Paper Multiple Lineair Regression

*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, 19 Dec 2010 12:08:20 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/19/t1292760495sgfoaa1xu6l6a06.htm/, Retrieved Sun, 19 Dec 2010 13:08:26 +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/2010/Dec/19/t1292760495sgfoaa1xu6l6a06.htm/}, year = {2010}, } @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 = {2010}, 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 «

631.923 21.454 97.06 130.678 654.294 23.899 97.73 120.877 671.833 24.939 98 137.114 586.840 23.580 97.76 134.406 600.969 24.562 97.48 120.262 625.568 24.696 97.77 130.846 558.110 23.785 97.96 120.343 630.577 23.812 98.22 98.881 628.654 21.917 98.51 115.678 603.184 19.713 98.19 120.796 656.255 19.282 98.37 94.261 600.730 18.788 98.31 89.151 670.326 21.453 98.6 119.880 678.423 24.482 98.96 131.468 641.502 27.474 99.11 155.089 625.311 27.264 99.64 149.581 628.177 27.349 100.02 122.788 589.767 30.632 99.98 143.900 582.471 29.429 100.32 112.115 636.248 30.084 100.44 109.600 599.885 26.290 100.51 117.446 621.694 24.379 101 118.456 637.406 23.335 100.88 101.901 595.994 21.346 100.55 89.940 696.308 21.106 100.82 129.143 674.201 24.514 101.5 126.102 648.861 28.353 102.15 143.048 649.605 30.805 102.39 142.258 672.392 31.348 102.54 131.011 598.396 34.556 102.85 146.471 613.177 33.855 103.47 114.073 638.104 34.787 103.56 114.642 615.632 32.529 103.69 118.226 634.465 29.998 103.49 111.338 etc...

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation WERKL[t] = -229.959847776113 -5.11489725323219VAC[t] + 9.08718983433677CPI[t] + 0.717907557025763INSCHR[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) -229.959847776113 121.51843 -1.8924 0.062057 0.031028 VAC -5.11489725323219 0.76071 -6.7238 0 0 CPI 9.08718983433677 1.272436 7.1416 0 0 INSCHR 0.717907557025763 0.195796 3.6666 0.000441 0.00022 Multiple Linear Regression - Regression Statistics Multiple R 0.639302254189665 R-squared 0.408707372211987 Adjusted R-squared 0.386533898669937 F-TEST (value) 18.4322664393065 F-TEST (DF numerator) 3 F-TEST (DF denominator) 80 p-value 3.48979267705829e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 32.5867879364038 Sum Squared Residuals 84951.8998409721 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 631.923 636.122515610781 -4.19951561078083 2 654.294 622.668797049227 31.6252029507726 3 671.833 631.459510164564 40.3734898354361 4 586.84 634.28563630704 -47.4456363070398 5 600.969 616.564309564179 -15.5953095641791 6 625.568 626.112531967764 -0.544531967764283 7 558.11 624.958586362541 -66.8485863625412 8 630.577 611.775421504745 18.8015784952554 9 628.654 636.162130086939 -7.50813008693905 10 603.184 648.201713762933 -45.0177137629328 11 656.255 632.992251623578 23.2627483764220 12 600.73 631.305271860213 -30.5752718602128 13 670.326 642.369937052151 27.9560629478488 14 678.423 638.467414383287 39.9555856167133 15 641.502 641.484414681272 0.0175853187278406 16 625.311 643.420518892552 -18.1095188925514 17 628.177 627.203987587683 0.973012412316605 18 589.767 625.204756655877 -35.4377566558766 19 582.471 611.628930895126 -29.1579308951255 20 636.248 607.563598468459 28.6844015315410 21 599.885 633.23832462805 -33.3533246280498 22 621.694 648.190702930397 -26.4967029303975 23 637.406 640.55523327609 -3.14923327608999 24 595.994 639.143098977853 -43.1490989778525 25 696.308 670.96834553198 25.3396544680199 26 674.201 657.532907899399 16.6680921006015 27 648.861 655.969152197918 -7.10815219791766 28 649.605 645.041202723183 4.56379727681725 29 672.392 635.55258569596 36.8394143040406 30 598.396 633.059874987853 -34.6638749878533 31 613.177 619.020706627137 -5.84370662713716 32 638.104 615.479958872163 22.6240411278373 33 615.632 630.783712232805 -15.1517122328052 34 634.465 636.967131961075 -2.50213196107492 35 638.686 638.682404801156 0.00359519884366832 36 604.243 623.900181652532 -19.6571816525323 37 706.669 659.987589926673 46.6814100733267 38 677.185 630.331340942343 46.8536590576568 39 644.328 632.512192752115 11.8158072478853 40 664.825 615.870037958228 48.9549620417723 41 605.707 597.503075793434 8.20392420656638 42 600.136 599.424713135498 0.711286864501612 43 612.166 589.502055295395 22.6639447046054 44 599.659 584.31983047839 15.3391695216096 45 634.21 613.243785053041 20.9662149469592 46 618.234 612.36137958672 5.87262041328069 47 613.576 625.868452505915 -12.2924525059149 48 627.2 612.349654212365 14.8503457876350 49 668.973 645.93683460439 23.0361653956103 50 651.479 624.320016753782 27.1589832462179 51 619.661 628.826006643577 -9.165006643577 52 644.26 606.731268105075 37.5287318949250 53 579.936 618.233004382386 -38.2970043823858 54 601.752 616.375394581715 -14.6233945817146 55 595.376 606.97366150835 -11.5976615083503 56 588.902 595.874600082813 -6.97260008281347 57 634.341 573.275401426119 61.0655985738806 58 594.305 619.530048105428 -25.2250481054281 59 606.2 606.941170898473 -0.741170898473257 60 610.926 622.801254113527 -11.8752541135268 61 633.685 641.422577762173 -7.73757776217326 62 639.696 637.797551023606 1.89844897639415 63 659.451 647.07510917297 12.3758908270298 64 593.248 647.527074099121 -54.2790740991208 65 606.677 629.448264196804 -22.7712641968042 66 599.434 654.436775282043 -55.0027752820429 67 569.578 650.657821385403 -81.0798213854033 68 629.873 619.00160858707 10.8713914129303 69 613.438 631.181538683885 -17.7435386838850 70 604.172 651.381733389248 -47.2097333892476 71 658.328 644.071466561392 14.2565334386082 72 612.633 644.676481964009 -32.0434819640091 73 707.372 678.706635167931 28.6653648320686 74 739.77 686.786154599285 52.9838454007151 75 777.535 697.403444977011 80.1315550229886 76 685.03 691.45657114709 -6.42657114709063 77 730.234 675.060831847406 55.1731681525944 78 714.154 684.616813117607 29.5371868823935 79 630.872 691.535247981449 -60.6632479814492 80 719.492 675.43336482083 44.0586351791699 81 677.023 695.348851702982 -18.3258517029816 82 679.272 685.977410237784 -6.7054102377838 83 718.317 686.652402397938 31.6645976020622 84 645.672 684.386741572752 -38.7147415727519 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.904101303034988 0.191797393930024 0.0958986969650119 8 0.922829973568106 0.154340052863787 0.0771700264318935 9 0.868359188138302 0.263281623723395 0.131640811861698 10 0.814819350280803 0.370361299438394 0.185180649719197 11 0.810493891699106 0.379012216601789 0.189506108300894 12 0.773785114081294 0.452429771837411 0.226214885918706 13 0.769336016538968 0.461327966922064 0.230663983461032 14 0.7175783066677 0.5648433866646 0.2824216933323 15 0.687440322906143 0.625119354187714 0.312559677093857 16 0.680879656682284 0.638240686635432 0.319120343317716 17 0.615069788643841 0.769860422712318 0.384930211356159 18 0.644545948218433 0.710908103563133 0.355454051781567 19 0.612089564571425 0.77582087085715 0.387910435428575 20 0.599551084313306 0.800897831373388 0.400448915686694 21 0.587755751039009 0.824488497921983 0.412244248960991 22 0.539504057549451 0.920991884901099 0.460495942450549 23 0.474302600943534 0.948605201887068 0.525697399056466 24 0.51338969614685 0.9732206077063 0.48661030385315 25 0.536119016355027 0.927761967289945 0.463880983644973 26 0.491258769102618 0.982517538205236 0.508741230897382 27 0.434062291727918 0.868124583455836 0.565937708272082 28 0.372557658702776 0.745115317405553 0.627442341297224 29 0.375690092855115 0.75138018571023 0.624309907144885 30 0.417403452060477 0.834806904120955 0.582596547939523 31 0.36127602848668 0.72255205697336 0.63872397151332 32 0.325588646941811 0.651177293883622 0.674411353058189 33 0.297272835408469 0.594545670816939 0.70272716459153 34 0.255506891174175 0.511013782348349 0.744493108825825 35 0.219608397036028 0.439216794072056 0.780391602963972 36 0.236294082076382 0.472588164152764 0.763705917923618 37 0.246421912262523 0.492843824525045 0.753578087737477 38 0.257407989991929 0.514815979983857 0.742592010008071 39 0.207720317026395 0.415440634052789 0.792279682973606 40 0.222347611901117 0.444695223802234 0.777652388098883 41 0.177498714957939 0.354997429915879 0.82250128504206 42 0.142643594519405 0.285287189038811 0.857356405480595 43 0.116576941367062 0.233153882734124 0.883423058632938 44 0.0897143052075147 0.179428610415029 0.910285694792485 45 0.0679471632592475 0.135894326518495 0.932052836740752 46 0.0494790317659758 0.0989580635319515 0.950520968234024 47 0.0449507747227703 0.0899015494455407 0.95504922527723 48 0.034184430907291 0.068368861814582 0.965815569092709 49 0.0237558334159300 0.0475116668318599 0.97624416658407 50 0.0169677382578426 0.0339354765156853 0.983032261742157 51 0.0142664232354666 0.0285328464709333 0.985733576764533 52 0.0136591632384268 0.0273183264768537 0.986340836761573 53 0.0231855008118650 0.0463710016237299 0.976814499188135 54 0.0194633177434053 0.0389266354868105 0.980536682256595 55 0.0151592405492589 0.0303184810985178 0.98484075945074 56 0.0110177648752358 0.0220355297504716 0.988982235124764 57 0.0269439848514477 0.0538879697028954 0.973056015148552 58 0.0261385782920360 0.0522771565840721 0.973861421707964 59 0.0178522331333161 0.0357044662666322 0.982147766866684 60 0.0133898988017589 0.0267797976035177 0.98661010119824 61 0.00988980023745973 0.0197796004749195 0.99011019976254 62 0.00614593354396835 0.0122918670879367 0.993854066456032 63 0.00380435823656268 0.00760871647312535 0.996195641763437 64 0.0117354459472965 0.0234708918945931 0.988264554052704 65 0.00922723137802734 0.0184544627560547 0.990772768621973 66 0.0119795375177935 0.0239590750355870 0.988020462482206 67 0.0271098981445650 0.0542197962891299 0.972890101855435 68 0.0209701446270546 0.0419402892541092 0.979029855372945 69 0.0127909330847354 0.0255818661694708 0.987209066915265 70 0.130602667387895 0.26120533477579 0.869397332612105 71 0.095670032942404 0.191340065884808 0.904329967057596 72 0.0940966977777474 0.188193395555495 0.905903302222253 73 0.0751802579464388 0.150360515892878 0.924819742053561 74 0.0626332042075244 0.125266408415049 0.937366795792476 75 0.472613381142656 0.945226762285313 0.527386618857344 76 0.383224402790821 0.766448805581641 0.61677559720918 77 0.257224854807937 0.514449709615874 0.742775145192063 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0140845070422535 NOK 5% type I error level 18 0.253521126760563 NOK 10% type I error level 24 0.338028169014085 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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