*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 03:20:13 -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/t1258626471gy3doxgh7h8wkct.htm/, Retrieved Thu, 19 Nov 2009 11:28:03 +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/t1258626471gy3doxgh7h8wkct.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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110.5 55 110.8 48.7 104.2 70.3 88.9 94.8 89.8 58.5 90 62.4 93.9 56.7 91.3 65.1 87.8 114.4 99.7 50.7 73.5 44.5 79.2 72 96.9 61.2 95.2 68.4 95.6 78.7 89.7 64.1 92.8 64.6 88 71.9 101.1 71 92.7 76.4 95.8 117.3 103.8 66.1 81.8 57.3 87.1 75 105.9 63.8 108.1 62.2 102.6 75.4 93.7 58 103.5 62.1 100.6 99.2 113.3 70.7 102.4 73.3 102.1 111.2 106.9 68.9 87.3 57.6 93.1 72.9 109.1 75.9 120.3 79.4 104.9 96.9 92.6 75.2 109.8 60.3 111.4 88.9 117.9 90.5 121.6 79.9 117.8 116.3 124.2 95.2 106.8 81.5 102.7 89.1 116.8 76 113.6 100.5 96.1 83.9 85 75.1 83.2 69.5 84.9 95.1 83 90.1 79.6 78.4 83.2 113.8 83.8 73.6 82.8 56.5 71.4 97.7

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 4 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 prod[t] = + 91.162508541583 + 0.0856714386324468`inv `[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) 91.162508541583 7.276378 12.5286 0 0 `inv ` 0.0856714386324468 0.092924 0.9219 0.360374 0.180187 Multiple Linear Regression - Regression Statistics Multiple R 0.120180468187982 R-squared 0.0144433449338825 Adjusted R-squared -0.00254901118794715 F-TEST (value) 0.849990715256226 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.360374230373602 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.5822570001917 Sum Squared Residuals 9182.16509069469 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 110.5 95.8744376663681 14.6255623336319 2 110.8 95.3347076029832 15.4652923970168 3 104.2 97.185210677444 7.01478932255595 4 88.9 99.284160923939 -10.384160923939 5 89.8 96.1742877015812 -6.37428770158119 6 90 96.5084063122477 -6.50840631224773 7 93.9 96.0200791120428 -2.12007911204278 8 91.3 96.7397191965553 -5.43971919655534 9 87.8 100.963321121135 -13.1633211211350 10 99.7 95.506050480248 4.1939495197519 11 73.5 94.974887560727 -21.4748875607269 12 79.2 97.3308521231192 -18.1308521231192 13 96.9 96.4056005858888 0.494399414111213 14 95.2 97.0224349440424 -1.82243494404241 15 95.6 97.9048507619566 -2.30485076195662 16 89.7 96.6540477579229 -6.95404775792288 17 92.8 96.696883477239 -3.89688347723911 18 88 97.322284979256 -9.32228497925597 19 101.1 97.2451806844868 3.85481931551322 20 92.7 97.707806453102 -5.00780645310198 21 95.8 101.211768293169 -5.41176829316906 22 103.8 96.8253906351878 6.97460936481222 23 81.8 96.0714819752223 -14.2714819752223 24 87.1 97.5878664390166 -10.4878664390166 25 105.9 96.6283463263332 9.27165367366685 26 108.1 96.4912720245212 11.6087279754788 27 102.6 97.6221350144695 4.97786498553046 28 93.7 96.131451982265 -2.43145198226496 29 103.5 96.482704880658 7.017295119342 30 100.6 99.6611152539218 0.938884746078225 31 113.3 97.219479252897 16.0805207471030 32 102.4 97.4422249933414 4.95777500665861 33 102.1 100.689172517511 1.41082748248886 34 106.9 97.0652706633586 9.83472933664137 35 87.3 96.097183406812 -8.79718340681199 36 93.1 97.4079564178884 -4.30795641788843 37 109.1 97.6649707337858 11.4350292662142 38 120.3 97.9648207689993 22.3351792310007 39 104.9 99.4640709450671 5.43592905493286 40 92.6 97.605000726743 -5.00500072674305 41 109.8 96.3284962911196 13.4715037088804 42 111.4 98.7786994360076 12.6213005639924 43 117.9 98.9157737378195 18.9842262621805 44 121.6 98.0076564883155 23.5923435116844 45 117.8 101.126096854537 16.6739031454634 46 124.2 99.318429499392 24.8815705006080 47 106.8 98.1447307901275 8.65526920987254 48 102.7 98.795833723734 3.90416627626595 49 116.8 97.673537877649 19.126462122351 50 113.6 99.772488124144 13.8275118758560 51 96.1 98.3503422428453 -2.25034224284534 52 85 97.5964335828798 -12.5964335828798 53 83.2 97.116673526538 -13.9166735265381 54 84.9 99.3098623555287 -14.4098623555287 55 83 98.8815051623665 -15.8815051623665 56 79.6 97.8791493303669 -18.2791493303669 57 83.2 100.911918257955 -17.7119182579555 58 83.8 97.4679264249311 -13.6679264249311 59 82.8 96.0029448243163 -13.2029448243163 60 71.4 99.5326080959731 -28.1326080959731 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.343950064271286 0.687900128542573 0.656049935728714 6 0.325143402108828 0.650286804217656 0.674856597891172 7 0.244557097135922 0.489114194271844 0.755442902864078 8 0.171337981386879 0.342675962773757 0.828662018613121 9 0.115145102371700 0.230290204743399 0.8848548976283 10 0.0653108479527721 0.130621695905544 0.934689152047228 11 0.36641172322532 0.73282344645064 0.63358827677468 12 0.409031414845979 0.818062829691959 0.590968585154021 13 0.317980564412423 0.635961128824845 0.682019435587577 14 0.237307446809065 0.47461489361813 0.762692553190935 15 0.174070878204942 0.348141756409883 0.825929121795058 16 0.128783508910556 0.257567017821111 0.871216491089444 17 0.0875046429618686 0.175009285923737 0.912495357038131 18 0.0644886409403305 0.128977281880661 0.93551135905967 19 0.0501710066522409 0.100342013304482 0.949828993347759 20 0.0321506825667225 0.064301365133445 0.967849317433278 21 0.0232235997403318 0.0464471994806636 0.976776400259668 22 0.0195088626184594 0.0390177252369188 0.98049113738154 23 0.0239659661827471 0.0479319323654943 0.976034033817253 24 0.0187408539111485 0.0374817078222971 0.981259146088851 25 0.0183381303639463 0.0366762607278926 0.981661869636054 26 0.0205072795917085 0.041014559183417 0.979492720408291 27 0.0154082408703558 0.0308164817407115 0.984591759129644 28 0.00940584826740083 0.0188116965348017 0.9905941517326 29 0.00693897506959461 0.0138779501391892 0.993061024930405 30 0.00465125409339284 0.00930250818678569 0.995348745906607 31 0.00849110213067076 0.0169822042613415 0.99150889786933 32 0.00567886913873672 0.0113577382774734 0.994321130861263 33 0.0036898456681214 0.0073796913362428 0.996310154331879 34 0.00317073463508974 0.00634146927017949 0.99682926536491 35 0.00235287870963842 0.00470575741927684 0.997647121290362 36 0.00134537132991366 0.00269074265982732 0.998654628670086 37 0.00131564742759180 0.00263129485518359 0.998684352572408 38 0.00523514989734997 0.0104702997946999 0.99476485010265 39 0.00331580227772813 0.00663160455545626 0.996684197722272 40 0.00194538947633111 0.00389077895266223 0.998054610523669 41 0.00235597995869436 0.00471195991738871 0.997644020041306 42 0.00234621218616514 0.00469242437233028 0.997653787813835 43 0.00467910113719957 0.00935820227439914 0.9953208988628 44 0.0204103447550928 0.0408206895101857 0.979589655244907 45 0.0246016208134189 0.0492032416268378 0.975398379186581 46 0.118197810693998 0.236395621387996 0.881802189306002 47 0.129573722916021 0.259147445832042 0.87042627708398 48 0.119676131407654 0.239352262815308 0.880323868592346 49 0.458143278028540 0.916286556057081 0.54185672197146 50 0.954321333555303 0.0913573328893948 0.0456786664446974 51 0.99212853902937 0.0157429219412589 0.00787146097062947 52 0.983747628280392 0.0325047434392165 0.0162523717196082 53 0.961958915457032 0.0760821690859354 0.0380410845429677 54 0.936155727947757 0.127688544104486 0.0638442720522432 55 0.868478210869702 0.263043578260596 0.131521789130298 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.215686274509804 NOK 5% type I error level 27 0.529411764705882 NOK 10% type I error level 30 0.588235294117647 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/10txoz1258626008.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/10txoz1258626008.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/1bkey1258626008.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/1bkey1258626008.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/293j41258626008.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/293j41258626008.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/3nj4q1258626008.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/3nj4q1258626008.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/4ldfw1258626008.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/4ldfw1258626008.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/5fh4a1258626008.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/5fh4a1258626008.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/6cgj41258626008.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/6cgj41258626008.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/7qm4y1258626008.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/7qm4y1258626008.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/8p8b51258626008.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/8p8b51258626008.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/9rro71258626008.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626471gy3doxgh7h8wkct/9rro71258626008.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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