Seatbelt Law 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: Mon, 14 Dec 2009 10:23:39 -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/t126081152952tuian1fkwrnad.htm/, Retrieved Mon, 14 Dec 2009 18:25:42 +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/t126081152952tuian1fkwrnad.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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8,9 -3 8,8 -1 8,3 -3 7,5 -4 7,2 -6 7,4 0 8,8 -4 9,3 -2 9,3 -2 8,7 -6 8,2 -7 8,3 -6 8,5 -6 8,6 -3 8,5 -2 8,2 -5 8,1 -11 7,9 -11 8,6 -11 8,7 -10 8,7 -14 8,5 -8 8,4 -9 8,5 -5 8,7 -1 8,7 -2 8,6 -5 8,5 -4 8,3 -6 8 -2 8,2 -2 8,1 -2 8,1 -2 8 2 7,9 1 7,9 -8 8 -1 8 1 7,9 -1 8 2 7,7 2 7,2 1 7,5 -1 7,3 -2 7 -2 7 -1 7 -8 7,2 -4 7,3 -6 7,1 -3 6,8 -3 6,4 -7 6,1 -9 6,5 -11 7,7 -13 7,9 -11 7,5 -9 6,9 -17 6,6 -22 6,9 -25

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation TW[t] = + 8.1012229396126 + 0.0364793011773642CV[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) 8.1012229396126 0.13096 61.8602 0 0 CV 0.0364793011773642 0.017241 2.1159 0.038657 0.019329 Multiple Linear Regression - Regression Statistics Multiple R 0.267691926597655 R-squared 0.0716589675655644 Adjusted R-squared 0.0556530876960053 F-TEST (value) 4.47704019707464 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.0386571938482783 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.722321951349474 Sum Squared Residuals 30.2614420812761 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.9 7.99178503608054 0.90821496391946 2 8.8 8.06474363843524 0.735256361564756 3 8.3 7.99178503608052 0.308214963919485 4 7.5 7.95530573490315 -0.455305734903152 5 7.2 7.88234713254842 -0.682347132548423 6 7.4 8.1012229396126 -0.701222939612608 7 8.8 7.95530573490315 0.844694265096849 8 9.3 8.02826433725788 1.27173566274212 9 9.3 8.02826433725788 1.27173566274212 10 8.7 7.88234713254842 0.817652867451576 11 8.2 7.84586783137106 0.35413216862894 12 8.3 7.88234713254842 0.417652867451577 13 8.5 7.88234713254842 0.617652867451577 14 8.6 7.99178503608052 0.608214963919484 15 8.5 8.02826433725788 0.47173566274212 16 8.2 7.91882643372579 0.281173566274212 17 8.1 7.6999506266616 0.400049373338397 18 7.9 7.6999506266616 0.200049373338398 19 8.6 7.6999506266616 0.900049373338397 20 8.7 7.73642992783897 0.963570072161033 21 8.7 7.59051272312951 1.10948727687049 22 8.5 7.8093885301937 0.690611469806305 23 8.4 7.77290922901633 0.62709077098367 24 8.5 7.91882643372579 0.581173566274212 25 8.7 8.06474363843524 0.635256361564755 26 8.7 8.02826433725788 0.671735662742119 27 8.6 7.91882643372579 0.681173566274212 28 8.5 7.95530573490315 0.544694265096848 29 8.3 7.88234713254842 0.417652867451577 30 8 8.02826433725788 -0.0282643372578802 31 8.2 8.02826433725788 0.171735662742119 32 8.1 8.02826433725788 0.0717356627421195 33 8.1 8.02826433725788 0.0717356627421195 34 8 8.17418154196734 -0.174181541967337 35 7.9 8.13770224078997 -0.237702240789972 36 7.9 7.8093885301937 0.0906114698063053 37 8 8.06474363843524 -0.0647436384352444 38 8 8.13770224078997 -0.137702240789973 39 7.9 8.06474363843524 -0.164743638435244 40 8 8.17418154196734 -0.174181541967337 41 7.7 8.17418154196734 -0.474181541967337 42 7.2 8.13770224078997 -0.937702240789973 43 7.5 8.06474363843524 -0.564743638435244 44 7.3 8.02826433725788 -0.72826433725788 45 7 8.02826433725788 -1.02826433725788 46 7 8.06474363843524 -1.06474363843524 47 7 7.8093885301937 -0.809388530193695 48 7.2 7.95530573490315 -0.755305734903152 49 7.3 7.88234713254842 -0.582347132548424 50 7.1 7.99178503608052 -0.891785036080516 51 6.8 7.99178503608052 -1.19178503608052 52 6.4 7.84586783137106 -1.44586783137106 53 6.1 7.77290922901633 -1.67290922901633 54 6.5 7.6999506266616 -1.19995062666160 55 7.7 7.62699202430687 0.0730079756931261 56 7.9 7.6999506266616 0.200049373338398 57 7.5 7.77290922901633 -0.272909229016331 58 6.9 7.48107481959742 -0.581074819597417 59 6.6 7.2986783137106 -0.698678313710597 60 6.9 7.1892404101785 -0.289240410178503 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.230740640539454 0.461481281078907 0.769259359460546 6 0.662326067688564 0.675347864622872 0.337673932311436 7 0.677380959116634 0.645238081766732 0.322619040883366 8 0.759396814749994 0.481206370500012 0.240603185250006 9 0.800503149458044 0.398993701083912 0.199496850541956 10 0.784468967216571 0.431062065566858 0.215531032783429 11 0.70631200844958 0.58737598310084 0.29368799155042 12 0.623356963165325 0.75328607366935 0.376643036834675 13 0.556269113986722 0.887461772026557 0.443730886013278 14 0.486522571687608 0.973045143375216 0.513477428312392 15 0.411251507618763 0.822503015237527 0.588748492381237 16 0.335103806789112 0.670207613578223 0.664896193210888 17 0.268775466690538 0.537550933381075 0.731224533309462 18 0.205584992821136 0.411169985642271 0.794415007178864 19 0.215129921706403 0.430259843412805 0.784870078293597 20 0.232652640103966 0.465305280207933 0.767347359896034 21 0.284671832921595 0.56934366584319 0.715328167078405 22 0.272229087304613 0.544458174609225 0.727770912695388 23 0.26126808682034 0.52253617364068 0.73873191317966 24 0.249027031597952 0.498054063195903 0.750972968402049 25 0.251631772536242 0.503263545072485 0.748368227463758 26 0.273014513605216 0.546029027210432 0.726985486394784 27 0.316037450671231 0.632074901342461 0.68396254932877 28 0.348968740081153 0.697937480162306 0.651031259918847 29 0.378858437202345 0.75771687440469 0.621141562797655 30 0.369251588564583 0.738503177129165 0.630748411435417 31 0.370510195732102 0.741020391464205 0.629489804267898 32 0.369265493634460 0.738530987268919 0.63073450636554 33 0.37447253194434 0.74894506388868 0.62552746805566 34 0.353861589369433 0.707723178738865 0.646138410630567 35 0.334959314390456 0.669918628780913 0.665040685609544 36 0.379810123766068 0.759620247532135 0.620189876233932 37 0.391166725830365 0.78233345166073 0.608833274169635 38 0.400530710634688 0.801061421269376 0.599469289365312 39 0.423912747103113 0.847825494206225 0.576087252896887 40 0.472845074233105 0.94569014846621 0.527154925766895 41 0.485218027786209 0.970436055572417 0.514781972213791 42 0.505616921268371 0.988766157463257 0.494383078731629 43 0.508864515724596 0.982270968550807 0.491135484275404 44 0.509806936534026 0.980386126931948 0.490193063465974 45 0.527864844517566 0.944270310964868 0.472135155482434 46 0.51834902579332 0.96330194841336 0.48165097420668 47 0.528402161328307 0.943195677343386 0.471597838671693 48 0.48467309602635 0.9693461920527 0.51532690397365 49 0.445942779589618 0.891885559179236 0.554057220410382 50 0.389382268015678 0.778764536031356 0.610617731984322 51 0.346388144988698 0.692776289977395 0.653611855011302 52 0.419661542278882 0.839323084557763 0.580338457721118 53 0.753384796282985 0.49323040743403 0.246615203717015 54 0.942776094668207 0.114447810663587 0.0572239053317933 55 0.886246104775537 0.227507790448927 0.113753895224463 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:

http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/10y58f1260811411.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/10y58f1260811411.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/1169x1260811411.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/1169x1260811411.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/2elcn1260811411.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/2elcn1260811411.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/3vh3e1260811411.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/3vh3e1260811411.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/4d3uz1260811411.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/4d3uz1260811411.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/5uieo1260811411.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/5uieo1260811411.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/6pspq1260811411.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/6pspq1260811411.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/7fhq51260811411.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/7fhq51260811411.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/88uhf1260811411.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/88uhf1260811411.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/9jipn1260811411.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/14/t126081152952tuian1fkwrnad/9jipn1260811411.ps (open in new window)

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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