w7

*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: Sun, 22 Nov 2009 06:32:33 -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/22/t12588968384s5p2uwca14kqi4.htm/, Retrieved Sun, 22 Nov 2009 14:34:10 +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/22/t12588968384s5p2uwca14kqi4.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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8 0 8,1 0 7,7 0 7,5 0 7,6 0 7,8 0 7,8 0 7,8 0 7,5 0 7,5 0 7,1 0 7,5 0 7,5 0 7,6 0 7,7 0 7,7 0 7,9 0 8,1 0 8,2 0 8,2 0 8,2 0 7,9 0 7,3 0 6,9 0 6,6 0 6,7 0 6,9 0 7 0 7,1 0 7,2 0 7,1 0 6,9 0 7 0 6,8 0 6,4 0 6,7 0 6,6 0 6,4 0 6,3 0 6,2 0 6,5 0 6,8 1 6,8 1 6,4 1 6,1 1 5,8 1 6,1 1 7,2 1 7,3 1 6,9 1 6,1 1 5,8 1 6,2 1 7,1 1 7,7 1 7,9 1 7,7 1 7,4 1 7,5 1 8 1 8,1 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 Y[t] = + 7.98971428571429 + 0.291428571428571X[t] -0.0081111111111108M1[t] -0.296793650793651M2[t] -0.473285714285714M3[t] -0.549777777777779M4[t] -0.306269841269842M5[t] -0.00104761904761940M6[t] + 0.142460317460317M7[t] + 0.0859682539682537M8[t] -0.0305238095238098M9[t] -0.227015873015873M10[t] -0.403507936507937M11[t] -0.0235079365079365t + 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) 7.98971428571429 0.337456 23.6763 0 0 X 0.291428571428571 0.29259 0.996 0.324335 0.162168 M1 -0.0081111111111108 0.372191 -0.0218 0.982705 0.491353 M2 -0.296793650793651 0.390476 -0.7601 0.451002 0.225501 M3 -0.473285714285714 0.389954 -1.2137 0.230928 0.115464 M4 -0.549777777777779 0.389587 -1.4112 0.164777 0.082388 M5 -0.306269841269842 0.389374 -0.7866 0.435481 0.217741 M6 -0.00104761904761940 0.390607 -0.0027 0.997871 0.498936 M7 0.142460317460317 0.389757 0.3655 0.71637 0.358185 M8 0.0859682539682537 0.38906 0.221 0.826077 0.413039 M9 -0.0305238095238098 0.388517 -0.0786 0.937712 0.468856 M10 -0.227015873015873 0.388129 -0.5849 0.561414 0.280707 M11 -0.403507936507937 0.387895 -1.0402 0.303546 0.151773 t -0.0235079365079365 0.007766 -3.0272 0.003998 0.001999 Multiple Linear Regression - Regression Statistics Multiple R 0.574299977559274 R-squared 0.329820464224582 Adjusted R-squared 0.144451656456913 F-TEST (value) 1.77926625410442 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 0.0752311735750701 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.613193676007708 Sum Squared Residuals 17.6723047619048 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8 7.95809523809523 0.0419047619047665 2 8.1 7.64590476190476 0.454095238095238 3 7.7 7.44590476190476 0.254095238095238 4 7.5 7.34590476190476 0.154095238095236 5 7.6 7.56590476190476 0.0340952380952374 6 7.8 7.84761904761905 -0.0476190476190475 7 7.8 7.96761904761905 -0.167619047619048 8 7.8 7.88761904761905 -0.0876190476190477 9 7.5 7.74761904761905 -0.247619047619048 10 7.5 7.52761904761905 -0.0276190476190476 11 7.1 7.32761904761905 -0.227619047619048 12 7.5 7.70761904761905 -0.207619047619048 13 7.5 7.676 -0.176000000000001 14 7.6 7.36380952380952 0.236190476190475 15 7.7 7.16380952380952 0.536190476190476 16 7.7 7.06380952380952 0.636190476190477 17 7.9 7.28380952380952 0.616190476190477 18 8.1 7.56552380952381 0.53447619047619 19 8.2 7.68552380952381 0.51447619047619 20 8.2 7.60552380952381 0.59447619047619 21 8.2 7.46552380952381 0.73447619047619 22 7.9 7.24552380952381 0.65447619047619 23 7.3 7.04552380952381 0.254476190476191 24 6.9 7.42552380952381 -0.525523809523809 25 6.6 7.39390476190476 -0.793904761904763 26 6.7 7.08171428571428 -0.381714285714285 27 6.9 6.88171428571429 0.0182857142857146 28 7 6.78171428571429 0.218285714285715 29 7.1 7.00171428571429 0.098285714285714 30 7.2 7.28342857142857 -0.0834285714285712 31 7.1 7.40342857142857 -0.303428571428571 32 6.9 7.32342857142857 -0.423428571428571 33 7 7.18342857142857 -0.183428571428571 34 6.8 6.96342857142857 -0.163428571428572 35 6.4 6.76342857142857 -0.363428571428571 36 6.7 7.14342857142857 -0.443428571428571 37 6.6 7.11180952380952 -0.511809523809525 38 6.4 6.79961904761905 -0.399619047619047 39 6.3 6.59961904761905 -0.299619047619048 40 6.2 6.49961904761905 -0.299619047619047 41 6.5 6.71961904761905 -0.219619047619047 42 6.8 7.2927619047619 -0.492761904761905 43 6.8 7.4127619047619 -0.612761904761905 44 6.4 7.3327619047619 -0.932761904761905 45 6.1 7.1927619047619 -1.09276190476191 46 5.8 6.9727619047619 -1.17276190476190 47 6.1 6.7727619047619 -0.672761904761905 48 7.2 7.1527619047619 0.0472380952380952 49 7.3 7.12114285714286 0.178857142857142 50 6.9 6.80895238095238 0.0910476190476197 51 6.1 6.60895238095238 -0.508952380952381 52 5.8 6.50895238095238 -0.70895238095238 53 6.2 6.72895238095238 -0.528952380952381 54 7.1 7.01066666666667 0.089333333333333 55 7.7 7.13066666666667 0.569333333333334 56 7.9 7.05066666666667 0.849333333333334 57 7.7 6.91066666666667 0.789333333333334 58 7.4 6.69066666666667 0.709333333333333 59 7.5 6.49066666666667 1.00933333333333 60 8 6.87066666666667 1.12933333333333 61 8.1 6.83904761904762 1.26095238095238 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.106514592759050 0.213029185518100 0.89348540724095 18 0.0586852418380361 0.117370483676072 0.941314758161964 19 0.036472185369894 0.072944370739788 0.963527814630106 20 0.0233517814189055 0.0467035628378110 0.976648218581095 21 0.0314827450966072 0.0629654901932144 0.968517254903393 22 0.0293989229416013 0.0587978458832025 0.970601077058399 23 0.0204355045337913 0.0408710090675825 0.979564495466209 24 0.030418815546125 0.06083763109225 0.969581184453875 25 0.138240617459518 0.276481234919037 0.861759382540482 26 0.205887829011737 0.411775658023474 0.794112170988263 27 0.249713056310982 0.499426112621963 0.750286943689018 28 0.437104405032881 0.874208810065762 0.562895594967119 29 0.778581248628103 0.442837502743794 0.221418751371897 30 0.785597073491165 0.428805853017671 0.214402926508835 31 0.747072752763712 0.505854494472577 0.252927247236288 32 0.718839233349604 0.562321533300792 0.281160766650396 33 0.722216805877635 0.55556638824473 0.277783194122365 34 0.806571452361237 0.386857095277526 0.193428547638763 35 0.751699784128516 0.496600431742968 0.248300215871484 36 0.679553372234216 0.640893255531569 0.320446627765784 37 0.691618844572622 0.616762310854756 0.308381155427378 38 0.750971829339954 0.498056341320092 0.249028170660046 39 0.664156890004778 0.671686219990445 0.335843109995222 40 0.561748420419732 0.876503159160537 0.438251579580268 41 0.435046171994913 0.870092343989826 0.564953828005087 42 0.647728819579358 0.704542360841284 0.352271180420642 43 0.571081789589114 0.857836420821773 0.428918210410886 44 0.43833454304332 0.87666908608664 0.56166545695668 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 2 0.0714285714285714 NOK 10% type I error level 6 0.214285714285714 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/10fm2u1258896748.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/10fm2u1258896748.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/1l0e21258896748.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/1l0e21258896748.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/2ehz61258896748.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/2ehz61258896748.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/3za601258896748.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/3za601258896748.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/4m7w51258896748.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/4m7w51258896748.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/5r2yv1258896748.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/5r2yv1258896748.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/68z5g1258896748.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/68z5g1258896748.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/71ku81258896748.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/71ku81258896748.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/8iqiz1258896748.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/8iqiz1258896748.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/9o3w31258896748.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12588968384s5p2uwca14kqi4/9o3w31258896748.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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