Model 1

*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: Wed, 18 Nov 2009 12:29:47 -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/18/t1258572796c6ykooubm3xznr3.htm/, Retrieved Wed, 18 Nov 2009 20:33:29 +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/18/t1258572796c6ykooubm3xznr3.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:

» Textbox « » Textfile « » CSV «

115.6 0 111.3 0 114.6 0 137.5 0 83.7 0 106.0 0 123.4 0 126.5 0 120.0 0 141.6 0 90.5 0 96.5 0 113.5 0 120.1 0 123.9 0 144.4 0 90.8 0 114.2 0 138.1 0 135.0 0 131.3 0 144.6 0 101.7 0 108.7 0 135.3 0 124.3 0 138.3 0 158.2 0 93.5 0 124.8 0 154.4 0 152.8 0 148.9 0 170.3 0 124.8 0 134.4 0 154.0 0 147.9 0 168.1 0 175.7 0 116.7 0 140.8 0 164.2 0 173.8 0 167.8 0 166.6 0 135.1 1 158.1 1 151.8 1 166.7 1 165.3 1 187.0 1 125.2 1 144.4 1 181.7 1 175.9 1 166.3 1 181.5 1 121.8 1 134.8 1 162.9 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] = + 131.936956521739 + 25.2963768115942X[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) 131.936956521739 3.46079 38.1234 0 0 X 25.2963768115942 6.979021 3.6246 0.000604 0.000302 Multiple Linear Regression - Regression Statistics Multiple R 0.426758218902933 R-squared 0.182122577401203 Adjusted R-squared 0.168260248204614 F-TEST (value) 13.1379492449225 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.0006041598561769 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 23.4722196083617 Sum Squared Residuals 32505.7605072464 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 115.6 131.936956521739 -16.3369565217391 2 111.3 131.936956521739 -20.6369565217391 3 114.6 131.936956521739 -17.3369565217391 4 137.5 131.936956521739 5.56304347826087 5 83.7 131.936956521739 -48.2369565217391 6 106 131.936956521739 -25.9369565217391 7 123.4 131.936956521739 -8.53695652173913 8 126.5 131.936956521739 -5.43695652173913 9 120 131.936956521739 -11.9369565217391 10 141.6 131.936956521739 9.66304347826086 11 90.5 131.936956521739 -41.4369565217391 12 96.5 131.936956521739 -35.4369565217391 13 113.5 131.936956521739 -18.4369565217391 14 120.1 131.936956521739 -11.8369565217391 15 123.9 131.936956521739 -8.03695652173913 16 144.4 131.936956521739 12.4630434782609 17 90.8 131.936956521739 -41.1369565217391 18 114.2 131.936956521739 -17.7369565217391 19 138.1 131.936956521739 6.16304347826086 20 135 131.936956521739 3.06304347826087 21 131.3 131.936956521739 -0.636956521739121 22 144.6 131.936956521739 12.6630434782609 23 101.7 131.936956521739 -30.2369565217391 24 108.7 131.936956521739 -23.2369565217391 25 135.3 131.936956521739 3.36304347826088 26 124.3 131.936956521739 -7.63695652173914 27 138.3 131.936956521739 6.36304347826088 28 158.2 131.936956521739 26.2630434782609 29 93.5 131.936956521739 -38.4369565217391 30 124.8 131.936956521739 -7.13695652173914 31 154.4 131.936956521739 22.4630434782609 32 152.8 131.936956521739 20.8630434782609 33 148.9 131.936956521739 16.9630434782609 34 170.3 131.936956521739 38.3630434782609 35 124.8 131.936956521739 -7.13695652173914 36 134.4 131.936956521739 2.46304347826087 37 154 131.936956521739 22.0630434782609 38 147.9 131.936956521739 15.9630434782609 39 168.1 131.936956521739 36.1630434782609 40 175.7 131.936956521739 43.7630434782609 41 116.7 131.936956521739 -15.2369565217391 42 140.8 131.936956521739 8.86304347826088 43 164.2 131.936956521739 32.2630434782609 44 173.8 131.936956521739 41.8630434782609 45 167.8 131.936956521739 35.8630434782609 46 166.6 131.936956521739 34.6630434782609 47 135.1 157.233333333333 -22.1333333333333 48 158.1 157.233333333333 0.866666666666659 49 151.8 157.233333333333 -5.43333333333332 50 166.7 157.233333333333 9.46666666666665 51 165.3 157.233333333333 8.06666666666668 52 187 157.233333333333 29.7666666666667 53 125.2 157.233333333333 -32.0333333333333 54 144.4 157.233333333333 -12.8333333333333 55 181.7 157.233333333333 24.4666666666667 56 175.9 157.233333333333 18.6666666666667 57 166.3 157.233333333333 9.06666666666668 58 181.5 157.233333333333 24.2666666666667 59 121.8 157.233333333333 -35.4333333333333 60 134.8 157.233333333333 -22.4333333333333 61 162.9 157.233333333333 5.66666666666667 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.559969476285832 0.880061047428336 0.440030523714168 6 0.411138784155425 0.822277568310849 0.588861215844575 7 0.303978371045705 0.607956742091409 0.696021628954295 8 0.227533325389391 0.455066650778781 0.77246667461061 9 0.146371890644611 0.292743781289222 0.853628109355389 10 0.178001987553032 0.356003975106065 0.821998012446968 11 0.252449827693602 0.504899655387203 0.747550172306398 12 0.263174694896184 0.526349389792368 0.736825305103816 13 0.199460773665902 0.398921547331803 0.800539226334098 14 0.1477989128606 0.2955978257212 0.8522010871394 15 0.110693466568688 0.221386933137376 0.889306533431312 16 0.147777437287829 0.295554874575658 0.852222562712171 17 0.231556467830152 0.463112935660305 0.768443532169848 18 0.193102208945102 0.386204417890204 0.806897791054898 19 0.192530608212992 0.385061216425985 0.807469391787008 20 0.173369646644401 0.346739293288802 0.826630353355599 21 0.144641080246282 0.289282160492563 0.855358919753718 22 0.155290204221697 0.310580408443393 0.844709795778303 23 0.193126334317041 0.386252668634082 0.80687366568296 24 0.205563870942112 0.411127741884225 0.794436129057888 25 0.184803936289809 0.369607872579617 0.815196063710191 26 0.161409517461645 0.322819034923290 0.838590482538355 27 0.148610388434825 0.297220776869649 0.851389611565175 28 0.216344324268576 0.432688648537152 0.783655675731424 29 0.449156207016908 0.898312414033815 0.550843792983092 30 0.450121462583305 0.90024292516661 0.549878537416695 31 0.487565531922396 0.975131063844793 0.512434468077604 32 0.49926335968324 0.99852671936648 0.50073664031676 33 0.484034377890088 0.968068755780176 0.515965622109912 34 0.598415472107164 0.803169055785672 0.401584527892836 35 0.610095873869731 0.779808252260537 0.389904126130269 36 0.593411477859205 0.81317704428159 0.406588522140795 37 0.572749269164982 0.854501461670035 0.427250730835018 38 0.537979485678751 0.924041028642498 0.462020514321249 39 0.568954305719807 0.862091388560385 0.431045694280193 40 0.644907552017757 0.710184895964487 0.355092447982243 41 0.767926379626638 0.464147240746724 0.232073620373362 42 0.773728237112224 0.452543525775552 0.226271762887776 43 0.750758253005952 0.498483493988096 0.249241746994048 44 0.744289157843698 0.511421684312604 0.255710842156302 45 0.710290652159589 0.579418695680823 0.289709347840411 46 0.664899773656058 0.670200452687884 0.335100226343942 47 0.646229442182287 0.707541115635427 0.353770557817713 48 0.56046628619408 0.87906742761184 0.43953371380592 49 0.469196416051689 0.938392832103378 0.530803583948311 50 0.383546443244852 0.767092886489703 0.616453556755148 51 0.295250427319725 0.590500854639449 0.704749572680275 52 0.337673762597438 0.675347525194875 0.662326237402562 53 0.407175447241252 0.814350894482505 0.592824552758748 54 0.326233459592893 0.652466919185787 0.673766540407107 55 0.299672362763237 0.599344725526474 0.700327637236763 56 0.242953052200924 0.485906104401849 0.757046947799076 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 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/10d14c1258572583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/10d14c1258572583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/1f4x01258572583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/1f4x01258572583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/218io1258572583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/218io1258572583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/30ab61258572583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/30ab61258572583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/4xdzy1258572583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/4xdzy1258572583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/55oep1258572583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/55oep1258572583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/66f2c1258572583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/66f2c1258572583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/70qgf1258572583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/70qgf1258572583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/86zdi1258572583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/86zdi1258572583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/9gay11258572583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258572796c6ykooubm3xznr3/9gay11258572583.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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