ws7 trend

*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: Tue, 23 Nov 2010 18:36:14 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics.htm/, Retrieved Tue, 23 Nov 2010 19:34:58 +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/2010/Nov/23/t1290537295nqx06hepanv1ics.htm/}, year = {2010}, } @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 = {2010}, 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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1 28 6 6 6.06 6.06 3.53 3.53 48 48 5 5 1 40 5 5 8.1 8.1 4.52 4.52 63 63 11 11 1 79 3 3 79.38 79.38 3.72 3.72 113 113 13 13 1 16 2 2 26.26 26.26 3.17 3.17 104 104 1 1 1 90 2 2 39.56 39.56 3.39 3.39 89 89 11 11 1 87 5 5 65.61 65.61 4.15 4.15 97 97 3 3 1 53 5 5 80.3 80.3 3.09 3.09 114 114 11 11 1 23 5 5 34.68 34.68 2.76 2.76 57 57 9 9 1 42 6 6 7.17 7.17 5.14 5.14 127 127 10 10 1 64 4 4 65.88 65.88 4.78 4.78 64 64 4 4 1 87 6 6 42.69 42.69 4.22 4.22 91 91 2 2 1 77 2 2 54.94 54.94 3.93 3.93 127 127 2 2 1 70 4 4 89.99 89.99 3.01 3.01 45 45 10 10 1 82 4 4 72.64 72.64 5.12 5.12 40 40 9 9 1 44 3 3 24.96 24.96 5.82 5.82 33 33 1 1 1 36 2 2 57.52 57.52 2.83 2.83 60 60 7 7 0 73 2 0 71.91 0 5.11 0 50 0 3 0 0 75 3 0 65.34 0 5.99 0 128 0 11 0 0 21 3 0 34.62 0 3.15 0 52 0 7 0 0 81 2 0 60.92 0 3.5 0 40 0 1 0 0 99 3 0 56.49 0 4.5 0 29 0 9 0 0 54 3 0 56.19 0 3.31 0 36 0 5 0 0 6 4 0 61.2 0 5.31 0 49 0 9 0 0 71 5 0 58.2 0 4.24 0 57 0 7 0 0 93 6 0 75.91 0 5.06 0 82 0 4 0 0 82 3 0 73.66 0 4.72 0 34 0 10 0 0 etc...

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 7 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation slaagkans[t] = + 36.7863750286887 -70.406096372685klant[t] -1.04098616693031verzekeraar[t] + 1.39545275131342verzekeraar_klant[t] + 0.554792741023526kost[t] + 0.056752300999544kost_klant[t] -2.24035951676562grootte[t] + 13.5990278640766grootte_klant[t] -0.0426617485641796snelheid[t] + 0.221180838587168snelheid_klant[t] + 0.854266191255212maand[t] -0.612581041231767maand_klant[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) 36.7863750286887 30.304006 1.2139 0.232272 0.116136 klant -70.406096372685 59.291374 -1.1875 0.242416 0.121208 verzekeraar -1.04098616693031 3.528783 -0.295 0.769601 0.3848 verzekeraar_klant 1.39545275131342 6.372526 0.219 0.827838 0.413919 kost 0.554792741023526 0.21581 2.5707 0.014189 0.007094 kost_klant 0.056752300999544 0.362904 0.1564 0.876558 0.438279 grootte -2.24035951676562 6.314635 -0.3548 0.72471 0.362355 grootte_klant 13.5990278640766 10.75192 1.2648 0.213645 0.106823 snelheid -0.0426617485641796 0.193603 -0.2204 0.826773 0.413387 snelheid_klant 0.221180838587168 0.304956 0.7253 0.472719 0.236359 maand 0.854266191255212 1.557287 0.5486 0.586516 0.293258 maand_klant -0.612581041231767 2.470105 -0.248 0.805471 0.402736 Multiple Linear Regression - Regression Statistics Multiple R 0.534618638587279 R-squared 0.285817088724916 Adjusted R-squared 0.0790799301979175 F-TEST (value) 1.38251435185315 F-TEST (DF numerator) 11 F-TEST (DF denominator) 38 p-value 0.220791410434633 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 28.8034976967944 Sum Squared Residuals 31526.3762236312 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 28 22.0864824541907 5.91351754580928 2 40 38.352546669858 1.647453330142 3 79 81.5569342198438 -2.55693421984381 4 16 37.9630338016859 -21.9630338016859 5 90 48.3345550468908 41.6654449531092 6 87 73.4559626086938 13.5440373913062 7 53 75.3676765584414 -22.3676765584414 8 23 33.0616727553791 -10.0616727553791 9 42 56.3641873519403 -14.3641873519403 10 64 74.7731294237277 -10.7731294237277 11 87 59.2761239240585 27.7238760759415 12 77 68.4823577714161 8.5176422285839 13 70 67.4708856018673 2.52911439813267 14 82 79.693088735455 2.3069112645451 15 44 54.948107560181 -10.948107560181 16 36 46.8132555163705 -10.8132555163705 17 73 63.5810227167142 9.4189772832858 18 75 60.4300450085413 14.5699549914587 19 21 49.5746611577697 -28.5746611577697 20 81 59.8089144179897 21.1910855820103 21 99 61.3732456558072 37.6267543441928 22 54 60.1571386534811 -6.1571386534811 23 6 60.2774071192339 -54.2774071192339 24 71 57.9194010411484 13.0805989588516 25 93 61.2373572261268 31.7626427738732 26 82 71.047115373927 10.952884626073 27 32 72.9137610915961 -40.9137610915961 28 93 73.0234999658563 19.9765000341437 29 24 58.1694888560453 -34.1694888560453 30 96 67.6941711417414 28.3058288582586 31 88 79.2986897327123 8.7013102672877 32 83 28.8943922420926 54.1056077579074 33 23 41.3240694232017 -18.3240694232017 34 20 46.9253824959355 -26.9253824959355 35 33 42.2274454905423 -9.2274454905423 36 88 71.5525322326523 16.4474677673477 37 98 62.2838797940016 35.7161202059984 38 34 40.7085608306196 -6.70856083061955 39 59 54.0210847731564 4.97891522684363 40 26 26.2093026200867 -0.209302620086662 41 13 59.6879811851952 -46.6879811851952 42 6 35.8505006547148 -29.8505006547148 43 49 26.1712952043024 22.8287047956976 44 3 40.2750804134853 -37.2750804134853 45 76 30.2723527483927 45.7276472516073 46 12 54.4435546855668 -42.4435546855668 47 63 40.3819286199605 22.6180713800395 48 35 48.9993791863894 -13.9993791863894 49 69 55.8200924441683 13.1799075558317 50 10 25.4452657968455 -15.4452657968455 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.673882392035549 0.652235215928902 0.326117607964451 16 0.522531755821112 0.954936488357776 0.477468244178888 17 0.366722757700708 0.733445515401416 0.633277242299292 18 0.241382895562999 0.482765791125998 0.758617104437001 19 0.156922864712849 0.313845729425699 0.84307713528715 20 0.0923967498791638 0.184793499758328 0.907603250120836 21 0.0588968581907692 0.117793716381538 0.94110314180923 22 0.0299216589941825 0.059843317988365 0.970078341005817 23 0.0284725753483957 0.0569451506967913 0.971527424651604 24 0.0967149328353585 0.193429865670717 0.903285067164642 25 0.0832517311551733 0.166503462310347 0.916748268844827 26 0.0586284225579604 0.117256845115921 0.94137157744204 27 0.0967743622148802 0.19354872442976 0.90322563778512 28 0.0643342191322938 0.128668438264588 0.935665780867706 29 0.067284676814565 0.13456935362913 0.932715323185435 30 0.0519505626935156 0.103901125387031 0.948049437306484 31 0.0302317017028123 0.0604634034056246 0.969768298297188 32 0.0694309457775505 0.138861891555101 0.93056905422245 33 0.0422247408952114 0.0844494817904228 0.957775259104789 34 0.0343292766774516 0.0686585533549032 0.965670723322548 35 0.0153750595530554 0.0307501191061108 0.984624940446945 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 0.0476190476190476 OK 10% type I error level 6 0.285714285714286 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/1088ve1290537364.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/1088ve1290537364.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/117yk1290537364.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/117yk1290537364.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/2ugx51290537364.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/2ugx51290537364.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/3ugx51290537364.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/3ugx51290537364.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/4ugx51290537364.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/4ugx51290537364.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/5ugx51290537364.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/5ugx51290537364.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/6npw81290537364.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/6npw81290537364.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/7gzdb1290537364.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/7gzdb1290537364.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/8gzdb1290537364.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/8gzdb1290537364.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/9gzdb1290537364.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290537295nqx06hepanv1ics/9gzdb1290537364.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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Software written by Ed van Stee & Patrick Wessa

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