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*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: Fri, 27 Nov 2009 11:06:27 -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/27/t1259345238e59hf5frzavmlny.htm/, Retrieved Fri, 27 Nov 2009 19:07:29 +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/27/t1259345238e59hf5frzavmlny.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}, }

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Dataseries X:

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8.9 1.9 9 1.6 9 1.7 9 2 9 2.5 9 2.4 9 2.3 9 2.3 9 2.1 9 2.4 9 2.2 9.1 2.4 9 1.9 9 2.1 9.1 2.1 9 2.1 9 2 9 2.1 9 2.2 8.9 2.2 8.9 2.6 8.9 2.5 8.9 2.3 8.8 2.2 8.8 2.4 8.7 2.3 8.7 2.2 8.5 2.5 8.5 2.5 8.4 2.5 8.2 2.4 8.2 2.3 8.1 1.7 8.1 1.6 8 1.9 7.9 1.9 7.8 1.8 7.7 1.8 7.6 1.9 7.5 1.9 7.5 1.9 7.5 1.9 7.5 1.8 7.5 1.7 7.4 2.1 7.4 2.6 7.3 3.1 7.3 3.1 7.3 3.2 7.2 3.3 7.2 3.6 7.3 3.3 7.4 3.7 7.4 4 7.5 4 7.6 3.8 7.7 3.6 7.9 3.2 8 2.1 8.2 1.6

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 werkl[t] = + 9.50211597447777 + 0.0382783046099624infl[t] -0.305734809579039M1[t] -0.308084260797827M2[t] -0.274261542477623M3[t] -0.299673258065217M4[t] -0.248912804113808M5[t] -0.234324519701403M6[t] -0.215908404828002M7[t] -0.175961157770203M8[t] -0.157545042896801M9[t] -0.0821911923921968M10[t] -0.0599472470577996M11[t] -0.0368849826890027t + 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) 9.50211597447777 0.217124 43.7635 0 0 infl 0.0382783046099624 0.081753 0.4682 0.641841 0.32092 M1 -0.305734809579039 0.213143 -1.4344 0.158218 0.079109 M2 -0.308084260797827 0.212605 -1.4491 0.154096 0.077048 M3 -0.274261542477623 0.212696 -1.2895 0.203686 0.101843 M4 -0.299673258065217 0.212761 -1.4085 0.165707 0.082854 M5 -0.248912804113808 0.214034 -1.163 0.250845 0.125422 M6 -0.234324519701403 0.214397 -1.0929 0.280108 0.140054 M7 -0.215908404828002 0.213464 -1.0115 0.31709 0.158545 M8 -0.175961157770203 0.212269 -0.829 0.411411 0.205705 M9 -0.157545042896801 0.211676 -0.7443 0.460495 0.230248 M10 -0.0821911923921968 0.211768 -0.3881 0.699718 0.349859 M11 -0.0599472470577996 0.210832 -0.2843 0.777429 0.388714 t -0.0368849826890027 0.002947 -12.5166 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.903710861624867 R-squared 0.81669332141876 Adjusted R-squared 0.764889260080584 F-TEST (value) 15.7650442903963 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 8.58646487245096e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.333088119752957 Sum Squared Residuals 5.10359399394578 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.9 9.23222496096872 -0.332224960968718 2 9 9.18150703567789 -0.181507035677888 3 9 9.18227260177009 -0.182272601770087 4 9 9.13145939487648 -0.131459394876479 5 9 9.16447401844387 -0.164474018443865 6 9 9.13834948970627 -0.138349489706272 7 9 9.11605279142967 -0.116052791429674 8 9 9.11911505579847 -0.119115055798471 9 9 9.09299052706088 -0.092990527060877 10 9 9.14294288625947 -0.142942886259467 11 9 9.12064618798287 -0.120646187982870 12 9.1 9.15136411327366 -0.0513641132736598 13 9 8.78960516870064 0.210394831299365 14 9 8.75802639571484 0.241973604285163 15 9.1 8.75496413134604 0.34503586865396 16 9 8.69266743306944 0.307332566930558 17 9 8.70271507387085 0.297284926129148 18 9 8.68424620605525 0.315753793944749 19 9 8.66960516870065 0.330394831299355 20 8.9 8.67266743306944 0.227332566930558 21 8.9 8.66950988709783 0.230490112902174 22 8.9 8.70415092445243 0.195849075547569 23 8.9 8.68185422617583 0.218145773824167 24 8.8 8.70108866008363 0.0989113399163669 25 8.8 8.36612452873758 0.433875471262416 26 8.7 8.3230622643688 0.376937735631202 27 8.7 8.316172169539 0.383827830460996 28 8.5 8.2653589626454 0.234641037354605 29 8.5 8.2792344339078 0.220765566092199 30 8.4 8.2569377356312 0.143062264368797 31 8.2 8.2346410373546 -0.0346410373546059 32 8.2 8.2338754712624 -0.0338754712624065 33 8.1 8.19243962068083 -0.092439620680827 34 8.1 8.22708065803543 -0.127080658035433 35 8 8.22392311206382 -0.223923112063816 36 7.9 8.24698537643261 -0.346985376432612 37 7.8 7.90053775370357 -0.100537753703574 38 7.7 7.86130331979578 -0.161303319795783 39 7.6 7.86206888588798 -0.262068885887982 40 7.5 7.79977218761139 -0.299772187611385 41 7.5 7.81364765887379 -0.313647658873791 42 7.5 7.79135096059719 -0.291350960597193 43 7.5 7.7690542623206 -0.269054262320595 44 7.5 7.7682886962284 -0.268288696228396 45 7.4 7.76513115025678 -0.365131150256779 46 7.4 7.82273917037736 -0.422739170377362 47 7.3 7.82723728532774 -0.527237285327739 48 7.3 7.85029954969654 -0.550299549696535 49 7.3 7.51150758788949 -0.211507587889489 50 7.2 7.4761009844427 -0.276100984442694 51 7.2 7.48452221145689 -0.284522211456886 52 7.3 7.4107420217973 -0.110742021797300 53 7.4 7.43992881490369 -0.0399288149036907 54 7.4 7.42911560801008 -0.0291156080100815 55 7.5 7.41064674019448 0.08935325980552 56 7.6 7.40605334364128 0.193946656358715 57 7.7 7.37992881490369 0.320071185096309 58 7.9 7.4030863608753 0.496913639124693 59 8 7.34633918844974 0.653660811550257 60 8.2 7.35026230051356 0.84973769948644 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00209231704246703 0.00418463408493406 0.997907682957533 18 0.000212792498804379 0.000425584997608758 0.999787207501196 19 2.109948659405e-05 4.21989731881e-05 0.999978900513406 20 1.40731864865647e-05 2.81463729731295e-05 0.999985926813513 21 6.94130624600258e-06 1.38826124920052e-05 0.999993058693754 22 1.85863720755150e-06 3.71727441510301e-06 0.999998141362793 23 4.40402031589288e-07 8.80804063178576e-07 0.999999559597968 24 2.69810284734183e-06 5.39620569468367e-06 0.999997301897153 25 1.09282810463414e-06 2.18565620926828e-06 0.999998907171895 26 2.13525277768341e-06 4.27050555536683e-06 0.999997864747222 27 5.83896801659248e-06 1.16779360331850e-05 0.999994161031983 28 4.28452850313248e-05 8.56905700626496e-05 0.999957154714969 29 0.000197440982472853 0.000394881964945705 0.999802559017527 30 0.00144446252000075 0.00288892504000149 0.99855553748 31 0.0148944659755933 0.0297889319511866 0.985105534024407 32 0.0547376250402027 0.109475250080405 0.945262374959797 33 0.0993976203080134 0.198795240616027 0.900602379691987 34 0.107702853892166 0.215405707784331 0.892297146107834 35 0.212476814846421 0.424953629692841 0.78752318515358 36 0.410650845314699 0.821301690629397 0.589349154685301 37 0.570119353658562 0.859761292682876 0.429880646341438 38 0.812599827891828 0.374800344216343 0.187400172108172 39 0.947094725629795 0.105810548740411 0.0529052743702053 40 0.988941058091569 0.0221178838168623 0.0110589419084311 41 0.991935931663711 0.0161281366725780 0.00806406833628901 42 0.994829847962508 0.0103403040749835 0.00517015203749175 43 0.99026033923048 0.0194793215390387 0.00973966076951937 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 14 0.518518518518518 NOK 5% type I error level 19 0.703703703703704 NOK 10% type I error level 19 0.703703703703704 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/10con21259345182.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/10con21259345182.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/115ur1259345182.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/115ur1259345182.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/29gxm1259345182.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/29gxm1259345182.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/33n4s1259345182.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/33n4s1259345182.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/4p2951259345182.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/4p2951259345182.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/5mbh81259345182.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/5mbh81259345182.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/6vcn01259345182.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/6vcn01259345182.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/7r8nx1259345182.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/7r8nx1259345182.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/8ksjp1259345182.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/8ksjp1259345182.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/92oyv1259345182.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259345238e59hf5frzavmlny/92oyv1259345182.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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