Model 4

*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, 27 Dec 2010 20:37:35 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo.htm/, Retrieved Mon, 27 Dec 2010 21:35:21 +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/2010/Dec/27/t1293482119sws1anuyfe7xfzo.htm/}, year = {2010}, } @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 = {2010}, 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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4,24 3,353 4,15 3,186 3,93 3,902 3,7 4,164 3,7 3,499 3,65 4,145 3,55 3,796 3,43 3,711 3,47 3,949 3,58 3,74 3,67 3,243 3,72 4,407 3,8 4,814 3,76 3,908 3,63 5,25 3,48 3,937 3,41 4,004 3,43 5,56 3,5 3,922 3,62 3,759 3,58 4,138 3,52 4,634 3,45 3,996 3,36 4,308 3,27 4,143 3,21 4,429 3,19 5,219 3,16 4,929 3,12 5,761 3,06 5,592 3,01 4,163 2,98 4,962 2,97 5,208 3,02 4,755 3,07 4,491 3,18 5,732 3,29 5,731 3,43 5,04 3,61 6,102 3,74 4,904 3,87 5,369 3,88 5,578 4,09 4,619 4,19 4,731 4,2 5,011 4,29 5,299 4,37 4,146 4,47 4,625 4,61 4,736 4,65 4,219 4,69 5,116 4,82 4,205 4,86 4,121 4,87 5,103 5,01 4,3 5,03 4,578 5,13 3,809 5,18 5,657 5,21 4,248 5,26 3,83 5,25 4,736 5,2 4,839 5,16 4,411 5,19 4,57 5,39 4,104 5,58 4,801 5,76 3,953 5,89 3,828

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Lening[t] = + 5.84015665054135 -0.702885208147645Huis[t] + 0.273548430791847M1[t] + 0.00364588524323202M2[t] + 0.446708863076928M3[t] + 0.00291691649522005M4[t] + 0.0254458225848275M5[t] + 0.466521896196587M6[t] -0.202668557765756M7[t] -0.108668912703735M8[t] -0.123855902024262M9[t] + 0.162821460073184M10[t] -0.396263611734106M11[t] + 0.0382594099127257t + 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) 5.84015665054135 0.424129 13.7698 0 0 Huis -0.702885208147645 0.089137 -7.8855 0 0 M1 0.273548430791847 0.243719 1.1224 0.266662 0.133331 M2 0.00364588524323202 0.244706 0.0149 0.988168 0.494084 M3 0.446708863076928 0.24677 1.8102 0.075827 0.037913 M4 0.00291691649522005 0.243581 0.012 0.99049 0.495245 M5 0.0254458225848275 0.243497 0.1045 0.917158 0.458579 M6 0.466521896196587 0.248222 1.8795 0.065582 0.032791 M7 -0.202668557765756 0.246871 -0.821 0.415284 0.207642 M8 -0.108668912703735 0.245279 -0.443 0.659505 0.329753 M9 -0.123855902024262 0.254517 -0.4866 0.628488 0.314244 M10 0.162821460073184 0.255252 0.6379 0.526244 0.263122 M11 -0.396263611734106 0.258772 -1.5313 0.131527 0.065763 t 0.0382594099127257 0.002676 14.2948 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.894432607624306 R-squared 0.800009689581615 Adjusted R-squared 0.751863874110523 F-TEST (value) 16.6163908899196 F-TEST (DF numerator) 13 F-TEST (DF denominator) 54 p-value 1.98729921407903e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.401866393352976 Sum Squared Residuals 8.72081629775257 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4.24 3.79519038832687 0.444809611673127 2 4.15 3.68092908245163 0.469070917548367 3 3.93 3.65898566116434 0.271014338835657 4 3.7 3.06929719996068 0.630702800039322 5 3.7 3.59750417938119 0.102495820618806 6 3.65 3.6227758184423 0.0272241815576986 7 3.55 3.23715171203621 0.312848287963789 8 3.43 3.42915600970351 0.000843990296492454 9 3.47 3.28494175075657 0.185058249243433 10 3.58 3.7567815312696 -0.176781531269597 11 3.67 3.58528981782441 0.084710182175588 12 3.72 3.20165445718739 0.518345542812615 13 3.8 3.22738801817587 0.572611981824134 14 3.76 3.63255888112174 0.127441118878256 15 3.63 3.17060931953403 0.459390680465973 16 3.48 3.6879650611629 -0.207965061162901 17 3.41 3.70166006821934 -0.291660068219342 18 3.43 3.08730616786609 0.342693832133908 19 3.5 3.60770109476232 -0.107701094762316 20 3.62 3.85453043866513 -0.234530438665129 21 3.58 3.61120936536937 -0.0312093653693708 22 3.52 3.58751507413831 -0.0675150741383104 23 3.45 3.51513017504194 -0.0651301750419436 24 3.36 3.73035301174671 -0.37035301174671 25 3.27 4.15813691179564 -0.888136911795644 26 3.21 3.72546860662953 -0.515468606629529 27 3.19 3.65151167993931 -0.461511679939312 28 3.16 3.44981585363315 -0.289815853633145 29 3.12 2.92580367645664 0.194196323543362 30 3.06 3.52392676015808 -0.463926760158076 31 3.01 3.89741867855144 -0.887418678551442 32 2.98 3.46807245221622 -0.488072452216221 33 2.97 3.3182351116041 -0.348235111604099 34 3.02 3.96157888290515 -0.941578882905154 35 3.07 3.62631491596157 -0.556314915961568 36 3.18 3.18855739429717 -0.00855739429717252 37 3.29 3.50106812020989 -0.211068120209893 38 3.43 3.75511866340403 -0.325118663404026 39 3.61 3.48997696009765 0.12002303990235 40 3.74 3.92650090278955 -0.186500902789545 41 3.87 3.66044759700322 0.209552402996776 42 3.88 3.99288007202485 -0.112880072024851 43 4.09 4.03601594258882 0.0539840574111749 44 4.19 4.08955185425104 0.100448145748965 45 4.2 3.91581641656189 0.284183583438107 46 4.29 4.03832224862554 0.251677751374457 47 4.37 4.32792323172521 0.0420767682747863 48 4.47 4.42576423866932 0.0442357613306763 49 4.61 4.65955182126951 -0.0495518212695073 50 4.65 4.79130033824595 -0.14130033824595 51 4.69 4.64213469428394 0.0478653057160645 52 4.82 4.87693058223746 -0.0569305822374566 53 4.86 4.99676125572419 -0.136761255724191 54 4.87 4.78586346484769 0.084136535152309 55 5.01 4.71934924294063 0.290650757059368 56 5.03 4.65620621005033 0.373793789949668 57 5.13 5.21979735570807 -0.08979735570807 58 5.18 4.2458022630614 0.934197736938605 59 5.21 4.71534185944686 0.494658140553137 60 5.26 5.44367089809941 -0.183670898099409 61 5.25 5.11866474022222 0.131335259777784 62 5.2 4.81462442814712 0.385375571852881 63 5.16 5.59678168498073 -0.436781684980733 64 5.19 5.07949040021627 0.110509599783726 65 5.39 5.46782322321541 -0.0778232232154109 66 5.58 5.45724771666099 0.122752283339012 67 5.76 5.42236332912057 0.337636670879427 68 5.89 5.64248303511378 0.247516964886225 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0049498619896452 0.0098997239792904 0.995050138010355 18 0.00474347245390603 0.00948694490781207 0.995256527546094 19 0.00562387012072593 0.0112477402414519 0.994376129879274 20 0.023358016902939 0.0467160338058779 0.976641983097061 21 0.0264031974662394 0.0528063949324788 0.97359680253376 22 0.0249823506359529 0.0499647012719059 0.975017649364047 23 0.0213872183311172 0.0427744366622345 0.978612781668883 24 0.0478205596936364 0.0956411193872728 0.952179440306364 25 0.227518496690497 0.455036993380994 0.772481503309503 26 0.337651515702389 0.675303031404778 0.662348484297611 27 0.36305415408795 0.7261083081759 0.63694584591205 28 0.351427840440132 0.702855680880264 0.648572159559868 29 0.311847378074547 0.623694756149093 0.688152621925453 30 0.242256040505927 0.484512081011855 0.757743959494073 31 0.188030441214971 0.376060882429943 0.811969558785029 32 0.159425551794229 0.318851103588458 0.840574448205771 33 0.15436553616904 0.308731072338079 0.84563446383096 34 0.286009529816131 0.572019059632263 0.713990470183869 35 0.496439109953954 0.992878219907907 0.503560890046046 36 0.513461925902579 0.973076148194843 0.486538074097421 37 0.65729988714322 0.68540022571356 0.34270011285678 38 0.847962919368848 0.304074161262303 0.152037080631152 39 0.940142003328116 0.119715993343768 0.059857996671884 40 0.98505693105518 0.0298861378896417 0.0149430689448209 41 0.994668437595115 0.0106631248097702 0.00533156240488508 42 0.997176037672756 0.00564792465448768 0.00282396232724384 43 0.998675317719025 0.00264936456194921 0.00132468228097461 44 0.999159815984855 0.00168036803029014 0.00084018401514507 45 0.998873701692417 0.00225259661516585 0.00112629830758292 46 0.999482674540882 0.00103465091823533 0.000517325459117667 47 0.999587365220202 0.000825269559595074 0.000412634779797537 48 0.998488826443025 0.00302234711395044 0.00151117355697522 49 0.99460460384028 0.0107907923194408 0.00539539615972039 50 0.984863526190635 0.03027294761873 0.015136473809365 51 0.999541996054065 0.00091600789187076 0.00045800394593538 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 10 0.285714285714286 NOK 5% type I error level 18 0.514285714285714 NOK 10% type I error level 20 0.571428571428571 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/10qbtg1293482246.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/10qbtg1293482246.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/1kax41293482246.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/1kax41293482246.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/2kax41293482246.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/2kax41293482246.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/3cje71293482246.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/3cje71293482246.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/4cje71293482246.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/4cje71293482246.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/5cje71293482246.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/5cje71293482246.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/65sds1293482246.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/65sds1293482246.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/7gkcu1293482246.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/7gkcu1293482246.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/8gkcu1293482246.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/8gkcu1293482246.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/9gkcu1293482246.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482119sws1anuyfe7xfzo/9gkcu1293482246.ps (open in new window)

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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