*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: Mon, 06 Dec 2010 14:54:48 +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/Dec/06/t1291647183i6fmqucffilsa65.htm/, Retrieved Mon, 06 Dec 2010 15:53:13 +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/Dec/06/t1291647183i6fmqucffilsa65.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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1635.25 8169.75 7977.64 10171 -14.9 -18 1.8 2.05 1833.42 7905.84 8334.59 9721 -16.2 -11 1.5 2.05 1910.43 8145.82 8623.36 9897 -14.4 -9 1 1.81 1959.67 8895.71 9098.03 9828 -17.3 -10 1.6 1.58 1969.6 9676.31 9154.34 9924 -15.7 -13 1.5 1.57 2061.41 9884.59 9284.73 10371 -12.6 -11 1.8 1.76 2093.48 10637.44 9492.49 10846 -9.4 -5 1.8 1.76 2120.88 10717.13 9682.35 10413 -8.1 -15 1.6 1.89 2174.56 10205.29 9762.12 10709 -5.4 -6 1.9 1.9 2196.72 10295.98 10124.63 10662 -4.6 -6 1.7 1.9 2350.44 10892.76 10540.05 10570 -4.9 -3 1.6 1.92 2440.25 10631.92 10601.61 10297 -4 -1 1.3 1.76 2408.64 11441.08 10323.73 10635 -3.1 -3 1.1 1.64 2472.81 11950.95 10418.4 10872 -1.3 -4 1.9 1.57 2407.6 11037.54 10092.96 10296 0 -6 2.6 1.69 2454.62 11527.72 10364.91 10383 -0.4 0 2.3 1.76 2448.05 11383.89 10152.09 10431 3 -4 2.4 1.89 2497.84 10989.34 10032.8 10574 0.4 -2 2.2 1.78 2645.64 11079.42 10204.59 10653 1.2 -2 2 1.88 2756.76 11028.93 10001.6 10805 0.6 -6 2.9 1.86 2849.27 10973 10411.75 10872 -1.3 -7 2.6 1.88 2921.44 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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation BEL_20[t] = -3817.4613946616 + 0.0945317983390053Nikkei[t] + 0.360449529459283DJ_Indust[t] + 0.0653703019067602Goudprijs[t] -14.6060391320068Conjunct_Seizoenzuiver[t] -3.86165594355231Cons_vertrouw[t] + 246.57375886206Alg_consumptie_index_BE[t] + 223.046859169249Gem_rente_kasbon_1j[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) -3817.4613946616 616.247741 -6.1947 0 0 Nikkei 0.0945317983390053 0.0505 1.8719 0.06803 0.034015 DJ_Indust 0.360449529459283 0.069986 5.1503 6e-06 3e-06 Goudprijs 0.0653703019067602 0.05824 1.1224 0.267912 0.133956 Conjunct_Seizoenzuiver -14.6060391320068 7.767973 -1.8803 0.066853 0.033427 Cons_vertrouw -3.86165594355231 8.349519 -0.4625 0.646054 0.323027 Alg_consumptie_index_BE 246.57375886206 52.887813 4.6622 3e-05 1.5e-05 Gem_rente_kasbon_1j 223.046859169249 118.488654 1.8824 0.066555 0.033278 Multiple Linear Regression - Regression Statistics Multiple R 0.98353031074024 R-squared 0.967331872144792 Adjusted R-squared 0.962013804819526 F-TEST (value) 181.895379087991 F-TEST (DF numerator) 7 F-TEST (DF denominator) 43 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 163.422316729577 Sum Squared Residuals 1148394.70502627 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1635.25 1683.47630700722 -48.2263070072228 2 1833.42 1675.75837539814 157.661624601860 3 1910.43 1603.20399216471 307.226007835285 4 1959.67 1983.53911687270 -23.8691168727036 5 1969.6 2045.23056138458 -75.6305613845798 6 2061.41 2204.48818114559 -143.078181145595 7 2093.48 2311.68507228755 -218.205072287552 8 2120.88 2358.65796671820 -237.777966718203 9 2174.56 2360.38686648766 -185.826866487657 10 2196.72 2435.55452693567 -238.834526935670 11 2350.44 2608.2934945071 -257.853494507099 12 2440.25 2457.45062860971 -17.2006286097128 13 2408.64 2374.37292714762 34.2670728523819 14 2472.81 2631.40488612628 -158.59488612628 15 2407.6 2578.20332275917 -170.603322759174 16 2454.62 2652.56601794605 -197.946017946046 17 2448.05 2584.83597332654 -136.785973326542 18 2497.84 2470.29086467044 27.5491353295608 19 2645.64 2507.19727042017 138.442729579831 20 2756.76 2680.85868887280 75.9013111271972 21 2849.27 2789.89164994631 59.3783500536878 22 2921.44 2824.72206535685 96.7179346431536 23 2981.85 2770.43108403516 211.418915964839 24 3080.58 2950.14330419146 130.436695808542 25 3106.22 3096.52930272616 9.69069727383502 26 3119.31 2894.34721707433 224.962782925666 27 3061.26 2858.61615078845 202.643849211553 28 3097.31 3046.6253788952 50.6846211047988 29 3161.69 3140.96061965951 20.7293803404903 30 3257.16 3180.08459715772 77.0754028422763 31 3277.01 3269.50868143511 7.50131856489218 32 3295.32 3058.89283843997 236.427161560029 33 3363.99 3375.41812964038 -11.4281296403797 34 3494.17 3689.96740301083 -195.797403010828 35 3667.03 3712.5213321679 -45.4913321679013 36 3813.06 3710.3815878858 102.678412114204 37 3917.96 3665.17578458137 252.784215418635 38 3895.51 3960.87192298474 -65.3619229847421 39 3801.06 4103.47835636203 -302.41835636203 40 3570.12 3626.52915156129 -56.4091515612881 41 3701.61 3611.96664528193 89.6433547180742 42 3862.27 3762.28442902992 99.9855709700786 43 3970.1 3761.48035005156 208.619649948443 44 4138.52 3926.01104938168 212.508950618324 45 4199.75 4107.2204100203 92.5295899796992 46 4290.89 4266.46593299463 24.4240670053703 47 4443.91 4446.53617052913 -2.62617052912971 48 4502.64 4643.01217215573 -140.372172155729 49 4356.98 4461.73902859837 -104.759028598376 50 4591.27 4646.50277396368 -55.2327739636803 51 4696.96 4758.51943930459 -61.5594393045858 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.0497425766103968 0.0994851532207936 0.950257423389603 12 0.0148321426277294 0.0296642852554589 0.98516785737227 13 0.0050989598031633 0.0101979196063266 0.994901040196837 14 0.00426894390616423 0.00853788781232845 0.995731056093836 15 0.00248796986266188 0.00497593972532376 0.997512030137338 16 0.00752968736296734 0.0150593747259347 0.992470312637033 17 0.00546500977281116 0.0109300195456223 0.994534990227189 18 0.0443184204840014 0.0886368409680028 0.955681579515999 19 0.59727394083236 0.80545211833528 0.40272605916764 20 0.979489691324259 0.0410206173514823 0.0205103086757411 21 0.994398109990278 0.0112037800194444 0.00560189000972219 22 0.999591095785232 0.000817808429535627 0.000408904214767813 23 0.99993982456802 0.000120350863960695 6.01754319803474e-05 24 0.999959840527761 8.03189444773857e-05 4.01594722386928e-05 25 0.999916746487762 0.000166507024476901 8.32535122384506e-05 26 0.999932954230886 0.000134091538227209 6.70457691136043e-05 27 0.999909030643443 0.000181938713113188 9.09693565565942e-05 28 0.999806962288686 0.000386075422627289 0.000193037711313644 29 0.99958441129392 0.000831177412160723 0.000415588706080362 30 0.99901887330792 0.00196225338416000 0.000981126692079998 31 0.998688432252603 0.00262313549479316 0.00131156774739658 32 0.999068369751712 0.00186326049657528 0.000931630248287642 33 0.997551003488711 0.00489799302257725 0.00244899651128862 34 0.997244734496097 0.0055105310078059 0.00275526550390295 35 0.996554918795097 0.00689016240980528 0.00344508120490264 36 0.990968239285597 0.0180635214288056 0.00903176071440281 37 0.987201599317017 0.0255968013659651 0.0127984006829826 38 0.968003728206333 0.0639925435873345 0.0319962717936672 39 0.980317124643933 0.0393657507121339 0.0196828753560669 40 0.990179485933287 0.0196410281334253 0.00982051406671264 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.533333333333333 NOK 5% type I error level 26 0.866666666666667 NOK 10% type I error level 29 0.966666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/10rjhb1291647280.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/10rjhb1291647280.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/120ki1291647280.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/120ki1291647280.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/2cr1k1291647280.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/2cr1k1291647280.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/3cr1k1291647280.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/3cr1k1291647280.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/4cr1k1291647280.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/4cr1k1291647280.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/550j51291647280.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/550j51291647280.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/650j51291647280.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/650j51291647280.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/7ya0q1291647280.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/7ya0q1291647280.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/8ya0q1291647280.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/8ya0q1291647280.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/9rjhb1291647280.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291647183i6fmqucffilsa65/9rjhb1291647280.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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