Multiple Regression

*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: Mon, 14 Dec 2009 15:31:19 -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/14/t1260830109ig5xr85vfh36vkm.htm/, Retrieved Mon, 14 Dec 2009 23:35:21 +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/14/t1260830109ig5xr85vfh36vkm.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:

Dataseries X:

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115.2 0 107.1 96.3 87.0 106.1 0 115.2 107.1 96.3 89.5 0 106.1 115.2 107.1 91.3 0 89.5 106.1 115.2 97.6 0 91.3 89.5 106.1 100.7 0 97.6 91.3 89.5 104.6 0 100.7 97.6 91.3 94.7 0 104.6 100.7 97.6 101.8 0 94.7 104.6 100.7 102.5 0 101.8 94.7 104.6 105.3 0 102.5 101.8 94.7 110.3 0 105.3 102.5 101.8 109.8 0 110.3 105.3 102.5 117.3 0 109.8 110.3 105.3 118.8 0 117.3 109.8 110.3 131.3 0 118.8 117.3 109.8 125.9 0 131.3 118.8 117.3 133.1 0 125.9 131.3 118.8 147.0 0 133.1 125.9 131.3 145.8 0 147.0 133.1 125.9 164.4 0 145.8 147.0 133.1 149.8 0 164.4 145.8 147.0 137.7 0 149.8 164.4 145.8 151.7 0 137.7 149.8 164.4 156.8 0 151.7 137.7 149.8 180.0 0 156.8 151.7 137.7 180.4 0 180.0 156.8 151.7 170.4 0 180.4 180.0 156.8 191.6 0 170.4 180.4 180.0 199.5 0 191.6 170.4 180.4 218.2 0 199.5 191.6 170.4 217.5 0 218.2 199.5 191.6 205.0 0 217.5 218.2 199.5 194.0 0 205.0 217.5 218.2 199.3 0 194.0 205.0 217.5 219.3 0 199.3 194.0 205.0 211.1 0 219.3 199.3 194.0 215.2 0 211.1 219.3 199.3 240.2 0 215.2 211 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 20.1262051422475 + 4.01724153834693D[t] + 1.26626523267027Y1[t] -0.0803516098227803Y2[t] -0.306798158642251Y3[t] -6.05261134619861M1[t] + 0.499696022840562M2[t] -4.75192659910419M3[t] -1.26056324317434M4[t] + 1.06889829441157M5[t] -8.30291994875169M6[t] -13.4549723969567M7[t] -18.0682013486370M8[t] -11.5986055519801M9[t] -6.88514406451962M10[t] -7.89200668271265M11[t] + 0.251263515884337t + 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) 20.1262051422475 9.910376 2.0308 0.046498 0.023249 D 4.01724153834693 8.038306 0.4998 0.618984 0.309492 Y1 1.26626523267027 0.121589 10.4143 0 0 Y2 -0.0803516098227803 0.203957 -0.394 0.694938 0.347469 Y3 -0.306798158642251 0.125386 -2.4468 0.017214 0.008607 M1 -6.05261134619861 10.769735 -0.562 0.576109 0.288054 M2 0.499696022840562 10.802744 0.0463 0.963252 0.481626 M3 -4.75192659910419 10.876344 -0.4369 0.663674 0.331837 M4 -1.26056324317434 10.930399 -0.1153 0.908553 0.454277 M5 1.06889829441157 10.93554 0.0977 0.922445 0.461222 M6 -8.30291994875169 11.034938 -0.7524 0.4546 0.2273 M7 -13.4549723969567 10.995851 -1.2236 0.225645 0.112822 M8 -18.0682013486370 10.848424 -1.6655 0.100776 0.050388 M9 -11.5986055519801 11.209761 -1.0347 0.30477 0.152385 M10 -6.88514406451962 11.158865 -0.617 0.539451 0.269725 M11 -7.89200668271265 11.116314 -0.7099 0.480356 0.240178 t 0.251263515884337 0.18774 1.3384 0.185589 0.092794 Multiple Linear Regression - Regression Statistics Multiple R 0.980540725989927 R-squared 0.961460115324854 Adjusted R-squared 0.95167220810577 F-TEST (value) 98.2293858946853 F-TEST (DF numerator) 16 F-TEST (DF denominator) 63 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 19.2071413719472 Sum Squared Residuals 23241.5996199639 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 115.2 115.512563903109 -0.312563903108759 2 106.1 128.851862911203 -22.7518629112027 3 89.5 108.364222034942 -18.8642220349417 4 91.3 89.332980608815 1.96701939118509 5 97.6 98.3186830477942 -0.718683047794248 6 100.7 102.123815822118 -1.42381582211837 7 104.6 100.089997283636 4.51000271636403 8 94.7 98.4845478653572 -3.7845478653572 9 101.8 91.404935804363 10.395064195637 10 102.5 104.959112078207 -2.45911207820746 11 105.3 107.556703979584 -2.25670397958450 12 110.3 117.011003776422 -6.71100377642231 13 109.8 117.101238890906 -7.301238890906 14 117.3 122.010884266182 -4.71088426618217 15 118.8 125.013699416849 -6.21369941684888 16 131.3 130.206486143319 1.09351385668127 17 125.9 146.194013000616 -20.2940130006163 18 133.1 128.77103365617 4.32896634383019 19 147 129.586276109090 17.4137238909101 20 145.8 143.903575873355 1.89642412664526 21 164.4 145.779082787931 18.6209172120691 22 149.8 170.128268645603 -20.3282686456027 23 137.7 149.758814993975 -12.0588149939751 24 151.7 138.046963629929 13.6530363700714 25 156.8 155.424836652031 1.37516334796950 26 180 171.273695405625 8.72630459437528 27 180.4 190.945722266427 -10.5457222664268 28 170.4 191.766027274345 -21.3660272743451 29 191.6 174.534242076683 17.0657579233166 30 199.5 192.939307116785 6.56069288321505 31 218.2 199.406540980739 18.7934590192610 32 217.5 211.584836715061 5.91516328493875 33 205 213.493029807774 -8.49302980777357 34 194 196.948559963006 -2.94855996300592 35 199.3 183.483197135159 15.8168028648414 36 219.3 203.056517757987 16.2434822420133 37 211.1 225.529390794082 -14.4293907940818 38 215.2 218.716524333850 -3.5165243338496 39 240.2 213.430772709439 26.7692272905609 40 242.2 251.016333698603 -8.81633369860295 41 240.7 252.862926521411 -12.1629265214110 42 255.4 234.012316759425 21.3876832405751 43 253 247.232557844807 5.76744215519323 44 218.2 239.110584424171 -20.9105844241707 45 203.7 197.448324571320 6.25167542867976 46 205.6 187.584755303520 18.0152446964797 47 215.6 201.076734406466 14.5232655935342 48 188.5 226.178562173415 -37.6785621734148 49 202.9 184.674993938088 18.2250060619118 50 214 208.822331213238 5.17766878676157 51 230.3 225.034683107575 5.26531689242510 52 230 244.107636918433 -14.1076369184332 53 241 241.593291601062 -0.593291601062043 54 259.6 245.442191468581 14.157808531419 55 247.8 263.302107603469 -15.5021076034694 56 270.3 239.128892734396 31.1711072656042 57 289.7 269.582423027181 20.117576972819 58 322.7 300.925000595295 21.7749994047051 59 315 333.494374371092 -18.4943743710924 60 320.2 323.283914876317 -3.08391487631701 61 329.5 314.561514416329 14.9384855836708 62 360.6 335.085869415553 25.5141305844469 63 382.2 367.123738649246 15.0762613507536 64 435.4 392.865536605877 42.5344633941231 65 464 451.534554531459 12.4654454685408 66 468.8 467.727639589305 1.07236041069461 67 403 450.285205693103 -47.2852056931028 68 351.6 353.442872883286 -1.84287288328556 69 252 298.892204001431 -46.8922040014313 70 188 202.054303414369 -14.0543034143687 71 146.5 144.030175113724 2.46982488627646 72 152.9 135.323037785931 17.5769622140695 73 148.1 160.595461405455 -12.4954614054555 74 165.1 173.538832454349 -8.4388324543493 75 177 188.487161815522 -11.4871618155223 76 206.1 207.404998750608 -1.30499875060823 77 244.9 240.662289220974 4.23771077902616 78 228.6 274.683695587616 -46.0836955876156 79 253.4 237.097314485156 16.3026855148438 80 241.1 253.544689504375 -12.4446895043748 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.118822816539425 0.237645633078850 0.881177183460575 21 0.0539593089165884 0.107918617833177 0.946040691083412 22 0.0431868154971408 0.0863736309942815 0.95681318450286 23 0.0187676767288260 0.0375353534576519 0.981232323271174 24 0.00800013111362059 0.0160002622272412 0.99199986888638 25 0.00449421460983426 0.00898842921966852 0.995505785390166 26 0.00597451244007324 0.0119490248801465 0.994025487559927 27 0.00287508170488989 0.00575016340977978 0.99712491829511 28 0.00290201527649479 0.00580403055298958 0.997097984723505 29 0.00461538274493286 0.00923076548986573 0.995384617255067 30 0.00202715263115156 0.00405430526230312 0.997972847368849 31 0.00134445810308242 0.00268891620616484 0.998655541896918 32 0.000599538188235415 0.00119907637647083 0.999400461811765 33 0.000849825365238857 0.00169965073047771 0.99915017463476 34 0.000426120864834664 0.000852241729669328 0.999573879135165 35 0.000183102477207801 0.000366204954415602 0.999816897522792 36 9.7837348278315e-05 0.00019567469655663 0.999902162651722 37 0.000102024603213640 0.000204049206427279 0.999897975396786 38 5.69998977736926e-05 0.000113999795547385 0.999943000102226 39 8.34840522075551e-05 0.000166968104415110 0.999916515947793 40 5.01978147724308e-05 0.000100395629544862 0.999949802185228 41 4.49479421650433e-05 8.98958843300865e-05 0.999955052057835 42 2.74538328833122e-05 5.49076657666244e-05 0.999972546167117 43 2.94665489343854e-05 5.89330978687708e-05 0.999970533451066 44 0.000167779801697804 0.000335559603395607 0.999832220198302 45 0.000257024911526724 0.000514049823053449 0.999742975088473 46 0.000168509703816091 0.000337019407632182 0.999831490296184 47 0.000146741368832183 0.000293482737664366 0.999853258631168 48 0.00202325986938098 0.00404651973876196 0.997976740130619 49 0.00145211953067848 0.00290423906135696 0.998547880469322 50 0.00100918535282068 0.00201837070564136 0.99899081464718 51 0.000694979002098836 0.00138995800419767 0.9993050209979 52 0.000378180809577303 0.000756361619154606 0.999621819190423 53 0.000162292555948603 0.000324585111897207 0.999837707444051 54 6.44699813740608e-05 0.000128939962748122 0.999935530018626 55 0.000292196868772886 0.000584393737545772 0.999707803131227 56 0.00209141966482261 0.00418283932964522 0.997908580335177 57 0.00104694081969726 0.00209388163939452 0.998953059180303 58 0.00107609336977189 0.00215218673954377 0.998923906630228 59 0.000542917914040404 0.00108583582808081 0.99945708208596 60 0.0113219743251798 0.0226439486503595 0.98867802567482 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 34 0.829268292682927 NOK 5% type I error level 38 0.926829268292683 NOK 10% type I error level 39 0.951219512195122 NOK

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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