*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, 12 Dec 2010 18:53:10 +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/12/t1292179942qgca4eg2yu4u8sz.htm/, Retrieved Sun, 12 Dec 2010 19:52:33 +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/12/t1292179942qgca4eg2yu4u8sz.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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31514 27071 29462 26105 22397 23843 21705 18089 20764 25316 17704 15548 28029 29383 36438 32034 22679 24319 18004 17537 20366 22782 19169 13807 29743 25591 29096 26482 22405 27044 17970 18730 19684 19785 18479 10698 31956 29506 34506 27165 26736 23691 18157 17328 18205 20995 17382 9367 31124 26551 30651 25859 25100 25778 20418 18688 20424 24776 19814 12738 31566 30111 30019 31934 25826 26835 20205 17789 20520 22518 15572 11509 25447 24090 27786 26195 20516 22759 19028 16971 20036 22485 18730 14538 27561 25985 34670 32066 27186 29586 21359 21553 19573 24256

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 8 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation X[t] = + 12600.7142857143 + 17016.7857142857M1[t] + 14685.2857142857M2[t] + 18977.7857142857M3[t] + 15879.2857142857M4[t] + 11504.9107142857M5[t] + 12881.1607142857M6[t] + 7005.03571428572M7[t] + 5734.91071428572M8[t] + 7345.78571428573M9[t] + 10263.4107142857M10[t] + 5520.71428571429M11[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) 12600.7142857143 814.601465 15.4686 0 0 M1 17016.7857142857 1115.438995 15.2557 0 0 M2 14685.2857142857 1115.438995 13.1655 0 0 M3 18977.7857142857 1115.438995 17.0137 0 0 M4 15879.2857142857 1115.438995 14.2359 0 0 M5 11504.9107142857 1115.438995 10.3142 0 0 M6 12881.1607142857 1115.438995 11.5481 0 0 M7 7005.03571428572 1115.438995 6.2801 0 0 M8 5734.91071428572 1115.438995 5.1414 2e-06 1e-06 M9 7345.78571428573 1115.438995 6.5856 0 0 M10 10263.4107142857 1115.438995 9.2012 0 0 M11 5520.71428571429 1152.02044 4.7922 7e-06 4e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.9348192921154 R-squared 0.873887108911139 Adjusted R-squared 0.856969525960194 F-TEST (value) 51.655553363924 F-TEST (DF numerator) 11 F-TEST (DF denominator) 82 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2155.23289484233 Sum Squared Residuals 380892364.142857 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 31514 29617.4999999999 1896.50000000005 2 27071 27286 -214.999999999987 3 29462 31578.4999999999 -2116.49999999993 4 26105 28479.9999999999 -2374.99999999994 5 22397 24105.625 -1708.62499999999 6 23843 25481.8750000001 -1638.87500000005 7 21705 19605.75 2099.25000000001 8 18089 18335.625 -246.624999999996 9 20764 19946.5 817.500000000001 10 25316 22864.125 2451.87499999999 11 17704 18121.4285714286 -417.428571428592 12 15548 12600.7142857143 2947.28571428571 13 28029 29617.5 -1588.50000000001 14 29383 27286 2097.00000000000 15 36438 31578.5 4859.49999999999 16 32034 28480 3553.99999999999 17 22679 24105.625 -1426.625 18 24319 25481.875 -1162.87499999999 19 18004 19605.75 -1601.75 20 17537 18335.625 -798.625000000001 21 20366 19946.5 419.5 22 22782 22864.125 -82.1249999999986 23 19169 18121.4285714286 1047.57142857143 24 13807 12600.7142857143 1206.28571428572 25 29743 29617.5 125.499999999993 26 25591 27286 -1695.00000000000 27 29096 31578.5 -2482.50000000001 28 26482 28480 -1998.00000000001 29 22405 24105.625 -1700.625 30 27044 25481.875 1562.12500000001 31 17970 19605.75 -1635.75 32 18730 18335.625 394.374999999999 33 19684 19946.5 -262.5 34 19785 22864.125 -3079.125 35 18479 18121.4285714286 357.571428571432 36 10698 12600.7142857143 -1902.71428571428 37 31956 29617.5 2338.49999999999 38 29506 27286 2220.00000000000 39 34506 31578.5 2927.49999999999 40 27165 28480 -1315.00000000001 41 26736 24105.625 2630.375 42 23691 25481.875 -1790.87499999999 43 18157 19605.75 -1448.75 44 17328 18335.625 -1007.625 45 18205 19946.5 -1741.5 46 20995 22864.125 -1869.125 47 17382 18121.4285714286 -739.428571428568 48 9367 12600.7142857143 -3233.71428571428 49 31124 29617.5 1506.49999999999 50 26551 27286 -735.000000000004 51 30651 31578.5 -927.50000000001 52 25859 28480 -2621.00000000001 53 25100 24105.625 994.375 54 25778 25481.875 296.125000000007 55 20418 19605.75 812.25 56 18688 18335.625 352.374999999999 57 20424 19946.5 477.5 58 24776 22864.125 1911.875 59 19814 18121.4285714286 1692.57142857143 60 12738 12600.7142857143 137.285714285717 61 31566 29617.5 1948.49999999999 62 30111 27286 2825.00000000000 63 30019 31578.5 -1559.50000000001 64 31934 28480 3453.99999999999 65 25826 24105.625 1720.375 66 26835 25481.875 1353.12500000001 67 20205 19605.75 599.25 68 17789 18335.625 -546.625000000001 69 20520 19946.5 573.5 70 22518 22864.125 -346.124999999999 71 15572 18121.4285714286 -2549.42857142857 72 11509 12600.7142857143 -1091.71428571428 73 25447 29617.5 -4170.50000000001 74 24090 27286 -3196.00000000000 75 27786 31578.5 -3792.50000000001 76 26195 28480 -2285.00000000001 77 20516 24105.625 -3589.625 78 22759 25481.875 -2722.87499999999 79 19028 19605.75 -577.75 80 16971 18335.625 -1364.625 81 20036 19946.5 89.5 82 22485 22864.125 -379.124999999999 83 18730 18121.4285714286 608.571428571432 84 14538 12600.7142857143 1937.28571428572 85 27561 29617.5 -2056.50000000001 86 25985 27286 -1301.00000000000 87 34670 31578.5 3091.49999999999 88 32066 28480 3585.99999999999 89 27186 24105.625 3080.375 90 29586 25481.875 4104.12500000001 91 21359 19605.75 1753.25 92 21553 18335.625 3217.375 93 19573 19946.5 -373.5 94 24256 22864.125 1391.875 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.92685977834978 0.146280443300441 0.0731402216502204 16 0.969866970380733 0.0602660592385346 0.0301330296192673 17 0.94316472896118 0.113670542077639 0.0568352710388195 18 0.903745452779862 0.192509094440275 0.0962545472201376 19 0.901396220182334 0.197207559635333 0.0986037798176665 20 0.84989373911244 0.30021252177512 0.15010626088756 21 0.784217021850576 0.431565956298848 0.215782978149424 22 0.744546347065226 0.510907305869548 0.255453652934774 23 0.679789439825982 0.640421120348037 0.320210560174018 24 0.624731103703467 0.750537792593066 0.375268896296533 25 0.537269294154575 0.92546141169085 0.462730705845425 26 0.522215215400212 0.955569569199576 0.477784784599788 27 0.582779987636198 0.834440024727604 0.417220012363802 28 0.569136423642726 0.861727152714548 0.430863576357274 29 0.511102937383103 0.977794125233794 0.488897062616897 30 0.506440199254642 0.987119601490716 0.493559800745358 31 0.467057897220519 0.934115794441038 0.532942102779481 32 0.397848685294001 0.795697370588003 0.602151314705999 33 0.331867023914892 0.663734047829784 0.668132976085108 34 0.419718430430460 0.839436860860921 0.580281569569540 35 0.350343456516012 0.700686913032024 0.649656543483988 36 0.386705430100845 0.77341086020169 0.613294569899155 37 0.382315351336166 0.764630702672332 0.617684648663834 38 0.373728944188797 0.747457888377593 0.626271055811203 39 0.410984374588245 0.821968749176491 0.589015625411754 40 0.364007006628667 0.728014013257333 0.635992993371333 41 0.424201267392250 0.848402534784499 0.57579873260775 42 0.395162737691529 0.790325475383058 0.604837262308471 43 0.354560299917814 0.709120599835628 0.645439700082186 44 0.303839708132058 0.607679416264116 0.696160291867942 45 0.28121384720459 0.56242769440918 0.71878615279541 46 0.261693059701925 0.52338611940385 0.738306940298075 47 0.215315238071449 0.430630476142898 0.784684761928551 48 0.276823856891296 0.553647713782592 0.723176143108704 49 0.257387140539078 0.514774281078156 0.742612859460922 50 0.212815105279205 0.42563021055841 0.787184894720795 51 0.177307960536416 0.354615921072833 0.822692039463584 52 0.207765531467573 0.415531062935146 0.792234468532427 53 0.171245630204443 0.342491260408886 0.828754369795557 54 0.135226659600937 0.270453319201873 0.864773340399063 55 0.106353233291295 0.212706466582590 0.893646766708705 56 0.0789401066654212 0.157880213330842 0.921059893334579 57 0.0571496760405953 0.114299352081191 0.942850323959405 58 0.0513007507943522 0.102601501588704 0.948699249205648 59 0.0453748377678132 0.0907496755356263 0.954625162232187 60 0.0309669405770236 0.0619338811540471 0.969033059422976 61 0.0438101862245342 0.0876203724490683 0.956189813775466 62 0.0734131266238489 0.146826253247698 0.926586873376151 63 0.0588397237849542 0.117679447569908 0.941160276215046 64 0.0791802272371751 0.158360454474350 0.920819772762825 65 0.0668388675428477 0.133677735085695 0.933161132457152 66 0.0496454888573111 0.0992909777146221 0.95035451114269 67 0.0332434306226999 0.0664868612453997 0.9667565693773 68 0.0227272792780502 0.0454545585561005 0.97727272072195 69 0.0142134542080186 0.0284269084160372 0.985786545791981 70 0.00850634210206705 0.0170126842041341 0.991493657897933 71 0.0082557442349064 0.0165114884698128 0.991744255765094 72 0.00624462468786345 0.0124892493757269 0.993755375312137 73 0.00769282602583937 0.0153856520516787 0.99230717397416 74 0.00643358637559222 0.0128671727511844 0.993566413624408 75 0.0223779769817871 0.0447559539635741 0.977622023018213 76 0.0433737612902434 0.0867475225804868 0.956626238709757 77 0.143545352431646 0.287090704863293 0.856454647568354 78 0.508753287340274 0.982493425319452 0.491246712659726 79 0.435123010688006 0.870246021376011 0.564876989311994 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 8 0.123076923076923 NOK 10% type I error level 15 0.230769230769231 NOK

Charts produced by software:

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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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