WS 7 Multiple Regression analysis

*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 11:53:22 -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/t125865691580awg69efbyr7ku.htm/, Retrieved Thu, 19 Nov 2009 19:55:27 +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/t125865691580awg69efbyr7ku.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:

WS 7 Multiple Regression analysis

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

14,5 14,8 14,3 14,7 15,3 16 14,4 15,4 13,7 15 14,2 15,5 13,5 15,1 11,9 11,7 14,6 16,3 15,6 16,7 14,1 15 14,9 14,9 14,2 14,6 14,6 15,3 17,2 17,9 15,4 16,4 14,3 15,4 17,5 17,9 14,5 15,9 14,4 13,9 16,6 17,8 16,7 17,9 16,6 17,4 16,9 16,7 15,7 16 16,4 16,6 18,4 19,1 16,9 17,8 16,5 17,2 18,3 18,6 15,1 16,3 15,7 15,1 18,1 19,2 16,8 17,7 18,9 19,1 19 18 18,1 17,5 17,8 17,8 21,5 21,1 17,1 17,2 18,7 19,4 19 19,8 16,4 17,6 16,9 16,2 18,6 19,5 19,3 19,9 19,4 20 17,6 17,3 18,6 18,9 18,1 18,6 20,4 21,4 18,1 18,6 19,6 19,8 19,9 20,8 19,2 19,6 17,8 17,7 19,2 19,8 22 22,2 21,1 20,7 19,5 17,9 22,2 20,9 20,9 21,2 22,2 21,4 23,5 23 21,5 21,3 24,3 23,9 22,8 22,4 20,3 18,3 23,7 22,8 23,3 22,3 19,6 17,8 18 16,4 17,3 16 16,8 16,4 18,2 17,7 16,5 16,6 16 16,2 18,4 18,3

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] = -1.53647721171885 + 1.07411692421246X[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) -1.53647721171885 0.695836 -2.2081 0.030253 0.015126 X 1.07411692421246 0.038394 27.9765 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.954721059341125 R-squared 0.91149230114944 Adjusted R-squared 0.910327726164563 F-TEST (value) 782.682363082568 F-TEST (DF numerator) 1 F-TEST (DF denominator) 76 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.817163446619703 Sum Squared Residuals 50.7494634853458 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 14.5 14.3604532666254 0.139546733374593 2 14.3 14.2530415742042 0.0469584257957522 3 15.3 15.6493935756804 -0.349393575680426 4 14.4 15.0049234211530 -0.604923421152954 5 13.7 14.5752766514680 -0.875276651467973 6 14.2 15.1123351135742 -0.9123351135742 7 13.5 14.6826883438892 -1.18268834388922 8 11.9 11.0306908015669 0.86930919843313 9 14.6 15.9716286529442 -1.37162865294416 10 15.6 16.4012754226291 -0.801275422629145 11 14.1 14.5752766514680 -0.475276651467972 12 14.9 14.4678649590467 0.432135040953273 13 14.2 14.145629881783 0.0543701182170092 14 14.6 14.8975117287317 -0.29751172873171 15 17.2 17.6902157316841 -0.49021573168409 16 15.4 16.0790403453654 -0.679040345365407 17 14.3 15.0049234211530 -0.704923421152954 18 17.5 17.6902157316841 -0.190215731684089 19 14.5 15.5419818832592 -1.04198188325918 20 14.4 13.3937480348343 1.00625196516573 21 16.6 17.5828040392628 -0.982804039262844 22 16.7 17.6902157316841 -0.99021573168409 23 16.6 17.1531572695779 -0.55315726957786 24 16.9 16.4012754226291 0.498724577370854 25 15.7 15.6493935756804 0.0506064243195725 26 16.4 16.2938637302079 0.106136269792098 27 18.4 18.9791560407390 -0.579156040739039 28 16.9 17.5828040392628 -0.682804039262847 29 16.5 16.9383338847354 -0.438333884735371 30 18.3 18.4420975786328 -0.142097578632809 31 15.1 15.9716286529442 -0.871628652944164 32 15.7 14.6826883438892 1.01731165611078 33 18.1 19.0865677331603 -0.986567733160279 34 16.8 17.4753923468416 -0.675392346841598 35 18.9 18.9791560407390 -0.0791560407390388 36 19 17.7976274241053 1.20237257589466 37 18.1 17.2605689619991 0.839431038000893 38 17.8 17.5828040392628 0.217195960737155 39 21.5 21.1273898891639 0.372610110836054 40 17.1 16.9383338847354 0.16166611526463 41 18.7 19.3013911180028 -0.601391118002771 42 19 19.7310378876878 -0.731037887687754 43 16.4 17.3679806544204 -0.967980654420357 44 16.9 15.8642169605229 1.03578303947708 45 18.6 19.4088028104240 -0.808802810424016 46 19.3 19.838449580109 -0.538449580108997 47 19.4 19.9458612725302 -0.545861272530246 48 17.6 17.0457455771566 0.554254422843383 49 18.6 18.7643326558965 -0.164332655896542 50 18.1 18.4420975786328 -0.342097578632809 51 20.4 21.4496249664277 -1.04962496642768 52 18.1 18.4420975786328 -0.342097578632809 53 19.6 19.7310378876878 -0.131037887687753 54 19.9 20.8051548119002 -0.90515481190021 55 19.2 19.5162145028453 -0.316214502845265 56 17.8 17.4753923468416 0.324607653158402 57 19.2 19.7310378876878 -0.531037887687755 58 22 22.3089185057976 -0.308918505797645 59 21.1 20.6977431194790 0.402256880521039 60 19.5 17.6902157316841 1.80978426831591 61 22.2 20.9125665043215 1.28743349567855 62 20.9 21.2348015815852 -0.334801581585191 63 22.2 21.4496249664277 0.75037503357232 64 23.5 23.1682120451676 0.331787954832392 65 21.5 21.3422132740064 0.157786725993563 66 24.3 24.1349172769588 0.165082723041184 67 22.8 22.5237418906401 0.276258109359867 68 20.3 18.1198625013691 2.18013749863093 69 23.7 22.9533886603251 0.746611339674881 70 23.3 22.4163301982189 0.88366980178111 71 19.6 17.5828040392628 2.01719596073716 72 18 16.0790403453654 1.92095965463459 73 17.3 15.6493935756804 1.65060642431957 74 16.8 16.0790403453654 0.720959654634594 75 18.2 17.4753923468416 0.7246076531584 76 16.5 16.2938637302079 0.206136269792099 77 16 15.8642169605229 0.135783039477083 78 18.4 18.1198625013691 0.280137498630926 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.177746941161458 0.355493882322915 0.822253058838542 6 0.120413185623959 0.240826371247918 0.879586814376041 7 0.157095258124916 0.314190516249832 0.842904741875084 8 0.0856731565477192 0.171346313095438 0.914326843452281 9 0.0612713925911435 0.122542785182287 0.938728607408857 10 0.0420027810876942 0.0840055621753884 0.957997218912306 11 0.0214886825258253 0.0429773650516505 0.978511317474175 12 0.0354875145626964 0.0709750291253928 0.964512485437304 13 0.0210847490481031 0.0421694980962062 0.978915250951897 14 0.0117209202096224 0.0234418404192447 0.988279079790378 15 0.0195343407679944 0.0390686815359889 0.980465659232006 16 0.0114813260665813 0.0229626521331625 0.988518673933419 17 0.00765336934512154 0.0153067386902431 0.992346630654878 18 0.0124051932915036 0.0248103865830073 0.987594806708496 19 0.0127512521710929 0.0255025043421859 0.987248747828907 20 0.0249982345404904 0.0499964690809808 0.97500176545951 21 0.0185713638545166 0.0371427277090332 0.981428636145483 22 0.0140723422912530 0.0281446845825060 0.985927657708747 23 0.0106269552604038 0.0212539105208076 0.989373044739596 24 0.0239047296937783 0.0478094593875565 0.976095270306222 25 0.0189716487342451 0.0379432974684901 0.981028351265755 26 0.0174640579100970 0.0349281158201941 0.982535942089903 27 0.0144845455528301 0.0289690911056602 0.98551545444717 28 0.0109620341836494 0.0219240683672988 0.989037965816351 29 0.00798953376024327 0.0159790675204865 0.992010466239757 30 0.00772714403539668 0.0154542880707934 0.992272855964603 31 0.00925282091341402 0.0185056418268280 0.990747179086586 32 0.0185919324359145 0.0371838648718290 0.981408067564085 33 0.0175111594395508 0.0350223188791015 0.98248884056045 34 0.0158283382240311 0.0316566764480621 0.984171661775969 35 0.0164543451605829 0.0329086903211657 0.983545654839417 36 0.0714121940677841 0.142824388135568 0.928587805932216 37 0.09985077025645 0.1997015405129 0.90014922974355 38 0.0866052888517718 0.173210577703544 0.913394711148228 39 0.0924747164885937 0.184949432977187 0.907525283511406 40 0.0749546518579667 0.149909303715933 0.925045348142033 41 0.0655715903311415 0.131143180662283 0.934428409668858 42 0.0611410095421159 0.122282019084232 0.938858990457884 43 0.0921079782770567 0.184215956554113 0.907892021722943 44 0.111217334399249 0.222434668798498 0.888782665600751 45 0.120233692639495 0.24046738527899 0.879766307360505 46 0.110207411152452 0.220414822304904 0.889792588847548 47 0.102630209748876 0.205260419497752 0.897369790251124 48 0.0938507250800404 0.187701450160081 0.90614927491996 49 0.0809500557862985 0.161900111572597 0.919049944213702 50 0.078124903182811 0.156249806365622 0.921875096817189 51 0.104946786076985 0.209893572153970 0.895053213923015 52 0.110848156806613 0.221696313613226 0.889151843193387 53 0.100138343603609 0.200276687207217 0.899861656396391 54 0.152037524871120 0.304075049742239 0.84796247512888 55 0.167174186164417 0.334348372328835 0.832825813835583 56 0.160878541905601 0.321757083811201 0.8391214580944 57 0.229316160731851 0.458632321463703 0.770683839268149 58 0.233365599491366 0.466731198982732 0.766634400508634 59 0.215207751542808 0.430415503085616 0.784792248457192 60 0.382453514965244 0.764907029930489 0.617546485034756 61 0.458512861685673 0.917025723371345 0.541487138314327 62 0.495102797110866 0.990205594221733 0.504897202889134 63 0.447804237498385 0.89560847499677 0.552195762501615 64 0.375551772745261 0.751103545490522 0.624448227254739 65 0.329658947071558 0.659317894143116 0.670341052928442 66 0.270837824962788 0.541675649925576 0.729162175037212 67 0.232641193278676 0.465282386557351 0.767358806721324 68 0.459654102518473 0.919308205036945 0.540345897481527 69 0.364705878521766 0.729411757043531 0.635294121478234 70 0.275802844581225 0.551605689162449 0.724197155418775 71 0.524679343733932 0.950641312532135 0.475320656266068 72 0.714233072104219 0.571533855791562 0.285766927895781 73 0.92710491694909 0.145790166101822 0.0728950830509108 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 24 0.347826086956522 NOK 10% type I error level 26 0.376811594202899 NOK

Charts produced by software:

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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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