*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: Fri, 20 Nov 2009 07:16:52 -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/20/t1258726662wbi452bzmjo8g78.htm/, Retrieved Fri, 20 Nov 2009 15:17:54 +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/20/t1258726662wbi452bzmjo8g78.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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22 0 22 0 20 0 21 0 20 0 21 0 21 0 21 0 19 0 21 0 21 0 22 0 19 0 24 0 22 0 22 0 22 0 24 0 22 0 23 0 24 0 21 0 20 0 22 0 23 0 23 0 22 0 20 0 21 1 21 1 20 1 20 1 17 1 18 1 19 1 19 1 20 1 21 1 20 1 21 1 19 1 22 1 20 1 18 1 16 1 17 1 18 1 19 1 18 1 20 1 21 1 18 1 19 1 19 1 19 1 21 1 19 1 19 1 17 1 16 1 16 1 17 1 16 1 15 1 16 1 16 1 16 1 18 1 19 1 16 1 16 1 16 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 22.15 -1.05X[t] -0.149999999999988M1[t] + 1.40833333333333M2[t] + 0.466666666666665M3[t] -0.141666666666668M4[t] + 0.091666666666664M5[t] + 1.15M6[t] + 0.374999999999999M7[t] + 0.93333333333333M8[t] -0.175000000000001M9[t] -0.450000000000001M10[t] -0.558333333333335M11[t] -0.0583333333333334t + 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) 22.15 0.745714 29.7031 0 0 X -1.05 0.711267 -1.4762 0.145289 0.072644 M1 -0.149999999999988 0.902221 -0.1663 0.868533 0.434267 M2 1.40833333333333 0.900829 1.5634 0.123405 0.061702 M3 0.466666666666665 0.899745 0.5187 0.605968 0.302984 M4 -0.141666666666668 0.89897 -0.1576 0.875329 0.437665 M5 0.091666666666664 0.903703 0.1014 0.919555 0.459778 M6 1.15 0.901696 1.2754 0.207261 0.103631 M7 0.374999999999999 0.899993 0.4167 0.678458 0.339229 M8 0.93333333333333 0.898598 1.0387 0.303276 0.151638 M9 -0.175000000000001 0.897511 -0.195 0.846087 0.423044 M10 -0.450000000000001 0.896734 -0.5018 0.617693 0.308847 M11 -0.558333333333335 0.896268 -0.623 0.535757 0.267878 t -0.0583333333333334 0.016698 -3.4934 0.00092 0.00046 Multiple Linear Regression - Regression Statistics Multiple R 0.792007423315082 R-squared 0.627275758586195 Adjusted R-squared 0.543734118269308 F-TEST (value) 7.50854012689762 F-TEST (DF numerator) 13 F-TEST (DF denominator) 58 p-value 2.18761772030618e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.55211202048626 Sum Squared Residuals 139.725 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 22 21.9416666666666 0.0583333333334018 2 22 23.4416666666667 -1.44166666666667 3 20 22.4416666666667 -2.44166666666667 4 21 21.775 -0.775000000000002 5 20 21.95 -1.95000000000000 6 21 22.95 -1.95 7 21 22.1166666666667 -1.11666666666667 8 21 22.6166666666667 -1.61666666666667 9 19 21.45 -2.45 10 21 21.1166666666667 -0.116666666666669 11 21 20.95 0.0499999999999965 12 22 21.45 0.549999999999997 13 19 21.2416666666667 -2.24166666666668 14 24 22.7416666666667 1.25833333333333 15 22 21.7416666666667 0.258333333333333 16 22 21.075 0.924999999999997 17 22 21.25 0.75 18 24 22.25 1.75000000000000 19 22 21.4166666666667 0.583333333333331 20 23 21.9166666666667 1.08333333333333 21 24 20.75 3.25 22 21 20.4166666666667 0.583333333333332 23 20 20.25 -0.250000000000001 24 22 20.75 1.25000000000000 25 23 20.5416666666667 2.45833333333332 26 23 22.0416666666667 0.958333333333332 27 22 21.0416666666667 0.958333333333334 28 20 20.375 -0.375000000000001 29 21 19.5 1.5 30 21 20.5 0.499999999999999 31 20 19.6666666666667 0.333333333333332 32 20 20.1666666666667 -0.166666666666666 33 17 19 -2 34 18 18.6666666666667 -0.666666666666668 35 19 18.5 0.499999999999999 36 19 19 -2.55611504185183e-15 37 20 18.7916666666667 1.20833333333332 38 21 20.2916666666667 0.708333333333333 39 20 19.2916666666667 0.708333333333335 40 21 18.625 2.375 41 19 18.8 0.200000000000001 42 22 19.8 2.2 43 20 18.9666666666667 1.03333333333333 44 18 19.4666666666667 -1.46666666666667 45 16 18.3 -2.3 46 17 17.9666666666667 -0.966666666666666 47 18 17.8 0.2 48 19 18.3 0.699999999999999 49 18 18.0916666666667 -0.0916666666666787 50 20 19.5916666666667 0.408333333333334 51 21 18.5916666666667 2.40833333333334 52 18 17.925 0.0750000000000002 53 19 18.1 0.900000000000002 54 19 19.1 -0.0999999999999987 55 19 18.2666666666667 0.733333333333334 56 21 18.7666666666667 2.23333333333334 57 19 17.6 1.40000000000000 58 19 17.2666666666667 1.73333333333333 59 17 17.1 -0.0999999999999988 60 16 17.6 -1.6 61 16 17.3916666666667 -1.39166666666668 62 17 18.8916666666667 -1.89166666666666 63 16 17.8916666666667 -1.89166666666666 64 15 17.225 -2.2250 65 16 17.4 -1.40000000000000 66 16 18.4 -2.40000000000000 67 16 17.5666666666667 -1.56666666666666 68 18 18.0666666666667 -0.0666666666666627 69 19 16.9 2.10000000000000 70 16 16.5666666666667 -0.566666666666664 71 16 16.4 -0.399999999999997 72 16 16.9 -0.899999999999998 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.787962266374968 0.424075467250065 0.212037733625032 18 0.74719251968757 0.505614960624859 0.252807480312429 19 0.621852816992459 0.756294366015082 0.378147183007541 20 0.505655766032402 0.988688467935195 0.494344233967598 21 0.692085120881221 0.615829758237557 0.307914879118779 22 0.630969962032912 0.738060075934176 0.369030037967088 23 0.638980901156641 0.722038197686718 0.361019098843359 24 0.563275806082032 0.873448387835935 0.436724193917968 25 0.497781470763963 0.995562941527925 0.502218529236037 26 0.472367846583705 0.94473569316741 0.527632153416295 27 0.386949607038544 0.773899214077088 0.613050392961456 28 0.448085373418454 0.896170746836909 0.551914626581546 29 0.364727979019566 0.729455958039131 0.635272020980434 30 0.300114345224298 0.600228690448597 0.699885654775702 31 0.232475248676997 0.464950497353993 0.767524751323003 32 0.186435007974626 0.372870015949252 0.813564992025374 33 0.320523600466573 0.641047200933145 0.679476399533427 34 0.293723712533104 0.587447425066208 0.706276287466896 35 0.231052034339139 0.462104068678278 0.768947965660861 36 0.183822699573305 0.36764539914661 0.816177300426695 37 0.137775770796201 0.275551541592403 0.862224229203799 38 0.0969607027704506 0.193921405540901 0.90303929722955 39 0.0664889927485148 0.132977985497030 0.933511007251485 40 0.0761147356881103 0.152229471376221 0.92388526431189 41 0.0594044848517782 0.118808969703556 0.940595515148222 42 0.0618211286842382 0.123642257368476 0.938178871315762 43 0.0419200066790394 0.0838400133580788 0.95807999332096 44 0.0928494756960445 0.185698951392089 0.907150524303955 45 0.545137218840792 0.909725562318416 0.454862781159208 46 0.802019538685507 0.395960922628986 0.197980461314493 47 0.82450908788226 0.350981824235479 0.175490912117740 48 0.761857769275734 0.476284461448531 0.238142230724265 49 0.696880210379276 0.606239579241448 0.303119789620724 50 0.609431292005524 0.781137415988952 0.390568707994476 51 0.746604127227132 0.506791745545737 0.253395872772868 52 0.679878599997144 0.640242800005713 0.320121400002857 53 0.594340748417704 0.811318503164592 0.405659251582296 54 0.531378017907029 0.937243964185942 0.468621982092971 55 0.457157315799615 0.914314631599231 0.542842684200385 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 0.0256410256410256 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/10oxey1258726607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/10oxey1258726607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/1zjsn1258726607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/1zjsn1258726607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/213t31258726607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/213t31258726607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/3jzt01258726607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/3jzt01258726607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/43jry1258726607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/43jry1258726607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/5tggo1258726607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/5tggo1258726607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/6obl51258726607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/6obl51258726607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/7sga01258726607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/7sga01258726607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/8y7ql1258726607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/8y7ql1258726607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/9baok1258726607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726662wbi452bzmjo8g78/9baok1258726607.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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