Multiple Linear Regression Y-2

*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: Sat, 19 Dec 2009 15:13:05 -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/Dec/19/t1261260873dfv5ibdy2cg3jyg.htm/, Retrieved Sat, 19 Dec 2009 23:14:45 +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/Dec/19/t1261260873dfv5ibdy2cg3jyg.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:

kvn paper

Dataseries X:

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9627 2249 8700 9487 8947 2687 9627 8700 9283 4359 8947 9627 8829 5382 9283 8947 9947 4459 8829 9283 9628 6398 9947 8829 9318 4596 9628 9947 9605 3024 9318 9628 8640 1887 9605 9318 9214 2070 8640 9605 9567 1351 9214 8640 8547 2218 9567 9214 9185 2461 8547 9567 9470 3028 9185 8547 9123 4784 9470 9185 9278 4975 9123 9470 10170 4607 9278 9123 9434 6249 10170 9278 9655 4809 9434 10170 9429 3157 9655 9434 8739 1910 9429 9655 9552 2228 8739 9429 9784 1594 9552 8739 9089 2467 9784 9552 9763 2222 9089 9784 9330 3607 9763 9089 9144 4685 9330 9763 9895 4962 9144 9330 10404 5770 9895 9144 10195 5480 10404 9895 9987 5000 10195 10404 9789 3228 9987 10195 9437 1993 9789 9987 10096 2288 9437 9789 9776 1580 10096 9437 9106 2111 9776 10096 10258 2192 9106 9776 9766 3601 10258 9106 9826 4665 9766 10258 9957 4876 9826 9766 10036 5813 9957 9826 10508 5589 10036 9957 10146 5331 10508 10036 10166 3075 10146 10508 9365 2002 10166 10146 9968 2306 9365 10166 10123 1507 9968 9365 9144 1992 etc...

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 9909.07359564333 -0.220163781194730X[t] -0.0277218242800674Y1[t] -0.0866784090448033Y2[t] + 923.519495450754M1[t] + 723.493989930962M2[t] + 1255.07123443339M3[t] + 1347.61506616514M4[t] + 1983.30789428388M5[t] + 1985.23271572194M6[t] + 1656.77157087654M7[t] + 1213.46390968443M8[t] + 107.672592588103M9[t] + 607.566958890386M10[t] + 702.371468107167M11[t] + 20.7394883620586t + 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) 9909.07359564333 1656.086072 5.9834 0 0 X -0.220163781194730 0.079119 -2.7827 0.007023 0.003512 Y1 -0.0277218242800674 0.127144 -0.218 0.828075 0.414037 Y2 -0.0866784090448033 0.119483 -0.7254 0.470742 0.235371 M1 923.519495450754 182.645029 5.0564 4e-06 2e-06 M2 723.493989930962 169.46058 4.2694 6.4e-05 3.2e-05 M3 1255.07123443339 264.28416 4.7489 1.1e-05 6e-06 M4 1347.61506616514 292.101814 4.6135 1.9e-05 9e-06 M5 1983.30789428388 298.419234 6.646 0 0 M6 1985.23271572194 329.505902 6.0249 0 0 M7 1656.77157087654 294.147605 5.6324 0 0 M8 1213.46390968443 168.72695 7.1919 0 0 M9 107.672592588103 139.659976 0.771 0.443481 0.22174 M10 607.566958890386 170.756346 3.5581 0.000698 0.000349 M11 702.371468107167 163.145993 4.3052 5.7e-05 2.8e-05 t 20.7394883620586 3.335416 6.218 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.928447759798554 R-squared 0.862015242674954 Adjusted R-squared 0.830655070555625 F-TEST (value) 27.4875800870894 F-TEST (DF numerator) 15 F-TEST (DF denominator) 66 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 241.321389857739 Sum Squared Residuals 3843576.87138949 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9627 9294.68629770461 332.313702295388 2 8947 9061.48632119418 -114.486321194180 3 9283 9184.189167227 98.8108327730044 4 8829 9121.87172435095 -292.871724350952 5 9947 9964.97697365858 -17.9769736585832 6 9628 9569.10270988334 58.8972901166561 7 9318 9570.05298774616 -252.052987746163 8 9605 9529.82645696634 75.1735430336645 9 8640 8714.01499068599 -74.0149906859897 10 9214 9196.2337304261 17.7662695739005 11 9567 9537.80782427543 29.1921757245712 12 8547 8605.75463547191 -58.7546354719067 13 9185 9494.19260282725 -309.192602827254 14 9470 9260.79917506712 209.200824932875 15 9123 9363.30676326326 -240.306763263264 16 9278 9419.4549275963 -141.45492759629 17 10170 10182.6880407319 -12.6880407318846 18 9434 9805.6804011505 -371.680401150492 19 9655 9758.08071138973 -103.080711389732 20 9429 9756.89189098445 -327.891890984448 21 8739 8933.4935012884 -194.493501288405 22 9552 9422.8326527302 129.167347269805 23 9784 9715.23074668771 68.7692533122867 24 9089 8764.4947761732 324.505223826795 25 9763 9761.85116335498 1.14883664502077 26 9330 9319.19529396392 10.8047060360822 27 9144 9587.75777291756 -443.75777291756 28 9895 9682.74373605292 212.256263947084 29 10404 10156.5868113764 247.413188623626 30 10195 10163.8927239718 31.1072760282372 31 9987 9923.52423353263 63.4757664673736 32 9789 9914.96820792025 -125.968207920248 33 9437 9125.33667925025 311.663320749753 34 10096 9607.9426255996 488.057374400401 35 9776 9891.60469804751 -115.604698047513 36 9106 9044.8156626971 61.1843373029007 37 10258 10017.5520933951 240.447906604879 38 9766 9554.1943010234 211.805698976606 39 9826 9786.04238102287 39.9576189771290 40 9957 9893.85361107783 63.1463889221739 41 10036 10335.1602010558 -299.160201055785 42 10508 10393.5963021405 114.403697859471 43 10146 10122.7446058307 23.2553941692996 44 10166 10165.9890146962 0.0109853038090577 45 9365 9347.9960707725 17.0039292275108 46 9968 9822.17174902107 145.828250978929 47 10123 10166.3397533785 -43.3397533785071 48 9144 9321.36437633653 -177.364376336527 49 10447 10170.3468010262 276.653198973809 50 9699 9818.97313674808 -119.973136748078 51 10451 10024.3543323464 426.645667653623 52 10192 9973.13118975567 218.868810244327 53 10404 10567.818510843 -163.818510843006 54 10597 10568.0865125669 28.9134874331131 55 10633 10145.050588303 487.949411697009 56 10727 10407.2981226456 319.701877354413 57 9784 9554.73723095608 229.262769043919 58 9667 9957.30377868797 -290.303778687972 59 10297 10387.9001207853 -90.9001207853224 60 9426 9569.9287898219 -143.928789821901 61 10274 10348.9858507933 -74.9858507932526 62 9598 10169.5098047809 -571.509804780895 63 10400 10323.3875375437 76.6124624562568 64 9985 10096.9928207986 -111.992820798621 65 10761 10649.8397075944 111.160292405594 66 11081 10853.8475676524 227.15243234761 67 10297 10402.4022011539 -105.402201153882 68 10751 10590.5095987915 160.490401208501 69 9760 9817.53890339826 -57.5389033982647 70 10133 10232.7234812101 -99.7234812100529 71 10806 10654.1168568255 151.883143174485 72 9734 9739.64175949936 -5.64175949936173 73 10083 10549.3851908986 -466.38519089859 74 10691 10316.8419672224 374.15803277759 75 10446 10403.9620456792 42.0379543208107 76 10517 10464.9519903677 52.0480096322775 77 11353 11217.9297547400 135.070245260038 78 10436 10524.7937826346 -88.7937826345953 79 10721 10835.1446720439 -114.144672043905 80 10701 10802.5167079957 -101.516707995691 81 9793 10024.8826236485 -231.882623648523 82 10142 10532.791982325 -390.79198232501 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.717906602701942 0.564186794596116 0.282093397298058 20 0.672749416862551 0.654501166274897 0.327250583137449 21 0.558766007345988 0.882467985308025 0.441233992654012 22 0.440510235811211 0.881020471622422 0.559489764188789 23 0.477537717733199 0.955075435466399 0.522462282266801 24 0.566629501390339 0.866740997219322 0.433370498609661 25 0.495751045974112 0.991502091948224 0.504248954025888 26 0.397522555063982 0.795045110127964 0.602477444936018 27 0.520891871234349 0.958216257531302 0.479108128765651 28 0.600197305135918 0.799605389728164 0.399802694864082 29 0.617021744291101 0.765956511417798 0.382978255708899 30 0.575008673633644 0.849982652732712 0.424991326366356 31 0.524488143807717 0.951023712384566 0.475511856192283 32 0.490777734180208 0.981555468360415 0.509222265819792 33 0.50063644446392 0.99872711107216 0.49936355553608 34 0.587252837668841 0.825494324662318 0.412747162331159 35 0.564267737914055 0.871464524171889 0.435732262085945 36 0.49441829058355 0.9888365811671 0.50558170941645 37 0.448213280009073 0.896426560018146 0.551786719990927 38 0.402405592700978 0.804811185401955 0.597594407299022 39 0.350635848848056 0.701271697696112 0.649364151151944 40 0.282001119745764 0.564002239491528 0.717998880254236 41 0.372113638326768 0.744227276653536 0.627886361673232 42 0.30648175388734 0.61296350777468 0.69351824611266 43 0.255800435527388 0.511600871054776 0.744199564472612 44 0.225209143979701 0.450418287959402 0.774790856020299 45 0.183287025801329 0.366574051602658 0.81671297419867 46 0.153695050777815 0.307390101555630 0.846304949222185 47 0.121627886665699 0.243255773331398 0.878372113334301 48 0.140515789555690 0.281031579111379 0.85948421044431 49 0.145592988662423 0.291185977324845 0.854407011337577 50 0.123296006753678 0.246592013507356 0.876703993246322 51 0.150302800010528 0.300605600021055 0.849697199989472 52 0.120978947362194 0.241957894724389 0.879021052637806 53 0.130320146552366 0.260640293104732 0.869679853447634 54 0.100187679065756 0.200375358131512 0.899812320934244 55 0.213508270228033 0.427016540456066 0.786491729771967 56 0.190452514342009 0.380905028684018 0.809547485657991 57 0.185523531017258 0.371047062034516 0.814476468982742 58 0.177175006741788 0.354350013483575 0.822824993258212 59 0.147311006162935 0.294622012325869 0.852688993837065 60 0.116316463697182 0.232632927394364 0.883683536302818 61 0.108875388148595 0.217750776297190 0.891124611851405 62 0.990218162958714 0.0195636740825719 0.00978183704128594 63 0.982937273422354 0.0341254531552921 0.0170627265776460 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 2 0.0444444444444444 OK 10% type I error level 2 0.0444444444444444 OK

Charts produced by software:

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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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