*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, 21 Dec 2010 20:26:33 +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/21/t1292963057xxrvnjd99mwzffz.htm/, Retrieved Tue, 21 Dec 2010 21:24:20 +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/21/t1292963057xxrvnjd99mwzffz.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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112.3 1 117.3 1 111.1 1 102.2 1 104.3 1 122.9 0 107.6 0 121.3 0 131.5 0 89 0 104.4 0 128.9 0 135.9 0 133.3 0 121.3 0 120.5 0 120.4 0 137.9 0 126.1 0 133.2 0 151.1 0 105 0 119 0 140.4 0 156.6 1 137.1 1 122.7 1 125.8 1 139.3 1 134.9 1 149.2 1 132.3 1 149 1 117.2 1 119.6 1 152 1 149.4 1 127.3 1 114.1 1 102.1 1 107.7 1 104.4 1 102.1 1 96 1 109.3 1 90 1 83.9 1 112 1 114.3 1 103.6 1 91.7 1 80.8 1 87.2 1 109.2 1 102.7 1 95.1 1 117.5 1 85.1 1 92.1 1 113.5 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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation Promet[t] = + 143.465911330049 -0.78177339901479Dummy[t] + 0.329540229885031M1[t] -9.27165845648604M2[t] -20.4328571428571M3[t] -25.9540558292282M4[t] -20.0752545155993M5[t] -9.7728078817734M6[t] -13.7140065681445M7[t] -15.2952052545156M8[t] + 1.1835960591133M9[t] -32.8576026272578M10[t] -25.9388013136289M11[t] -0.378801313628899t + 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) 143.465911330049 8.231945 17.4279 0 0 Dummy -0.78177339901479 6.030553 -0.1296 0.89742 0.44871 M1 0.329540229885031 10.292548 0.032 0.974597 0.487298 M2 -9.27165845648604 10.25309 -0.9043 0.370559 0.185279 M3 -20.4328571428571 10.216034 -2.0001 0.051416 0.025708 M4 -25.9540558292282 10.181407 -2.5492 0.014198 0.007099 M5 -20.0752545155993 10.149234 -1.978 0.053936 0.026968 M6 -9.7728078817734 9.968671 -0.9804 0.33204 0.16602 M7 -13.7140065681445 9.954262 -1.3777 0.174961 0.087481 M8 -15.2952052545156 9.942457 -1.5384 0.130809 0.065404 M9 1.1835960591133 9.933266 0.1192 0.905672 0.452836 M10 -32.8576026272578 9.926696 -3.31 0.00182 0.00091 M11 -25.9388013136289 9.922751 -2.6141 0.012053 0.006027 t -0.378801313628899 0.161546 -2.3449 0.023406 0.011703 Multiple Linear Regression - Regression Statistics Multiple R 0.683211258579278 R-squared 0.466777623849482 Adjusted R-squared 0.316084343633031 F-TEST (value) 3.09753443006229 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0.002306270961007 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 15.6871682668679 Sum Squared Residuals 11320.0134187192 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 112.3 142.634876847291 -30.3348768472907 2 117.3 132.654876847291 -15.3548768472906 3 111.1 121.114876847291 -10.0148768472906 4 102.2 115.214876847291 -13.0148768472906 5 104.3 120.714876847291 -16.4148768472906 6 122.9 131.420295566502 -8.52029556650246 7 107.6 127.100295566502 -19.5002955665025 8 121.3 125.140295566502 -3.84029556650247 9 131.5 141.240295566502 -9.74029556650246 10 89 106.820295566502 -17.8202955665025 11 104.4 113.360295566502 -8.96029556650245 12 128.9 138.920295566502 -10.0202955665025 13 135.9 138.871034482759 -2.97103448275858 14 133.3 128.891034482759 4.40896551724139 15 121.3 117.351034482759 3.94896551724137 16 120.5 111.451034482759 9.04896551724137 17 120.4 116.951034482759 3.44896551724137 18 137.9 126.874679802956 11.0253201970443 19 126.1 122.554679802956 3.54532019704433 20 133.2 120.594679802956 12.6053201970443 21 151.1 136.694679802956 14.4053201970443 22 105 102.274679802956 2.72532019704434 23 119 108.814679802956 10.1853201970443 24 140.4 134.374679802956 6.02532019704433 25 156.6 133.543645320197 23.056354679803 26 137.1 123.563645320197 13.536354679803 27 122.7 112.023645320197 10.676354679803 28 125.8 106.123645320197 19.676354679803 29 139.3 111.623645320197 27.676354679803 30 134.9 121.547290640394 13.3527093596059 31 149.2 117.227290640394 31.9727093596059 32 132.3 115.267290640394 17.0327093596059 33 149 131.367290640394 17.6327093596059 34 117.2 96.947290640394 20.2527093596059 35 119.6 103.487290640394 16.1127093596059 36 152 129.047290640394 22.9527093596059 37 149.4 128.99802955665 20.4019704433498 38 127.3 119.01802955665 8.28197044334975 39 114.1 107.47802955665 6.62197044334975 40 102.1 101.57802955665 0.521970443349749 41 107.7 107.07802955665 0.621970443349752 42 104.4 117.001674876847 -12.6016748768473 43 102.1 112.681674876847 -10.5816748768473 44 96 110.721674876847 -14.7216748768473 45 109.3 126.821674876847 -17.5216748768473 46 90 92.4016748768473 -2.40167487684729 47 83.9 98.9416748768473 -15.0416748768473 48 112 124.501674876847 -12.5016748768473 49 114.3 124.452413793103 -10.1524137931034 50 103.6 114.472413793103 -10.8724137931035 51 91.7 102.932413793103 -11.2324137931035 52 80.8 97.0324137931034 -16.2324137931035 53 87.2 102.532413793103 -15.3324137931035 54 109.2 112.4560591133 -3.2560591133005 55 102.7 108.1360591133 -5.43605911330049 56 95.1 106.1760591133 -11.0760591133005 57 117.5 122.276059113301 -4.7760591133005 58 85.1 87.8560591133005 -2.7560591133005 59 92.1 94.3960591133005 -2.2960591133005 60 113.5 119.9560591133 -6.4560591133005 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.081435893624075 0.16287178724815 0.918564106375925 18 0.0260545578135157 0.0521091156270315 0.973945442186484 19 0.00910346594098418 0.0182069318819684 0.990896534059016 20 0.00383174834562902 0.00766349669125804 0.99616825165437 21 0.00166371651509202 0.00332743303018404 0.998336283484908 22 0.000474333552480204 0.000948667104960409 0.99952566644752 23 0.00012861888735342 0.000257237774706839 0.999871381112647 24 5.23033130340177e-05 0.000104606626068035 0.999947696686966 25 0.000100224171622767 0.000200448343245534 0.999899775828377 26 0.000818718830199015 0.00163743766039803 0.9991812811698 27 0.00318716720016651 0.00637433440033302 0.996812832799833 28 0.0015544521378876 0.00310890427577521 0.998445547862112 29 0.00169882462892149 0.00339764925784299 0.998301175371079 30 0.0020653177422913 0.00413063548458261 0.997934682257709 31 0.00978153088158911 0.0195630617631782 0.99021846911841 32 0.0122550119248149 0.0245100238496298 0.987744988075185 33 0.0107959132386351 0.0215918264772702 0.989204086761365 34 0.00693878841067921 0.0138775768213584 0.993061211589321 35 0.00572415819034554 0.0114483163806911 0.994275841809654 36 0.0115280269597219 0.0230560539194437 0.988471973040278 37 0.048534348227113 0.097068696454226 0.951465651772887 38 0.213193156793389 0.426386313586777 0.786806843206611 39 0.458271326838713 0.916542653677425 0.541728673161287 40 0.787242306162259 0.425515387675482 0.212757693837741 41 0.986131614265108 0.0277367714697832 0.0138683857348916 42 0.977306982249933 0.0453860355001342 0.0226930177500671 43 0.944096074216107 0.111807851567786 0.055903925783893 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.407407407407407 NOK 5% type I error level 20 0.740740740740741 NOK 10% type I error level 22 0.814814814814815 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/10uxqg1292963184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/10uxqg1292963184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/1net51292963184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/1net51292963184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/2yob81292963184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/2yob81292963184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/3yob81292963184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/3yob81292963184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/4yob81292963184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/4yob81292963184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/5yob81292963184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/5yob81292963184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/6qfsa1292963184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/6qfsa1292963184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/7169v1292963184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/7169v1292963184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/8169v1292963184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/8169v1292963184.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/9169v1292963184.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292963057xxrvnjd99mwzffz/9169v1292963184.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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