*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, 13 Dec 2010 19:23:19 +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/13/t1292268255fg0fw0fcgqok2jy.htm/, Retrieved Mon, 13 Dec 2010 20:24:25 +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/13/t1292268255fg0fw0fcgqok2jy.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:

» Textbox « » Textfile « » CSV «

15 15 11 12 13 6 1 9 12 12 7 11 4 0 12 15 12 13 14 6 0 15 12 11 11 12 5 0 17 14 11 16 12 5 0 14 8 10 10 6 4 0 9 11 11 15 10 5 1 12 15 9 5 11 3 1 11 4 10 4 10 2 0 13 13 12 7 12 5 0 16 19 12 15 15 6 1 16 10 12 5 13 6 1 10 6 9 15 11 6 0 16 7 12 13 12 3 1 12 14 12 13 13 6 0 15 16 12 15 14 6 0 13 16 12 15 16 7 1 18 14 13 10 16 8 1 13 15 11 17 16 6 0 17 14 12 14 15 7 1 14 12 12 9 13 4 1 13 9 15 6 8 4 0 13 12 11 11 14 2 1 15 14 12 13 15 6 1 13 12 10 12 13 6 1 15 14 11 10 16 6 1 13 10 13 4 13 6 1 14 14 6 13 12 6 1 13 16 12 15 15 7 1 14 8 10 10 14 3 1 15 11 12 7 13 6 1 9 8 11 9 12 4 0 16 13 9 14 14 6 0 16 11 10 5 13 3 1 13 16 12 16 14 6 0 17 16 11 14 15 6 1 15 13 12 16 16 6 1 14 14 11 15 15 8 1 10 5 14 4 5 2 0 13 14 10 12 15 6 0 16 14 11 15 16 6 0 16 14 11 15 16 6 0 15 11 10 12 14 5 1 15 15 12 13 13 6 1 12 16 11 14 14 6 1 15 11 12 15 12 6 0 17 10 11 13 15 6 1 10 8 7 4 13 6 1 11 9 11 8 10 4 0 15 12 8 13 13 5 1 15 14 11 15 14 6 0 7 12 12 15 13 6 1 14 14 14 17 18 6 0 1 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 8 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Liked[t] = + 3.98257961482792 + 0.125971845421722Perceived_happiness[t] + 0.303663490104812Popularity[t] + 0.0425109403840113Finding_friends[t] + 0.101947787907027Knowing_people[t] + 0.308821987184138Celebrity[t] + 0.817714644076724Gender[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) 3.98257961482792 1.935829 2.0573 0.043385 0.021692 Perceived_happiness 0.125971845421722 0.093491 1.3474 0.18219 0.091095 Popularity 0.303663490104812 0.097995 3.0988 0.002798 0.001399 Finding_friends 0.0425109403840113 0.135155 0.3145 0.754051 0.377026 Knowing_people 0.101947787907027 0.075687 1.347 0.182336 0.091168 Celebrity 0.308821987184138 0.198098 1.5589 0.123521 0.06176 Gender 0.817714644076724 0.460074 1.7774 0.079854 0.039927 Multiple Linear Regression - Regression Statistics Multiple R 0.703702909052231 R-squared 0.495197784208573 Adjusted R-squared 0.451929022855022 F-TEST (value) 11.4446951730901 F-TEST (DF numerator) 6 F-TEST (DF denominator) 70 p-value 7.02814140218777e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.80833332620379 Sum Squared Residuals 228.904859306149 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13 14.7887500140159 -1.78875001401595 2 11 11.2193418535750 -0.219341853575041 3 14 13.7375785619651 0.262421438034914 4 12 12.6492751245336 -0.649275124533604 5 12 14.0182847351218 -2.01828473512181 6 6 10.8553686032175 -4.85536860321745 7 10 12.8152863576033 -2.81528635760329 8 11 12.6857121200811 -1.68571212008114 9 10 8.0334684047226 1.96653159527740 10 12 12.3357147125509 -0.335714712550877 11 15 16.477730123962 -1.47773012396199 12 13 12.7252808339484 0.274719166051585 13 11 10.8290262148403 0.170973785159657 14 12 11.7034067053378 0.296593294662223 15 13 13.4339150718603 -0.433915071860267 16 14 14.6230531641491 -0.62305316414911 17 16 15.4976461045665 0.502353895433471 18 16 15.3617723394985 0.638227660501471 19 16 14.2288306186309 1.77116938136910 20 15 15.2922587181368 -0.292258718136765 21 13 12.8708113005744 0.129188699425574 22 8 10.8378237981925 -2.8378237981925 23 14 12.2885801162145 1.71141988378553 24 15 14.6295452522022 0.370454747797843 25 13 13.5833049124740 -0.583304912474038 26 16 14.2811909480971 1.71880905190294 27 13 12.2879284501602 0.712071549839767 28 12 14.2485077644764 -2.24850776447637 29 15 15.4976461045665 -0.497646104566529 30 14 11.3642612601100 2.63573873988996 31 13 13.1068680544456 -0.106868054445558 32 12 10.1660725285858 1.83392747141417 33 14 13.6085539301973 0.391446069802666 34 13 12.0174564817328 0.98254351826721 35 14 14.4730572612127 -0.473057261212694 36 15 15.5482527707782 -0.548252770778241 37 16 14.6317251258184 1.36827487418158 38 15 15.2826020165788 -0.282602016578752 39 5 8.38120381094174 -3.38120381094174 40 15 13.3729172486069 1.62708275139306 41 16 14.0991870889772 1.90081291102280 42 16 14.0991870889772 1.90081291102280 43 14 13.2227631260285 0.777236873971468 44 13 14.9332087423070 -1.93320874230697 45 14 14.9183935436696 -0.91839354366963 46 12 13.1047357136250 -1.10473571362505 47 15 13.6243240422423 1.37567595775766 48 13 11.0476202913814 1.95237970861863 49 10 10.6197319216271 -0.619731921627061 50 13 13.5433525232723 -0.543352523272348 51 14 13.9732152435555 0.0267847564445257 52 13 13.2183390844328 -0.21833908443281 53 18 14.1786717950998 3.82132820490016 54 16 15.5785484584219 0.421451541578082 55 15 14.0385252812456 0.96147471875442 56 14 12.4299582877596 1.57004171224043 57 16 14.5434844012674 1.45651559873256 58 11 10.3772867126996 0.62271328730036 59 13 12.3463046951977 0.653695304802288 60 14 13.7545736342187 0.245426365781316 61 14 11.0453168953556 2.95468310464444 62 12 12.0432817352977 -0.0432817352976996 63 16 12.5394520687229 3.46054793127706 64 14 15.8427074020057 -1.84270740200570 65 12 13.9972393010702 -1.99723930107017 66 13 14.2389202280330 -1.23892022803296 67 13 13.3026265097172 -0.302626509717229 68 10 10.7461516097294 -0.746151609729437 69 15 15.3746528826079 -0.374652882607865 70 13 12.8467872430597 0.153212756940269 71 14 13.6043503465257 0.395649653474324 72 15 11.4436026382351 3.55639736176488 73 14 13.2321993696625 0.76780063033752 74 12 13.605911322256 -1.60591132225599 75 13 14.4780575386500 -1.47805753865003 76 14 13.1257336148809 0.874266385119072 77 4 10.0915880999005 -6.09158809990052 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.84192399953315 0.3161520009337 0.15807600046685 11 0.751820686841549 0.496358626316903 0.248179313158451 12 0.627200168964775 0.74559966207045 0.372799831035225 13 0.709298062947969 0.581403874104062 0.290701937052031 14 0.674746295349624 0.650507409300752 0.325253704650376 15 0.572189433607494 0.855621132785011 0.427810566392506 16 0.484192416250455 0.96838483250091 0.515807583749545 17 0.444726895556917 0.889453791113833 0.555273104443083 18 0.352965639249335 0.70593127849867 0.647034360750665 19 0.493382649022217 0.986765298044435 0.506617350977783 20 0.40380255210018 0.80760510420036 0.59619744789982 21 0.326864392899042 0.653728785798084 0.673135607100958 22 0.512692718402568 0.974614563194864 0.487307281597432 23 0.57202820006246 0.85594359987508 0.42797179993754 24 0.499710517599151 0.999421035198302 0.500289482400849 25 0.423337238793872 0.846674477587745 0.576662761206128 26 0.423295914788476 0.846591829576951 0.576704085211524 27 0.355195450082464 0.710390900164928 0.644804549917536 28 0.366906146311526 0.733812292623052 0.633093853688474 29 0.301659947877475 0.603319895754949 0.698340052122525 30 0.386347934191975 0.772695868383949 0.613652065808025 31 0.317136042503450 0.634272085006901 0.68286395749655 32 0.318572183306984 0.637144366613969 0.681427816693016 33 0.280458906978885 0.56091781395777 0.719541093021115 34 0.233738341566351 0.467476683132702 0.766261658433649 35 0.192060935598243 0.384121871196485 0.807939064401757 36 0.153410561749095 0.30682112349819 0.846589438250905 37 0.139251947372773 0.278503894745545 0.860748052627228 38 0.102993434355486 0.205986868710972 0.897006565644514 39 0.223019121654959 0.446038243309917 0.776980878345041 40 0.211344435873583 0.422688871747166 0.788655564126417 41 0.212551714571639 0.425103429143278 0.78744828542836 42 0.210201944374844 0.420403888749687 0.789798055625156 43 0.170935349721899 0.341870699443798 0.8290646502781 44 0.175795074898341 0.351590149796682 0.82420492510166 45 0.152201975969907 0.304403951939814 0.847798024030093 46 0.122019246860301 0.244038493720601 0.8779807531397 47 0.134208650148132 0.268417300296264 0.865791349851868 48 0.121716344000253 0.243432688000507 0.878283655999747 49 0.115847207835565 0.231694415671130 0.884152792164435 50 0.0871204463907079 0.174240892781416 0.912879553609292 51 0.0620793676114744 0.124158735222949 0.937920632388526 52 0.0453383958150519 0.0906767916301039 0.954661604184948 53 0.150887873768172 0.301775747536345 0.849112126231828 54 0.110547990263357 0.221095980526714 0.889452009736643 55 0.0855059370623598 0.171011874124720 0.91449406293764 56 0.0628738087193125 0.125747617438625 0.937126191280688 57 0.046196238773681 0.092392477547362 0.953803761226319 58 0.0297272079488836 0.0594544158977672 0.970272792051116 59 0.0192272967290860 0.0384545934581721 0.980772703270914 60 0.0118694983498478 0.0237389966996957 0.988130501650152 61 0.0480348176530805 0.096069635306161 0.95196518234692 62 0.0286915983737231 0.0573831967474462 0.971308401626277 63 0.157711305524631 0.315422611049262 0.84228869447537 64 0.149196911092176 0.298393822184352 0.850803088907824 65 0.500418233961791 0.999163532076417 0.499581766038209 66 0.509389633988693 0.981220732022614 0.490610366011307 67 0.456099411492222 0.912198822984445 0.543900588507778 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 2 0.0344827586206897 OK 10% type I error level 7 0.120689655172414 NOK

Charts produced by software:

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Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; 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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