paper multiple regressie 5 vertragingen

*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: Mon, 28 Dec 2009 08:42:47 -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/Dec/28/t1262015038pu557kxn7fm153i.htm/, Retrieved Mon, 28 Dec 2009 16:44:10 +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/Dec/28/t1262015038pu557kxn7fm153i.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:

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User-defined keywords:

Dataseries X:

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106.3 0 97.7 98.3 91.6 104.6 111.6 102.3 0 106.3 97.7 98.3 91.6 104.6 106.6 0 102.3 106.3 97.7 98.3 91.6 108.1 0 106.6 102.3 106.3 97.7 98.3 93.8 0 108.1 106.6 102.3 106.3 97.7 88.2 0 93.8 108.1 106.6 102.3 106.3 108.9 0 88.2 93.8 108.1 106.6 102.3 114.2 0 108.9 88.2 93.8 108.1 106.6 102.5 0 114.2 108.9 88.2 93.8 108.1 94.2 0 102.5 114.2 108.9 88.2 93.8 97.4 0 94.2 102.5 114.2 108.9 88.2 98.5 0 97.4 94.2 102.5 114.2 108.9 106.5 0 98.5 97.4 94.2 102.5 114.2 102.9 0 106.5 98.5 97.4 94.2 102.5 97.1 0 102.9 106.5 98.5 97.4 94.2 103.7 0 97.1 102.9 106.5 98.5 97.4 93.4 0 103.7 97.1 102.9 106.5 98.5 85.8 0 93.4 103.7 97.1 102.9 106.5 108.6 0 85.8 93.4 103.7 97.1 102.9 110.2 0 108.6 85.8 93.4 103.7 97.1 101.2 0 110.2 108.6 85.8 93.4 103.7 101.2 0 101.2 110.2 108.6 85.8 93.4 96.9 0 101.2 101.2 110.2 108.6 85.8 99.4 0 96.9 101.2 101.2 110.2 108.6 118.7 0 99.4 96.9 101.2 101.2 110.2 108.0 0 118.7 99.4 96.9 101.2 101.2 101.2 0 108.0 118.7 99.4 96.9 101.2 119.9 0 101.2 108.0 118.7 99.4 96.9 94. 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation y[t] = + 38.2434492472197 -9.6442840760713x[t] + 0.0694968060235102y1[t] + 0.448373151946162y2[t] + 0.548341840327527y3[t] -0.228183028240301y4[t] -0.211678098153006y5[t] + 14.2132730623937M1[t] + 1.26208625936005M2[t] -7.0104913604995M3[t] -0.0442328718516249M4[t] -13.5073967711877M5[t] -16.9682899190018M6[t] + 7.2052177084541M7[t] + 21.3464877391464M8[t] + 1.66102089570285M9[t] -21.6998501830883M10[t] -13.119192025781M11[t] + 0.165545242405437t + 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) 38.2434492472197 8.675644 4.4081 3.6e-05 1.8e-05 x -9.6442840760713 2.371055 -4.0675 0.00012 6e-05 y1 0.0694968060235102 0.112458 0.618 0.538538 0.269269 y2 0.448373151946162 0.108694 4.1251 9.8e-05 4.9e-05 y3 0.548341840327527 0.100827 5.4385 1e-06 0 y4 -0.228183028240301 0.104174 -2.1904 0.031735 0.015867 y5 -0.211678098153006 0.11377 -1.8606 0.066887 0.033444 M1 14.2132730623937 2.476775 5.7386 0 0 M2 1.26208625936005 3.451357 0.3657 0.715677 0.357839 M3 -7.0104913604995 3.743771 -1.8726 0.065188 0.032594 M4 -0.0442328718516249 3.33722 -0.0133 0.989461 0.494731 M5 -13.5073967711877 2.92367 -4.62 1.6e-05 8e-06 M6 -16.9682899190018 2.929475 -5.7923 0 0 M7 7.2052177084541 2.721787 2.6472 0.009963 0.004981 M8 21.3464877391464 3.048199 7.003 0 0 M9 1.66102089570285 4.667236 0.3559 0.722964 0.361482 M10 -21.6998501830883 4.801832 -4.5191 2.4e-05 1.2e-05 M11 -13.119192025781 3.933529 -3.3352 0.00135 0.000675 t 0.165545242405437 0.042404 3.904 0.000211 0.000105 Multiple Linear Regression - Regression Statistics Multiple R 0.960141697819114 R-squared 0.92187207989097 Adjusted R-squared 0.902340099863713 F-TEST (value) 47.1980863488736 F-TEST (DF numerator) 18 F-TEST (DF denominator) 72 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.08537947263239 Sum Squared Residuals 1201.70343134924 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 106.3 106.224078403014 0.0759215969862502 2 102.3 101.889101867410 0.410898132590319 3 106.6 98.254075255181 8.3459247448189 4 108.1 107.325629030487 0.774370969513379 5 93.8 92.0315255906748 1.76847440932515 6 88.2 89.865103469303 -1.66510346930294 7 108.9 108.091276284273 0.808723715727493 8 114.2 112.232007110056 1.96799288994366 9 102.5 102.236528677001 0.263471322998906 10 94.2 96.2599657719685 -2.05996577196847 11 97.4 98.5516002227344 -1.15160022273444 12 98.5 96.33052389577 2.16947610422993 13 106.5 109.217193008905 -2.71719300890492 14 102.9 103.605983135438 -0.705983135438309 15 97.1 100.46566602053 -3.36566602053011 16 103.7 109.038608407107 -5.33860840710717 17 93.4 89.5667636295742 3.83323637042583 18 85.8 84.4625128675094 1.33748713249061 19 108.6 109.359705409853 -0.759705409852815 20 110.2 115.917215933024 -5.71721593302364 21 101.2 103.517208842572 -2.31720884257172 22 101.2 96.8304781801582 4.36952181984184 23 96.9 98.8248506589635 -1.92485065896345 24 99.4 101.684321615228 -2.28432161522802 25 118.7 116.023839678835 2.67616032116463 26 108 105.247652324295 2.75234767570504 27 101.2 107.402647577202 -6.20264757720235 28 119.9 120.187036071250 -0.2870360712503 29 94.8 94.3396848718006 0.460315128199434 30 95.3 92.311991670186 2.98800832981397 31 118 119.502219485606 -1.50221948560640 32 115.9 119.019807078537 -3.11980707853675 33 111.4 111.575197227561 -0.175197227561280 34 108.2 104.771940669938 3.42805933006197 35 108.8 104.840963451799 3.95903654820135 36 109.5 109.939157967129 -0.439157967128984 37 124.8 124.352301671467 0.447698328533122 38 115.3 114.955564685615 0.344435314385339 39 109.5 113.972721261088 -4.4727212610882 40 124.2 124.544793752068 -0.344793752067768 41 92.9 100.819591378500 -7.91959137849979 42 98.4 97.688759970806 0.711240029193965 43 120.9 119.770994166944 1.12900583305626 44 111.7 118.817882763178 -7.11788276317774 45 116.1 115.793327328386 0.306672671614345 46 109.4 106.486963664835 2.91303633516451 47 111.7 105.397287726492 6.30271227350775 48 114.3 115.586998279303 -1.28699827930273 49 133.7 128.447309377795 5.25269062220489 50 114.3 120.034304939173 -5.73430493917306 51 126.5 121.596584750142 4.90341524985756 52 131 130.435506570104 0.564493429895654 53 104 107.305830488609 -3.30583048860904 54 108.9 111.161714100013 -2.26171410001266 55 128.5 127.525486657953 0.974513342046673 56 132.4 126.976941660257 5.42305833974324 57 128 124.411436719260 3.58856328074036 58 116.4 118.003692111134 -1.60369211113375 59 120.9 120.599813835228 0.300186164771633 60 118.6 121.544649536568 -2.94464953656791 61 133.1 131.598999764932 1.50100023506799 62 121.1 124.835648683103 -3.73564868310308 63 127.6 122.563521415326 5.03647858467359 64 135.4 132.289802770192 3.11019722980811 65 114.9 113.046788320232 1.85321167976806 66 114.3 115.057152354315 -0.75715235431532 67 128.9 135.496871374495 -6.59687137449504 68 138.9 136.152573139385 2.74742686061489 69 129.4 126.571525426132 2.82847457386770 70 115 119.681813149848 -4.68181314984759 71 128 125.446670649192 2.55332935080809 72 127 122.596715009111 4.40328499088918 73 128.8 134.889722769223 -6.08972276922297 74 137.9 134.206023770863 3.69397622913735 75 128.4 127.071927407677 1.32807259232257 76 135.9 136.087090229058 -0.187090229058247 77 122.2 123.842012068116 -1.64201206811637 78 113.1 115.290622943008 -2.19062294300817 79 136.2 127.564290497869 8.63570950213122 80 138 132.183572315564 5.81642768443633 81 115.2 119.694775779088 -4.49477577908832 82 111 113.365146452119 -2.36514645211851 83 99.2 109.238813455591 -10.0388134555909 84 102.4 102.017633696891 0.382366303108538 85 112.7 113.846555325829 -1.14655532582898 86 105.5 102.525720594104 2.97427940589641 87 98.3 103.872856312852 -5.57285631285196 88 116.4 114.691533169734 1.70846683026634 89 97.4 92.4478036524933 4.95219634750672 90 93.3 91.4621426248595 1.83785737514053 91 117.4 120.089156123007 -2.68915612300738 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 22 0.695819880522355 0.608360238955291 0.304180119477645 23 0.540317707257508 0.919364585484985 0.459682292742492 24 0.446477529358958 0.892955058717915 0.553522470641042 25 0.572366676485743 0.855266647028513 0.427633323514257 26 0.4643694423994 0.9287388847988 0.5356305576006 27 0.379338196325443 0.758676392650885 0.620661803674557 28 0.336771889767109 0.673543779534217 0.663228110232891 29 0.347117708004044 0.694235416008088 0.652882291995956 30 0.4351960158754 0.8703920317508 0.5648039841246 31 0.347211508473455 0.69442301694691 0.652788491526545 32 0.282070345602967 0.564140691205935 0.717929654397033 33 0.21288499920394 0.42576999840788 0.78711500079606 34 0.169896162811265 0.33979232562253 0.830103837188735 35 0.1669578476177 0.3339156952354 0.8330421523823 36 0.117592125137220 0.235184250274440 0.88240787486278 37 0.080442846807531 0.160885693615062 0.919557153192469 38 0.0573539066395286 0.114707813279057 0.942646093360471 39 0.0580974277028485 0.116194855405697 0.941902572297151 40 0.0380361619641665 0.076072323928333 0.961963838035834 41 0.133834311308258 0.267668622616516 0.866165688691742 42 0.0957385810015397 0.191477162003079 0.90426141899846 43 0.0671250997094315 0.134250199418863 0.932874900290569 44 0.122534760978576 0.245069521957153 0.877465239021424 45 0.0957541356124579 0.191508271224916 0.904245864387542 46 0.0754507571688458 0.150901514337692 0.924549242831154 47 0.132577394355298 0.265154788710596 0.867422605644702 48 0.100222378690500 0.200444757380999 0.8997776213095 49 0.118138674323958 0.236277348647915 0.881861325676042 50 0.140021974587475 0.280043949174951 0.859978025412525 51 0.142338945916555 0.284677891833111 0.857661054083445 52 0.109398179233557 0.218796358467115 0.890601820766443 53 0.106614780125276 0.213229560250552 0.893385219874724 54 0.0889453716736335 0.177890743347267 0.911054628326367 55 0.0607491333877873 0.121498266775575 0.939250866612213 56 0.0766219582516171 0.153243916503234 0.923378041748383 57 0.0612270573386076 0.122454114677215 0.938772942661392 58 0.0439425904908723 0.0878851809817445 0.956057409509128 59 0.0298697123352384 0.0597394246704768 0.970130287664762 60 0.0310133468275741 0.0620266936551482 0.968986653172426 61 0.0219173659486063 0.0438347318972125 0.978082634051394 62 0.0384595216763758 0.0769190433527517 0.961540478323624 63 0.0537847090596111 0.107569418119222 0.946215290940389 64 0.0372431279599746 0.0744862559199493 0.962756872040025 65 0.0224320256048178 0.0448640512096356 0.977567974395182 66 0.0184188000058725 0.0368376000117451 0.981581199994128 67 0.122988045597665 0.245976091195329 0.877011954402335 68 0.103822977481191 0.207645954962381 0.89617702251881 69 0.0517136564287966 0.103427312857593 0.948286343571203 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 3 0.0625 NOK 10% type I error level 9 0.1875 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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