*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 05:46:38 -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/t12587213319yvp5p7k43axy2m.htm/, Retrieved Fri, 20 Nov 2009 13:49:04 +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/t12587213319yvp5p7k43axy2m.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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1.4816 133.91 0.91557 43.6188 1.4562 133.14 0.89135 44.7624 1.4268 135.31 0.86265 45.1972 1.4088 133.09 0.86092 44.3881 1.4016 135.39 0.85670 43.5552 1.3650 131.85 0.88444 43.5678 1.3190 130.25 0.89756 44.2135 1.3050 127.65 0.91966 45.1450 1.2785 118.30 0.88691 45.8079 1.3239 119.73 0.91819 42.3282 1.3449 122.51 0.90448 37.8999 1.2732 123.28 0.83063 34.7964 1.3322 133.52 0.78668 35.2144 1.4369 153.20 0.79924 36.3727 1.4975 163.63 0.79279 36.2502 1.5770 168.45 0.79308 36.8261 1.5553 166.26 0.79152 36.7723 1.5557 162.31 0.79209 36.9042 1.5750 161.56 0.79487 37.0494 1.5527 156.59 0.77494 36.8259 1.4748 157.97 0.75094 36.1357 1.4718 158.68 0.74725 36.0300 1.4570 163.55 0.72064 35.7927 1.4684 162.89 0.70896 35.9174 1.4227 164.95 0.69614 35.4008 1.3896 159.82 0.68887 35.1723 1.3622 159.05 0.67766 34.9211 1.3716 166.76 0.67440 35.0292 1.3419 164.55 0.67562 34.7739 1.3511 163.22 0.68136 34.8999 1.3516 160.68 0.67934 34.9054 1.3242 155.24 0.68021 34.5680 1.3074 157.60 0.66800 34.4060 1 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 8 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation dollar/euro[t] = -0.490341429831448 + 0.0069849570837074`Japanseyen/euro`[t] + 0.928187504675763`pond/euro`[t] + 0.00347990209464552`roebel/euro`[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) -0.490341429831448 0.123628 -3.9663 0.00021 0.000105 `Japanseyen/euro` 0.0069849570837074 0.000505 13.8237 0 0 `pond/euro` 0.928187504675763 0.147549 6.2907 0 0 `roebel/euro` 0.00347990209464552 0.003528 0.9863 0.328233 0.164116 Multiple Linear Regression - Regression Statistics Multiple R 0.906051036391935 R-squared 0.8209284805469 Adjusted R-squared 0.811335363433341 F-TEST (value) 85.574737682145 F-TEST (DF numerator) 3 F-TEST (DF denominator) 56 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0461281743809974 Sum Squared Residuals 0.119157274416528 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.4816 1.44662396038971 0.0349760396102876 2 1.4562 1.42274445810746 0.0334555418925436 3 1.4268 1.41277589502566 0.0140241049743409 4 1.4088 1.39284793713196 0.0159520628680381 5 1.4016 1.40209797670013 -0.000497976700126948 6 1.365 1.4031629967699 -0.0381629967699009 7 1.319 1.40641185827983 -0.0874118582798278 8 1.305 1.41200544251669 -0.107005442516685 9 1.2785 1.31860478010443 -0.0401047801044301 10 1.3239 1.34551795856165 -0.0216179585616515 11 1.3449 1.33680063811953 0.00809936188046522 12 1.2732 1.26283253170295 0.0103674682970481 13 1.3322 1.29501925048518 0.037180749514822 14 1.4369 1.44817201154749 -0.0112720115474950 15 1.4975 1.51461201651881 -0.0171120165188105 16 1.577 1.55055275965494 0.0264472403450574 17 1.5553 1.53362051240164 0.0216794875983627 18 1.5557 1.50701799788494 0.0486820021150581 19 1.575 1.50486492311930 0.0701350768806974 20 1.5527 1.45087315132694 0.101826848673064 21 1.4748 1.43583406356451 0.0389659364354909 22 1.4718 1.43700054555028 0.0347994544497162 23 1.457 1.44549243628146 0.0115075637185426 24 1.4684 1.4304750783428 0.0379249216572 25 1.4227 1.4311670087032 -0.00846700870319987 26 1.3896 1.38779109807616 0.00180890192383823 27 1.3622 1.37113354778812 -0.00893354778811688 28 1.3716 1.42233785305469 -0.0507378530546891 29 1.3419 1.40714506765064 -0.0652450676506372 30 1.3511 1.40362133867007 -0.0525213386700705 31 1.3516 1.38402374837953 -0.0324237483795294 32 1.3242 1.34565898600650 -0.0214589860064955 33 1.3074 1.35024657115262 -0.0428465711526214 34 1.2999 1.33890209406761 -0.0390020940676065 35 1.3213 1.33647243785466 -0.0151724378546556 36 1.2881 1.30994810935688 -0.0218481093568753 37 1.2611 1.29711675662686 -0.0360167566268556 38 1.2727 1.29548371019472 -0.0227837101947153 39 1.2811 1.29450483259568 -0.0134048325956764 40 1.2684 1.29157912539424 -0.0231791253942422 41 1.265 1.27946385622636 -0.0144638562263600 42 1.277 1.26083341444467 0.0161665855553298 43 1.2271 1.27499161111732 -0.0478916111173178 44 1.202 1.25067150147115 -0.0486715014711534 45 1.1938 1.24405899743853 -0.0502589974385282 46 1.2103 1.24240295556499 -0.0321029555649924 47 1.1856 1.24089923408211 -0.0552992340821139 48 1.1786 1.23326705824168 -0.0546670582416781 49 1.2015 1.22582283091650 -0.0243228309165039 50 1.2256 1.20998527948738 0.0156147205126247 51 1.2292 1.21737006988776 0.0118299301122367 52 1.2037 1.20930127915572 -0.00560127915571699 53 1.2165 1.17485617819313 0.0416438218068685 54 1.2694 1.21352574891656 0.0558742510834422 55 1.2938 1.23854189366279 0.0552581063372052 56 1.3201 1.24893521773219 0.0711647822678146 57 1.3014 1.23024393529982 0.0711560647001775 58 1.3119 1.23313446509509 0.0787655349049095 59 1.3408 1.25684052729893 0.08395947270107 60 1.2991 1.23786048010697 0.061239519893027 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.372537788880416 0.745075577760832 0.627462211119584 8 0.294496345832938 0.588992691665875 0.705503654167062 9 0.906101323543443 0.187797352913113 0.0938986764565567 10 0.998336892110027 0.00332621577994571 0.00166310788997285 11 0.999867228725017 0.000265542549965279 0.000132771274982639 12 0.999836929572446 0.000326140855108308 0.000163070427554154 13 0.999703793130679 0.00059241373864285 0.000296206869321425 14 0.999976553583157 4.68928336859157e-05 2.34464168429578e-05 15 0.99999099429621 1.80114075820852e-05 9.00570379104259e-06 16 0.999986350252534 2.72994949328160e-05 1.36497474664080e-05 17 0.999985820062801 2.83598743971819e-05 1.41799371985909e-05 18 0.999985713866819 2.85722663624448e-05 1.42861331812224e-05 19 0.999989001048615 2.19979027702950e-05 1.09989513851475e-05 20 0.999997227847556 5.54430488890952e-06 2.77215244445476e-06 21 0.999993545959475 1.29080810499248e-05 6.45404052496242e-06 22 0.999986337105197 2.73257896066549e-05 1.36628948033274e-05 23 0.999983385013616 3.32299727687359e-05 1.66149863843680e-05 24 0.999990588440074 1.88231198509696e-05 9.41155992548479e-06 25 0.999993737410045 1.25251799103851e-05 6.26258995519253e-06 26 0.999995828967032 8.34206593652637e-06 4.17103296826318e-06 27 0.999995482133329 9.03573334233633e-06 4.51786667116816e-06 28 0.999997153038782 5.69392243629754e-06 2.84696121814877e-06 29 0.999998914282173 2.17143565346138e-06 1.08571782673069e-06 30 0.999998910235376 2.17952924869400e-06 1.08976462434700e-06 31 0.99999744776029 5.10447942170033e-06 2.55223971085016e-06 32 0.999993518683295 1.29626334091588e-05 6.48131670457938e-06 33 0.999987982914923 2.40341701530138e-05 1.20170850765069e-05 34 0.999986490729335 2.70185413306563e-05 1.35092706653282e-05 35 0.99997123560954 5.75287809219423e-05 2.87643904609711e-05 36 0.999928604460823 0.000142791078354719 7.13955391773595e-05 37 0.9998396934742 0.000320613051599773 0.000160306525799886 38 0.999619058096077 0.00076188380784572 0.00038094190392286 39 0.999152027122437 0.00169594575512567 0.000847972877562836 40 0.998388225304869 0.00322354939026257 0.00161177469513129 41 0.997991555023313 0.0040168899533735 0.00200844497668675 42 0.999483499866368 0.00103300026726339 0.000516500133631695 43 0.999468181399912 0.00106363720017695 0.000531818600088474 44 0.999718264599379 0.000563470801241896 0.000281735400620948 45 0.999629497495575 0.000741005008849226 0.000370502504424613 46 0.999164066333486 0.00167186733302823 0.000835933666514114 47 0.998487853155667 0.00302429368866503 0.00151214684433251 48 0.997346667677195 0.00530666464561073 0.00265333232280537 49 0.994982723245615 0.0100345535087692 0.00501727675438461 50 0.989477086680946 0.0210458266381073 0.0105229133190536 51 0.978615487972674 0.0427690240546521 0.0213845120273261 52 0.991600512464463 0.0167989750710732 0.00839948753553661 53 0.97109332610466 0.0578133477906806 0.0289066738953403 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 39 0.829787234042553 NOK 5% type I error level 43 0.914893617021277 NOK 10% type I error level 44 0.936170212765957 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/108l9n1258721189.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/108l9n1258721189.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/1f80x1258721189.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/1f80x1258721189.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/25e2w1258721189.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/25e2w1258721189.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/356j01258721189.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/356j01258721189.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/4fv8s1258721189.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/4fv8s1258721189.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/5nhg11258721189.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/5nhg11258721189.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/6lr4u1258721189.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/6lr4u1258721189.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/7xuny1258721189.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/7xuny1258721189.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/8jj0t1258721189.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/8jj0t1258721189.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/9nd4v1258721189.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587213319yvp5p7k43axy2m/9nd4v1258721189.ps (open in new window)

Parameters (Session):

par1 = 0 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 0 ; 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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