Multiple lineair regression aantal werklozen(onder 25jaar) - Rente op basis-herfinancieringstransacties

*Unverified author*

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 02:50:17 -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/t1258710905finywma2ggt9lxz.htm/, Retrieved Fri, 20 Nov 2009 10:55:20 +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/t1258710905finywma2ggt9lxz.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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127 2.75 123 2.75 118 2.55 114 2.5 108 2.5 111 2.1 151 2 159 2 158 2 148 2 138 2 137 2 136 2 133 2 126 2 120 2 114 2 116 2 153 2 162 2 161 2 149 2 139 2 135 2 130 2 127 2 122 2 117 2 112 2 113 2 149 2 157 2 157 2 147 2 137 2 132 2.21 125 2.25 123 2.25 117 2.45 114 2.5 111 2.5 112 2.64 144 2.75 150 2.93 149 3 134 3.17 123 3.25 116 3.39 117 3.5 111 3.5 105 3.65 102 3.75 95 3.75 93 3.9 124 4 130 4 124 4 115 4 106 4 105 4 105 4 101 4 95 4 93 4 84 4 87 4 116 4.18 120 4.25 117 4.25 109 3.97 105 3.42 107 2.75

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Werkloosheid[t] = + 163.536249738331 -14.1801697936507Rente[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) 163.536249738331 6.161964 26.5396 0 0 Rente -14.1801697936507 2.110015 -6.7204 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.626235410899096 R-squared 0.392170789863960 Adjusted R-squared 0.383487515433445 F-TEST (value) 45.1639289996166 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 4.02302191560011e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 15.0961419653212 Sum Squared Residuals 15952.5451565993 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 127 124.540782805791 2.45921719420949 2 123 124.540782805792 -1.54078280579150 3 118 127.376816764522 -9.37681676452159 4 114 128.085825254204 -14.0858252542041 5 108 128.085825254204 -20.0858252542041 6 111 133.757893171664 -22.7578931716644 7 151 135.175910151029 15.8240898489705 8 159 135.175910151029 23.8240898489705 9 158 135.175910151029 22.8240898489705 10 148 135.175910151029 12.8240898489705 11 138 135.175910151029 2.82408984897055 12 137 135.175910151029 1.82408984897055 13 136 135.175910151029 0.824089848970548 14 133 135.175910151029 -2.17591015102945 15 126 135.175910151029 -9.17591015102945 16 120 135.175910151029 -15.1759101510295 17 114 135.175910151029 -21.1759101510295 18 116 135.175910151029 -19.1759101510295 19 153 135.175910151029 17.8240898489705 20 162 135.175910151029 26.8240898489705 21 161 135.175910151029 25.8240898489705 22 149 135.175910151029 13.8240898489705 23 139 135.175910151029 3.82408984897055 24 135 135.175910151029 -0.175910151029452 25 130 135.175910151029 -5.17591015102945 26 127 135.175910151029 -8.17591015102945 27 122 135.175910151029 -13.1759101510295 28 117 135.175910151029 -18.1759101510295 29 112 135.175910151029 -23.1759101510295 30 113 135.175910151029 -22.1759101510295 31 149 135.175910151029 13.8240898489705 32 157 135.175910151029 21.8240898489705 33 157 135.175910151029 21.8240898489705 34 147 135.175910151029 11.8240898489705 35 137 135.175910151029 1.82408984897055 36 132 132.198074494363 -0.198074494362813 37 125 131.630867702617 -6.63086770261679 38 123 131.630867702617 -8.63086770261678 39 117 128.794833743887 -11.7948337438867 40 114 128.085825254204 -14.0858252542041 41 111 128.085825254204 -17.0858252542041 42 112 126.100601483093 -14.1006014830930 43 144 124.540782805791 19.4592171942085 44 150 121.988352242934 28.0116477570657 45 149 120.995740357379 28.0042596426212 46 134 118.585111492458 15.4148885075418 47 123 117.450697908966 5.54930209103388 48 116 115.465474137855 0.534525862144974 49 117 113.905655460553 3.09434453944655 50 111 113.905655460553 -2.90565546055345 51 105 111.778629991506 -6.77862999150586 52 102 110.360613012141 -8.36061301214079 53 95 110.360613012141 -15.3606130121408 54 93 108.233587543093 -15.2335875430932 55 124 106.815570563728 17.1844294362719 56 130 106.815570563728 23.1844294362719 57 124 106.815570563728 17.1844294362719 58 115 106.815570563728 8.18442943627188 59 106 106.815570563728 -0.81557056372812 60 105 106.815570563728 -1.81557056372812 61 105 106.815570563728 -1.81557056372812 62 101 106.815570563728 -5.81557056372812 63 95 106.815570563728 -11.8155705637281 64 93 106.815570563728 -13.8155705637281 65 84 106.815570563728 -22.8155705637281 66 87 106.815570563728 -19.8155705637281 67 116 104.263140000871 11.736859999129 68 120 103.270528115315 16.7294718846845 69 117 103.270528115315 13.7294718846845 70 109 107.240975657538 1.75902434246236 71 105 115.040069044046 -10.0400690440455 72 107 124.540782805791 -17.5407828057915 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0174596669919574 0.0349193339839147 0.982540333008043 6 0.0273204913629827 0.0546409827259655 0.972679508637017 7 0.556555299754242 0.886889400491517 0.443444700245758 8 0.706282638394999 0.587434723210002 0.293717361605001 9 0.713245753960866 0.573508492078267 0.286754246039134 10 0.625372034480653 0.749255931038694 0.374627965519347 11 0.538359410970483 0.923281178059035 0.461640589029517 12 0.452532953760507 0.905065907521013 0.547467046239493 13 0.372108484306131 0.744216968612261 0.62789151569387 14 0.309800181171093 0.619600362342187 0.690199818828907 15 0.298760093192312 0.597520186384624 0.701239906807688 16 0.338893846421705 0.67778769284341 0.661106153578295 17 0.441887015968024 0.883774031936048 0.558112984031976 18 0.491922454003753 0.983844908007506 0.508077545996247 19 0.520070409070753 0.959859181858493 0.479929590929247 20 0.657498905525515 0.68500218894897 0.342501094474485 21 0.751470169302379 0.497059661395242 0.248529830697621 22 0.728095763461577 0.543808473076847 0.271904236538423 23 0.666634085914914 0.666731828170173 0.333365914085086 24 0.601119725151318 0.797760549697363 0.398880274848682 25 0.545823807380141 0.908352385239718 0.454176192619859 26 0.503266081240638 0.993467837518724 0.496733918759362 27 0.494622081562188 0.989244163124376 0.505377918437812 28 0.532884790949535 0.93423041810093 0.467115209050465 29 0.630821004274748 0.738357991450505 0.369178995725252 30 0.712637047788715 0.574725904422571 0.287362952211285 31 0.693323196799157 0.613353606401685 0.306676803200843 32 0.743372943353877 0.513254113292247 0.256627056646123 33 0.796479557510522 0.407040884978957 0.203520442489478 34 0.782389980879847 0.435220038240306 0.217610019120153 35 0.733490573772632 0.533018852454736 0.266509426227368 36 0.676745768956788 0.646508462086424 0.323254231043212 37 0.615489618892088 0.769020762215824 0.384510381107912 38 0.556305832364414 0.887388335271171 0.443694167635586 39 0.505643451324822 0.988713097350355 0.494356548675178 40 0.47242363054483 0.94484726108966 0.52757636945517 41 0.480616366087418 0.961232732174835 0.519383633912582 42 0.489132694601244 0.978265389202488 0.510867305398756 43 0.568968536115401 0.862062927769198 0.431031463884599 44 0.751813529781834 0.496372940436331 0.248186470218166 45 0.89923891190955 0.201522176180901 0.100761088090451 46 0.929520532526558 0.140958934946884 0.0704794674734419 47 0.92661778144672 0.146764437106559 0.0733822185532793 48 0.909690282102012 0.180619435795976 0.090309717897988 49 0.895068639046844 0.209862721906312 0.104931360953156 50 0.865430744296367 0.269138511407267 0.134569255703633 51 0.822821216113206 0.354357567773589 0.177178783886794 52 0.774301643720287 0.451396712559425 0.225698356279713 53 0.753193963737925 0.49361207252415 0.246806036262075 54 0.749579277073105 0.50084144585379 0.250420722926895 55 0.77431372684416 0.451372546311679 0.225686273155840 56 0.873994666270665 0.25201066745867 0.126005333729335 57 0.910309638414822 0.179380723170355 0.0896903615851776 58 0.893879115781293 0.212241768437414 0.106120884218707 59 0.842774172889144 0.314451654221713 0.157225827110856 60 0.774447222652287 0.451105554695427 0.225552777347713 61 0.688829474255919 0.622341051488163 0.311170525744081 62 0.59142860857967 0.81714278284066 0.40857139142033 63 0.524717059264183 0.950565881471635 0.475282940735817 64 0.487066171929257 0.974132343858514 0.512933828070743 65 0.708568332994592 0.582863334010815 0.291431667005408 66 0.990035995591466 0.0199280088170675 0.00996400440853373 67 0.961340905175529 0.077318189648943 0.0386590948244715 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 2 0.0317460317460317 OK 10% type I error level 4 0.0634920634920635 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/10bpww1258710612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/10bpww1258710612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/14irx1258710612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/14irx1258710612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/2qxmx1258710612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/2qxmx1258710612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/33s001258710612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/33s001258710612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/47me81258710612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/47me81258710612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/5tqab1258710612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/5tqab1258710612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/6vd731258710612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/6vd731258710612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/7r8171258710612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/7r8171258710612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/8yz121258710612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/8yz121258710612.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/91yqs1258710612.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258710905finywma2ggt9lxz/91yqs1258710612.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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