eigen reeks 2

*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: Thu, 27 Nov 2008 01:43:57 -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/Nov/27/t1227775653pibo9eg7xww5b2g.htm/, Retrieved Thu, 27 Nov 2008 08:47:44 +0000

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/Nov/27/t1227775653pibo9eg7xww5b2g.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}, }

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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97,8 0 107,4 0 117,5 0 105,6 0 97,4 0 99,5 0 98,0 0 104,3 0 100,6 0 101,1 0 103,9 0 96,9 0 95,5 0 108,4 0 117,0 0 103,8 0 100,8 0 110,6 0 104,0 0 112,6 0 107,3 0 98,9 0 109,8 0 104,9 0 102,2 0 123,9 0 124,9 0 112,7 0 121,9 0 100,6 0 104,3 0 120,4 0 107,5 0 102,9 0 125,6 0 107,5 0 108,8 0 128,4 0 121,1 0 119,5 0 128,7 0 108,7 0 105,5 0 119,8 0 111,3 0 110,6 0 120,1 0 97,5 0 107,7 0 127,3 0 117,2 0 119,8 0 116,2 0 111,0 0 112,4 0 130,6 0 109,1 0 118,8 0 123,9 0 101,6 0 112,8 0 128,0 0 129,6 0 125,8 0 119,5 0 115,7 0 113,6 0 129,7 0 112,0 0 116,8 0 127,0 0 112,1 1 114,2 1 121,1 1 131,6 1 125,0 1 120,4 1 117,7 1 117,5 1 120,6 1 127,5 1 112,3 1 124,5 1 115,2 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 5 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation C[t] = + 92.2386051335235 -3.45408348457351D[t] + 3.15155129202308M1[t] + 17.9344741163253M2[t] + 19.7031112263417M3[t] + 12.7431769077867M4[t] + 11.4118140178031M5[t] + 5.25187969924812M6[t] + 3.74908823783596M7[t] + 15.2748682049952M8[t] + 6.02921960072595M9[t] + 3.75499956788522M10[t] + 13.9522081064731M11[t] + 0.288505747126437t + 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) 92.2386051335235 2.160907 42.6851 0 0 D -3.45408348457351 1.912074 -1.8065 0.075145 0.037572 M1 3.15155129202308 2.63012 1.1983 0.234859 0.11743 M2 17.9344741163253 2.628716 6.8225 0 0 M3 19.7031112263417 2.627623 7.4985 0 0 M4 12.7431769077867 2.626843 4.8511 7e-06 4e-06 M5 11.4118140178031 2.626374 4.3451 4.6e-05 2.3e-05 M6 5.25187969924812 2.626218 1.9998 0.049405 0.024703 M7 3.74908823783596 2.626374 1.4275 0.157889 0.078944 M8 15.2748682049952 2.626843 5.8149 0 0 M9 6.02921960072595 2.627623 2.2946 0.024762 0.012381 M10 3.75499956788522 2.628716 1.4285 0.157608 0.078804 M11 13.9522081064731 2.63012 5.3048 1e-06 1e-06 t 0.288505747126437 0.028643 10.0726 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.887940440757827 R-squared 0.788438226333204 Adjusted R-squared 0.749148182652228 F-TEST (value) 20.0671252171439 F-TEST (DF numerator) 13 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.89712167843117 Sum Squared Residuals 1678.72605133523 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 97.8 95.6786621726733 2.12133782732672 2 107.4 110.750090744102 -3.3500907441016 3 117.5 112.807233601244 4.6927663987556 4 105.6 106.135805029816 -0.535805029815878 5 97.4 105.092947886959 -7.69294788695878 6 99.5 99.2215193155302 0.278480684469789 7 98 98.0072336012445 -0.00723360124447175 8 104.3 109.821519315530 -5.52151931553021 9 100.6 100.864376458387 -0.264376458387351 10 101.1 98.878662172673 2.22133782732694 11 103.9 109.364376458387 -5.46437645838736 12 96.9 95.7006740990407 1.19932590095930 13 95.5 99.1407311381903 -3.64073113819025 14 108.4 114.212159709619 -5.81215970961885 15 117 116.269302566762 0.730697433238259 16 103.8 109.597873995333 -5.79787399533317 17 100.8 108.555016852476 -7.75501685247602 18 110.6 102.683588281047 7.91641171895255 19 104 101.469302566762 2.53069743323827 20 112.6 113.283588281047 -0.683588281047447 21 107.3 104.326445423905 2.97355457609541 22 98.9 102.340731138190 -3.44073113819029 23 109.8 112.826445423905 -3.02644542390459 24 104.9 99.162743064558 5.73725693544206 25 102.2 102.602800103708 -0.402800103707509 26 123.9 117.674228675136 6.22577132486388 27 124.9 119.731371532279 5.16862846772103 28 112.7 113.059942960850 -0.359942960850407 29 121.9 112.017085817993 9.88291418200675 30 100.6 106.145657246565 -5.54565724656469 31 104.3 104.931371532279 -0.631371532278978 32 120.4 116.745657246565 3.65434275343533 33 107.5 107.788514389422 -0.288514389421827 34 102.9 105.802800103708 -2.90280010370754 35 125.6 116.288514389422 9.31148561057817 36 107.5 102.624812030075 4.87518796992481 37 108.8 106.064869069225 2.73513093077524 38 128.4 121.136297640653 7.26370235934663 39 121.1 123.193440497796 -2.09344049779623 40 119.5 116.522011926368 2.97798807363235 41 128.7 115.479154783511 13.2208452164895 42 108.7 109.607726212082 -0.907726212081925 43 105.5 108.393440497796 -2.89344049779622 44 119.8 120.207726212082 -0.407726212081924 45 111.3 111.250583354939 0.0494166450609272 46 110.6 109.264869069225 1.33513093077521 47 120.1 119.750583354939 0.349416645060921 48 97.5 106.086880995592 -8.58688099559243 49 107.7 109.526938034742 -1.82693803474199 50 127.3 124.598366606171 2.70163339382938 51 117.2 126.655509463313 -9.45550946331346 52 119.8 119.984080891885 -0.184080891884893 53 116.2 118.941223749028 -2.74122374902774 54 111 113.069795177599 -2.06979517759917 55 112.4 111.855509463313 0.544490536686545 56 130.6 123.669795177599 6.93020482240083 57 109.1 114.712652320456 -5.61265232045632 58 118.8 112.726938034742 6.07306196525797 59 123.9 123.212652320456 0.687347679543698 60 101.6 109.548949961110 -7.94894996110968 61 112.8 112.989007000259 -0.189007000259241 62 128 128.060435571688 -0.0604355716878555 63 129.6 130.117578428831 -0.517578428830714 64 125.8 123.446149857402 2.35385014259786 65 119.5 122.403292714545 -2.90329271454498 66 115.7 116.531864143116 -0.831864143116408 67 113.6 115.317578428831 -1.71757842883071 68 129.7 127.131864143116 2.56813585688358 69 112 118.174721285974 -6.17472128597355 70 116.8 116.189007000259 0.610992999740726 71 127 126.674721285974 0.325278714026447 72 112.1 109.556935442053 2.54306455794658 73 114.2 112.996992481203 1.20300751879703 74 121.1 128.068421052632 -6.9684210526316 75 131.6 130.125563909774 1.47443609022555 76 125 123.454135338346 1.54586466165413 77 120.4 122.411278195489 -2.01127819548871 78 117.7 116.539849624060 1.16015037593986 79 117.5 115.325563909774 2.17443609022556 80 120.6 127.139849624060 -6.53984962406014 81 127.5 118.182706766917 9.31729323308271 82 112.3 116.196992481203 -3.89699248120301 83 124.5 126.682706766917 -2.18270676691729 84 115.2 113.019004407571 2.18099559242935 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0768705571668881 0.153741114333776 0.923129442833112 18 0.412194794695032 0.824389589390064 0.587805205304968 19 0.319146933731328 0.638293867462656 0.680853066268672 20 0.294782495640924 0.589564991281849 0.705217504359076 21 0.219367303444563 0.438734606889127 0.780632696555437 22 0.211318428525791 0.422636857051582 0.78868157147421 23 0.170183442405822 0.340366884811644 0.829816557594178 24 0.146756252428253 0.293512504856507 0.853243747571747 25 0.0964689595472329 0.192937919094466 0.903531040452767 26 0.222179778558716 0.444359557117432 0.777820221441284 27 0.169203706906738 0.338407413813476 0.830796293093262 28 0.122872056162506 0.245744112325013 0.877127943837494 29 0.473363528967128 0.946727057934256 0.526636471032872 30 0.697201595265308 0.605596809469384 0.302798404734692 31 0.651840352826005 0.69631929434799 0.348159647173995 32 0.600797994105928 0.798404011788143 0.399202005894071 33 0.548835362381777 0.902329275236446 0.451164637618223 34 0.543658044599758 0.912683910800485 0.456341955400242 35 0.661841443235482 0.676317113529036 0.338158556764518 36 0.624422325789532 0.751155348420936 0.375577674210468 37 0.550982166930046 0.898035666139909 0.449017833069954 38 0.561135450462487 0.877729099075026 0.438864549537513 39 0.588912237011747 0.822175525976506 0.411087762988253 40 0.519097410124336 0.96180517975133 0.480902589875664 41 0.839467975199414 0.321064049601173 0.160532024800586 42 0.81203947051121 0.37592105897758 0.18796052948879 43 0.798504796162699 0.402990407674602 0.201495203837301 44 0.746529235608276 0.506941528783449 0.253470764391724 45 0.695015452468935 0.60996909506213 0.304984547531065 46 0.625935671886349 0.748128656227302 0.374064328113651 47 0.561621599056811 0.876756801886378 0.438378400943189 48 0.7173851740803 0.565229651839399 0.282614825919700 49 0.66509599890042 0.66980800219916 0.33490400109958 50 0.647210677879183 0.705578644241635 0.352789322120817 51 0.794879616928947 0.410240766142107 0.205120383071053 52 0.738530130006896 0.522939739986207 0.261469869993104 53 0.684687622726505 0.63062475454699 0.315312377273495 54 0.625034053147491 0.749931893705018 0.374965946852509 55 0.541448399142022 0.917103201715955 0.458551600857977 56 0.598216263874669 0.803567472250662 0.401783736125331 57 0.630949819786359 0.738100360427283 0.369050180213641 58 0.663338531504261 0.673322936991477 0.336661468495739 59 0.611469466317315 0.777061067365371 0.388530533682685 60 0.702731867953659 0.594536264092681 0.297268132046341 61 0.607285446860816 0.785429106278369 0.392714553139184 62 0.594849735657524 0.810300528684953 0.405150264342476 63 0.481962920263644 0.963925840527287 0.518037079736356 64 0.36924621170827 0.73849242341654 0.63075378829173 65 0.260656269620029 0.521312539240057 0.739343730379971 66 0.162727971366479 0.325455942732958 0.837272028633521 67 0.0954066049353497 0.190813209870699 0.90459339506465 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 0 0 OK 10% type I error level 0 0 OK

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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