M5

*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 01:44:55 -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/t1258620646445xi8qzcapuk3p.htm/, Retrieved Thu, 19 Nov 2009 09:50:58 +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/2009/Nov/19/t1258620646445xi8qzcapuk3p.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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19 2407.6 21 25 2454.62 19 21 2448.05 25 23 2497.84 21 23 2645.64 23 19 2756.76 23 18 2849.27 19 19 2921.44 18 19 2981.85 19 22 3080.58 19 23 3106.22 22 20 3119.31 23 14 3061.26 20 14 3097.31 14 14 3161.69 14 15 3257.16 14 11 3277.01 15 17 3295.32 11 16 3363.99 17 20 3494.17 16 24 3667.03 20 23 3813.06 24 20 3917.96 23 21 3895.51 20 19 3801.06 21 23 3570.12 19 23 3701.61 23 23 3862.27 23 23 3970.1 23 27 4138.52 23 26 4199.75 27 17 4290.89 26 24 4443.91 17 26 4502.64 24 24 4356.98 26 27 4591.27 24 27 4696.96 27 26 4621.4 27 24 4562.84 26 23 4202.52 24 23 4296.49 23 24 4435.23 23 17 4105.18 24 21 4116.68 17 19 3844.49 21 22 3720.98 19 22 3674.4 22 18 3857.62 22 16 3801.06 18 14 3504.37 16 12 3032.6 14 14 3047.03 12 16 2962.34 14 8 2197.82 16 3 2014.45 8 0 1862.83 3 5 1905.41 0 1 1810.99 5 1 1670.07 1 3 1864.44 1

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 Multiple Linear Regression - Estimated Regression Equation Consvertr[t] = + 0.74343004601014 + 0.00334799485690966Aand[t] + 0.512904370369499Y1[t] -1.99293616351209M1[t] + 1.09126781260913M2[t] -0.893483298458545M3[t] + 0.858889235258036M4[t] -0.0371475938890308M5[t] + 0.292557415183294M6[t] -2.3050400438417M7[t] -1.36406165673480M8[t] + 1.74372986889002M9[t] + 0.955268414103237M10[t] + 0.0881616057021931M11[t] -0.104961670239249t + 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) 0.74343004601014 2.197679 0.3383 0.736726 0.368363 Aand 0.00334799485690966 0.000835 4.0097 0.000226 0.000113 Y1 0.512904370369499 0.115951 4.4234 6.1e-05 3e-05 M1 -1.99293616351209 1.829644 -1.0892 0.281844 0.140922 M2 1.09126781260913 1.822776 0.5987 0.552385 0.276193 M3 -0.893483298458545 1.827577 -0.4889 0.627294 0.313647 M4 0.858889235258036 1.817565 0.4725 0.638819 0.319409 M5 -0.0371475938890308 1.816997 -0.0204 0.983779 0.49189 M6 0.292557415183294 1.816653 0.161 0.872781 0.436391 M7 -2.3050400438417 1.818369 -1.2676 0.211446 0.105723 M8 -1.36406165673480 1.824681 -0.7476 0.458613 0.229307 M9 1.74372986889002 1.833477 0.9511 0.346659 0.173329 M10 0.955268414103237 1.810148 0.5277 0.60028 0.30014 M11 0.0881616057021931 1.816656 0.0485 0.961509 0.480754 t -0.104961670239249 0.028622 -3.6671 0.000646 0.000323 Multiple Linear Regression - Regression Statistics Multiple R 0.932200781887413 R-squared 0.868998297751504 Adjusted R-squared 0.828242212607527 F-TEST (value) 21.3219276258170 F-TEST (DF numerator) 14 F-TEST (DF denominator) 45 p-value 2.66453525910038e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.85994923312552 Sum Squared Residuals 368.068932722487 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 19 17.4771564075140 1.52284359248602 2 25 19.5880126908288 5.41198730917116 3 21 20.5537298055290 0.446270194470978 4 23 20.3162198514539 2.68378014854611 5 23 20.8358637326578 2.16413626734218 6 19 21.4326362599907 -2.43263625999069 7 18 16.9881826534612 1.01181734653883 8 19 17.5529197887825 1.44708021121751 9 19 21.2709063838435 -2.27090638384348 10 22 20.7080307910401 1.29196920895987 11 23 21.3605180116395 1.6394819883605 12 20 21.7241243587445 -1.72412435874450 13 14 17.8931623124411 -3.89316231244106 14 14 17.9156736106976 -3.91567361069763 15 14 16.0415047382786 -2.04150473827856 16 15 18.0085486707451 -3.00854867074506 17 11 17.5869122396379 -6.5869122396379 18 17 15.821339882823 1.17866011717701 19 16 16.4261137825997 -0.426113782599727 20 20 17.1850680995704 2.81493190042962 21 24 22.8182498273994 1.18175017260064 22 23 24.4653518728058 -1.46535187280583 23 20 23.3315836842859 -3.33158368428587 24 21 21.5245848126983 -0.524584812698307 25 19 19.6233732350813 -0.623373235081348 26 23 20.8036208679696 2.19637913203040 27 23 21.2057534118757 1.79424658812427 28 23 23.3910531290642 -0.391053129064169 29 23 22.7510689150984 0.248931084901579 30 27 23.5396815477322 3.46031845226778 31 26 23.0937376250345 2.90626237496545 32 17 23.7219862227915 -6.72198622279145 33 24 22.6209869178559 1.37901308214414 34 26 25.5145221233626 0.485477876637384 35 24 25.0805934546039 -1.08059345460386 36 27 24.6460631529488 2.35393684705121 37 27 24.4407280067327 2.55927199326728 38 26 27.1669958212266 -1.16699582122660 39 24 24.3683200907296 -0.368320090729552 40 23 23.7835727066262 -0.7835727066262 41 23 22.5842809135742 0.415719086425817 42 24 23.2735250588549 0.726474941145096 43 17 19.9788645974371 -2.97886459743713 44 21 17.2630526625727 3.73694733742725 45 19 21.4062092793341 -2.40620927933408 46 22 19.0734665687921 2.92653343120787 47 22 19.4841616008255 2.51583839917451 48 18 19.9044579425670 -1.90445794256703 49 16 15.5655800382309 0.434419961769114 50 14 16.5256970092773 -2.52569700927733 51 12 11.8306919535871 0.169308046412859 52 14 12.5006056421107 1.49939435788932 53 16 12.2418741990317 3.75812580096831 54 8 10.9328172505992 -2.93281725059918 55 3 3.51310134146743 -0.513101341467427 56 0 1.27697322628293 -1.27697322628293 57 5 2.88364759156723 2.11635240843277 58 1 4.23862864399928 -3.23862864399928 59 1 0.743143248645286 0.256856751354713 60 3 1.20076973304137 1.79923026695863 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.806060903722517 0.387878192554965 0.193939096277483 19 0.733478827327265 0.53304234534547 0.266521172672735 20 0.795385170250875 0.40922965949825 0.204614829749125 21 0.939254270373974 0.121491459252053 0.0607457296260263 22 0.893961770932382 0.212076458135237 0.106038229067618 23 0.868980373480185 0.26203925303963 0.131019626519815 24 0.88788235261768 0.224235294764640 0.112117647382320 25 0.886912317931032 0.226175364137936 0.113087682068968 26 0.84648036153397 0.307039276932061 0.153519638466030 27 0.814930160321393 0.370139679357213 0.185069839678607 28 0.736457059458561 0.527085881082879 0.263542940541439 29 0.68461556154148 0.630768876917041 0.315384438458520 30 0.6875000501445 0.624999899711 0.3124999498555 31 0.79916503271968 0.401669934560642 0.200834967280321 32 0.950352979539844 0.099294040920313 0.0496470204601565 33 0.933091330681804 0.133817338636391 0.0669086693181956 34 0.889617868289393 0.220764263421213 0.110382131710607 35 0.858342149251178 0.283315701497645 0.141657850748822 36 0.811877752448137 0.376244495103726 0.188122247551863 37 0.789911672212669 0.420176655574662 0.210088327787331 38 0.773915352445743 0.452169295108514 0.226084647554257 39 0.660007582736888 0.679984834526224 0.339992417263112 40 0.543328418602098 0.913343162795803 0.456671581397902 41 0.464357132355874 0.928714264711749 0.535642867644126 42 0.400662763401171 0.801325526802343 0.599337236598829 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 0 0 OK 10% type I error level 1 0.04 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/10rufi1258620290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/10rufi1258620290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/14r561258620290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/14r561258620290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/21r7f1258620290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/21r7f1258620290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/3kzx81258620290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/3kzx81258620290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/4a6441258620290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/4a6441258620290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/5qccs1258620290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/5qccs1258620290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/6iak01258620290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/6iak01258620290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/7f6u81258620290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/7f6u81258620290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/8gi8h1258620290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/8gi8h1258620290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/9nmpy1258620290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620646445xi8qzcapuk3p/9nmpy1258620290.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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