187775.459627452 -22087.4596274520 4 161570 173565.354420142 -11995.3544201417 5 156145 196144.641115882 -39999.6411158815 6 153730 180929.715512981 -27199.7155129813 7 182698 178913.592009572 3784.40799042814 8 200765 189811.031267550 10953.9687324498 9 176512 185033.272354326 -8521.27235432592 10 166618 202316.183158473 -35698.1831584725 11 158644 200060.199302567 -41416.1993025674 12 159585 182628.186117140 -23043.1861171397 13 163095 194951.822065633 -31856.8220656329 14 159044 184145.140778869 -25101.1407788690 15 155511 196663.258094251 -41152.2580942505 16 153745 182200.327109985 -28455.3271099853 17 150569 188786.762735271 -38217.7627352715 18 150605 182025.293879786 -31420.2938797858 19 179612 183075.493260983 -3463.493260983 20 194690 182647.634253829 12042.3657461714 21 189917 177020.640038525 12896.3599614750 22 184128 192773.630756483 -8645.6307564831 23 175335 189642.480749580 -14307.4807495803 24 179566 173766.318499260 5799.68150074037 25 181140 195243.544115965 -14103.5441159654 26 177876 180391.650397923 -2515.65039792347 27 175041 187736.563354074 -12695.5633540743 28 169292 187237.394512394 -17945.3945123941 29 166070 191062.194727865 -24992.1947278654 30 166972 185370.373390266 -18398.3733902658 31 206348 179438.691700170 26909.3082998295 32 215706 183425.559721382 32280.4402786179 33 202108 190828.817087599 11279.1829124006 34 195411 201622.532949904 -6211.53294990398 35 193111 190770.472677533 2340.52732246713 36 195198 186375.193785856 8822.80621414431 37 198770 196728.085216547 2041.91478345335 38 194163 188339.455591428 5823.54440857177 39 190420 191217.779821376 -797.779821376132 40 189733 195379.681072787 -5646.68107278728 41 186029 201000.192575861 -14971.1925758612 42 191531 187315.187059149 4215.81294085052 43 232571 189759.169569713 42811.8304302866 44 243477 188514.488821628 54962.5111783722 45 227247 194361.895252738 32885.1047472619 46 217859 204209.135129519 13649.8648704807 47 208679 190148.13230349 18530.8676965099 48 213188 195353.750223869 17834.2497761312 49 216234 204915.750762547 11318.2492374529 50 213586 193486.729101740 20099.2708982595 51 209465 210348.263610962 -883.263610962228 52 204045 204079.480884927 -34.4808849270783 53 200237 207411.594970948 -7174.59497094783 54 203666 220130.676365447 -16464.6763654473 55 241476 206737.392899068 34738.6071009319 56 260307 209524.959157801 50782.0408421985 57 243324 213181.208855303 30142.7911446972 58 244460 216532.771078012 27927.2289219876 59 233575 213660.929560294 19914.0704397058 60 237217 208312.691970864 28904.3080291360 61 235243 215145.470660875 20097.5293391246 62 230354 222821.001940736 7532.99805926357 63 227184 220856.740135164 6327.25986483612 64 221678 214412.924178929 7265.07582107082 65 217142 215450.158135667 1691.84186433283 66 219452 240901.286349125 -21449.2863491253 67 256446 212578.316617949 43867.6833820511 68 265845 228337.790048137 37507.2099518634 69 248624 231851.420076586 16772.5799234135 70 241114 218516.481020274 22597.5189797262 71 229245 227151.453710117 2093.54628988251 72 231805 210361.229035421 21443.7709645785 73 219277 234431.539543972 -15154.5395439722 74 219313 214698.163517032 4614.83648296786 75 212610 230107.570486821 -17497.5704868207 76 214771 214250.856373189 520.143626811123 77 211142 229887.158271014 -18745.1582710139 78 211457 223683.202667275 -12226.2026672749 79 240048 218153.449135416 21894.5508645845 80 240636 224480.576271517 16155.4237284828 81 230580 232499.691299548 -1919.69129954771 82 208795 251143.971671913 -42348.9716719129 83 197922 240531.771752037 -42609.7717520374 84 194596 223605.410120520 -29009.4101205195 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0705126495466477 0.141025299093295 0.929487350453352 6 0.0576273525282811 0.115254705056562 0.942372647471719 7 0.0521777614150371 0.104355522830074 0.947822238584963 8 0.213782682771964 0.427565365543928 0.786217317228036 9 0.134125331582700 0.268250663165401 0.8658746684173 10 0.08751455473567 0.17502910947134 0.91248544526433 11 0.0665906799383064 0.133181359876613 0.933409320061694 12 0.0496939262554628 0.0993878525109257 0.950306073744537 13 0.0323671311022896 0.0647342622045792 0.96763286889771 14 0.0241910456819122 0.0483820913638244 0.975808954318088 15 0.0208698810885134 0.0417397621770268 0.979130118911487 16 0.0214867040587752 0.0429734081175504 0.978513295941225 17 0.0261986981070086 0.0523973962140173 0.973801301892991 18 0.0328670464372102 0.0657340928744204 0.96713295356279 19 0.0281382095334587 0.0562764190669173 0.971861790466541 20 0.0498712456831351 0.0997424913662702 0.950128754316865 21 0.0494065447079478 0.0988130894158955 0.950593455292052 22 0.0514367390346997 0.102873478069399 0.9485632609653 23 0.0417309557617093 0.0834619115234187 0.95826904423829 24 0.0292282971308885 0.058456594261777 0.970771702869111 25 0.0284896016470720 0.0569792032941441 0.971510398352928 26 0.0212235104227697 0.0424470208455395 0.97877648957723 27 0.0174602821206059 0.0349205642412117 0.982539717879394 28 0.016035462653559 0.032070925307118 0.98396453734644 29 0.0182779906181964 0.0365559812363928 0.981722009381804 30 0.0215780774357720 0.0431561548715439 0.978421922564228 31 0.0500578461565727 0.100115692313145 0.949942153843427 32 0.146524790374871 0.293049580749743 0.853475209625129 33 0.196212793249008 0.392425586498016 0.803787206750992 34 0.236157103863029 0.472314207726059 0.76384289613697 35 0.243159072432503 0.486318144865006 0.756840927567497 36 0.247706556290270 0.495413112580539 0.75229344370973 37 0.270670946994566 0.541341893989132 0.729329053005434 38 0.277242657951963 0.554485315903926 0.722757342048037 39 0.293658266044472 0.587316532088943 0.706341733955528 40 0.325558952495799 0.651117904991598 0.674441047504201 41 0.391091797451492 0.782183594902984 0.608908202548508 42 0.465077602600852 0.930155205201704 0.534922397399148 43 0.674958731409182 0.650082537181635 0.325041268590818 44 0.870401414638326 0.259197170723348 0.129598585361674 45 0.90040649882087 0.199187002358261 0.0995935011791304 46 0.897524928627722 0.204950142744557 0.102475071372278 47 0.899850070832432 0.200299858335136 0.100149929167568 48 0.899384000536073 0.201231998927854 0.100615999463927 49 0.889473181901375 0.221053636197251 0.110526818098625 50 0.895119563920942 0.209760872158115 0.104880436079058 51 0.889919120088661 0.220161759822678 0.110080879911339 52 0.913760359831503 0.172479280336995 0.0862396401684973 53 0.951016810490575 0.0979663790188498 0.0489831895094249 54 0.960494584357636 0.0790108312847276 0.0395054156423638 55 0.961625351739195 0.0767492965216109 0.0383746482608054 56 0.981560996017796 0.0368780079644079 0.0184390039822040 57 0.978389364679862 0.0432212706402763 0.0216106353201382 58 0.975028907476157 0.0499421850476861 0.0249710925238431 59 0.963559101122242 0.0728817977555156 0.0364408988777578 60 0.951105133476565 0.0977897330468707 0.0488948665234353 61 0.9310913631322 0.137817273735599 0.0689086368677993 62 0.902769918820886 0.194460162358228 0.097230081179114 63 0.865078001478235 0.269843997043529 0.134921998521765 64 0.82694272156495 0.346114556870100 0.173057278435050 65 0.797619968291855 0.404760063416291 0.202380031708145 66 0.763848659937813 0.472302680124374 0.236151340062187 67 0.799361584133765 0.40127683173247 0.200638415866235 68 0.943433903166565 0.113132193666870 0.0565660968334348 69 0.974352228594912 0.0512955428101766 0.0256477714050883 70 0.973261916465924 0.0534761670681524 0.0267380835340762 71 0.961985641033243 0.0760287179335141 0.0380143589667571 72 0.93900717545719 0.12198564908562 0.06099282454281 73 0.908111626446219 0.183776747107563 0.0918883735537814 74 0.852551135403902 0.294897729192196 0.147448864596098 75 0.782513813175678 0.434972373648644 0.217486186824322 76 0.709182467967583 0.581635064064834 0.290817532032417 77 0.603933079447648 0.792133841104703 0.396066920552352 78 0.504865935609164 0.990268128781673 0.495134064390836 79 0.394493144320052 0.788986288640104 0.605506855679948 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 11 0.146666666666667 NOK 10% type I error level 29 0.386666666666667 NOK

Charts produced by software:

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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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