Ws7.1

*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: Wed, 18 Nov 2009 11:38:50 -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/18/t12585697269nhvod248vixgup.htm/, Retrieved Wed, 18 Nov 2009 19:42:19 +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/18/t12585697269nhvod248vixgup.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:

WS7.1

Dataseries X:

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8.2 9.9 8.0 9.8 7.5 9.3 6.8 8.3 6.5 8.0 6.6 8.5 7.6 10.4 8.0 11.1 8.1 10.9 7.7 10.0 7.5 9.2 7.6 9.2 7.8 9.5 7.8 9.6 7.8 9.5 7.5 9.1 7.5 8.9 7.1 9.0 7.5 10.1 7.5 10.3 7.6 10.2 7.7 9.6 7.7 9.2 7.9 9.3 8.1 9.4 8.2 9.4 8.2 9.2 8.2 9.0 7.9 9.0 7.3 9.0 6.9 9.8 6.6 10.0 6.7 9.8 6.9 9.3 7.0 9.0 7.1 9.0 7.2 9.1 7.1 9.1 6.9 9.1 7.0 9.2 6.8 8.8 6.4 8.3 6.7 8.4 6.6 8.1 6.4 7.7 6.3 7.9 6.2 7.9 6.5 8.0 6.8 7.9 6.8 7.6 6.4 7.1 6.1 6.8 5.8 6.5 6.1 6.9 7.2 8.2 7.3 8.7 6.9 8.3 6.1 7.9 5.8 7.5 6.2 7.8 7.1 8.3 7.7 8.4 7.9 8.2 7.7 7.7 7.4 7.2 7.5 7.3 8.0 8.1 8.1 8.5

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 Y[t] = + 3.55332206483892 + 0.415158378689023X[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) 3.55332206483892 0.571921 6.213 0 0 X 0.415158378689023 0.06471 6.4157 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.619763320732683 R-squared 0.384106573725603 Adjusted R-squared 0.374774855145688 F-TEST (value) 41.1613970605934 F-TEST (DF numerator) 1 F-TEST (DF denominator) 66 p-value 1.74689942511463e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.52271667279695 Sum Squared Residuals 18.0333595213143 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.2 7.66339001386027 0.536609986139731 2 8 7.62187417599135 0.378125824008652 3 7.5 7.41429498664683 0.0857050133531662 4 6.8 6.99913660795781 -0.199136607957811 5 6.5 6.8745890943511 -0.374589094351104 6 6.6 7.08216828369561 -0.482168283695615 7 7.6 7.87096920320476 -0.270969203204759 8 8 8.16158006828707 -0.161580068287074 9 8.1 8.07854839254927 0.0214516074507294 10 7.7 7.70490585172915 -0.00490585172914927 11 7.5 7.37277914877793 0.127220851222069 12 7.6 7.37277914877793 0.227220851222069 13 7.8 7.49732666238464 0.302673337615362 14 7.8 7.53884250025354 0.261157499746460 15 7.8 7.49732666238464 0.302673337615362 16 7.5 7.33126331090903 0.168736689090971 17 7.5 7.24823163517122 0.251768364828776 18 7.1 7.28974747304013 -0.189747473040127 19 7.5 7.74642168959805 -0.246421689598052 20 7.5 7.82945336533586 -0.329453365335857 21 7.6 7.78793752746695 -0.187937527466954 22 7.7 7.53884250025354 0.16115749974646 23 7.7 7.37277914877793 0.327220851222069 24 7.9 7.41429498664683 0.485705013353167 25 8.1 7.45581082451574 0.644189175484264 26 8.2 7.45581082451574 0.744189175484263 27 8.2 7.37277914877793 0.827220851222068 28 8.2 7.28974747304013 0.910252526959873 29 7.9 7.28974747304013 0.610252526959874 30 7.3 7.28974747304013 0.0102525269598732 31 6.9 7.62187417599135 -0.721874175991345 32 6.6 7.70490585172915 -1.10490585172915 33 6.7 7.62187417599135 -0.921874175991345 34 6.9 7.41429498664683 -0.514294986646833 35 7 7.28974747304013 -0.289747473040127 36 7.1 7.28974747304013 -0.189747473040127 37 7.2 7.33126331090903 -0.131263310909029 38 7.1 7.33126331090903 -0.231263310909029 39 6.9 7.33126331090903 -0.431263310909028 40 7 7.37277914877793 -0.372779148777931 41 6.8 7.20671579730232 -0.406715797302322 42 6.4 6.99913660795781 -0.599136607957811 43 6.7 7.04065244582671 -0.340652445826713 44 6.6 6.91610493222 -0.316104932220006 45 6.4 6.7500415807444 -0.350041580744397 46 6.3 6.8330732564822 -0.533073256482202 47 6.2 6.8330732564822 -0.633073256482201 48 6.5 6.8745890943511 -0.374589094351104 49 6.8 6.8330732564822 -0.0330732564822018 50 6.8 6.7085257428755 0.0914742571245054 51 6.4 6.50094655353098 -0.100946553530983 52 6.1 6.37639903992428 -0.276399039924277 53 5.8 6.25185152631757 -0.45185152631757 54 6.1 6.41791487779318 -0.317914877793179 55 7.2 6.95762077008891 0.242379229911092 56 7.3 7.16519995943342 0.134800040566580 57 6.9 6.99913660795781 -0.0991366079578105 58 6.1 6.8330732564822 -0.733073256482202 59 5.8 6.66700990500659 -0.867009905006592 60 6.2 6.7915574186133 -0.591557418613299 61 7.1 6.99913660795781 0.100863392042189 62 7.7 7.04065244582671 0.659347554173287 63 7.9 6.95762077008891 0.942379229911092 64 7.7 6.7500415807444 0.949958419255603 65 7.4 6.54246239139989 0.857537608600115 66 7.5 6.58397822926879 0.916021770731212 67 8 6.91610493222 1.08389506777999 68 8.1 7.08216828369562 1.01783171630438 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00531436083752519 0.0106287216750504 0.994685639162475 6 0.0146716525989501 0.0293433051979003 0.98532834740105 7 0.164088697294765 0.32817739458953 0.835911302705235 8 0.155221703059549 0.310443406119098 0.84477829694045 9 0.0894998358640397 0.178999671728079 0.91050016413596 10 0.0475229845136698 0.0950459690273397 0.95247701548633 11 0.0286243899948375 0.0572487799896750 0.971375610005162 12 0.0198904270057495 0.0397808540114989 0.98010957299425 13 0.0146426031280258 0.0292852062560517 0.985357396871974 14 0.00917313286774008 0.0183462657354802 0.99082686713226 15 0.006176719465023 0.012353438930046 0.993823280534977 16 0.0032966469808305 0.006593293961661 0.99670335301917 17 0.00203197560465869 0.00406395120931738 0.99796802439534 18 0.00115870320747261 0.00231740641494521 0.998841296792527 19 0.000909239796073948 0.00181847959214790 0.999090760203926 20 0.00087009979455612 0.00174019958911224 0.999129900205444 21 0.000501139595921213 0.00100227919184243 0.999498860404079 22 0.000251349097713491 0.000502698195426982 0.999748650902286 23 0.000184253965149068 0.000368507930298137 0.99981574603485 24 0.000229092580662964 0.000458185161325927 0.999770907419337 25 0.00051709781026264 0.00103419562052528 0.999482902189737 26 0.00151342194880855 0.00302684389761709 0.998486578051191 27 0.0049203558743675 0.009840711748735 0.995079644125632 28 0.0165911446787849 0.0331822893575699 0.983408855321215 29 0.0205022801048652 0.0410045602097305 0.979497719895135 30 0.0142655456563562 0.0285310913127123 0.985734454343644 31 0.027799950650329 0.055599901300658 0.97220004934967 32 0.101247298232637 0.202494596465274 0.898752701767363 33 0.17484963101145 0.3496992620229 0.82515036898855 34 0.175739433526775 0.35147886705355 0.824260566473225 35 0.14935588073565 0.2987117614713 0.85064411926435 36 0.118138501177357 0.236277002354714 0.881861498822643 37 0.0890478276725832 0.178095655345166 0.910952172327417 38 0.0690501387558105 0.138100277511621 0.93094986124419 39 0.0640186371535499 0.128037274307100 0.93598136284645 40 0.0573791655629999 0.114758331126000 0.942620834437 41 0.0554387025958795 0.110877405191759 0.94456129740412 42 0.0692011175010984 0.138402235002197 0.930798882498902 43 0.0623381591018052 0.124676318203610 0.937661840898195 44 0.0523975617253456 0.104795123450691 0.947602438274654 45 0.0418636843780074 0.0837273687560147 0.958136315621993 46 0.0440950831469392 0.0881901662938784 0.95590491685306 47 0.0565956824493524 0.113191364898705 0.943404317550648 48 0.0535691387472096 0.107138277494419 0.94643086125279 49 0.038575716878998 0.077151433757996 0.961424283121002 50 0.0262839145353454 0.0525678290706909 0.973716085464654 51 0.0165281800619475 0.0330563601238951 0.983471819938052 52 0.0101321158246560 0.0202642316493120 0.989867884175344 53 0.00670355629754487 0.0134071125950897 0.993296443702455 54 0.00476199417408763 0.00952398834817527 0.995238005825912 55 0.0028871657904085 0.005774331580817 0.997112834209592 56 0.00171715881162064 0.00343431762324127 0.99828284118838 57 0.00127755038301914 0.00255510076603828 0.99872244961698 58 0.00624644107171026 0.0124928821434205 0.99375355892829 59 0.0848971832109769 0.169794366421954 0.915102816789023 60 0.732416466007722 0.535167067984556 0.267583533992278 61 0.98716684458191 0.0256663108361825 0.0128331554180913 62 0.99932598448813 0.00134803102373992 0.000674015511869962 63 0.997651394504473 0.00469721099105298 0.00234860549552649 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 18 0.305084745762712 NOK 5% type I error level 32 0.542372881355932 NOK 10% type I error level 39 0.661016949152542 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/10idq41258569525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/10idq41258569525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/1s6581258569525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/1s6581258569525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/2nbfb1258569525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/2nbfb1258569525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/3wd1i1258569525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/3wd1i1258569525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/4m3jt1258569525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/4m3jt1258569525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/5yaq51258569525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/5yaq51258569525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/676sr1258569525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/676sr1258569525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/77l3f1258569525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/77l3f1258569525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/8455l1258569525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/8455l1258569525.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/9sb441258569525.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585697269nhvod248vixgup/9sb441258569525.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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