Model 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: Fri, 20 Nov 2009 04:15:35 -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/t12587159997dbw7i1r2xsug0k.htm/, Retrieved Fri, 20 Nov 2009 12:20:12 +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/t12587159997dbw7i1r2xsug0k.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:

» Textbox « » Textfile « » CSV «

98.71 153.4 98.54 145 98.2 137.7 96.92 148.3 99.06 152.2 99.65 169.4 99.82 168.6 99.99 161.1 100.33 174.1 99.31 179 101.1 190.6 101.1 190 100.93 181.6 100.85 174.8 100.93 180.5 99.6 196.8 101.88 193.8 101.81 197 102.38 216.3 102.74 221.4 102.82 217.9 101.72 229.7 103.47 227.4 102.98 204.2 102.68 196.6 102.9 198.8 103.03 207.5 101.29 190.7 103.69 201.6 103.68 210.5 104.2 223.5 104.08 223.8 104.16 231.2 103.05 244 104.66 234.7 104.46 250.2 104.95 265.7 105.85 287.6 106.23 283.3 104.86 295.4 107.44 312.3 108.23 333.8 108.45 347.7 109.39 383.2 110.15 407.1 109.13 413.6 110.28 362.7 110.17 321.9 109.99 239.4 109.26 191 109.11 159.7 107.06 163.4 109.53 157.6 108.92 166.2 109.24 176.7 109.12 198.3 109 226.2 107.23 216.2 109.49 235.9 109.04 226.9

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 96.4375783171408 + 0.0352788806088186X[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) 96.4375783171408 1.436953 67.1126 0 0 X 0.0352788806088186 0.00618 5.7087 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.599788787480733 R-squared 0.359746589587608 Adjusted R-squared 0.348707737683946 F-TEST (value) 32.5891308921600 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 4.09724077887752e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.10991109710113 Sum Squared Residuals 560.949727848618 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 98.71 101.849358602534 -3.13935860253352 2 98.54 101.553016005420 -3.0130160054195 3 98.2 101.295480176975 -3.09548017697513 4 96.92 101.669436311429 -4.74943631142861 5 99.06 101.807023945803 -2.747023945803 6 99.65 102.413820692275 -2.76382069227468 7 99.82 102.385597587788 -2.56559758778763 8 99.99 102.121005983221 -2.13100598322149 9 100.33 102.579631431136 -2.24963143113613 10 99.31 102.752497946119 -3.44249794611934 11 101.1 103.161732961182 -2.06173296118164 12 101.1 103.140565632816 -2.04056563281635 13 100.93 102.844223035702 -1.91422303570226 14 100.85 102.604326647562 -1.75432664756231 15 100.93 102.805416267033 -1.87541626703256 16 99.6 103.380462020956 -3.78046202095632 17 101.88 103.274625379130 -1.39462537912986 18 101.81 103.387517797078 -1.57751779707807 19 102.38 104.068400192828 -1.68840019282828 20 102.74 104.248322483933 -1.50832248393326 21 102.82 104.124846401802 -1.30484640180239 22 101.72 104.541137192986 -2.82113719298645 23 103.47 104.459995767586 -0.989995767586163 24 102.98 103.641525737462 -0.661525737461566 25 102.68 103.373406244835 -0.693406244834542 26 102.9 103.451019782174 -0.551019782173944 27 103.03 103.757946043471 -0.72794604347067 28 101.29 103.165260849243 -1.87526084924251 29 103.69 103.549800647879 0.140199352121356 30 103.68 103.863782685297 -0.183782685297121 31 104.2 104.322408133212 -0.122408133211766 32 104.08 104.332991797394 -0.252991797394417 33 104.16 104.594055513900 -0.434055513899676 34 103.05 105.045625185693 -1.99562518569255 35 104.66 104.717531596031 -0.0575315960305406 36 104.46 105.264354245467 -0.804354245467232 37 104.95 105.811176894904 -0.861176894903911 38 105.85 106.583784380237 -0.733784380237048 39 106.23 106.432085193619 -0.202085193619118 40 104.86 106.858959648986 -1.99895964898583 41 107.44 107.455172731275 -0.0151727312748642 42 108.23 108.213668664364 0.0163313356355422 43 108.45 108.704045104827 -0.254045104827037 44 109.39 109.956445366440 -0.5664453664401 45 110.15 110.799610612991 -0.649610612990861 46 109.13 111.028923336948 -1.89892333694819 47 110.28 109.233228313959 1.04677168604068 48 110.17 107.793849985120 2.37615001488048 49 109.99 104.883342334892 5.10665766510801 50 109.26 103.175844513425 6.08415548657484 51 109.11 102.071615550369 7.03838444963086 52 107.06 102.202147408622 4.85785259137823 53 109.53 101.997529901091 7.53247009890938 54 108.92 102.300928274326 6.61907172567354 55 109.24 102.671356520719 6.56864347928094 56 109.12 103.433380341870 5.68661965813046 57 109 104.417661110856 4.58233888914442 58 107.23 104.064872304767 3.16512769523261 59 109.49 104.759866252761 4.73013374723887 60 109.04 104.442356327282 4.59764367271825 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0358554199903961 0.0717108399807923 0.964144580009604 6 0.00972985846649186 0.0194597169329837 0.990270141533508 7 0.00247273084140800 0.00494546168281601 0.997527269158592 8 0.000955663219489067 0.00191132643897813 0.99904433678051 9 0.000227048790089961 0.000454097580179922 0.99977295120991 10 0.000156629096389302 0.000313258192778603 0.99984337090361 11 4.15943811837635e-05 8.3188762367527e-05 0.999958405618816 12 1.05934263619861e-05 2.11868527239722e-05 0.999989406573638 13 3.25549715380238e-06 6.51099430760477e-06 0.999996744502846 14 1.46578474621620e-06 2.93156949243240e-06 0.999998534215254 15 4.54946237088145e-07 9.0989247417629e-07 0.999999545053763 16 3.42981794052023e-06 6.85963588104046e-06 0.99999657018206 17 1.83136944449096e-06 3.66273888898191e-06 0.999998168630555 18 7.4450303426787e-07 1.48900606853574e-06 0.999999255496966 19 2.61414513116035e-07 5.2282902623207e-07 0.999999738585487 20 8.82399348133479e-08 1.76479869626696e-07 0.999999911760065 21 3.10695095238061e-08 6.21390190476123e-08 0.99999996893049 22 8.63965045551294e-08 1.72793009110259e-07 0.999999913603495 23 4.08004903738326e-08 8.16009807476652e-08 0.99999995919951 24 5.52666573714547e-08 1.10533314742909e-07 0.999999944733343 25 8.79496479926245e-08 1.75899295985249e-07 0.999999912050352 26 1.42799719221884e-07 2.85599438443768e-07 0.99999985720028 27 1.3714584700427e-07 2.7429169400854e-07 0.999999862854153 28 2.35718134081456e-07 4.71436268162913e-07 0.999999764281866 29 1.02297625952874e-06 2.04595251905749e-06 0.99999897702374 30 1.77203587476277e-06 3.54407174952554e-06 0.999998227964125 31 2.00869973245732e-06 4.01739946491464e-06 0.999997991300268 32 2.54377576228906e-06 5.08755152457811e-06 0.999997456224238 33 3.38905168206004e-06 6.77810336412008e-06 0.999996610948318 34 6.29058400230478e-05 0.000125811680046096 0.999937094159977 35 0.000146733746550689 0.000293467493101378 0.99985326625345 36 0.000643843745028401 0.00128768749005680 0.999356156254972 37 0.00324458624598546 0.00648917249197092 0.996755413754015 38 0.0100803283896061 0.0201606567792123 0.989919671610394 39 0.0211032773011086 0.0422065546022172 0.978896722698891 40 0.51233636412883 0.97532727174234 0.48766363587117 41 0.642020127465142 0.715959745069717 0.357979872534858 42 0.667321641939035 0.66535671612193 0.332678358060965 43 0.701304787582416 0.597390424835169 0.298695212417584 44 0.681582037841102 0.636835924317796 0.318417962158898 45 0.643312442136808 0.713375115726384 0.356687557863192 46 0.759935622682247 0.480128754635506 0.240064377317753 47 0.700647301508385 0.598705396983231 0.299352698491616 48 0.725634100296243 0.548731799407514 0.274365899703757 49 0.928403251170304 0.143193497659391 0.0715967488296957 50 0.971359940815886 0.0572801183682282 0.0286400591841141 51 0.981986333561158 0.0360273328776831 0.0180136664388415 52 0.994851573244166 0.0102968535116687 0.00514842675583434 53 0.991799731804517 0.0164005363909658 0.0082002681954829 54 0.97880250409199 0.0423949918160204 0.0211974959080102 55 0.950017765340781 0.0999644693184371 0.0499822346592186 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 31 0.607843137254902 NOK 5% type I error level 38 0.745098039215686 NOK 10% type I error level 41 0.80392156862745 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/10vack1258715731.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/10vack1258715731.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/1nsi01258715731.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/1nsi01258715731.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/247gz1258715731.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/247gz1258715731.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/3rrvp1258715731.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/3rrvp1258715731.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/4nqjo1258715731.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/4nqjo1258715731.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/597ww1258715731.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/597ww1258715731.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/6w3g61258715731.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/6w3g61258715731.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/7e2uo1258715731.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/7e2uo1258715731.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/89hj61258715731.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/89hj61258715731.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/9nvae1258715731.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587159997dbw7i1r2xsug0k/9nvae1258715731.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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