*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, 28 Dec 2010 00:44:59 +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/28/t1293497011gige8uvplb3glud.htm/, Retrieved Tue, 28 Dec 2010 01:43:31 +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/28/t1293497011gige8uvplb3glud.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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99 94.6 106.3 95.9 128.9 104.7 111.1 102.8 102.9 98.1 130 113.9 87 80.9 87.5 95.7 117.6 113.2 103.4 105.9 110.8 108.8 112.6 102.3 102.5 99 112.4 100.7 135.6 115.5 105.1 100.7 127.7 109.9 137 114.6 91 85.4 90.5 100.5 122.4 114.8 123.3 116.5 124.3 112.9 120 102 118.1 106 119 105.3 142.7 118.8 123.6 106.1 129.6 109.3 151.6 117.2 110.4 92.5 99.2 104.2 130.5 112.5 136.2 122.4 129.7 113.3 128 100 121.6 110.7 135.8 112.8 143.8 109.8 147.5 117.3 136.2 109.1 156.6 115.9 123.3 96 104.5 99.8 139.8 116.8 136.5 115.7 112.1 99.4 118.5 94.3 94.4 91 102.3 93.2 111.4 103.1 99.2 94.1 87.8 91.8 115.8 102.7 79.7 82.6 72.7 89.1 104.5 104.5 103 105.1 95.1 95.1 104.2 88.7 78.3 86.3

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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation ipi[t] = -70.5918402602645 + 1.92131992879401`tip `[t] -15.2527580996292M1[t] -9.41583810663133M2[t] -9.00345348001863M3[t] -12.3096963200716M4[t] -11.693757159947M5[t] -8.0483269034278M6[t] + 0.794772889364236M7[t] -26.5485279715176M8[t] -22.3276669390308M9[t] -26.2678700849142M10[t] -18.4759401990215M11[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) -70.5918402602645 13.335151 -5.2937 3e-06 1e-06 `tip ` 1.92131992879401 0.133273 14.4164 0 0 M1 -15.2527580996292 4.089094 -3.7301 0.000506 0.000253 M2 -9.41583810663133 4.305566 -2.1869 0.033655 0.016828 M3 -9.00345348001863 4.604486 -1.9554 0.056375 0.028188 M4 -12.3096963200716 4.36386 -2.8208 0.006945 0.003473 M5 -11.693757159947 4.349111 -2.6888 0.009831 0.004916 M6 -8.0483269034278 4.738012 -1.6987 0.095855 0.047927 M7 0.794772889364236 4.472748 0.1777 0.859712 0.429856 M8 -26.5485279715176 4.270743 -6.2164 0 0 M9 -22.3276669390308 4.70953 -4.741 1.9e-05 1e-05 M10 -26.2678700849142 4.753125 -5.5264 1e-06 1e-06 M11 -18.4759401990215 4.416065 -4.1838 0.000121 6.1e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.94817767238409 R-squared 0.89904089840771 Adjusted R-squared 0.873801123009637 F-TEST (value) 35.6200039116181 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.75211221609483 Sum Squared Residuals 2188.36893017938 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 99 95.91226690402 3.08773309598005 2 106.3 104.24690280445 2.05309719554997 3 128.9 121.56690280445 7.33309719554998 4 111.1 114.610152099688 -3.51015209968838 5 102.9 106.195887594481 -3.29588759448116 6 130 140.198172725946 -10.1981727259458 7 87 85.6377148685354 1.36228513146459 8 87.5 86.729948953805 0.77005104619506 9 117.6 124.573908740187 -6.97390874018698 10 103.4 106.608070114107 -3.20807011410722 11 110.8 119.971827793503 -9.17182779350262 12 112.6 125.959188455363 -13.3591884553630 13 102.5 104.366074590714 -1.86607459071361 14 112.4 113.469238462661 -1.06923846266127 15 135.6 142.317158035425 -6.71715803542535 16 105.1 110.575380249221 -5.47538024922097 17 127.7 128.867462754251 -1.16746275425052 18 137 141.543096676102 -4.54309667610157 19 91 94.2836545481085 -3.28365454810847 20 90.5 95.9522846120162 -5.45228461201619 21 122.4 127.648020626257 -5.24802062625738 22 123.3 126.974061359324 -3.67406135932376 23 124.3 127.849239501558 -3.54923950155809 24 120 125.382792476725 -5.38279247672481 25 118.1 117.815314092272 0.284685907728300 26 119 122.307310135114 -3.30731013511372 27 142.7 148.657513800446 -5.95751380044559 28 123.6 120.950507864709 2.64949213529138 29 129.6 127.714670796974 1.88532920302589 30 151.6 146.538528490966 5.06147150903398 31 110.4 107.925026042546 2.47497395745407 32 99.2 103.061168348554 -3.86116834855404 33 130.5 123.228984790031 7.27101520996883 34 136.2 138.309848939208 -2.10984893920845 35 129.7 128.617767473076 1.08223252692431 36 128 121.540152619137 6.45984738086321 37 121.6 126.845517757604 -5.24551775760356 38 135.8 136.717209601069 -0.917209601068798 39 143.8 131.365634441299 12.4343655587005 40 147.5 142.469291067202 5.03070893279844 41 136.2 127.330406811215 8.8695931887847 42 156.6 144.040812583534 12.5591874164662 43 123.3 114.649645793325 8.65035420667502 44 104.5 94.6073606618604 9.89263933813961 45 139.8 131.490660483845 8.3093395161546 46 136.5 125.437005416289 11.0629945837114 47 112.1 101.911420462839 10.1885795371611 48 118.5 110.588629025011 7.91137097498908 49 94.4 88.9955151603615 5.40448483963849 50 102.3 99.0593389967062 3.24066100329381 51 111.4 118.492790918380 -7.09279091837958 52 99.2 97.8946687191805 1.30533128081953 53 87.8 94.0915720430789 -6.2915720430789 54 115.8 118.679389523453 -2.87938952345284 55 79.7 88.9039587474852 -9.2039587474852 56 72.7 74.0492374237645 -1.34923742376444 57 104.5 107.858425359679 -3.35842535967907 58 103 105.071014171072 -2.07101417107201 59 95.1 93.6497447690247 1.45025523097534 60 104.2 99.8292374237645 4.37076257623554 61 78.3 79.9653114950297 -1.66531149502966 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0416459383256458 0.0832918766512917 0.958354061674354 17 0.208754920828958 0.417509841657916 0.791245079171042 18 0.171437152055514 0.342874304111028 0.828562847944486 19 0.0975249229766727 0.195049845953345 0.902475077023327 20 0.0610022263551397 0.122004452710279 0.93899777364486 21 0.0381365527066469 0.0762731054132939 0.961863447293353 22 0.0276237807804865 0.0552475615609729 0.972376219219514 23 0.0318658801987914 0.0637317603975828 0.968134119801209 24 0.0476127306373149 0.0952254612746297 0.952387269362685 25 0.0289898711285964 0.0579797422571929 0.971010128871404 26 0.0164946287955907 0.0329892575911813 0.98350537120441 27 0.0139705808580587 0.0279411617161173 0.986029419141941 28 0.0206662584328961 0.0413325168657921 0.979333741567104 29 0.0164045903461241 0.0328091806922482 0.983595409653876 30 0.0457404509185833 0.0914809018371666 0.954259549081417 31 0.0327353438341914 0.0654706876683828 0.967264656165809 32 0.0275351325346125 0.0550702650692251 0.972464867465388 33 0.0524827271677833 0.104965454335567 0.947517272832217 34 0.0478790895483947 0.0957581790967894 0.952120910451605 35 0.0602685126401806 0.120537025280361 0.93973148735982 36 0.103066138786688 0.206132277573375 0.896933861213312 37 0.252917453800927 0.505834907601854 0.747082546199073 38 0.496625835210786 0.993251670421571 0.503374164789214 39 0.778144136377215 0.443711727245571 0.221855863622785 40 0.998771992681473 0.00245601463705438 0.00122800731852719 41 0.999054459359756 0.00189108128048877 0.000945540640244383 42 0.99777104506156 0.00445790987687922 0.00222895493843961 43 0.99525383544556 0.00949232910887979 0.00474616455443989 44 0.984881253675234 0.0302374926495327 0.0151187463247663 45 0.964707601808473 0.0705847963830542 0.0352923981915271 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.133333333333333 NOK 5% type I error level 9 0.3 NOK 10% type I error level 20 0.666666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/10gvzf1293497091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/10gvzf1293497091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/1rc331293497091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/1rc331293497091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/2rc331293497091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/2rc331293497091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/32l261293497091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/32l261293497091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/42l261293497091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/42l261293497091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/52l261293497091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/52l261293497091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/6ucj91293497091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/6ucj91293497091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/75mju1293497091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/75mju1293497091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/85mju1293497091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/85mju1293497091.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/9gvzf1293497091.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293497011gige8uvplb3glud/9gvzf1293497091.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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