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: Tue, 28 Dec 2010 19:38:03 +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/28/t1293564961rht8dzhxry4ic6c.htm/, Retrieved Tue, 28 Dec 2010 20:36:12 +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/28/t1293564961rht8dzhxry4ic6c.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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4.24 3.353 0 4.15 3.186 0 3.93 3.902 0 3.7 4.164 0 3.7 3.499 0 3.65 4.145 0 3.55 3.796 0 3.43 3.711 0 3.47 3.949 0 3.58 3.74 0 3.67 3.243 0 3.72 4.407 0 3.8 4.814 0 3.76 3.908 0 3.63 5.25 0 3.48 3.937 0 3.41 4.004 0 3.43 5.56 0 3.5 3.922 0 3.62 3.759 0 3.58 4.138 0 3.52 4.634 0 3.45 3.996 0 3.36 4.308 0 3.27 4.143 0 3.21 4.429 0 3.19 5.219 0 3.16 4.929 0 3.12 5.761 0 3.06 5.592 0 3.01 4.163 0 2.98 4.962 0 2.97 5.208 0 3.02 4.755 0 3.07 4.491 0 3.18 5.732 0 3.29 5.731 1 3.43 5.04 1 3.61 6.102 1 3.74 4.904 1 3.87 5.369 1 3.88 5.578 1 4.09 4.619 1 4.19 4.731 1 4.2 5.011 1 4.29 5.299 1 4.37 4.146 1 4.47 4.625 1 4.61 4.736 1 4.65 4.219 1 4.69 5.116 1 4.82 4.205 1 4.86 4.121 1 4.87 5.103 1 5.01 4.3 1 5.03 4.578 1 5.13 3.809 1 5.18 5.657 1 5.21 4.248 1 5.26 3.83 1 5.25 4.736 1 5.2 4.839 1 5.16 4.411 1 5.19 4.57 1 5.39 4.104 1 5.58 4.801 1 5.76 3.953 1 5.89 3.828 1 5.98 4.44 1 6.02 4.026 1 5.62 4.109 1 4.87 4.785 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 9 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Rente[t] = + 5.65536394245622 -0.504266519015653Woonhuis[t] + 1.48949454760005dummy[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) 5.65536394245622 0.374536 15.0996 0 0 Woonhuis -0.504266519015653 0.08425 -5.9853 0 0 dummy 1.48949454760005 0.110501 13.4795 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.856681932753487 R-squared 0.73390393390625 Adjusted R-squared 0.726191004454257 F-TEST (value) 95.1524240529174 F-TEST (DF numerator) 2 F-TEST (DF denominator) 69 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.455985844110793 Sum Squared Residuals 14.3466932120308 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4.24 3.9645583041967 0.275441695803297 2 4.15 4.04877081287235 0.101229187127649 3 3.93 3.68771598525715 0.242284014742850 4 3.7 3.55559815727504 0.144401842724957 5 3.7 3.89093539242045 -0.190935392420452 6 3.65 3.56517922113634 0.0848207788636594 7 3.55 3.7411682362728 -0.191168236272804 8 3.43 3.78403089038913 -0.354030890389134 9 3.47 3.66401545886341 -0.194015458863408 10 3.58 3.76940716133768 -0.189407161337680 11 3.67 4.02002762128846 -0.350027621288459 12 3.72 3.43306139315424 0.286938606845761 13 3.8 3.22782491991487 0.572175080085131 14 3.76 3.68469038614305 0.0753096138569496 15 3.63 3.00796471762404 0.622035282375956 16 3.48 3.67006665709160 -0.190066657091596 17 3.41 3.63628080031755 -0.226280800317548 18 3.43 2.85164209672919 0.578357903270808 19 3.5 3.67763065487683 -0.177630654876831 20 3.62 3.75982609747638 -0.139826097476382 21 3.58 3.56870908676945 0.0112909132305501 22 3.52 3.31859289333769 0.201407106662314 23 3.45 3.64031493246967 -0.190314932469673 24 3.36 3.48298377853679 -0.122983778536789 25 3.27 3.56618775417437 -0.296187754174372 26 3.21 3.42196752973589 -0.211967529735895 27 3.19 3.02359697971353 0.166403020286471 28 3.16 3.16983427022807 -0.00983427022806826 29 3.12 2.75028452640705 0.369715473592955 30 3.06 2.83550556812069 0.224494431879309 31 3.01 3.55610242379406 -0.546102423794059 32 2.98 3.15319347510055 -0.173193475100552 33 2.97 3.0291439114227 -0.059143911422701 34 3.02 3.25757664453679 -0.237576644536792 35 3.07 3.39070300555692 -0.320703005556925 36 3.18 2.7649082554585 0.415091744541501 37 3.29 4.25490706957756 -0.964907069577562 38 3.43 4.60335523421738 -1.17335523421738 39 3.61 4.06782419102275 -0.457824191022754 40 3.74 4.67193548080351 -0.931935480803506 41 3.87 4.43745154946123 -0.567451549461228 42 3.88 4.33205984698696 -0.452059846986957 43 4.09 4.81565143872297 -0.725651438722968 44 4.19 4.75917358859321 -0.569173588593214 45 4.2 4.61797896326883 -0.417978963268832 46 4.29 4.47275020579232 -0.182750205792324 47 4.37 5.05416950221737 -0.684169502217371 48 4.47 4.81262583960887 -0.342625839608874 49 4.61 4.75665225599814 -0.146652255998136 50 4.65 5.01735804632923 -0.367358046329228 51 4.69 4.56503097877219 0.124969021227812 52 4.82 5.02441777759545 -0.204417777595448 53 4.86 5.06677616519276 -0.206776165192762 54 4.87 4.57158644351939 0.298413556480608 55 5.01 4.97651245828896 0.0334875417110387 56 5.03 4.83632636600261 0.193673633997391 57 5.13 5.22410731912565 -0.0941073191256464 58 5.18 4.29222279198472 0.88777720801528 59 5.21 5.00273431727777 0.207265682722225 60 5.26 5.21351772222632 0.046482277773682 61 5.25 4.75665225599814 0.493347744001864 62 5.2 4.70471280453952 0.495287195460476 63 5.16 4.92053887467822 0.239461125321776 64 5.19 4.84036049815473 0.349639501845266 65 5.39 5.07534869601603 0.314651303983971 66 5.58 4.72387493226212 0.856125067737881 67 5.76 5.15149294038739 0.608507059612607 68 5.89 5.21452625526435 0.67547374473565 69 5.98 4.90591514562677 1.07408485437323 70 6.02 5.11468148449925 0.90531851550075 71 5.62 5.07282736342095 0.547172636579049 72 4.87 4.73194319656637 0.138056803433631 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.105184446118469 0.210368892236938 0.89481555388153 7 0.0830066975856192 0.166013395171238 0.916993302414381 8 0.102305929343947 0.204611858687895 0.897694070656053 9 0.0617286507861995 0.123457301572399 0.9382713492138 10 0.034951610654606 0.069903221309212 0.965048389345394 11 0.0279304231676423 0.0558608463352845 0.972069576832358 12 0.0177604117251215 0.0355208234502429 0.982239588274879 13 0.0147270892759974 0.0294541785519948 0.985272910724003 14 0.0069334686927204 0.0138669373854408 0.99306653130728 15 0.00380086052394477 0.00760172104788954 0.996199139476055 16 0.00262302855402228 0.00524605710804456 0.997376971445978 17 0.00214354155817314 0.00428708311634627 0.997856458441827 18 0.00120663384310876 0.00241326768621753 0.99879336615689 19 0.000709572125323163 0.00141914425064633 0.999290427874677 20 0.000329444057754916 0.000658888115509833 0.999670555942245 21 0.000142811394521020 0.000285622789042040 0.99985718860548 22 6.141641138261e-05 0.00012283282276522 0.999938583588617 23 3.74670604932174e-05 7.49341209864349e-05 0.999962532939507 24 2.56682158928046e-05 5.13364317856092e-05 0.999974331784107 25 3.09378551804832e-05 6.18757103609663e-05 0.99996906214482 26 3.38514320610285e-05 6.77028641220571e-05 0.99996614856794 27 1.97376434716325e-05 3.9475286943265e-05 0.999980262356528 28 1.32036660320788e-05 2.64073320641577e-05 0.999986796333968 29 7.47423674924633e-06 1.49484734984927e-05 0.99999252576325 30 4.52783051385829e-06 9.05566102771658e-06 0.999995472169486 31 2.0490028715916e-05 4.0980057431832e-05 0.999979509971284 32 1.94306326009419e-05 3.88612652018837e-05 0.999980569367399 33 1.32511099494678e-05 2.65022198989356e-05 0.99998674889005 34 1.19464056112581e-05 2.38928112225162e-05 0.999988053594389 35 1.90619077862173e-05 3.81238155724345e-05 0.999980938092214 36 9.086368809662e-06 1.8172737619324e-05 0.99999091363119 37 6.47184209220595e-06 1.29436841844119e-05 0.999993528157908 38 1.38984649143397e-05 2.77969298286794e-05 0.999986101535086 39 1.35923257971883e-05 2.71846515943767e-05 0.999986407674203 40 2.51681159427953e-05 5.03362318855905e-05 0.999974831884057 41 3.17892980634681e-05 6.35785961269362e-05 0.999968210701937 42 3.87052356182561e-05 7.74104712365123e-05 0.999961294764382 43 9.15220846889517e-05 0.000183044169377903 0.99990847791531 44 0.000206815444496306 0.000413630888992613 0.999793184555504 45 0.000437707932892951 0.000875415865785902 0.999562292067107 46 0.000984956475783232 0.00196991295156646 0.999015043524217 47 0.00344686109733596 0.00689372219467191 0.996553138902664 48 0.00855340875681051 0.0171068175136210 0.99144659124319 49 0.0184857595298137 0.0369715190596273 0.981514240470186 50 0.0420260999888586 0.0840521999777172 0.957973900011141 51 0.0754472114885709 0.150894422977142 0.924552788511429 52 0.123716319459396 0.247432638918792 0.876283680540604 53 0.202144647160239 0.404289294320478 0.797855352839761 54 0.264917598846123 0.529835197692246 0.735082401153877 55 0.323082392793751 0.646164785587502 0.676917607206249 56 0.367153295301416 0.734306590602832 0.632846704698584 57 0.452028306316759 0.904056612633519 0.54797169368324 58 0.573019883496491 0.853960233007018 0.426980116503509 59 0.575044565476572 0.849910869046857 0.424955434523428 60 0.70133820240146 0.59732359519708 0.29866179759854 61 0.647905835360454 0.704188329279092 0.352094164639546 62 0.576125296511296 0.847749406977407 0.423874703488704 63 0.558489448729883 0.883021102540233 0.441510551270117 64 0.49428061083775 0.9885612216755 0.50571938916225 65 0.483535316671146 0.967070633342293 0.516464683328854 66 0.460363047480949 0.920726094961899 0.539636952519051 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 33 0.540983606557377 NOK 5% type I error level 38 0.622950819672131 NOK 10% type I error level 41 0.672131147540984 NOK

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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