paper 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: Thu, 16 Dec 2010 08:02:53 +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/16/t12924866285524h3b9uqz0ib7.htm/, Retrieved Thu, 16 Dec 2010 09:03:59 +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/16/t12924866285524h3b9uqz0ib7.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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9 12 9 24 13 14 9 15 6 25 12 8 9 14 13 19 15 12 8 10 7 18 12 7 14 10 8 18 10 10 14 9 8 23 12 7 15 18 11 23 15 16 11 11 11 23 9 11 14 14 8 17 7 12 8 24 20 30 11 7 16 18 16 26 10 11 11 14 8 23 14 15 7 18 11 35 11 7 9 12 8 21 15 14 16 5 4 23 12 7 10 12 8 20 14 15 14 11 8 24 15 17 11 9 6 20 9 15 6 11 8 17 13 14 12 16 14 27 16 8 14 14 10 18 13 8 13 8 9 24 12 14 14 18 10 26 11 8 10 10 8 26 16 16 14 13 10 25 12 10 8 12 7 20 13 14 10 12 8 26 16 16 9 12 7 18 14 13 9 13 6 19 15 5 15 7 5 21 8 10 12 14 7 24 17 15 14 9 9 23 13 16 11 9 5 31 6 15 12 10 8 23 8 8 13 10 6 19 14 13 14 11 8 26 12 14 15 13 8 14 11 12 11 13 6 25 16 16 9 13 8 27 8 10 8 6 6 20 15 15 10 13 6 24 16 16 10 21 12 32 14 19 10 11 5 26 16 14 9 9 7 21 9 6 13 18 12 21 14 13 8 9 11 24 13 7 10 15 10 23 15 13 11 11 8 24 15 14 10 14 9 21 13 13 16 14 9 21 11 11 11 8 4 13 11 14 6 8 11 29 12 14 9 11 10 21 7 7 20 8 7 19 12 12 12 13 9 21 12 11 9 13 10 19 16 14 14 15 11 22 14 10 8 12 7 14 10 13 7 12 6 19 12 11 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 10 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation DoubtsAboutActions[t] = + 13.7331110184792 + 0.0197387874653406ParentalExpectations[t] -0.00659949411248572ParentalCritism[t] -0.0365198552546792PersonalStandards[t] -0.187967271206596Popularity[t] + 0.0342413656924875KnowingPeople[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) 13.7331110184792 2.427945 5.6563 0 0 ParentalExpectations 0.0197387874653406 0.119334 0.1654 0.869093 0.434547 ParentalCritism -0.00659949411248572 0.159171 -0.0415 0.967044 0.483522 PersonalStandards -0.0365198552546792 0.083993 -0.4348 0.66503 0.332515 Popularity -0.187967271206596 0.128885 -1.4584 0.149136 0.074568 KnowingPeople 0.0342413656924875 0.12021 0.2848 0.776592 0.388296 Multiple Linear Regression - Regression Statistics Multiple R 0.188707663494420 R-squared 0.0356105822615232 Adjusted R-squared -0.0323041654665386 F-TEST (value) 0.524342406514001 F-TEST (DF numerator) 5 F-TEST (DF denominator) 71 p-value 0.757067623825922 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.90488610873579 Sum Squared Residuals 599.123794635556 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9 11.0699090889478 -2.06990908894776 2 9 11.0949231554782 -2.09492315547819 3 9 10.8211706899037 -1.82117068990369 4 8 11.2110273451293 -3.21102734512927 5 14 11.6830864905074 2.31691350949256 6 14 11.0020897872780 2.99791021272195 7 15 10.9042108697413 4.09578913025875 8 11 11.722636156261 -0.722636156261008 9 14 12.4309460406282 1.56905395937176 10 8 11.1513059543322 -3.15130595433217 11 16 11.5302833609853 4.46971663901467 12 11 10.9987801077315 0.00121989226854191 13 7 10.9096694002791 -3.9096694002791 14 9 10.8101336064111 -1.81013360641105 15 16 10.9495326138666 5.05046738613337 16 10 11.0688620985648 -1.06886209856481 17 14 10.7835593502591 3.21644064974086 18 11 11.9626810804267 -0.962681080426742 19 6 11.3124087823776 -5.31240878237762 20 12 10.2369571947079 1.76304280529210 21 14 11.1164581071391 2.88354189286093 22 13 11.1789212102930 1.82107878970705 23 14 11.2791889573762 2.72081104262381 24 10 10.4685722153854 -0.468572215385354 25 14 11.0975303354825 2.90246966451746 26 8 11.2291874981914 -3.22918749819141 27 10 10.5080497903160 -0.508049790316035 28 9 11.0800185718017 -2.08001857180168 29 9 10.6079388013783 -1.60793880137834 30 15 11.9100435870980 3.08995641290197 31 12 10.4049579329695 1.59504206703052 32 14 11.1156953131914 2.88430468680865 33 11 12.1314639803575 -1.13146398035754 34 12 11.8079390252623 0.192060974737742 35 13 11.0106206357288 1.98937936427119 36 14 11.1716973562921 2.8283026437079 37 15 11.7688977341006 3.23110226589944 38 11 10.5775074212610 0.422492578738973 39 9 11.7895586980245 -2.78955869802454 40 8 10.7756610907911 -2.77566109079115 41 10 10.6140272765157 -0.614027276515706 42 10 10.9188404090167 -0.918840409016737 43 10 10.4396267538032 -0.439626753803177 44 9 11.6113894398272 -2.61138943982719 45 13 11.0558942602673 1.94410573973274 46 8 10.7578041784793 -2.75780417847931 47 10 10.7488699043803 -0.748869904380256 48 11 10.6808352531817 0.319164746818326 49 10 11.1847048639500 -1.18470486394995 50 16 11.4921566749782 4.50784332502183 51 11 11.8016043598634 -0.801604359863449 52 6 10.9831229457946 -4.98312294579459 53 9 12.0412444405261 -3.04124444052609 54 20 11.3062367434063 8.69376325659365 55 12 11.2844506163062 0.715549383693768 56 9 10.7017458449542 -1.70174584495418 57 14 10.8640334396516 3.13596656034842 58 8 11.9779670776468 -3.97796707764678 59 7 11.3575500216877 -4.35755002168771 60 11 11.4366115075522 -0.436611507552224 61 14 11.9411623681666 2.05883763183339 62 14 11.7943208109905 2.20567918900952 63 9 10.6024227230924 -1.60242272309244 64 16 11.6095542058047 4.39044579419528 65 13 11.0418397622265 1.95816023777349 66 13 12.0751777408216 0.924822259178398 67 8 12.0174961183552 -4.01749611835517 68 9 11.6761192361259 -2.67611923612586 69 11 11.4236300491196 -0.423630049119585 70 8 10.7350513809136 -2.73505138091359 71 7 10.4132287773532 -3.41322877735323 72 11 11.4582316037771 -0.458231603777067 73 9 12.7653350434308 -3.7653350434308 74 16 11.5139173523864 4.48608264761357 75 13 10.7380042759429 2.26199572405708 76 12 12.0334889425049 -0.0334889425049476 77 9 10.2182221609422 -1.21822216094217 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.861251141321761 0.277497717356478 0.138748858678239 10 0.768133223234704 0.463733553530592 0.231866776765296 11 0.827514942518818 0.344970114962364 0.172485057481182 12 0.746118711341276 0.507762577317449 0.253881288658724 13 0.699904607544912 0.600190784910176 0.300095392455088 14 0.637651219790001 0.724697560419998 0.362348780209999 15 0.788954220972669 0.422091558054662 0.211045779027331 16 0.734827123260722 0.530345753478557 0.265172876739278 17 0.690321727988358 0.619356544023285 0.309678272011642 18 0.691796676328052 0.616406647343897 0.308203323671948 19 0.831176713589422 0.337646572821155 0.168823286410578 20 0.798306591688613 0.403386816622775 0.201693408311387 21 0.809247415089116 0.381505169821768 0.190752584910884 22 0.758867234283166 0.482265531433668 0.241132765716834 23 0.766445318285115 0.467109363429771 0.233554681714886 24 0.70683337021974 0.58633325956052 0.29316662978026 25 0.69145585207661 0.617088295846781 0.308544147923390 26 0.694531356969936 0.610937286060129 0.305468643030064 27 0.626052512280379 0.747894975439243 0.373947487719621 28 0.579142035185467 0.841715929629065 0.420857964814533 29 0.515652030618506 0.968695938762988 0.484347969381494 30 0.495875991180185 0.99175198236037 0.504124008819815 31 0.464073004106228 0.928146008212457 0.535926995893772 32 0.447942678938311 0.895885357876622 0.552057321061689 33 0.404779804104153 0.809559608208305 0.595220195895847 34 0.342311381517413 0.684622763034825 0.657688618482587 35 0.307758356644037 0.615516713288073 0.692241643355963 36 0.307822028856086 0.615644057712172 0.692177971143914 37 0.325093552380698 0.650187104761397 0.674906447619302 38 0.267197076702667 0.534394153405333 0.732802923297333 39 0.253082654120364 0.506165308240729 0.746917345879636 40 0.258208112511867 0.516416225023735 0.741791887488133 41 0.20522413203061 0.41044826406122 0.79477586796939 42 0.162808875668692 0.325617751337385 0.837191124331308 43 0.123277180090799 0.246554360181598 0.876722819909201 44 0.114906913122173 0.229813826244346 0.885093086877827 45 0.0964331716339272 0.192866343267854 0.903566828366073 46 0.0946643110573637 0.189328622114727 0.905335688942636 47 0.069131965125597 0.138263930251194 0.930868034874403 48 0.048344997730387 0.096689995460774 0.951655002269613 49 0.0344725722390342 0.0689451444780684 0.965527427760966 50 0.054017401694584 0.108034803389168 0.945982598305416 51 0.0381573429379159 0.0763146858758318 0.961842657062084 52 0.0730238030806108 0.146047606161222 0.92697619691939 53 0.0868597339359006 0.173719467871801 0.9131402660641 54 0.50543506944428 0.98912986111144 0.49456493055572 55 0.434386949182466 0.868773898364932 0.565613050817534 56 0.367853263180781 0.735706526361561 0.63214673681922 57 0.387297570525536 0.774595141051071 0.612702429474465 58 0.384119674631787 0.768239349263573 0.615880325368213 59 0.428920107664703 0.857840215329405 0.571079892335297 60 0.405754832077946 0.811509664155892 0.594245167922054 61 0.710581451814085 0.57883709637183 0.289418548185915 62 0.658055567409207 0.683888865181586 0.341944432590793 63 0.677253178310592 0.645493643378817 0.322746821689408 64 0.629966904134001 0.740066191731998 0.370033095865999 65 0.683200235037056 0.633599529925888 0.316799764962944 66 0.571000216707073 0.857999566585854 0.428999783292927 67 0.449799201691226 0.899598403382453 0.550200798308774 68 0.314067523871506 0.628135047743013 0.685932476128494 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 3 0.05 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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