Investeringsgoederen met seizoenale en lineaire trend

*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: Sat, 20 Dec 2008 07:18:37 -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/20/t1229782807ksor6ns3dyaisv3.htm/, Retrieved Sat, 20 Dec 2008 15:20:17 +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/20/t1229782807ksor6ns3dyaisv3.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 «

97.7 0 101.5 0 119.6 0 108.1 0 117.8 0 125.5 0 89.2 0 92.3 0 104.6 0 122.8 0 96.0 0 94.6 0 93.3 0 101.1 0 114.2 0 104.7 0 113.3 0 118.2 0 83.6 0 73.9 0 99.5 0 97.7 0 103.0 0 106.3 0 92.2 0 101.8 0 122.8 0 111.8 0 106.3 0 121.5 0 81.9 0 85.4 0 110.9 0 117.3 0 106.3 0 105.5 0 101.3 0 105.9 0 126.3 0 111.9 0 108.9 0 127.2 0 94.2 0 85.7 0 116.2 0 107.2 0 110.6 0 112.0 0 104.5 0 112.0 0 132.8 0 110.8 0 128.7 0 136.8 0 94.9 0 88.8 0 123.2 0 125.3 0 122.7 0 125.7 0 116.3 0 118.7 0 142.0 0 127.9 0 131.9 0 152.3 0 110.8 1 99.1 1 135.0 1 133.2 1 131.0 1 133.9 1 119.9 1 136.9 1 148.9 1 145.1 1 142.4 1 159.6 1 120.7 1 109.0 1 142.0 1

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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 97.0556224899598 + 11.2265060240964X[t] -7.4573101931536M1[t] -0.263817173455726M2[t] + 17.7868187033849M3[t] + 5.12316886593993M4[t] + 8.93094759992352M5[t] + 21.7101549053356M6[t] -18.2001386498374M7[t] -24.4066456301396M8[t] + 3.42970453241538M9[t] + 4.92015681774718M10[t] -1.06492159112641M11[t] + 0.33507840887359t + 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) 97.0556224899598 2.902477 33.4389 0 0 X 11.2265060240964 2.416381 4.646 1.6e-05 8e-06 M1 -7.4573101931536 3.442468 -2.1663 0.033852 0.016926 M2 -0.263817173455726 3.440839 -0.0767 0.939113 0.469556 M3 17.7868187033849 3.439671 5.1711 2e-06 1e-06 M4 5.12316886593993 3.438964 1.4897 0.140985 0.070492 M5 8.93094759992352 3.43872 2.5972 0.011544 0.005772 M6 21.7101549053356 3.438937 6.313 0 0 M7 -18.2001386498374 3.448464 -5.2778 2e-06 1e-06 M8 -24.4066456301396 3.446921 -7.0807 0 0 M9 3.42970453241538 3.445838 0.9953 0.323163 0.161581 M10 4.92015681774718 3.569144 1.3785 0.172627 0.086313 M11 -1.06492159112641 3.568476 -0.2984 0.766303 0.383151 t 0.33507840887359 0.039855 8.4074 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.946900247831163 R-squared 0.896620079342718 Adjusted R-squared 0.876561288767424 F-TEST (value) 44.6996081831119 F-TEST (DF numerator) 13 F-TEST (DF denominator) 67 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.18039698072037 Sum Squared Residuals 2559.21955823293 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 97.7 89.93339070568 7.76660929432007 2 101.5 97.4619621342513 4.0380378657487 3 119.6 115.847676419966 3.75232358003445 4 108.1 103.519104991394 4.58089500860584 5 117.8 107.661962134251 10.1380378657488 6 125.5 120.776247848537 4.72375215146305 7 89.2 81.2010327022375 7.99896729776248 8 92.3 75.329604130809 16.9703958691910 9 104.6 103.501032702238 1.09896729776248 10 122.8 105.326563396443 17.4734366035571 11 96 99.6765633964429 -3.67656339644291 12 94.6 101.076563396443 -6.47656339644294 13 93.3 93.954331612163 -0.65433161216293 14 101.1 101.482903040734 -0.382903040734384 15 114.2 119.868617326449 -5.66861732644865 16 104.7 107.540045897877 -2.84004589787722 17 113.3 111.682903040734 1.61709695926563 18 118.2 124.79718875502 -6.59718875502009 19 83.6 85.2219736087206 -1.62197360872060 20 73.9 79.350545037292 -5.45054503729202 21 99.5 107.521973608721 -8.0219736087206 22 97.7 109.347504302926 -11.6475043029260 23 103 103.697504302926 -0.697504302925992 24 106.3 105.097504302926 1.20249569707401 25 92.2 97.975272518646 -5.775272518646 26 101.8 105.503843947217 -3.70384394721745 27 122.8 123.889558232932 -1.08955823293173 28 111.8 111.560986804360 0.239013195639700 29 106.3 115.703843947217 -9.40384394721745 30 121.5 128.818129661503 -7.31812966150317 31 81.9 89.2429145152037 -7.34291451520367 32 85.4 83.3714859437751 2.02851405622491 33 110.9 111.542914515204 -0.64291451520367 34 117.3 113.368445209409 3.93155479059094 35 106.3 107.718445209409 -1.41844520940907 36 105.5 109.118445209409 -3.61844520940906 37 101.3 101.996213425129 -0.696213425129082 38 105.9 109.524784853701 -3.62478485370051 39 126.3 127.910499139415 -1.61049913941481 40 111.9 115.581927710843 -3.68192771084337 41 108.9 119.724784853701 -10.8247848537005 42 127.2 132.839070567986 -5.63907056798624 43 94.2 93.2638554216867 0.936144578313256 44 85.7 87.3924268502582 -1.69242685025817 45 116.2 115.563855421687 0.63614457831325 46 107.2 117.389386115892 -10.1893861158921 47 110.6 111.739386115892 -1.13938611589215 48 112 113.139386115892 -1.13938611589214 49 104.5 106.017154331612 -1.51715433161215 50 112 113.545725760184 -1.54572576018359 51 132.8 131.931440045898 0.868559954102127 52 110.8 119.602868617326 -8.80286861732645 53 128.7 123.745725760184 4.95427423981639 54 136.8 136.860011474469 -0.0600114744693075 55 94.9 97.2847963281698 -2.38479632816982 56 88.8 91.4133677567413 -2.61336775674126 57 123.2 119.584796328170 3.61520367183018 58 125.3 121.410327022375 3.88967297762478 59 122.7 115.760327022375 6.93967297762479 60 125.7 117.160327022375 8.53967297762478 61 116.3 110.038095238095 6.26190476190477 62 118.7 117.566666666667 1.13333333333333 63 142 135.952380952381 6.04761904761904 64 127.9 123.623809523810 4.27619047619048 65 131.9 127.766666666667 4.13333333333333 66 152.3 140.880952380952 11.4190476190476 67 110.8 112.532243258749 -1.73224325874929 68 99.1 106.660814687321 -7.56081468732072 69 135 134.832243258749 0.167756741250714 70 133.2 136.657773952955 -3.45777395295468 71 131 131.007773952955 -0.00777395295467587 72 133.9 132.407773952955 1.49222604704533 73 119.9 125.285542168675 -5.38554216867468 74 136.9 132.814113597246 4.08588640275388 75 148.9 151.199827882960 -2.29982788296041 76 145.1 138.871256454389 6.22874354561101 77 142.4 143.014113597246 -0.614113597246129 78 159.6 156.128399311532 3.47160068846814 79 120.7 116.553184165232 4.14681583476765 80 109 110.681755593804 -1.68175559380379 81 142 138.853184165232 3.14681583476764 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0679500661330789 0.135900132266158 0.93204993386692 18 0.0562681616482577 0.112536323296515 0.943731838351742 19 0.0244700081105663 0.0489400162211327 0.975529991889434 20 0.528756300729183 0.942487398541634 0.471243699270817 21 0.413459314069142 0.826918628138284 0.586540685930858 22 0.908999498905026 0.182001002189948 0.0910005010949741 23 0.965419921938953 0.0691601561220932 0.0345800780610466 24 0.993204243672018 0.0135915126559640 0.00679575632798202 25 0.988855294678707 0.0222894106425869 0.0111447053212934 26 0.986338552118666 0.0273228957626681 0.0136614478813340 27 0.99067621002009 0.0186475799598179 0.00932378997990894 28 0.99373712631715 0.0125257473657006 0.0062628736828503 29 0.991438472178545 0.0171230556429102 0.0085615278214551 30 0.986657896685759 0.0266842066284826 0.0133421033142413 31 0.979082350002679 0.041835299994642 0.020917649997321 32 0.98837280028799 0.0232543994240216 0.0116271997120108 33 0.991430061751568 0.0171398764968631 0.00856993824843156 34 0.998551003543606 0.00289799291278701 0.00144899645639350 35 0.99834929357619 0.00330141284761935 0.00165070642380967 36 0.99741228612407 0.00517542775185974 0.00258771387592987 37 0.99803055664731 0.00393888670538083 0.00196944335269041 38 0.99697682216905 0.00604635566190172 0.00302317783095086 39 0.996819064537213 0.00636187092557393 0.00318093546278696 40 0.995674685493246 0.00865062901350735 0.00432531450675367 41 0.99537219405862 0.0092556118827596 0.0046278059413798 42 0.993252395502617 0.0134952089947662 0.0067476044973831 43 0.99398110591906 0.0120377881618822 0.00601889408094108 44 0.995654127332381 0.00869174533523771 0.00434587266761886 45 0.9960083465523 0.00798330689540141 0.00399165344770071 46 0.996741475261297 0.00651704947740574 0.00325852473870287 47 0.994806098134425 0.0103878037311492 0.00519390186557461 48 0.992539407629173 0.0149211847416546 0.0074605923708273 49 0.98837746145644 0.0232450770871205 0.0116225385435603 50 0.98125560583888 0.0374887883222387 0.0187443941611193 51 0.976972596532317 0.0460548069353658 0.0230274034676829 52 0.992762183293376 0.0144756334132471 0.00723781670662353 53 0.996984146888507 0.0060317062229852 0.0030158531114926 54 0.995946555379189 0.00810688924162216 0.00405344462081108 55 0.996620407383857 0.00675918523228512 0.00337959261614256 56 0.992544126113688 0.0149117477726250 0.00745587388631249 57 0.986792093032 0.0264158139360009 0.0132079069680004 58 0.97669655303788 0.0466068939242392 0.0233034469621196 59 0.963339305930744 0.073321388138511 0.0366606940692555 60 0.945316270201081 0.109367459597838 0.0546837297989192 61 0.960550275100945 0.0788994497981092 0.0394497248990546 62 0.96960765996147 0.0607846800770602 0.0303923400385301 63 0.953022117964658 0.0939557640706848 0.0469778820353424 64 0.98816133417682 0.0236773316463603 0.0118386658231801 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 14 0.291666666666667 NOK 5% type I error level 37 0.770833333333333 NOK 10% type I error level 42 0.875 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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