Ws 7

*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: Fri, 20 Nov 2009 08:39:05 -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/20/t1258731594ee7k6cf46ar7un1.htm/, Retrieved Fri, 20 Nov 2009 16:40:06 +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/20/t1258731594ee7k6cf46ar7un1.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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100 100 96.21064363 97.82226485 96.31280765 94.04971502 107.1793443 91.12460521 114.9066592 93.13202153 92.56060184 93.88342812 114.9995356 92.55349954 107.1236185 94.43494835 117.7765394 96.25017563 107.3650971 100.4355715 106.2970187 101.5036685 114.5072908 99.39789728 98.0031578 99.68990733 103.0649206 101.6895041 100.2879168 103.6652759 104.6066685 103.0532766 111.1544534 100.9500712 104.9874617 102.345366 109.9284852 101.6472299 111.5352466 99.56809393 132.4974459 95.67727392 100.3436426 96.58494865 123.0983561 96.32604937 114.2379493 95.37109101 104.569518 96.00056203 109.0833101 96.88367859 106.9843039 94.85280372 133.6769759 92.46943974 124.8537197 93.99180173 122.5132349 93.45262168 116.8013374 92.26698759 116.0118882 90.39653498 129.7575926 90.43001228 125.1973623 91.04995327 143.7912139 89.07845784 127.9465032 89.69314509 130.2962757 87.92459054 108.4424631 85.8789319 129.3675118 83.20612366 143.6797622 83.85722053 131.8844618 83.01393462 117.6186496 82.8450 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Import[t] = + 221.603731633023 -1.11914602538942Wisselkoers[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) 221.603731633023 24.267354 9.1318 0 0 Wisselkoers -1.11914602538942 0.262683 -4.2605 7.6e-05 3.8e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.488221079733609 R-squared 0.238359822696251 Adjusted R-squared 0.225228095501359 F-TEST (value) 18.1514449058129 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 7.58171647913253e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 14.0068187711533 Sum Squared Residuals 11379.0763811000 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 109.689129094081 -9.68912909408146 2 96.21064363 112.126332731555 -15.9156891015547 3 96.31280765 116.348366879383 -20.0355592293827 4 107.1793443 119.621991897072 -12.4426475970718 5 114.9066592 117.375399901242 -2.46874070124189 6 92.56060184 116.534466202592 -23.973864362592 7 114.9995356 118.022850486951 -3.02331488695078 8 107.1236185 115.917234529266 -8.79361602926562 9 117.7765394 113.885730133675 3.89080926632483 10 107.3650971 109.201660981083 -1.83656388108339 11 106.2970187 108.006304468803 -1.70928576880302 12 114.5072908 110.362969960045 4.14432083995456 13 98.0031578 110.036168073214 -12.0330102732142 14 103.0649206 107.798327295687 -4.73340669568717 15 100.2879168 105.587150138641 -5.29923333864065 16 104.6066685 106.272066722777 -1.66539822277676 17 111.1544534 108.625860686764 2.52859271323566 18 104.9874617 107.064322057098 -2.07686035709781 19 109.9284852 107.845638298594 2.08284690140632 20 111.5352466 110.172495055663 1.36275154433664 21 132.4974459 114.526890805360 17.9705550946395 22 100.3436426 113.511070238935 -13.1674276389346 23 123.0983561 113.800816339123 9.29753976087727 24 114.2379493 114.869554192129 -0.631604892129148 25 104.569518 114.165084201998 -9.59556620199832 26 109.0833101 113.176747813919 -4.09343771391873 27 106.9843039 115.449593352743 -8.4652894527425 28 133.6769759 118.116925678016 15.5600502219842 29 124.8537197 116.413180307703 8.44053939229662 30 122.5132349 117.016601517630 5.49663338236984 31 116.8013374 118.343499197020 -1.54216179701986 32 116.0118882 120.436808801181 -4.42492060118062 33 129.7575926 120.399342813945 9.35824978605516 34 125.1973623 119.705538319010 5.49182398098963 35 143.7912139 121.911929593568 21.8792843064317 36 127.9465032 121.224004800873 6.72249839912677 37 130.2962757 123.20327559619 7.09300010380989 38 108.4424631 125.492666332450 -17.0502032324496 39 129.3675118 128.483929050874 0.883582749126271 40 143.6797622 127.755256576670 15.9245056233303 41 131.8844618 128.699016651113 3.18544514888687 42 117.6186496 128.88798744562 -11.2693378456200 43 118.9560695 133.539649844881 -14.5835803448814 44 104.8202842 134.792025739201 -29.9717415392007 45 134.624315 133.709477422795 0.914837577205257 46 140.401226 133.686779059788 6.71444694021177 47 143.8005015 134.896629688201 8.90387181179927 48 153.4317823 130.293372257086 23.1384100429143 49 153.2924677 126.44914296101 26.8433247389899 50 127.3149438 118.963602341644 8.35134145835622 51 153.5525216 114.207199256121 39.3453223438789 52 136.9276493 119.932795524321 16.9948537756787 53 131.7730101 118.320117136515 13.4528929634850 54 144.3391845 114.652415544160 29.6867689558395 55 107.4208229 116.824212302780 -9.40338940278017 56 113.6249652 117.936432582523 -4.31146738252315 57 124.2221603 121.4299310046 2.79222929539996 58 102.0618557 124.045850141977 -21.9839944419766 59 96.36853348 124.544393928381 -28.1758604483806 60 111.6838488 125.768875443761 -14.0850266437610 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.173143306004147 0.346286612008295 0.826856693995853 6 0.185960863569801 0.371921727139602 0.814039136430199 7 0.162237749210483 0.324475498420966 0.837762250789517 8 0.0942392899482458 0.188478579896492 0.905760710051754 9 0.144189888403412 0.288379776806824 0.855810111596588 10 0.0986368407787577 0.197273681557515 0.901363159221242 11 0.0594050696074827 0.118810139214965 0.940594930392517 12 0.0487310349469404 0.0974620698938808 0.95126896505306 13 0.0368653303971662 0.0737306607943324 0.963134669602834 14 0.0207298579391103 0.0414597158782206 0.97927014206089 15 0.0118095531224210 0.0236191062448421 0.98819044687758 16 0.00626345971527014 0.0125269194305403 0.99373654028473 17 0.00413705758288181 0.00827411516576362 0.995862942417118 18 0.00211198601462723 0.00422397202925446 0.997888013985373 19 0.0012119849852575 0.002423969970515 0.998788015014743 20 0.000730106819765163 0.00146021363953033 0.999269893180235 21 0.00940698765410692 0.0188139753082138 0.990593012345893 22 0.00843081916546616 0.0168616383309323 0.991569180834534 23 0.0103518161363753 0.0207036322727506 0.989648183863625 24 0.00669743069802546 0.0133948613960509 0.993302569301975 25 0.00515955778335027 0.0103191155667005 0.99484044221665 26 0.00343860347600537 0.00687720695201074 0.996561396523995 27 0.00265880094210373 0.00531760188420745 0.997341199057896 28 0.00758948427083864 0.0151789685416773 0.992410515729161 29 0.00689006645872663 0.0137801329174533 0.993109933541273 30 0.0050348906533944 0.0100697813067888 0.994965109346606 31 0.00318990919841279 0.00637981839682557 0.996810090801587 32 0.00204642294711697 0.00409284589423394 0.997953577052883 33 0.00169903877169550 0.00339807754339100 0.998300961228304 34 0.00106154769797632 0.00212309539595264 0.998938452302024 35 0.00264815310873198 0.00529630621746395 0.997351846891268 36 0.00152818509256722 0.00305637018513445 0.998471814907433 37 0.00084342862746298 0.00168685725492596 0.999156571372537 38 0.00187068263186045 0.0037413652637209 0.99812931736814 39 0.000990529304032918 0.00198105860806584 0.999009470695967 40 0.00108512875989066 0.00217025751978132 0.99891487124011 41 0.000570406121873627 0.00114081224374725 0.999429593878126 42 0.000518172663039628 0.00103634532607926 0.99948182733696 43 0.000513708702069692 0.00102741740413938 0.99948629129793 44 0.00317494163437145 0.0063498832687429 0.996825058365629 45 0.00172033955052016 0.00344067910104031 0.99827966044948 46 0.00108409142375942 0.00216818284751883 0.99891590857624 47 0.000911807547220216 0.00182361509444043 0.99908819245278 48 0.00911488917097907 0.0182297783419581 0.99088511082902 49 0.310653394535344 0.621306789070689 0.689346605464656 50 0.232991474225611 0.465982948451223 0.767008525774389 51 0.377262362439920 0.754524724879841 0.62273763756008 52 0.46400633966624 0.92801267933248 0.53599366033376 53 0.417381568613655 0.83476313722731 0.582618431386345 54 0.682783502747706 0.634432994504588 0.317216497252294 55 0.59096206517949 0.81807586964102 0.40903793482051 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 23 0.450980392156863 NOK 5% type I error level 35 0.686274509803922 NOK 10% type I error level 37 0.725490196078431 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/10qzey1258731540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/10qzey1258731540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/17kt81258731540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/17kt81258731540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/28uyn1258731540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/28uyn1258731540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/36f9v1258731540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/36f9v1258731540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/4mq7k1258731540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/4mq7k1258731540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/5xoj81258731540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/5xoj81258731540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/63n071258731540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/63n071258731540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/7e0ae1258731540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/7e0ae1258731540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/8gdvj1258731540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/8gdvj1258731540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/9prct1258731540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731594ee7k6cf46ar7un1/9prct1258731540.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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