*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: Wed, 18 Nov 2009 10:18:34 -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/18/t1258564967voigo6vjkccxoi0.htm/, Retrieved Wed, 18 Nov 2009 18:23:00 +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/18/t1258564967voigo6vjkccxoi0.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:

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Dataseries X:

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8.9 1.6 8.8 1.8 8.3 1.6 7.5 1.5 7.2 1.5 7.4 1.3 8.8 1.4 9.3 1.4 9.3 1.3 8.7 1.3 8.2 1.2 8.3 1.1 8.5 1.4 8.6 1.2 8.5 1.5 8.2 1.1 8.1 1.3 7.9 1.5 8.6 1.1 8.7 1.4 8.7 1.3 8.5 1.5 8.4 1.6 8.5 1.7 8.7 1.1 8.7 1.6 8.6 1.3 8.5 1.7 8.3 1.6 8 1.7 8.2 1.9 8.1 1.8 8.1 1.9 8 1.6 7.9 1.5 7.9 1.6 8 1.6 8 1.7 7.9 2 8 2 7.7 1.9 7.2 1.7 7.5 1.8 7.3 1.9 7 1.7 7 2 7 2.1 7.2 2.4 7.3 2.5 7.1 2.5 6.8 2.6 6.4 2.2 6.1 2.5 6.5 2.8 7.7 2.8 7.9 2.9 7.5 3 6.9 3.1 6.6 2.9 6.9 2.7

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 inflatie[t] = + 2.14074298052491 -0.126086309974952graad[t] + 0.0216233697380451t + 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) 2.14074298052491 0.681777 3.1399 0.002677 0.001339 graad -0.126086309974952 0.076435 -1.6496 0.104529 0.052265 t 0.0216233697380451 0.003253 6.6468 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.840905099321339 R-squared 0.707121386064631 Adjusted R-squared 0.696844943470407 F-TEST (value) 68.8099388072399 F-TEST (DF numerator) 2 F-TEST (DF denominator) 57 p-value 6.66133814775094e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.295370228940042 Sum Squared Residuals 4.97288361221327 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.6 1.04019819148589 0.55980180851411 2 1.8 1.07443019222142 0.725569807778577 3 1.6 1.15909671694694 0.440903283053055 4 1.5 1.28158913466495 0.218410865335049 5 1.5 1.34103839739548 0.158961602604518 6 1.3 1.33744450513854 -0.0374445051385365 7 1.4 1.18254704091165 0.217452959088351 8 1.4 1.14112725566222 0.258872744337782 9 1.3 1.16275062540026 0.137249374599737 10 1.3 1.26002578112328 0.0399742188767206 11 1.2 1.3446923058488 -0.144692305848801 12 1.1 1.35370704458935 -0.253707044589350 13 1.4 1.35011315233241 0.0498868476675948 14 1.2 1.35912789107295 -0.159127891072955 15 1.5 1.39335989180850 0.106640108191505 16 1.1 1.45280915453903 -0.352809154539026 17 1.3 1.48704115527457 -0.187041155274567 18 1.5 1.53388178700760 -0.0338817870076020 19 1.1 1.46724473976318 -0.367244739763181 20 1.4 1.47625947850373 -0.0762594785037309 21 1.3 1.49788284824178 -0.197882848241776 22 1.5 1.54472347997481 -0.0447234799748113 23 1.6 1.57895548071035 0.0210445192896485 24 1.7 1.5879702194509 0.112029780549098 25 1.1 1.58437632719396 -0.484376327193956 26 1.6 1.60599969693200 -0.00599969693200142 27 1.3 1.64023169766754 -0.340231697667542 28 1.7 1.67446369840308 0.0255363015969178 29 1.6 1.72130433013612 -0.121304330136118 30 1.7 1.78075359286665 -0.0807535928666485 31 1.9 1.77715970060970 0.122840299390297 32 1.8 1.81139170134524 -0.0113917013452435 33 1.9 1.83301507108329 0.0669849289167112 34 1.6 1.86724707181883 -0.267247071818829 35 1.5 1.90147907255437 -0.401479072554369 36 1.6 1.92310244229241 -0.323102442292414 37 1.6 1.93211718103296 -0.332117181032964 38 1.7 1.95374055077101 -0.253740550771010 39 2 1.98797255150655 0.0120274484934502 40 2 1.9969872902471 0.00301270975290019 41 1.9 2.05643655297763 -0.156436552977631 42 1.7 2.14110307770315 -0.441103077703152 43 1.8 2.12490055444871 -0.324900554448711 44 1.9 2.17174118618175 -0.271741186181747 45 1.7 2.23119044891228 -0.531190448912278 46 2 2.25281381865032 -0.252813818650323 47 2.1 2.27443718838837 -0.174437188388368 48 2.4 2.27084329613142 0.129156703868577 49 2.5 2.27985803487197 0.220141965128027 50 2.5 2.32669866660501 0.173301333394992 51 2.6 2.38614792933554 0.213852070664461 52 2.2 2.45820582306356 -0.258205823063565 53 2.5 2.51765508579410 -0.0176550857940955 54 2.8 2.48884393154216 0.31115606845784 55 2.8 2.35916372931026 0.440836270689737 56 2.9 2.35556983705332 0.544430162946683 57 3 2.42762773078134 0.572372269218657 58 3.1 2.52490288650436 0.575097113495641 59 2.9 2.58435214923489 0.315647850765109 60 2.7 2.56814962598045 0.131850374019550 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.133024263084961 0.266048526169923 0.866975736915039 7 0.0572763653624738 0.114552730724948 0.942723634637526 8 0.0236361426074174 0.0472722852148347 0.976363857392583 9 0.00907711614644565 0.0181542322928913 0.990922883853554 10 0.00383428408062381 0.00766856816124761 0.996165715919376 11 0.00128935462861263 0.00257870925722527 0.998710645371387 12 0.000435559648633516 0.000871119297267033 0.999564440351366 13 0.00629513989484603 0.0125902797896921 0.993704860105154 14 0.00301306541527571 0.00602613083055141 0.996986934584724 15 0.0327721924661388 0.0655443849322777 0.967227807533861 16 0.0190349217896785 0.0380698435793569 0.980965078210322 17 0.0186052556041227 0.0372105112082455 0.981394744395877 18 0.0792552315257623 0.158510463051525 0.920744768474238 19 0.0549898165993446 0.109979633198689 0.945010183400655 20 0.0636140993613484 0.127228198722697 0.936385900638652 21 0.0458683039264177 0.0917366078528354 0.954131696073582 22 0.0765943919871915 0.153188783974383 0.923405608012809 23 0.167423283713519 0.334846567427039 0.83257671628648 24 0.369027157982438 0.738054315964877 0.630972842017562 25 0.389968222505057 0.779936445010113 0.610031777494943 26 0.427474974948787 0.854949949897574 0.572525025051213 27 0.365461228744557 0.730922457489114 0.634538771255443 28 0.456667404689358 0.913334809378715 0.543332595310642 29 0.441736945740312 0.883473891480623 0.558263054259688 30 0.506670119838717 0.986659760322566 0.493329880161283 31 0.742934608608698 0.514130782782604 0.257065391391302 32 0.825591881024614 0.348816237950772 0.174408118975386 33 0.943008374040408 0.113983251919184 0.0569916259595922 34 0.927527730962889 0.144944538074223 0.0724722690371113 35 0.899501487092118 0.200997025815764 0.100498512907882 36 0.860042249856099 0.279915500287803 0.139957750143901 37 0.812659967954395 0.374680064091211 0.187340032045605 38 0.755929853597801 0.488140292804397 0.244070146402199 39 0.80903649119764 0.381927017604722 0.190963508802361 40 0.829938163291764 0.340123673416473 0.170061836708236 41 0.795903003373011 0.408193993253977 0.204096996626989 42 0.739365758418261 0.521268483163478 0.260634241581739 43 0.681213756088759 0.637572487822482 0.318786243911241 44 0.609140841901875 0.78171831619625 0.390859158098125 45 0.759128792686293 0.481742414627414 0.240871207313707 46 0.756876521267592 0.486246957464816 0.243123478732408 47 0.77490092923363 0.450198141532739 0.225099070766370 48 0.756787278328688 0.486425443342625 0.243212721671312 49 0.734275489208403 0.531449021583194 0.265724510791597 50 0.67654497833497 0.646910043330059 0.323455021665029 51 0.626084104933123 0.747831790133754 0.373915895066877 52 0.764751404228075 0.47049719154385 0.235248595771925 53 0.756500096762566 0.486999806474867 0.243499903237434 54 0.711770814199669 0.576458371600662 0.288229185800331 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.0816326530612245 NOK 5% type I error level 9 0.183673469387755 NOK 10% type I error level 11 0.224489795918367 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/102k3u1258564709.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/102k3u1258564709.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/1kzt31258564709.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/1kzt31258564709.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/2nw0f1258564709.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/2nw0f1258564709.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/3jcia1258564709.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/3jcia1258564709.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/4kufn1258564709.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/4kufn1258564709.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/5j30i1258564709.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/5j30i1258564709.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/6ltf51258564709.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/6ltf51258564709.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/7a0gr1258564709.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/7a0gr1258564709.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/89i4m1258564709.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/89i4m1258564709.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/9i14y1258564709.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564967voigo6vjkccxoi0/9i14y1258564709.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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