WS7 Multiple Regression 1 (eenvoudigste model)

*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: Fri, 20 Nov 2009 06:48:22 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5.htm/, Retrieved Fri, 20 Nov 2009 14:52:31 +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/2009/Nov/20/t1258725139ot68frauw2ws9l5.htm/}, year = {2009}, } @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 = {2009}, 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:

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96.8 92.9 114.1 107.7 110.3 103.5 103.9 91.1 101.6 79.8 94.6 71.9 95.9 82.9 104.7 90.1 102.8 100.7 98.1 90.7 113.9 108.8 80.9 44.1 95.7 93.6 113.2 107.4 105.9 96.5 108.8 93.6 102.3 76.5 99 76.7 100.7 84 115.5 103.3 100.7 88.5 109.9 99 114.6 105.9 85.4 44.7 100.5 94 114.8 107.1 116.5 104.8 112.9 102.5 102 77.7 106 85.2 105.3 91.3 118.8 106.5 106.1 92.4 109.3 97.5 117.2 107 92.5 51.1 104.2 98.6 112.5 102.2 122.4 114.3 113.3 99.4 100 72.5 110.7 92.3 112.8 99.4 109.8 85.9 117.3 109.4 109.1 97.6 115.9 104.7 96 56.9 99.8 86.7 116.8 108.5 115.7 103.4 99.4 86.2 94.3 71 91 75.9 93.2 87.1 103.1 102 94.1 88.5 91.8 87.8 102.7 100.8 82.6 50.6

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Totind[t] = + 59.9181251189299 + 0.494158060055627Bouw[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) 59.9181251189299 3.602081 16.6343 0 0 Bouw 0.494158060055627 0.039156 12.6204 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.856186966701773 R-squared 0.733056121949982 Adjusted R-squared 0.728453641293947 F-TEST (value) 159.274134262530 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.93290987300361 Sum Squared Residuals 1411.34878928024 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 96.8 105.825408898098 -9.02540889809781 2 114.1 113.138948186921 0.961051813079056 3 110.3 111.063484334687 -0.763484334687316 4 103.9 104.935924389998 -1.03592438999753 5 101.6 99.351938311369 2.24806168863105 6 94.6 95.4480896369295 -0.848089636929504 7 95.9 100.883828297541 -4.98382829754139 8 104.7 104.441766329942 0.258233670058096 9 102.8 109.679841766532 -6.87984176653156 10 98.1 104.738261165975 -6.63826116597529 11 113.9 113.682522052982 0.21747794701787 12 80.9 81.710495567383 -0.810495567383055 13 95.7 106.171319540137 -10.4713195401366 14 113.2 112.990700768904 0.209299231095741 15 105.9 107.604377914298 -1.70437791429792 16 108.8 106.171319540137 2.62868045986340 17 102.3 97.7212167131854 4.57878328681462 18 99 97.8200483251965 1.17995167480349 19 100.7 101.427402163603 -0.727402163602581 20 115.5 110.964652722676 4.53534727732381 21 100.7 103.651113433853 -2.95111343385290 22 109.9 108.839773064437 1.06022693556301 23 114.6 112.249463678821 2.35053632117917 24 85.4 82.0069904034164 3.39300959658357 25 100.5 106.368982764159 -5.86898276415886 26 114.8 112.842453350888 1.95754664911243 27 116.5 111.705889812760 4.79411018724037 28 112.9 110.569326274632 2.33067372536832 29 102 98.3142063852521 3.68579361474787 30 106 102.020391835669 3.97960816433066 31 105.3 105.034756002009 0.265243997991337 32 118.8 112.545958514854 6.2540414851458 33 106.1 105.578329868070 0.52167013193014 34 109.3 108.098535974354 1.20146402564645 35 117.2 112.793037544882 4.40696245511799 36 92.5 85.1696019877725 7.33039801222755 37 104.2 108.642109840415 -4.44210984041473 38 112.5 110.421078856615 2.078921143385 39 122.4 116.400391383288 5.99960861671192 40 113.3 109.037436288459 4.26256371154075 41 100 95.7445844729629 4.25541552703713 42 110.7 105.528914062064 5.17108593793572 43 112.8 109.037436288459 3.76256371154075 44 109.8 102.366302477708 7.43369752229172 45 117.3 113.979016889016 3.32098311098448 46 109.1 108.147951780359 0.952048219640884 47 115.9 111.656474006754 4.24352599324594 48 96 88.0357187360951 7.96428126390491 49 99.8 102.761628925753 -2.96162892575278 50 116.8 113.534274634965 3.26572536503455 51 115.7 111.014068528682 4.68593147131825 52 99.4 102.514549895725 -3.11454989572496 53 94.3 95.0033473828794 -0.703347382879434 54 91 97.424721877152 -6.42472187715201 55 93.2 102.959292149775 -9.75929214977502 56 103.1 110.322247244604 -7.22224724460388 57 94.1 103.651113433853 -9.55111343385291 58 91.8 103.305202791814 -11.5052027918140 59 102.7 109.729257572537 -7.02925757253712 60 82.6 84.9225229577446 -2.32252295774464 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.615546064036884 0.768907871926231 0.384453935963116 6 0.445983508389495 0.891967016778989 0.554016491610505 7 0.374125733713701 0.748251467427403 0.625874266286299 8 0.270109750369982 0.540219500739963 0.729890249630018 9 0.289169187052795 0.57833837410559 0.710830812947205 10 0.286125977184051 0.572251954368103 0.713874022815949 11 0.229738792944707 0.459477585889414 0.770261207055293 12 0.158874603256543 0.317749206513085 0.841125396743457 13 0.344979829300885 0.68995965860177 0.655020170699115 14 0.295807938248181 0.591615876496362 0.704192061751819 15 0.225352803096728 0.450705606193456 0.774647196903272 16 0.228357395941746 0.456714791883491 0.771642604058254 17 0.273704072649988 0.547408145299975 0.726295927350012 18 0.220011523707733 0.440023047415467 0.779988476292267 19 0.163296624874863 0.326593249749726 0.836703375125137 20 0.195520810082760 0.391041620165520 0.80447918991724 21 0.154068032698131 0.308136065396263 0.845931967301869 22 0.118990154050549 0.237980308101097 0.881009845949451 23 0.0992525341234844 0.198505068246969 0.900747465876516 24 0.0846574150731824 0.169314830146365 0.915342584926818 25 0.0925366081772324 0.185073216354465 0.907463391822768 26 0.0745975014864312 0.149195002972862 0.925402498513569 27 0.082904869708018 0.165809739416036 0.917095130291982 28 0.0647833598511365 0.129566719702273 0.935216640148864 29 0.0565245537946615 0.113049107589323 0.943475446205339 30 0.0505725550115556 0.101145110023111 0.949427444988444 31 0.0336252975771566 0.0672505951543133 0.966374702422843 32 0.0447237956658827 0.0894475913317654 0.955276204334117 33 0.0295029248099291 0.0590058496198582 0.97049707519007 34 0.0192835424840967 0.0385670849681934 0.980716457515903 35 0.0174066384498570 0.0348132768997139 0.982593361550143 36 0.0297246812463599 0.0594493624927198 0.97027531875364 37 0.0269691076594302 0.0539382153188605 0.97303089234057 38 0.0182923802583329 0.0365847605166658 0.981707619741667 39 0.0216600895254156 0.0433201790508313 0.978339910474584 40 0.0190354678808942 0.0380709357617885 0.980964532119106 41 0.0171786152992522 0.0343572305985044 0.982821384700748 42 0.0185710939621887 0.0371421879243774 0.981428906037811 43 0.0159950569028705 0.031990113805741 0.98400494309713 44 0.0349346064746468 0.0698692129492935 0.965065393525353 45 0.0327132815239354 0.0654265630478707 0.967286718476065 46 0.0236876958078819 0.0473753916157638 0.976312304192118 47 0.0356208538091681 0.0712417076183363 0.964379146190832 48 0.143736175209477 0.287472350418954 0.856263824790523 49 0.106086757072659 0.212173514145318 0.893913242927341 50 0.172586591945979 0.345173183891957 0.827413408054021 51 0.771947192971107 0.456105614057787 0.228052807028893 52 0.785106426258967 0.429787147482066 0.214893573741033 53 0.871074697498484 0.257850605003031 0.128925302501516 54 0.78321669675952 0.43356660648096 0.21678330324048 55 0.707327340588216 0.585345318823568 0.292672659411784 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 9 0.176470588235294 NOK 10% type I error level 17 0.333333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/10rfym1258724895.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/10rfym1258724895.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/1b47n1258724895.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/1b47n1258724895.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/2af8c1258724895.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/2af8c1258724895.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/3zi6z1258724895.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/3zi6z1258724895.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/419sk1258724895.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/419sk1258724895.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/5hove1258724895.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/5hove1258724895.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/61q3p1258724895.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/61q3p1258724895.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/7t8sz1258724895.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/7t8sz1258724895.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/8lzwy1258724895.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/8lzwy1258724895.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/9zqey1258724895.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725139ot68frauw2ws9l5/9zqey1258724895.ps (open in new window)

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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