berekening 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: Thu, 19 Nov 2009 08:41:37 -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/19/t1258645429txa2sdh7o31ug9w.htm/, Retrieved Thu, 19 Nov 2009 16:44:01 +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/19/t1258645429txa2sdh7o31ug9w.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:

sdws7

Dataseries X:

» Textbox « » Textfile « » CSV «

593530,00 0 610943,00 0 612613,00 0 611324,00 0 594167,00 0 595454,00 0 590865,00 0 589379,00 0 584428,00 0 573100,00 0 567456,00 0 569028,00 0 620735,00 0 628884,00 0 628232,00 0 612117,00 0 595404,00 0 597141,00 0 593408,00 0 590072,00 0 579799,00 0 574205,00 0 572775,00 0 572942,00 0 619567,00 0 625809,00 0 619916,00 0 587625,00 0 565742,00 0 557274,00 0 560576,00 0 548854,00 0 531673,00 0 525919,00 0 511038,00 0 498662,00 0 555362,00 0 564591,00 0 541657,00 0 527070,00 0 509846,00 0 514258,00 0 516922,00 0 507561,00 0 492622,00 0 490243,00 0 469357,00 0 477580,00 0 528379,00 1 533590,00 1 517945,00 1 506174,00 1 501866,00 1 516141,00 1 528222,00 1 532638,00 1 536322,00 1 536535,00 1 523597,00 1 536214,00 1 586570,00 1 596594,00 1 580523,00 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation werklozen[t] = + 566202.604166667 -28781.9375`crisis `[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) 566202.604166667 5863.381902 96.5659 0 0 `crisis ` -28781.9375 12016.357134 -2.3952 0.019695 0.009848 Multiple Linear Regression - Regression Statistics Multiple R 0.293199650306945 R-squared 0.0859660349401147 Adjusted R-squared 0.0709818715784772 F-TEST (value) 5.73712611544302 F-TEST (DF numerator) 1 F-TEST (DF denominator) 61 p-value 0.0196951101646206 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 40622.7014338511 Sum Squared Residuals 100662436178.812 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 593530 566202.604166667 27327.3958333327 2 610943 566202.604166667 44740.3958333334 3 612613 566202.604166667 46410.3958333333 4 611324 566202.604166667 45121.3958333333 5 594167 566202.604166667 27964.3958333333 6 595454 566202.604166667 29251.3958333333 7 590865 566202.604166667 24662.3958333333 8 589379 566202.604166667 23176.3958333333 9 584428 566202.604166667 18225.3958333333 10 573100 566202.604166667 6897.39583333335 11 567456 566202.604166667 1253.39583333335 12 569028 566202.604166667 2825.39583333335 13 620735 566202.604166667 54532.3958333333 14 628884 566202.604166667 62681.3958333333 15 628232 566202.604166667 62029.3958333333 16 612117 566202.604166667 45914.3958333333 17 595404 566202.604166667 29201.3958333333 18 597141 566202.604166667 30938.3958333333 19 593408 566202.604166667 27205.3958333333 20 590072 566202.604166667 23869.3958333333 21 579799 566202.604166667 13596.3958333333 22 574205 566202.604166667 8002.39583333335 23 572775 566202.604166667 6572.39583333335 24 572942 566202.604166667 6739.39583333335 25 619567 566202.604166667 53364.3958333333 26 625809 566202.604166667 59606.3958333333 27 619916 566202.604166667 53713.3958333333 28 587625 566202.604166667 21422.3958333333 29 565742 566202.604166667 -460.604166666652 30 557274 566202.604166667 -8928.60416666665 31 560576 566202.604166667 -5626.60416666665 32 548854 566202.604166667 -17348.6041666667 33 531673 566202.604166667 -34529.6041666667 34 525919 566202.604166667 -40283.6041666667 35 511038 566202.604166667 -55164.6041666667 36 498662 566202.604166667 -67540.6041666667 37 555362 566202.604166667 -10840.6041666667 38 564591 566202.604166667 -1611.60416666665 39 541657 566202.604166667 -24545.6041666667 40 527070 566202.604166667 -39132.6041666667 41 509846 566202.604166667 -56356.6041666667 42 514258 566202.604166667 -51944.6041666667 43 516922 566202.604166667 -49280.6041666667 44 507561 566202.604166667 -58641.6041666667 45 492622 566202.604166667 -73580.6041666667 46 490243 566202.604166667 -75959.6041666667 47 469357 566202.604166667 -96845.6041666667 48 477580 566202.604166667 -88622.6041666667 49 528379 537420.666666667 -9041.66666666667 50 533590 537420.666666667 -3830.66666666667 51 517945 537420.666666667 -19475.6666666667 52 506174 537420.666666667 -31246.6666666667 53 501866 537420.666666667 -35554.6666666667 54 516141 537420.666666667 -21279.6666666667 55 528222 537420.666666667 -9198.66666666667 56 532638 537420.666666667 -4782.66666666667 57 536322 537420.666666667 -1098.66666666667 58 536535 537420.666666667 -885.666666666667 59 523597 537420.666666667 -13823.6666666667 60 536214 537420.666666667 -1206.66666666667 61 586570 537420.666666667 49149.3333333333 62 596594 537420.666666667 59173.3333333333 63 580523 537420.666666667 43102.3333333333 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0279517691024954 0.0559035382049908 0.972048230897505 6 0.00869413446564348 0.0173882689312870 0.991305865534357 7 0.00349247328554265 0.0069849465710853 0.996507526714457 8 0.00140844312283937 0.00281688624567874 0.99859155687716 9 0.000788433825877486 0.00157686765175497 0.999211566174123 10 0.00115339681775454 0.00230679363550909 0.998846603182245 11 0.00167504342422635 0.00335008684845269 0.998324956575774 12 0.00144121322400796 0.00288242644801593 0.998558786775992 13 0.00213760893429407 0.00427521786858814 0.997862391065706 14 0.00458012158931647 0.00916024317863295 0.995419878410684 15 0.00736347951758157 0.0147269590351631 0.992636520482418 16 0.00544563108756515 0.0108912621751303 0.994554368912435 17 0.00312522830119974 0.00625045660239947 0.9968747716988 18 0.0018204518101216 0.0036409036202432 0.998179548189878 19 0.00106836983085288 0.00213673966170576 0.998931630169147 20 0.000647145742953183 0.00129429148590637 0.999352854257047 21 0.000480776502670846 0.000961553005341693 0.999519223497329 22 0.000424383444266399 0.000848766888532798 0.999575616555734 23 0.000379636071582501 0.000759272143165003 0.999620363928418 24 0.000326157116766365 0.00065231423353273 0.999673842883234 25 0.000774182382634728 0.00154836476526946 0.999225817617365 26 0.00356132824813359 0.00712265649626717 0.996438671751866 27 0.0140683921967972 0.0281367843935944 0.985931607803203 28 0.0206972230416772 0.0413944460833543 0.979302776958323 29 0.0339502847463865 0.067900569492773 0.966049715253613 30 0.0600800529988217 0.120160105997643 0.939919947001178 31 0.0947842284175064 0.189568456835013 0.905215771582494 32 0.156160868136609 0.312321736273219 0.84383913186339 33 0.273491405070326 0.546982810140653 0.726508594929674 34 0.401377615893614 0.802755231787227 0.598622384106386 35 0.566744733885308 0.866510532229384 0.433255266114692 36 0.730872179373394 0.538255641253212 0.269127820626606 37 0.765685671343246 0.468628657313509 0.234314328656754 38 0.849690269975741 0.300619460048518 0.150309730024259 39 0.884544896433479 0.230910207133042 0.115455103566521 40 0.905218753978005 0.189562492043989 0.0947812460219947 41 0.919359745477394 0.161280509045213 0.0806402545226064 42 0.927717060495249 0.144565879009502 0.072282939504751 43 0.937045224135798 0.125909551728403 0.0629547758642016 44 0.943313352097692 0.113373295804615 0.0566866479023076 45 0.94625768901829 0.107484621963419 0.0537423109817097 46 0.946709248321458 0.106581503357083 0.0532907516785417 47 0.949970085622192 0.100059828755617 0.0500299143778083 48 0.943237017017703 0.113525965964594 0.0567629829822969 49 0.911160260174158 0.177679479651684 0.088839739825842 50 0.863471034311035 0.27305793137793 0.136528965688965 51 0.818543007675382 0.362913984649236 0.181456992324618 52 0.804818967769322 0.390362064461355 0.195181032230678 53 0.825691728022087 0.348616543955826 0.174308271977913 54 0.8057668806558 0.388466238688400 0.194233119344200 55 0.747241430116796 0.505517139766409 0.252758569883204 56 0.6675082760124 0.664983447975201 0.332491723987601 57 0.565920702525185 0.86815859494963 0.434079297474815 58 0.460758804119815 0.92151760823963 0.539241195880185 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 18 0.333333333333333 NOK 5% type I error level 23 0.425925925925926 NOK 10% type I error level 25 0.462962962962963 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/10xhtb1258645293.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/10xhtb1258645293.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/1etdf1258645293.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/1etdf1258645293.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/2k4py1258645293.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/2k4py1258645293.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/3amto1258645293.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/3amto1258645293.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/4weix1258645293.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/4weix1258645293.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/5rbmm1258645293.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/5rbmm1258645293.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/6mtwe1258645293.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/6mtwe1258645293.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/77xxs1258645293.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/77xxs1258645293.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/8d5a01258645293.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/8d5a01258645293.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/98npo1258645293.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258645429txa2sdh7o31ug9w/98npo1258645293.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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