multicollineariteit

*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, 19 Nov 2010 13:07:10 +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/Nov/19/t1290171960e3m7uvo97nlwnm0.htm/, Retrieved Fri, 19 Nov 2010 14:06:03 +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/Nov/19/t1290171960e3m7uvo97nlwnm0.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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101.82 107.34 93.63 99.85 101.76 101.68 107.34 93.63 99.91 102.37 101.68 107.34 93.63 99.87 102.38 102.45 107.34 96.13 99.86 102.86 102.45 107.34 96.13 100.10 102.87 102.45 107.34 96.13 100.10 102.92 102.45 107.34 96.13 100.12 102.95 102.45 107.34 96.13 99.95 103.02 102.45 112.60 96.13 99.94 104.08 102.52 112.60 96.13 100.18 104.16 102.52 112.60 96.13 100.31 104.24 102.85 112.60 96.13 100.65 104.33 102.85 112.61 96.13 100.65 104.73 102.85 112.61 96.13 100.69 104.86 103.25 112.61 96.13 101.26 105.03 103.25 112.61 98.73 101.26 105.62 103.25 112.61 98.73 101.38 105.63 103.25 112.61 98.73 101.38 105.63 104.45 112.61 98.73 101.38 105.94 104.45 112.61 98.73 101.44 106.61 104.45 118.65 98.73 101.40 107.69 104.80 118.65 98.73 101.40 107.78 104.80 118.65 98.73 100.58 107.93 105.29 118.65 98.73 100.58 108.48 105.29 114.29 98.73 100.58 108.14 105.29 114.29 98.73 100.59 108.48 105.29 114.29 98.73 100.81 108.48 106.04 114.29 101.67 100.75 108.89 105.94 114.29 101.67 100.75 108.93 105.94 114.29 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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Eendagsattracties[t] = -81.283726637103 -0.0643130276182469Bioscoop[t] -0.427201176091097Schouwburgabonnement[t] + 0.42473910932952HuurvaneenDVD[t] + 1.8217064126921`And.dienstenrecr.&cultuur`[t] -0.104368938422877t + 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) -81.283726637103 45.490792 -1.7868 0.079797 0.039898 Bioscoop -0.0643130276182469 0.096705 -0.665 0.508961 0.254481 Schouwburgabonnement -0.427201176091097 0.103081 -4.1443 0.000126 6.3e-05 HuurvaneenDVD 0.42473910932952 0.339972 1.2493 0.217137 0.108569 `And.dienstenrecr.&cultuur` 1.8217064126921 0.465457 3.9138 0.000265 0.000133 t -0.104368938422877 0.104995 -0.994 0.324808 0.162404 Multiple Linear Regression - Regression Statistics Multiple R 0.979106475949625 R-squared 0.958649491246493 Adjusted R-squared 0.954673480789425 F-TEST (value) 241.108392846992 F-TEST (DF numerator) 5 F-TEST (DF denominator) 52 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.14802596690307 Sum Squared Residuals 68.5341082755535 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 93.63 93.9948223328667 -0.36482233286675 2 93.63 95.0361824766126 -1.40618247661258 3 93.63 94.9330410379434 -1.30304103794344 4 96.13 95.6493227552534 0.480677244746571 5 96.13 95.6651082671966 0.464891732803438 6 96.13 95.6518246494083 0.478175350591712 7 96.13 95.6106016855528 0.51939831444723 8 96.13 95.5615465474323 0.568453452567691 9 96.13 95.1368608291306 0.993139170869396 10 96.13 95.2756638780289 0.854336121971097 11 96.13 95.3722475368342 0.757752463165774 12 96.13 95.5550201736117 0.574979826388338 13 96.13 96.1750617885047 -0.0450617885047232 14 96.13 96.324504248105 -0.194504248104989 15 96.13 96.7462014811103 -0.616201481110303 16 98.73 97.7166393261758 1.01336067382424 17 98.73 97.6814561449993 1.04854385500067 18 98.73 97.5770872065765 1.15291279342355 19 98.73 97.9602716229462 0.769728377053766 20 98.73 99.1019303275868 -0.371930327586841 21 98.73 98.367719646908 0.362280353091971 22 98.73 98.404794725961 0.325205274038938 23 98.73 98.2253956797918 0.5046043202082 24 98.73 99.0914518848166 -0.361451884816633 25 98.73 100.230299893836 -1.50029989383562 26 98.73 100.749558526821 -2.01955852682136 27 98.73 100.738632192451 -2.00863219245097 28 101.67 101.307443765958 0.362556234041603 29 101.67 101.282374386805 0.387625613194959 30 101.67 101.777278456895 -0.107278456895125 31 101.67 102.295211874038 -0.62521187403754 32 101.67 102.910303528492 -1.24030352849159 33 101.67 102.370613689498 -0.700613689498008 34 101.67 102.553866830508 -0.883866830508186 35 101.67 102.880261247275 -1.21026124727486 36 101.67 102.787171939184 -1.11717193918363 37 101.67 102.774453213227 -1.10445321322742 38 101.67 103.5431132271 -1.8731132270996 39 101.67 103.739276988726 -2.06927698872611 40 107.94 104.800800154426 3.13919984557381 41 107.94 104.877374357519 3.06262564248121 42 107.94 106.56577096936 1.37422903064019 43 107.94 106.590616155322 1.3493838446784 44 107.94 107.259303782709 0.680696217290513 45 107.94 107.980846376675 -0.0408463766750154 46 107.94 108.774288874514 -0.834288874513695 47 107.94 108.388169192 -0.448169192000423 48 107.94 108.43212294405 -0.4921229440501 49 107.94 108.356470570567 -0.416470570566868 50 107.94 108.838861361416 -0.898861361416381 51 107.94 108.226810210424 -0.286810210424231 52 110.3 109.009211003291 1.29078899670928 53 110.3 108.994970973588 1.30502902641233 54 110.3 109.520825221283 0.779174778717133 55 110.3 109.884566298514 0.415433701485716 56 110.3 110.284235372966 0.0157646270339175 57 110.3 110.275436039255 0.0245639607449071 58 110.3 110.814704127954 -0.514704127953556 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 1.0932548537229e-06 2.18650970744581e-06 0.999998906745146 10 2.72627639846001e-05 5.45255279692002e-05 0.999972737236015 11 1.94194921276017e-06 3.88389842552035e-06 0.999998058050787 12 0.000129628563667612 0.000259257127335225 0.999870371436332 13 0.000135996284332359 0.000271992568664717 0.999864003715668 14 4.43212445887518e-05 8.86424891775037e-05 0.999955678755411 15 1.20419053190271e-05 2.40838106380542e-05 0.99998795809468 16 0.00189565021843934 0.00379130043687867 0.99810434978156 17 0.00336397917477558 0.00672795834955117 0.996636020825224 18 0.00357456635014568 0.00714913270029137 0.996425433649854 19 0.00839592042299005 0.0167918408459801 0.99160407957701 20 0.0211809525179049 0.0423619050358097 0.978819047482095 21 0.0142925881942979 0.0285851763885957 0.985707411805702 22 0.0120434143548136 0.0240868287096272 0.987956585645186 23 0.0193608526231059 0.0387217052462119 0.980639147376894 24 0.0268757893722252 0.0537515787444504 0.973124210627775 25 0.0358242437613217 0.0716484875226433 0.964175756238678 26 0.0345272490420184 0.0690544980840368 0.965472750957982 27 0.0344891919089543 0.0689783838179087 0.965510808091046 28 0.0614132999912679 0.122826599982536 0.938586700008732 29 0.0739229086773515 0.147845817354703 0.926077091322648 30 0.0601811785720765 0.120362357144153 0.939818821427924 31 0.0391411307707481 0.0782822615414962 0.960858869229252 32 0.0481533815257214 0.0963067630514428 0.951846618474279 33 0.0343473684596218 0.0686947369192436 0.965652631540378 34 0.0217587037109314 0.0435174074218629 0.978241296289069 35 0.0150751671450438 0.0301503342900876 0.984924832854956 36 0.00945630986349538 0.0189126197269908 0.990543690136505 37 0.00600007216164375 0.0120001443232875 0.993999927838356 38 0.0144188564293422 0.0288377128586843 0.985581143570658 39 0.891203534637522 0.217592930724956 0.108796465362478 40 0.990248313136087 0.0195033737278263 0.00975168686391314 41 0.996741500303598 0.0065169993928033 0.00325849969640165 42 0.99358077727859 0.0128384454428215 0.00641922272141073 43 0.989081913732802 0.0218361725343956 0.0109180862671978 44 0.983393597228527 0.0332128055429467 0.0166064027714734 45 0.96437892246074 0.0712421550785191 0.0356210775392595 46 0.929629632264859 0.140740735470283 0.0703703677351414 47 0.863605069069171 0.272789861861658 0.136394930930829 48 0.753818479673416 0.492363040653169 0.246181520326584 49 0.598597997326502 0.802804005346995 0.401402002673498 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.268292682926829 NOK 5% type I error level 25 0.609756097560976 NOK 10% type I error level 33 0.804878048780488 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/10fws91290172019.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/10fws91290172019.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/1qvex1290172019.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/1qvex1290172019.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/2qvex1290172019.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/2qvex1290172019.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/31mdi1290172019.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/31mdi1290172019.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/41mdi1290172019.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/41mdi1290172019.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/51mdi1290172019.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/51mdi1290172019.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/6uvul1290172019.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/6uvul1290172019.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/7m4t61290172019.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/7m4t61290172019.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/8m4t61290172019.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/8m4t61290172019.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/9m4t61290172019.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0/9m4t61290172019.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 3 ; par2 = Do not include Seasonal 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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