dummy variabele model 5

*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, 24 Dec 2009 08:54:01 -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/Dec/24/t1261670100u7916lgzkgg3hbn.htm/, Retrieved Thu, 24 Dec 2009 16:55:12 +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/Dec/24/t1261670100u7916lgzkgg3hbn.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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8,6 0 8,5 8,3 8,5 0 8,6 8,5 8,2 0 8,5 8,6 8,1 0 8,2 8,5 7,9 0 8,1 8,2 8,6 0 7,9 8,1 8,7 0 8,6 7,9 8,7 0 8,7 8,6 8,5 0 8,7 8,7 8,4 0 8,5 8,7 8,5 0 8,4 8,5 8,7 0 8,5 8,4 8,7 0 8,7 8,5 8,6 0 8,7 8,7 8,5 0 8,6 8,7 8,3 0 8,5 8,6 8 0 8,3 8,5 8,2 0 8 8,3 8,1 0 8,2 8 8,1 0 8,1 8,2 8 0 8,1 8,1 7,9 0 8 8,1 7,9 0 7,9 8 8 0 7,9 7,9 8 0 8 7,9 7,9 0 8 8 8 0 7,9 8 7,7 0 8 7,9 7,2 0 7,7 8 7,5 0 7,2 7,7 7,3 0 7,5 7,2 7 0 7,3 7,5 7 0 7 7,3 7 0 7 7 7,2 0 7 7 7,3 0 7,2 7 7,1 0 7,3 7,2 6,8 0 7,1 7,3 6,4 0 6,8 7,1 6,1 0 6,4 6,8 6,5 0 6,1 6,4 7,7 0 6,5 6,1 7,9 0 7,7 6,5 7,5 1 7,9 7,7 6,9 1 7,5 7,9 6,6 1 6,9 7,5 6,9 1 6,6 6,9 7,7 1 6,9 6,6 8 1 7,7 6,9 8 1 8 7,7 7,7 1 8 8 7,3 1 7,7 8 7,4 1 7,3 7,7 8,1 1 7,4 7,3 8,3 1 8,1 7,4 8,2 1 8,3 8,1

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 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 2.31498095419434 + 0.213396160967166X[t] + 1.40972702884716Y1[t] -0.654418798366198Y2[t] -0.282977229990250M1[t] -0.266105111881319M2[t] -0.260737180764732M3[t] -0.307298095078962M4[t] -0.161628891531660M5[t] + 0.439218859498125M6[t] -0.450229842503455M7[t] -0.293534565143451M8[t] -0.293447343788018M9[t] -0.205758116291212M10[t] -0.0167625315974305M11[t] -0.0100239357202803t + 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) 2.31498095419434 0.668448 3.4632 0.001286 0.000643 X 0.213396160967166 0.112491 1.897 0.065064 0.032532 Y1 1.40972702884716 0.125498 11.2331 0 0 Y2 -0.654418798366198 0.131587 -4.9733 1.3e-05 6e-06 M1 -0.282977229990250 0.12875 -2.1979 0.033808 0.016904 M2 -0.266105111881319 0.131076 -2.0302 0.049027 0.024514 M3 -0.260737180764732 0.134806 -1.9342 0.060189 0.030094 M4 -0.307298095078962 0.135857 -2.2619 0.029207 0.014603 M5 -0.161628891531660 0.136675 -1.1826 0.243957 0.121979 M6 0.439218859498125 0.130478 3.3662 0.001693 0.000847 M7 -0.450229842503455 0.140175 -3.2119 0.002604 0.001302 M8 -0.293534565143451 0.132759 -2.211 0.032813 0.016406 M9 -0.293447343788018 0.141087 -2.0799 0.043987 0.021993 M10 -0.205758116291212 0.141655 -1.4525 0.154153 0.077076 M11 -0.0167625315974305 0.137847 -0.1216 0.903823 0.451911 t -0.0100239357202803 0.004123 -2.4315 0.01961 0.009805 Multiple Linear Regression - Regression Statistics Multiple R 0.96975621217004 R-squared 0.940427111042382 Adjusted R-squared 0.918087277683275 F-TEST (value) 42.096424620778 F-TEST (DF numerator) 15 F-TEST (DF denominator) 40 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.189042547101552 Sum Squared Residuals 1.42948338458570 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.6 8.57298350724525 0.0270164927547522 2 8.5 8.58992063284537 -0.089920632845368 3 8.2 8.37885004552034 -0.17885004552034 4 8.1 7.9647889666683 0.135211033331702 5 7.9 8.15578717112047 -0.255787171120464 6 8.6 8.53010746049716 0.0698925395028428 7 8.7 8.74832750264155 -0.0483275026415501 8 8.7 8.57787838830965 0.122121611690351 9 8.5 8.50249979410818 -0.00249979410818188 10 8.4 8.29821968011528 0.101780319884724 11 8.5 8.4671023858773 0.0328976141226986 12 8.7 8.68025556447579 0.0197444355242121 13 8.7 8.60375792469807 0.0962420753019308 14 8.6 8.47972234741348 0.120277652586520 15 8.5 8.33409363992507 0.165906360074929 16 8.3 8.20197796684246 0.0980220331575366 17 8 8.12111970873667 -0.121119708736675 18 8.2 8.41990917506527 -0.219909175065269 19 8.1 7.9987075826227 0.101292417377299 20 8.1 7.87352246170447 0.226477538295532 21 8 7.92902762717624 0.0709723728237596 22 7.9 7.86572021606805 0.0342797839319496 23 7.9 7.96916104199346 -0.0691610419934553 24 8 8.04134151770723 -0.0413415177072254 25 8 7.88931305488141 0.110686945118589 26 7.9 7.83071935743344 0.0692806425665585 27 8 7.68509064994503 0.314909350054967 28 7.7 7.83492038263186 -0.134920382631857 29 7.2 7.48220566196811 -0.282205661968112 30 7.5 7.56449160236389 -0.0644916023638944 31 7.3 7.41514647247928 -0.115146472479282 32 7 7.08354676883971 -0.0835467688397126 33 7 6.78157570549396 0.218424294506043 34 7 7.05556663678034 -0.0555666367803428 35 7.2 7.23453828575384 -0.0345382857538436 36 7.3 7.52322228740043 -0.223222287400427 37 7.1 7.24031006490137 -0.140310064901373 38 6.8 6.89977096168397 -0.0997709616839707 39 6.4 6.60308060809937 -0.203080608099369 40 6.1 6.17893058603585 -0.0789305860358536 41 6.5 6.1534252645552 0.346574735444795 42 7.7 7.50446553091343 0.195534469086566 43 7.9 8.03489780846169 -0.13489780846169 44 7.5 7.89160815879857 -0.391608158798573 45 6.9 7.18689687322162 -0.286896873221621 46 6.6 6.68049346703633 -0.0804934670363303 47 6.9 6.8291982863754 0.0708017136246003 48 7.7 7.45518063041656 0.24481936958344 49 8 8.0936354482739 -0.0936354482738995 50 8 7.99986670062374 0.000133299376260403 51 7.7 7.79888505651019 -0.0988850565101869 52 7.3 7.31938209782153 -0.0193820978215279 53 7.4 7.08746219361954 0.312537806380456 54 8.1 8.08102623116024 0.0189737688397549 55 8.3 8.10292063379478 0.197079366205223 56 8.2 8.0734442223476 0.126555777652404 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.48840438535538 0.97680877071076 0.51159561464462 20 0.358416718574836 0.716833437149672 0.641583281425164 21 0.221790342686721 0.443580685373442 0.778209657313279 22 0.140791008053726 0.281582016107452 0.859208991946274 23 0.0847214645095539 0.169442929019108 0.915278535490446 24 0.0448659510587191 0.0897319021174381 0.95513404894128 25 0.0278706760674612 0.0557413521349223 0.97212932393254 26 0.0153577752851903 0.0307155505703806 0.98464222471481 27 0.110413905589405 0.220827811178810 0.889586094410595 28 0.158886631056787 0.317773262113574 0.841113368943213 29 0.148416605478291 0.296833210956582 0.851583394521709 30 0.10176642180543 0.20353284361086 0.89823357819457 31 0.0881543189514336 0.176308637902867 0.911845681048566 32 0.136238082764914 0.272476165529829 0.863761917235086 33 0.571864180000288 0.856271639999424 0.428135819999712 34 0.542511889786503 0.914976220426995 0.457488110213497 35 0.600259556002104 0.799480887995792 0.399740443997896 36 0.508370844863831 0.983258310272339 0.491629155136169 37 0.486514085355074 0.973028170710149 0.513485914644926 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 1 0.0526315789473684 NOK 10% type I error level 3 0.157894736842105 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/10u4j81261670035.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/10u4j81261670035.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/1ii751261670035.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/1ii751261670035.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/2or5h1261670035.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/2or5h1261670035.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/3t5b91261670035.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/3t5b91261670035.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/4arkp1261670035.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/4arkp1261670035.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/51z9z1261670035.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/51z9z1261670035.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/603kh1261670035.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/603kh1261670035.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/7wuy91261670035.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/7wuy91261670035.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/8aduv1261670035.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/8aduv1261670035.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/9cemn1261670035.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/24/t1261670100u7916lgzkgg3hbn/9cemn1261670035.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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