dummie eenvoudig model belgie

*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 Dec 2008 03:30:26 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5.htm/, Retrieved Fri, 19 Dec 2008 11:31:17 +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/2008/Dec/19/t1229682677e9cyw90l0bv45x5.htm/}, year = {2008}, } @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 = {2008}, 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:

» Textbox « » Textfile « » CSV «

565464 0 547344 0 554788 0 562325 0 560854 0 555332 0 543599 0 536662 0 542722 0 593530 0 610763 0 612613 0 611324 0 594167 0 595454 0 590865 0 589379 0 584428 0 573100 0 567456 0 569028 0 620735 0 628884 0 628232 0 612117 0 595404 0 597141 0 593408 0 590072 0 579799 0 574205 0 572775 0 572942 0 619567 0 625809 0 619916 0 587625 0 565742 0 557274 0 560576 1 548854 1 531673 1 525919 1 511038 1 498662 1 555362 1 564591 1 541657 1 527070 1 509846 1 514258 1 516922 1 507561 1 492622 1 490243 1 469357 1 477580 1 528379 1 533590 1 517945 1

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 11 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 584688.307692308 -64511.8791208791X[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) 584688.307692308 4151.892684 140.8245 0 0 X -64511.8791208791 7017.979534 -9.1924 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.77005299297022 R-squared 0.592981611982393 Adjusted R-squared 0.585964053568297 F-TEST (value) 84.4997044543625 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 6.3948846218409e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 25928.5614989401 Sum Squared Residuals 38992837481.4506 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 565464 584688.307692309 -19224.3076923086 2 547344 584688.307692308 -37344.3076923077 3 554788 584688.307692308 -29900.3076923077 4 562325 584688.307692308 -22363.3076923077 5 560854 584688.307692308 -23834.3076923077 6 555332 584688.307692308 -29356.3076923077 7 543599 584688.307692308 -41089.3076923077 8 536662 584688.307692308 -48026.3076923077 9 542722 584688.307692308 -41966.3076923077 10 593530 584688.307692308 8841.69230769233 11 610763 584688.307692308 26074.6923076923 12 612613 584688.307692308 27924.6923076923 13 611324 584688.307692308 26635.6923076923 14 594167 584688.307692308 9478.69230769233 15 595454 584688.307692308 10765.6923076923 16 590865 584688.307692308 6176.69230769233 17 589379 584688.307692308 4690.69230769233 18 584428 584688.307692308 -260.307692307667 19 573100 584688.307692308 -11588.3076923077 20 567456 584688.307692308 -17232.3076923077 21 569028 584688.307692308 -15660.3076923077 22 620735 584688.307692308 36046.6923076923 23 628884 584688.307692308 44195.6923076923 24 628232 584688.307692308 43543.6923076923 25 612117 584688.307692308 27428.6923076923 26 595404 584688.307692308 10715.6923076923 27 597141 584688.307692308 12452.6923076923 28 593408 584688.307692308 8719.69230769233 29 590072 584688.307692308 5383.69230769233 30 579799 584688.307692308 -4889.30769230767 31 574205 584688.307692308 -10483.3076923077 32 572775 584688.307692308 -11913.3076923077 33 572942 584688.307692308 -11746.3076923077 34 619567 584688.307692308 34878.6923076923 35 625809 584688.307692308 41120.6923076923 36 619916 584688.307692308 35227.6923076923 37 587625 584688.307692308 2936.69230769233 38 565742 584688.307692308 -18946.3076923077 39 557274 584688.307692308 -27414.3076923077 40 560576 520176.428571429 40399.5714285714 41 548854 520176.428571429 28677.5714285714 42 531673 520176.428571429 11496.5714285714 43 525919 520176.428571429 5742.57142857143 44 511038 520176.428571429 -9138.42857142857 45 498662 520176.428571429 -21514.4285714286 46 555362 520176.428571429 35185.5714285714 47 564591 520176.428571429 44414.5714285714 48 541657 520176.428571429 21480.5714285714 49 527070 520176.428571429 6893.57142857143 50 509846 520176.428571429 -10330.4285714286 51 514258 520176.428571429 -5918.42857142857 52 516922 520176.428571429 -3254.42857142857 53 507561 520176.428571429 -12615.4285714286 54 492622 520176.428571429 -27554.4285714286 55 490243 520176.428571429 -29933.4285714286 56 469357 520176.428571429 -50819.4285714286 57 477580 520176.428571429 -42596.4285714286 58 528379 520176.428571429 8202.57142857143 59 533590 520176.428571429 13413.5714285714 60 517945 520176.428571429 -2231.42857142857 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0431374997972793 0.0862749995945585 0.95686250020272 6 0.0125338662905779 0.0250677325811557 0.987466133709422 7 0.0136915733756852 0.0273831467513704 0.986308426624315 8 0.0264544957981383 0.0529089915962766 0.973545504201862 9 0.0201963631939329 0.0403927263878659 0.979803636806067 10 0.183357782277810 0.366715564555621 0.81664221772219 11 0.570766803279893 0.858466393440214 0.429233196720107 12 0.773611379476835 0.452777241046329 0.226388620523165 13 0.852719144179439 0.294561711641122 0.147280855820561 14 0.830365002350519 0.339269995298962 0.169634997649481 15 0.804939920851867 0.390120158296267 0.195060079148133 16 0.760875109036246 0.478249781927508 0.239124890963754 17 0.706912494401407 0.586175011197185 0.293087505598593 18 0.639825523507958 0.720348952984085 0.360174476492042 19 0.575147266793685 0.84970546641263 0.424852733206315 20 0.527518791387305 0.94496241722539 0.472481208612695 21 0.479324737711916 0.958649475423832 0.520675262288084 22 0.591941475999033 0.816117048001935 0.408058524000967 23 0.737495607855687 0.525008784288627 0.262504392144313 24 0.83206463190508 0.335870736189841 0.167935368094920 25 0.832430014884371 0.335139970231258 0.167569985115629 26 0.787891798767473 0.424216402465055 0.212108201232527 27 0.740185390571649 0.519629218856702 0.259814609428351 28 0.680577585110782 0.638844829778437 0.319422414889218 29 0.611691974748491 0.776616050503018 0.388308025251509 30 0.541038992185947 0.917922015628106 0.458961007814053 31 0.481625022030282 0.963250044060564 0.518374977969718 32 0.430425301708665 0.860850603417331 0.569574698291335 33 0.386023819945456 0.772047639890913 0.613976180054544 34 0.413860246557343 0.827720493114686 0.586139753442657 35 0.508548693440963 0.982902613118074 0.491451306559037 36 0.60706886573098 0.78586226853804 0.39293113426902 37 0.562514045964431 0.874971908071139 0.437485954035569 38 0.501963438243579 0.996073123512842 0.498036561756421 39 0.452254306159441 0.904508612318883 0.547745693840559 40 0.504119104502622 0.991761790994755 0.495880895497378 41 0.508624573319146 0.982750853361707 0.491375426680854 42 0.459028395128307 0.918056790256615 0.540971604871693 43 0.396510975391779 0.793021950783559 0.60348902460822 44 0.340703594010359 0.681407188020718 0.659296405989641 45 0.318474392077535 0.63694878415507 0.681525607922465 46 0.394498520880316 0.788997041760632 0.605501479119684 47 0.647905579700741 0.704188840598517 0.352094420299259 48 0.703793642638615 0.592412714722769 0.296206357361385 49 0.674646235579864 0.650707528840271 0.325353764420136 50 0.587001040279759 0.825997919440482 0.412998959720241 51 0.495247323665333 0.990494647330665 0.504752676334667 52 0.409880015459667 0.819760030919333 0.590119984540333 53 0.302900517092728 0.605801034185457 0.697099482907272 54 0.219542790126657 0.439085580253313 0.780457209873343 55 0.150019331156555 0.300038662313109 0.849980668843445 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 3 0.0588235294117647 NOK 10% type I error level 5 0.0980392156862745 OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/10o8gn1229682600.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/10o8gn1229682600.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/15j6q1229682600.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/15j6q1229682600.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/2zdbv1229682600.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/2zdbv1229682600.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/37qre1229682600.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/37qre1229682600.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/4eg6d1229682600.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/4eg6d1229682600.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/5dbzg1229682600.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/5dbzg1229682600.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/6sxzi1229682600.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/6sxzi1229682600.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/74aku1229682600.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/74aku1229682600.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/8w9ah1229682600.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/8w9ah1229682600.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/98nop1229682600.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/19/t1229682677e9cyw90l0bv45x5/98nop1229682600.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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