Model 2

*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, 19 Nov 2009 14:13:22 -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/Nov/19/t1258665286qo5vqbk2vx2sosy.htm/, Retrieved Thu, 19 Nov 2009 22:14:59 +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/2009/Nov/19/t1258665286qo5vqbk2vx2sosy.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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4.3 96.2 4.1 96.8 3.9 109.9 3.8 88 3.7 91.1 3.7 106.4 4.1 68.6 4.1 100.1 3.8 108 3.7 106 3.5 108.6 3.6 91.5 4.1 99.2 3.8 98 3.7 96.6 3.6 102.8 3.3 96.9 3.4 110 3.7 70.5 3.7 101.9 3.4 109.6 3.3 107.8 3 113 3 93.8 3.3 108 3 102.8 2.9 116.3 2.8 89.2 2.5 106.7 2.6 112.1 2.8 74.2 2.7 108.8 2.4 111.5 2.2 118.8 2.1 118.9 2.1 97.6 2.3 116.4 2.1 107.9 2 121.2 1.9 97.9 1.7 113.4 1.8 117.6 2.1 79.6 2 115.9 1.8 115.7 1.7 129.1 1.6 123.3 1.6 96.7 1.8 121.2 1.7 118.2 1.7 102.1 1.5 125.4 1.5 116.7 1.5 121.3 1.8 85.3 1.8 114.2 1.7 124.4 1.7 131 1.8 118.3 2 99.6

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation unempl[t] = + 10.7362119129704 -0.0863544648682223proman[t] + 1.76734118577122M1[t] + 1.24855473732718M2[t] + 1.53542273993681M3[t] + 0.676228520664832M4[t] + 0.867552719598188M5[t] + 1.66329276027544M6[t] -1.30436019033809M7[t] + 1.46561409647386M8[t] + 1.71438036762800M9[t] + 2.02024635250865M10[t] + 1.71717488698802M11[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) 10.7362119129704 0.906545 11.843 0 0 proman -0.0863544648682223 0.009075 -9.5155 0 0 M1 1.76734118577122 0.3785 4.6693 2.5e-05 1.3e-05 M2 1.24855473732718 0.37041 3.3707 0.001507 0.000753 M3 1.53542273993681 0.381345 4.0263 0.000205 0.000103 M4 0.676228520664832 0.364134 1.8571 0.069572 0.034786 M5 0.867552719598188 0.370851 2.3394 0.023618 0.011809 M6 1.66329276027544 0.395358 4.2071 0.000115 5.8e-05 M7 -1.30436019033809 0.405322 -3.2181 0.00234 0.00117 M8 1.46561409647386 0.378446 3.8727 0.000332 0.000166 M9 1.71438036762800 0.396692 4.3217 8e-05 4e-05 M10 2.02024635250865 0.416075 4.8555 1.4e-05 7e-06 M11 1.71717488698802 0.406893 4.2202 0.000111 5.5e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.823911288067274 R-squared 0.678829810604674 Adjusted R-squared 0.596828911184591 F-TEST (value) 8.27832152336635 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 4.40602229145881e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.571577374027925 Sum Squared Residuals 15.3549326415309 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4.3 4.1962535784187 0.103746421581302 2 4.1 3.62565445105368 0.474345548946316 3 3.9 2.78127896388961 1.11872103611039 4 3.8 3.81324752523170 -0.0132475252316958 5 3.7 3.73687288307356 -0.0368728830735613 6 3.7 3.21138961126701 0.488610388732986 7 4.1 3.50793543267229 0.592064567327714 8 4.1 3.55774407613524 0.542255923864763 9 3.8 3.12431007483042 0.675689925169582 10 3.7 3.60288498944751 0.0971150105524916 11 3.5 3.0752919152695 0.424708084730501 12 3.6 2.83477837752808 0.765221622471915 13 4.1 3.93719018381399 0.162809816186008 14 3.8 3.52202909321182 0.277970906788181 15 3.7 3.92979334663697 -0.229793346636966 16 3.6 2.53520144518200 1.06479855481800 17 3.3 3.23601698683787 0.0639830131621285 18 3.4 2.90051353774141 0.499486462258587 19 3.7 3.34386194942266 0.356138050577338 20 3.7 3.40230603937244 0.297693960627565 21 3.4 2.98614293104126 0.413857068958737 22 3.3 3.44744695268471 -0.147446952684708 23 3 2.69533226984932 0.30466773015068 24 3 2.63616310833117 0.363836891668826 25 3.3 3.17727089297364 0.122729107026363 26 3 3.10752766184435 -0.107527661844352 27 2.9 2.22861038873299 0.671389611267013 28 2.8 3.70962216738983 -0.909622167389827 29 2.5 2.38974323112929 0.110256768870707 30 2.6 2.71916916151815 -0.119169161518147 31 2.8 3.02435042941024 -0.224350429410240 32 2.7 2.8064602317817 -0.106460231781703 33 2.4 2.82206944779164 -0.42206944779164 34 2.2 2.49754783913426 -0.297547839134261 35 2.1 2.18584092712681 -0.0858409271268084 36 2.1 2.30801614183193 -0.208016141831930 37 2.3 2.45189338808057 -0.151893388080570 38 2.1 2.66711989101642 -0.567119891016417 39 2 1.80547351087870 0.194526489121303 40 1.9 2.95833832303629 -1.05833832303629 41 1.7 1.81116831651220 -0.111168316512204 42 1.8 2.24421960474292 -0.444219604742924 43 2.1 2.55803631912184 -0.45803631912184 44 2 2.19334353121732 -0.193343531217323 45 1.8 2.45938069534511 -0.659380695345107 46 1.7 1.60809685099157 0.091903149008428 47 1.6 1.80588128170663 -0.20588128170663 48 1.6 2.38573516021333 -0.785735160213329 49 1.8 2.03739195671310 -0.237391956713103 50 1.7 1.77766890287373 -0.077668902873728 51 1.7 3.45484378986174 -1.75484378986174 52 1.5 0.58359053916018 0.91640946083982 53 1.5 1.52619858244707 -0.0261985824470702 54 1.5 1.9247080847305 -0.424708084730502 55 1.8 2.06581586937297 -0.265815869372972 56 1.8 2.3401461214933 -0.540146121493301 57 1.7 1.70809685099157 -0.0080968509915719 58 1.7 1.44402336774195 0.255976632258050 59 1.8 2.23765360604774 -0.437653606047742 60 2 2.13530721209548 -0.135307212095485 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0582863136775296 0.116572627355059 0.94171368632247 17 0.0421536801067582 0.0843073602135165 0.957846319893242 18 0.0257098329130918 0.0514196658261836 0.974290167086908 19 0.0236956741249314 0.0473913482498628 0.976304325875069 20 0.0226911532318448 0.0453823064636895 0.977308846768155 21 0.0256932593873758 0.0513865187747517 0.974306740612624 22 0.0217445071547057 0.0434890143094113 0.978255492845294 23 0.0266546371420014 0.0533092742840027 0.973345362857999 24 0.0519606546763428 0.103921309352686 0.948039345323657 25 0.108844498128987 0.217688996257975 0.891155501871012 26 0.233068005095795 0.466136010191589 0.766931994904205 27 0.426767598545104 0.853535197090208 0.573232401454896 28 0.749899479244073 0.500201041511854 0.250100520755927 29 0.79162160299797 0.416756794004059 0.208378397002030 30 0.90441847076856 0.191163058462879 0.0955815292314396 31 0.962549376406415 0.0749012471871693 0.0374506235935846 32 0.988149866749536 0.0237002665009275 0.0118501332504638 33 0.996196241718742 0.00760751656251572 0.00380375828125786 34 0.995960042415628 0.00807991516874396 0.00403995758437198 35 0.996428461654079 0.00714307669184253 0.00357153834592126 36 0.996490716951788 0.007018566096423 0.0035092830482115 37 0.996969935624565 0.00606012875086924 0.00303006437543462 38 0.996632470527194 0.00673505894561256 0.00336752947280628 39 0.99851661940334 0.00296676119332109 0.00148338059666055 40 0.997132052279432 0.00573589544113611 0.00286794772056805 41 0.991887431358986 0.0162251372820272 0.0081125686410136 42 0.984875120781473 0.0302497584370533 0.0151248792185266 43 0.971319598702153 0.0573608025956931 0.0286804012978466 44 0.932759078023476 0.134481843953048 0.067240921976524 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.275862068965517 NOK 5% type I error level 14 0.482758620689655 NOK 10% type I error level 20 0.689655172413793 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/10wt721258665198.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/10wt721258665198.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/1f1fd1258665198.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/1f1fd1258665198.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/2z4f71258665198.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/2z4f71258665198.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/33z9t1258665198.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/33z9t1258665198.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/401871258665198.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/401871258665198.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/568g31258665198.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/568g31258665198.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/6wvyi1258665198.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/6wvyi1258665198.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/74wyb1258665198.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/74wyb1258665198.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/8pk3r1258665198.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/8pk3r1258665198.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/9jnte1258665198.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665286qo5vqbk2vx2sosy/9jnte1258665198.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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