Q3 - b

*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, 23 Nov 2008 11:10:56 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk.htm/, Retrieved Sun, 23 Nov 2008 18:11:39 +0000

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/2008/Nov/23/t1227463890m48t2kho7y0yfvk.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

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User-defined keywords:

Dataseries X:

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1515 0 1510 0 1225 0 1577 0 1417 0 1224 0 1693 0 1633 0 1639 0 1914 0 1586 0 1552 0 2081 1 1500 0 1437 0 1470 0 1849 0 1387 0 1592 0 1589 0 1798 0 1935 0 1887 0 2027 1 2080 1 1556 0 1682 0 1785 0 1869 0 1781 0 2082 1 2570 1 1862 0 1936 0 1504 0 1765 0 1607 0 1577 0 1493 0 1615 0 1700 0 1335 0 1523 0 1623 0 1540 0 1637 0 1524 0 1419 0 1821 0 1593 0 1357 0 1263 0 1750 0 1405 0 1393 0 1639 0 1679 0 1551 0 1744 0 1429 0 1784 0

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 Gebouwen[t] = + 1527.70085653105 + 563.279443254816Dummy[t] + 100.890970735190M1[t] + 20.912348322627M2[t] -87.4332976445398M3[t] + 15.8210563882938M4[t] + 190.875410421128M5[t] -99.6702355460388M6[t] + 17.9282298358316M7[t] + 172.182583868665M8[t] + 177.692826552462M9[t] + 268.747180585296M10[t] + 123.20153461813M11[t] -0.0543540328337076t + 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) 1527.70085653105 87.538483 17.4518 0 0 Dummy 563.279443254816 84.852411 6.6383 0 0 M1 100.890970735190 99.072356 1.0184 0.313722 0.156861 M2 20.912348322627 105.068915 0.199 0.843094 0.421547 M3 -87.4332976445398 104.909547 -0.8334 0.408826 0.204413 M4 15.8210563882938 104.763913 0.151 0.880609 0.440305 M5 190.875410421128 104.632069 1.8243 0.074475 0.037237 M6 -99.6702355460388 104.514068 -0.9537 0.345138 0.172569 M7 17.9282298358316 102.884434 0.1743 0.862413 0.431206 M8 172.182583868665 102.820379 1.6746 0.100656 0.050328 M9 177.692826552462 104.243563 1.7046 0.094873 0.047437 M10 268.747180585296 104.181347 2.5796 0.013079 0.006539 M11 123.20153461813 104.133155 1.1831 0.242714 0.121357 t -0.0543540328337076 1.209975 -0.0449 0.96436 0.48218 Multiple Linear Regression - Regression Statistics Multiple R 0.800089685202874 R-squared 0.640143504368034 Adjusted R-squared 0.540608728980469 F-TEST (value) 6.43135529140912 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 8.85797678540357e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 162.393085798019 Sum Squared Residuals 1239461.17280514 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1515 1628.5374732334 -113.5374732334 2 1510 1548.50449678801 -38.5044967880068 3 1225 1440.10449678801 -215.104496788009 4 1577 1543.30449678801 33.6955032119909 5 1417 1718.30449678801 -301.304496788009 6 1224 1427.70449678801 -203.704496788009 7 1693 1545.24860813705 147.751391862954 8 1633 1699.44860813705 -66.4486081370455 9 1639 1704.90449678801 -65.904496788009 10 1914 1795.90449678801 118.095503211991 11 1586 1650.30449678801 -64.3044967880089 12 1552 1527.04860813705 24.9513918629545 13 2081 2191.16466809422 -110.164668094219 14 1500 1547.85224839401 -47.8522483940051 15 1437 1439.45224839400 -2.45224839400456 16 1470 1542.65224839400 -72.6522483940045 17 1849 1717.65224839400 131.347751605995 18 1387 1427.05224839400 -40.0522483940045 19 1592 1544.59635974304 47.4036402569588 20 1589 1698.79635974304 -109.796359743041 21 1798 1704.25224839400 93.7477516059955 22 1935 1795.25224839400 139.747751605995 23 1887 1649.65224839400 237.347751605995 24 2027 2089.67580299786 -62.6758029978582 25 2080 2190.51241970022 -110.512419700215 26 1556 1547.2 8.79999999999944 27 1682 1438.8 243.2 28 1785 1542 243 29 1869 1717 152 30 1781 1426.4 354.6 31 2082 2107.22355460385 -25.2235546038539 32 2570 2261.42355460385 308.576445396146 33 1862 1703.6 158.4 34 1936 1794.6 141.4 35 1504 1649 -145 36 1765 1525.74411134904 239.255888650963 37 1607 1626.58072805139 -19.5807280513932 38 1577 1546.54775160600 30.4522483940039 39 1493 1438.14775160600 54.8522483940044 40 1615 1541.34775160600 73.6522483940045 41 1700 1716.34775160600 -16.3477516059955 42 1335 1425.74775160600 -90.7477516059955 43 1523 1543.29186295503 -20.2918629550322 44 1623 1697.49186295503 -74.491862955032 45 1540 1702.94775160600 -162.947751605995 46 1637 1793.94775160600 -156.947751605996 47 1524 1648.34775160600 -124.347751605995 48 1419 1525.09186295503 -106.091862955032 49 1821 1625.92847965739 195.071520342611 50 1593 1545.89550321199 47.1044967880084 51 1357 1437.49550321199 -80.4955032119911 52 1263 1540.69550321199 -277.695503211991 53 1750 1715.69550321199 34.3044967880089 54 1405 1425.09550321199 -20.095503211991 55 1393 1542.63961456103 -149.639614561028 56 1639 1696.83961456103 -57.8396145610276 57 1679 1702.29550321199 -23.2955032119910 58 1551 1793.29550321199 -242.295503211991 59 1744 1647.69550321199 96.304496788009 60 1429 1524.43961456103 -95.4396145610275 61 1784 1625.27623126338 158.723768736616 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.652210764944433 0.695578470111134 0.347789235055567 18 0.513778520144093 0.972442959711814 0.486221479855907 19 0.486259103918197 0.972518207836393 0.513740896081803 20 0.468851218285489 0.937702436570977 0.531148781714511 21 0.35823517550098 0.71647035100196 0.64176482449902 22 0.257431820686173 0.514863641372345 0.742568179313827 23 0.25383766904477 0.50767533808954 0.74616233095523 24 0.186443186224309 0.372886372448617 0.813556813775691 25 0.220209631386322 0.440419262772644 0.779790368613678 26 0.177813058985061 0.355626117970123 0.822186941014938 27 0.199776049195023 0.399552098390046 0.800223950804977 28 0.182140019188066 0.364280038376131 0.817859980811934 29 0.126472559037634 0.252945118075267 0.873527440962366 30 0.269818447729881 0.539636895459762 0.730181552270119 31 0.267069992877938 0.534139985755877 0.732930007122062 32 0.413359733952095 0.82671946790419 0.586640266047905 33 0.379146801101641 0.758293602203283 0.620853198898359 34 0.510485942741256 0.979028114517488 0.489514057258744 35 0.657805809477671 0.684388381044657 0.342194190522329 36 0.80590236020872 0.388195279582559 0.194097639791280 37 0.839721959021906 0.320556081956189 0.160278040978094 38 0.772800636348609 0.454398727302783 0.227199363651391 39 0.71930849521618 0.561383009567639 0.280691504783820 40 0.938091682640938 0.123816634718124 0.0619083173590618 41 0.889534104131476 0.220931791737048 0.110465895868524 42 0.832928131597918 0.334143736804164 0.167071868402082 43 0.828352744941728 0.343294510116544 0.171647255058272 44 0.700915105700464 0.598169788599073 0.299084894299536 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://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/10wlnh1227463851.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/10wlnh1227463851.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/1if921227463851.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/1if921227463851.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/2hsy91227463851.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/2hsy91227463851.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/3v4wy1227463851.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/3v4wy1227463851.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/46wia1227463851.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/46wia1227463851.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/5z6nu1227463851.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/5z6nu1227463851.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/6lqjv1227463851.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/6lqjv1227463851.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/7jwai1227463851.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/7jwai1227463851.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/87fk11227463851.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/87fk11227463851.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/90ct21227463851.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/23/t1227463890m48t2kho7y0yfvk/90ct21227463851.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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