Multiple Linear Regression zonder seizonaliteit en zonder trend

*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 05:35:34 -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/t126122687128g7he3s4rv6wzf.htm/, Retrieved Sat, 19 Dec 2009 13:48:04 +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/t126122687128g7he3s4rv6wzf.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:

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No (this computation is public)

User-defined keywords:

kvn paper

Dataseries X:

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9487 1169 8700 2154 9627 2249 8947 2687 9283 4359 8829 5382 9947 4459 9628 6398 9318 4596 9605 3024 8640 1887 9214 2070 9567 1351 8547 2218 9185 2461 9470 3028 9123 4784 9278 4975 10170 4607 9434 6249 9655 4809 9429 3157 8739 1910 9552 2228 9784 1594 9089 2467 9763 2222 9330 3607 9144 4685 9895 4962 10404 5770 10195 5480 9987 5000 9789 3228 9437 1993 10096 2288 9776 1580 9106 2111 10258 2192 9766 3601 9826 4665 9957 4876 10036 5813 10508 5589 10146 5331 10166 3075 9365 2002 9968 2306 10123 1507 9144 1992 10447 2487 9699 3490 10451 4647 10192 5594 10404 5611 10597 5788 10633 6204 10727 3013 9784 1931 9667 2549 10297 1504 9426 2090 10274 2702 9598 2939 10400 4500 9985 6208 10761 6415 11081 5657 10297 5964 10751 3163 9760 1997 10133 2422 10806 1376 9734 2202 10083 2683 10691 3303 10446 5202 10517 5231 11353 4880 10436 7998 10721 4977 10701 3531 9793 2025 10142 2205

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 Y[t] = + 9380.59311151319 + 0.135405250981971X[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) 9380.59311151319 149.259483 62.8476 0 0 X 0.135405250981971 0.037598 3.6014 0.000541 0.000271 Multiple Linear Regression - Regression Statistics Multiple R 0.36955225400964 R-squared 0.136568868443605 Adjusted R-squared 0.126039220497796 F-TEST (value) 12.9699368057176 F-TEST (DF numerator) 1 F-TEST (DF denominator) 82 p-value 0.000541023645005412 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 556.442302261611 Sum Squared Residuals 25389498.9311885 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9487 9538.88184991107 -51.881849911075 2 8700 9672.25602212835 -972.256022128347 3 9627 9685.11952097164 -58.1195209716347 4 8947 9744.42702090174 -797.427020901738 5 9283 9970.8246005436 -687.824600543594 6 8829 10109.3441722981 -1280.34417229815 7 9947 9984.3651256418 -37.365125641791 8 9628 10246.9159072958 -618.915907295833 9 9318 10002.9156450263 -684.915645026321 10 9605 9790.05859048266 -185.058590482662 11 8640 9636.10282011616 -996.10282011616 12 9214 9660.88198104586 -446.881981045862 13 9567 9563.52560558982 3.47439441017536 14 8547 9680.9219581912 -1133.92195819119 15 9185 9713.82543417981 -528.825434179813 16 9470 9790.6002114866 -320.60021148659 17 9123 10028.3718322109 -905.371832210932 18 9278 10054.2342351485 -776.234235148488 19 10170 10004.4051027871 165.594897212877 20 9434 10226.7405248995 -792.74052489952 21 9655 10031.7569634855 -376.756963485481 22 9429 9808.06748886326 -379.067488863264 23 8739 9639.21714088875 -900.217140888746 24 9552 9682.27601070101 -130.276010701013 25 9784 9596.42908157844 187.570918421556 26 9089 9714.6378656857 -625.637865685704 27 9763 9681.46357919512 81.5364208048785 28 9330 9868.99985180515 -538.999851805152 29 9144 10014.9667123637 -870.966712363717 30 9895 10052.4739668857 -157.473966885722 31 10404 10161.8814096792 242.118590320845 32 10195 10122.6138868944 72.3861131056164 33 9987 10057.6193664230 -70.6193664230374 34 9789 9817.68126168298 -28.6812616829845 35 9437 9650.45577672025 -213.45577672025 36 10096 9690.40032575993 405.599674240068 37 9776 9594.5334080647 181.466591935304 38 9106 9666.43359633612 -560.433596336123 39 10258 9677.40142166566 580.598578334338 40 9766 9868.18742029926 -102.187420299260 41 9826 10012.2586073441 -186.258607344077 42 9957 10040.8291153013 -83.829115301273 43 10036 10167.7038354714 -131.70383547138 44 10508 10137.3730592514 370.626940748582 45 10146 10102.4385044981 43.5614955019302 46 10166 9796.96425828274 369.035741717257 47 9365 9651.6744239791 -286.674423979088 48 9968 9692.8376202776 275.162379722393 49 10123 9584.64882474301 538.351175256988 50 9144 9650.32037146927 -506.320371469268 51 10447 9717.34597070534 729.654029294656 52 9699 9853.15743744026 -154.157437440261 53 10451 10009.8213128264 441.178687173599 54 10192 10138.0500855063 53.9499144936718 55 10404 10140.3519747730 263.648025226978 56 10597 10164.3187041968 432.681295803169 57 10633 10220.6472886053 412.352711394669 58 10727 9788.56913272186 938.43086727814 59 9784 9642.06065115937 141.939348840632 60 9667 9725.74109626623 -58.7410962662261 61 10297 9584.24260899007 712.757391009934 62 9426 9663.5900860655 -237.590086065501 63 10274 9746.45809966647 527.541900333532 64 9598 9778.5491441492 -180.549144149195 65 10400 9989.91674093205 410.083259067948 66 9985 10221.1889096093 -236.188909609259 67 10761 10249.2177965625 511.782203437473 68 11081 10146.5806163182 934.419383681808 69 10297 10188.1500283697 108.849971630342 70 10751 9808.87992036916 942.120079630844 71 9760 9650.99739772418 109.002602275822 72 10133 9708.54462939152 424.455370608484 73 10806 9566.91073686437 1239.08926313563 74 9734 9678.75547417548 55.2445258245179 75 10083 9743.8853998978 339.11460010219 76 10691 9827.83665550663 863.163344493368 77 10446 10084.9712271214 361.028772878604 78 10517 10088.8979793999 428.102020600127 79 11353 10041.3707363052 1311.6292636948 80 10436 10463.564308867 -27.5643088669869 81 10721 10054.5050456505 666.494954349548 82 10701 9858.70905273052 842.290947269478 83 9793 9654.78874475167 138.211255248327 84 10142 9679.16168992843 462.838310071572 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.407018800943063 0.814037601886126 0.592981199056937 6 0.288676076069641 0.577352152139282 0.711323923930359 7 0.498562086534858 0.997124173069715 0.501437913465142 8 0.415805053718751 0.831610107437502 0.584194946281249 9 0.314227439123331 0.628454878246663 0.685772560876669 10 0.252129847169449 0.504259694338898 0.747870152830551 11 0.315531859502721 0.631063719005442 0.684468140497279 12 0.237119683491193 0.474239366982387 0.762880316508807 13 0.208577456487363 0.417154912974726 0.791422543512637 14 0.323285171613607 0.646570343227213 0.676714828386393 15 0.262949796380243 0.525899592760486 0.737050203619757 16 0.216792627286315 0.433585254572631 0.783207372713685 17 0.207532090274321 0.415064180548642 0.79246790972568 18 0.184192452331587 0.368384904663174 0.815807547668413 19 0.297690393073206 0.595380786146412 0.702309606926794 20 0.285171896726162 0.570343793452325 0.714828103273838 21 0.25669752334437 0.51339504668874 0.74330247665563 22 0.220558365770602 0.441116731541205 0.779441634229397 23 0.287006592236964 0.574013184473927 0.712993407763036 24 0.266475475921762 0.532950951843525 0.733524524078238 25 0.292907425633723 0.585814851267445 0.707092574366277 26 0.299813801361544 0.599627602723088 0.700186198638456 27 0.301973887457454 0.603947774914909 0.698026112542546 28 0.297088454710886 0.594176909421771 0.702911545289114 29 0.384173865392398 0.768347730784797 0.615826134607602 30 0.397740953280682 0.795481906561364 0.602259046719318 31 0.512519328421782 0.974961343156436 0.487480671578218 32 0.53335528757913 0.93328942484174 0.46664471242087 33 0.520660198588613 0.958679602822773 0.479339801411386 34 0.502734770722253 0.994530458555495 0.497265229277747 35 0.481450297378975 0.96290059475795 0.518549702621025 36 0.538731735521192 0.922536528957617 0.461268264478808 37 0.522485898459642 0.955028203080715 0.477514101540358 38 0.588763795737752 0.822472408524495 0.411236204262248 39 0.668167971817397 0.663664056365205 0.331832028182603 40 0.647851785657661 0.704296428684677 0.352148214342339 41 0.637020360857577 0.725959278284845 0.362979639142423 42 0.622154878721164 0.755690242557672 0.377845121278836 43 0.614171431916334 0.771657136167332 0.385828568083666 44 0.638144251569519 0.723711496860962 0.361855748430481 45 0.616898535335355 0.76620292932929 0.383101464664645 46 0.615215841239151 0.769568317521698 0.384784158760849 47 0.633159687341431 0.733680625317139 0.366840312658569 48 0.611750134587387 0.776499730825226 0.388249865412613 49 0.622002588055934 0.755994823888131 0.377997411944066 50 0.731389975135013 0.537220049729973 0.268610024864987 51 0.777059205418271 0.445881589163458 0.222940794581729 52 0.783529388157969 0.432941223684062 0.216470611842031 53 0.776030048007874 0.447939903984251 0.223969951992126 54 0.754441687524567 0.491116624950867 0.245558312475433 55 0.72669102963198 0.54661794073604 0.27330897036802 56 0.70581143538736 0.58837712922528 0.29418856461264 57 0.675690871524672 0.648618256950657 0.324309128475328 58 0.756782630038462 0.486434739923076 0.243217369961538 59 0.722789734819876 0.554420530360249 0.277210265180124 60 0.718228484594456 0.563543030811088 0.281771515405544 61 0.712147896045805 0.57570420790839 0.287852103954195 62 0.767429389274088 0.465141221451825 0.232570610725912 63 0.73061351895848 0.538772962083040 0.269386481041520 64 0.782125644197567 0.435748711604866 0.217874355802433 65 0.737036021694982 0.525927956610036 0.262963978305018 66 0.785133628956191 0.429732742087617 0.214866371043809 67 0.74109150464563 0.51781699070874 0.25890849535437 68 0.778381984137309 0.443236031725383 0.221618015862692 69 0.74094160893613 0.518116782127741 0.259058391063871 70 0.754360781329678 0.491278437340643 0.245639218670322 71 0.746132696375794 0.507734607248413 0.253867303624206 72 0.682094562284354 0.635810875431293 0.317905437715647 73 0.786005705410424 0.427988589179153 0.213994294589576 74 0.785379544285215 0.42924091142957 0.214620455714785 75 0.723704673459806 0.552590653080389 0.276295326540194 76 0.664676205369063 0.670647589261875 0.335323794630937 77 0.54809428118737 0.90381143762526 0.45190571881263 78 0.411481296530543 0.822962593061086 0.588518703469457 79 0.692251217705384 0.615497564589231 0.307748782294616 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:

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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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