mr 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, 14 Dec 2010 20:53:42 +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/14/t1292360230aojjjadxayvrid0.htm/, Retrieved Tue, 14 Dec 2010 21:57:10 +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/14/t1292360230aojjjadxayvrid0.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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113 14.3 15.89 110 14.2 16.93 107 15.9 20.28 103 15.3 22.52 98 15.5 23.51 98 15.1 22.59 137 15 23.51 148 12.1 24.76 147 15.8 26.08 139 16.9 25.29 130 15.1 23.38 128 13.7 25.29 127 14.8 28.42 123 14.7 31.85 118 16 30.1 114 15.4 25.45 108 15 24.95 111 15.5 26.84 151 15.1 27.52 159 11.7 27.94 158 16.3 25.23 148 16.7 26.53 138 15 27.21 137 14.9 28.53 136 14.6 30.35 133 15.3 31.21 126 17.9 32.86 120 16.4 33.2 114 15.4 35.73 116 17.9 34.53 153 15.9 36.54 162 13.9 40.1 161 17.8 40.56 149 17.9 46.14 139 17.4 42.85 135 16.7 38.22 130 16 40.18 127 16.6 42.19 122 19.1 47.56 117 17.8 47.26 112 17.2 44.03 113 18.6 49.83 149 16.3 53.35 157 15.1 58.9 157 19.2 59.64 147 17.7 56.99 137 19.1 53.2 132 18 53.24 125 17.5 57.85 123 17.8 55.69 117 21.1 55.64 114 17.2 62.52 111 19.4 64.4 112 19.8 64.65 144 17.6 67.71 150 16.2 67.21 149 19.5 59.37 134 19.9 53.26 123 20 52.42 116 17.3 55.03

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation werklozen-25[t] = + 173.218398257015 -3.97737434591834buitenlandse_handel[t] + 0.575419054811092ruwe_aardolie[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) 173.218398257015 20.835746 8.3135 0 0 buitenlandse_handel -3.97737434591834 1.54868 -2.5682 0.012867 0.006433 ruwe_aardolie 0.575419054811092 0.203605 2.8262 0.006485 0.003243 Multiple Linear Regression - Regression Statistics Multiple R 0.362985939755321 R-squared 0.131758792460053 Adjusted R-squared 0.101294188686722 F-TEST (value) 4.32497968594604 F-TEST (DF numerator) 2 F-TEST (DF denominator) 57 p-value 0.0178341013100068 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 16.3536743151294 Sum Squared Residuals 15244.2318255034 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 113 125.485353891331 -12.485353891331 2 110 126.481527142926 -16.4815271429261 3 107 121.647644588482 -14.6476445884821 4 103 125.32300787881 -22.3230078788099 5 98 125.097197873889 -27.0971978738893 6 98 126.158762081830 -28.1587620818304 7 137 127.085885046848 9.91411495315156 8 148 139.339544468526 8.6604555314745 9 147 125.382812540978 21.6171874590217 10 139 120.553119707167 18.4468802928327 11 130 126.613343135131 3.38665686486884 12 128 133.280717614106 -5.28071761410603 13 127 130.706667475155 -3.70666747515457 14 123 133.078092267748 -10.0780922677485 15 118 126.900522272135 -8.9005222721352 16 114 126.611248274815 -12.6112482748146 17 108 127.914488485776 -19.9144884857764 18 111 127.013343326410 -16.0133433264102 19 151 128.995578022049 22.0044219779509 20 159 142.760326801192 16.2396731988079 21 158 122.905019171430 35.0949808285703 22 148 122.062114204317 25.9378857956833 23 138 129.214935549649 8.78506445035052 24 137 130.372226136592 6.62777386340805 25 136 132.612701120124 3.38729887987635 26 133 130.323399465118 2.67660053488166 27 126 120.931667606169 5.06833239383104 28 120 127.093371603682 -7.09337160368225 29 114 132.526556158273 -18.5265561582726 30 116 121.892617427703 -5.89261742770348 31 153 131.003958419710 21.9960415802895 32 162 141.007198946675 20.9928010533254 33 161 125.760131762806 35.2398682371938 34 149 128.573232654060 20.4267673459397 35 139 128.668791136691 10.3312088633091 36 135 128.788762955058 6.21123704494158 37 130 132.700746344631 -2.700746344631 38 127 131.470914037250 -4.47091403725029 39 122 124.61747849679 -2.61747849679000 40 117 129.615439430041 -12.6154394300405 41 112 130.143260490552 -18.1432604905517 42 113 127.912366924170 -14.9123669241703 43 149 139.085802992718 9.91419700728241 44 157 147.052227962021 9.94777203797884 45 157 131.170803244316 25.8291967556838 46 147 135.612004267944 11.3879957320557 47 137 127.862841965925 9.13715803407544 48 132 132.260970508627 -0.260970508627183 49 125 136.902339524265 -11.9023395242655 50 123 134.466222062098 -11.4662220620980 51 117 121.312115767827 -4.31211576782693 52 114 140.782758814009 -26.7827588140088 53 111 133.114323076033 -22.1143230760333 54 112 131.667228101369 -19.6672281013687 55 144 142.178233970111 1.82176602988898 56 150 147.458848526991 2.54115147300884 57 149 129.822227795742 19.1777722042583 58 134 124.715467632479 9.28453236752144 59 123 123.834378191845 -0.8343781918454 60 116 136.075132658882 -20.0751326588819 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.00247292313421816 0.00494584626843633 0.997527076865782 7 0.584708501377585 0.830582997244831 0.415291498622415 8 0.43582241221643 0.87164482443286 0.56417758778357 9 0.727487966115488 0.545024067769024 0.272512033884512 10 0.768386620713444 0.463226758573111 0.231613379286556 11 0.684093054849715 0.631813890300569 0.315906945150285 12 0.615193673183997 0.769612653632006 0.384806326816003 13 0.567835345925046 0.864329308149908 0.432164654074954 14 0.594104150959736 0.811791698080528 0.405895849040264 15 0.54816644550003 0.903667108999941 0.451833554499971 16 0.506702860179238 0.986594279641524 0.493297139820762 17 0.54987946941518 0.90024106116964 0.45012053058482 18 0.569170743943838 0.861658512112324 0.430829256056162 19 0.65407156828292 0.69185686343416 0.34592843171708 20 0.611895591718584 0.776208816562833 0.388104408281416 21 0.852713510858007 0.294572978283987 0.147286489141994 22 0.887675295470428 0.224649409059144 0.112324704529572 23 0.848921028817399 0.302157942365202 0.151078971182601 24 0.79792401046838 0.40415197906324 0.20207598953162 25 0.740032551813881 0.519934896372237 0.259967448186119 26 0.67819516889273 0.64360966221454 0.32180483110727 27 0.611626981583062 0.776746036833875 0.388373018416938 28 0.596188260997373 0.807623478005254 0.403811739002627 29 0.709666093066054 0.580667813867892 0.290333906933946 30 0.686533674039576 0.626932651920848 0.313466325960424 31 0.667534370322076 0.664931259355847 0.332465629677924 32 0.636806887846319 0.726386224307363 0.363193112153681 33 0.774921846612308 0.450156306775384 0.225078153387692 34 0.785085660780931 0.429828678438138 0.214914339219069 35 0.754225903304782 0.491548193390436 0.245774096695218 36 0.709286176400551 0.581427647198898 0.290713823599449 37 0.663824396525815 0.67235120694837 0.336175603474185 38 0.619342988152111 0.761314023695777 0.380657011847889 39 0.570549288660239 0.858901422679522 0.429450711339761 40 0.560731280564019 0.878537438871962 0.439268719435981 41 0.607783303962624 0.784433392074753 0.392216696037376 42 0.63589015438087 0.728219691238261 0.364109845619130 43 0.562212104343538 0.875575791312924 0.437787895656462 44 0.526957901897649 0.946084196204702 0.473042098102351 45 0.687483781446749 0.625032437106501 0.312516218553251 46 0.679724392899227 0.640551214201545 0.320275607100772 47 0.632543966000666 0.734912067998668 0.367456033999334 48 0.547486024222993 0.905027951554015 0.452513975777008 49 0.469832735367704 0.939665470735407 0.530167264632296 50 0.384302323615748 0.768604647231497 0.615697676384252 51 0.281657006980615 0.56331401396123 0.718342993019385 52 0.341680459488987 0.683360918977973 0.658319540511013 53 0.377080226138143 0.754160452276285 0.622919773861857 54 0.858610245529508 0.282779508940984 0.141389754470492 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0204081632653061 NOK 5% type I error level 1 0.0204081632653061 OK 10% type I error level 1 0.0204081632653061 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/10qapu1292360014.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/10qapu1292360014.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/11rb01292360014.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/11rb01292360014.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/21rb01292360014.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/21rb01292360014.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/3c0sl1292360014.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/3c0sl1292360014.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/4c0sl1292360014.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/4c0sl1292360014.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/5c0sl1292360014.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/5c0sl1292360014.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/6n9r61292360014.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/6n9r61292360014.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/7g08r1292360014.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/7g08r1292360014.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/8g08r1292360014.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/8g08r1292360014.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/9g08r1292360014.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292360230aojjjadxayvrid0/9g08r1292360014.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 0 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 0 ;

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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