Workshop7

*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 11:30:59 -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/t1258655672fx4vs250r56uqt8.htm/, Retrieved Thu, 19 Nov 2009 19:34:45 +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/t1258655672fx4vs250r56uqt8.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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5.4 2.7 5.4 2.5 5.6 2.2 5.7 2.9 5.8 3.1 5.8 3 5.8 2.8 5.9 2.5 6.1 1.9 6.4 1.9 6.4 1.8 6.3 2 6.2 2.6 6.2 2.5 6.3 2.5 6.4 1.6 6.5 1.4 6.6 0.8 6.6 1.1 6.6 1.3 6.8 1.2 7 1.3 7.2 1.1 7.3 1.3 7.5 1.2 7.6 1.6 7.6 1.7 7.7 1.5 7.7 0.9 7.7 1.5 7.7 1.4 7.6 1.6 7.7 1.7 7.9 1.4 7.9 1.8 7.9 1.7 7.8 1.4 7.6 1.2 7.4 1 7 1.7 7 2.4 7.2 2 7.5 2.1 7.8 2 7.8 1.8 7.7 2.7 7.6 2.3 7.6 1.9 7.5 2 7.5 2.3 7.6 2.8 7.6 2.4 7.9 2.3 7.6 2.7 7.5 2.7 7.5 2.9 7.6 3 7.7 2.2 7.8 2.3 7.9 2.8 7.9 2.8

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 Y[t] = + 7.70546056652312 -0.309228385849713X[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) 7.70546056652312 0.324173 23.7696 0 0 X -0.309228385849713 0.155277 -1.9915 0.051066 0.025533 Multiple Linear Regression - Regression Statistics Multiple R 0.250968372521009 R-squared 0.0629851240058438 Adjusted R-squared 0.0471035159381463 F-TEST (value) 3.96591602924345 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.0510662732824697 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.74579163111317 Sum Squared Residuals 32.8161042652681 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5.4 6.8705439247289 -1.47054392472889 2 5.4 6.93238960189883 -1.53238960189883 3 5.6 7.02515811765375 -1.42515811765375 4 5.7 6.80869824755895 -1.10869824755895 5 5.8 6.746852570389 -0.946852570389005 6 5.8 6.77777540897398 -0.977775408973976 7 5.8 6.83962108614392 -1.03962108614392 8 5.9 6.93238960189883 -1.03238960189883 9 6.1 7.11792663340866 -1.01792663340866 10 6.4 7.11792663340866 -0.717926633408661 11 6.4 7.14884947199363 -0.748849471993632 12 6.3 7.08700379482369 -0.78700379482369 13 6.2 6.90146676331386 -0.701466763313861 14 6.2 6.93238960189883 -0.732389601898833 15 6.3 6.93238960189883 -0.632389601898833 16 6.4 7.21069514916358 -0.810695149163575 17 6.5 7.27254082633352 -0.772540826333518 18 6.6 7.45807785784335 -0.858077857843347 19 6.6 7.36530934208843 -0.765309342088433 20 6.6 7.30346366491849 -0.70346366491849 21 6.8 7.33438650350346 -0.534386503503461 22 7 7.30346366491849 -0.303463664918490 23 7.2 7.36530934208843 -0.165309342088432 24 7.3 7.30346366491849 -0.00346366491848968 25 7.5 7.33438650350346 0.165613496496539 26 7.6 7.21069514916358 0.389304850836424 27 7.6 7.1797723105786 0.420227689421396 28 7.7 7.24161798774855 0.458382012251453 29 7.7 7.42715501925838 0.272844980741625 30 7.7 7.24161798774855 0.458382012251453 31 7.7 7.27254082633352 0.427459173666482 32 7.6 7.21069514916358 0.389304850836424 33 7.7 7.1797723105786 0.520227689421396 34 7.9 7.27254082633352 0.627459173666482 35 7.9 7.14884947199363 0.751150528006368 36 7.9 7.1797723105786 0.720227689421396 37 7.8 7.27254082633352 0.527459173666482 38 7.6 7.33438650350346 0.265613496496539 39 7.4 7.39623218067340 0.0037678193265967 40 7 7.1797723105786 -0.179772310578604 41 7 6.96331244048380 0.0366875595161955 42 7.2 7.08700379482369 0.112996205176310 43 7.5 7.05608095623872 0.443919043761281 44 7.8 7.08700379482369 0.71299620517631 45 7.8 7.14884947199363 0.651150528006367 46 7.7 6.87054392472889 0.82945607527111 47 7.6 6.99423527906878 0.605764720931224 48 7.6 7.11792663340866 0.482073366591338 49 7.5 7.08700379482369 0.41299620517631 50 7.5 6.99423527906878 0.505764720931224 51 7.6 6.83962108614392 0.760378913856081 52 7.6 6.96331244048380 0.636687559516195 53 7.9 6.99423527906878 0.905764720931225 54 7.6 6.87054392472889 0.72945607527111 55 7.5 6.87054392472889 0.62945607527111 56 7.5 6.80869824755895 0.691301752441053 57 7.6 6.77777540897398 0.822224591026024 58 7.7 7.02515811765375 0.674841882346253 59 7.8 6.99423527906878 0.805764720931224 60 7.9 6.83962108614392 1.06037891385608 61 7.9 6.83962108614392 1.06037891385608 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0250964473554747 0.0501928947109494 0.974903552644525 6 0.00808807516244473 0.0161761503248895 0.991911924837555 7 0.00362281824282741 0.00724563648565482 0.996377181757173 8 0.00575133818551505 0.0115026763710301 0.994248661814485 9 0.0143788168960079 0.0287576337920157 0.985621183103992 10 0.0288702907906709 0.0577405815813417 0.97112970920933 11 0.0258361420904582 0.0516722841809165 0.974163857909542 12 0.0222763819609424 0.0445527639218847 0.977723618039058 13 0.0393816861219376 0.0787633722438753 0.960618313878062 14 0.0668008641661893 0.133601728332379 0.93319913583381 15 0.148875668308548 0.297751336617096 0.851124331691452 16 0.176396926584069 0.352793853168139 0.82360307341593 17 0.203264153808227 0.406528307616454 0.796735846191773 18 0.216057773958275 0.432115547916549 0.783942226041725 19 0.267248008247553 0.534496016495107 0.732751991752447 20 0.41092928092471 0.82185856184942 0.58907071907529 21 0.573923001319233 0.852153997361534 0.426076998680767 22 0.7728524432904 0.4542951134192 0.2271475567096 23 0.875509536159304 0.248980927681392 0.124490463840696 24 0.954212949923442 0.091574100153117 0.0457870500765585 25 0.982421326803677 0.0351573463926463 0.0175786731963232 26 0.99749170260408 0.00501659479183989 0.00250829739591994 27 0.999493670515697 0.00101265896860615 0.000506329484303077 28 0.999799622669284 0.000400754661432992 0.000200377330716496 29 0.999707960862645 0.000584078274710898 0.000292039137355449 30 0.999809315256334 0.000381369487332947 0.000190684743666474 31 0.999817234786196 0.000365530427608374 0.000182765213804187 32 0.999815782399926 0.000368435200148294 0.000184217600074147 33 0.999853065793984 0.000293868412032814 0.000146934206016407 34 0.999903303322528 0.000193393354943482 9.6696677471741e-05 35 0.999962288705724 7.54225885522416e-05 3.77112942761208e-05 36 0.999981516437207 3.69671255865273e-05 1.84835627932637e-05 37 0.999981627305686 3.6745388627857e-05 1.83726943139285e-05 38 0.99996396756905 7.20648618987994e-05 3.60324309493997e-05 39 0.999912832345487 0.000174335309026698 8.7167654513349e-05 40 0.999964467091453 7.10658170942894e-05 3.55329085471447e-05 41 0.999998647473245 2.70505351066890e-06 1.35252675533445e-06 42 0.99999983477046 3.30459081437200e-07 1.65229540718600e-07 43 0.999999776918393 4.46163214019253e-07 2.23081607009627e-07 44 0.99999962322536 7.53549279051757e-07 3.76774639525879e-07 45 0.99999929529489 1.40941022172089e-06 7.04705110860446e-07 46 0.999998721258512 2.55748297697487e-06 1.27874148848743e-06 47 0.999996178287939 7.64342412227562e-06 3.82171206113781e-06 48 0.99998634159904 2.7316801918806e-05 1.3658400959403e-05 49 0.999971626301324 5.67473973513371e-05 2.83736986756686e-05 50 0.999957617940749 8.47641185018995e-05 4.23820592509497e-05 51 0.999868815800677 0.000262368398646118 0.000131184199323059 52 0.999647590370066 0.000704819259868467 0.000352409629934233 53 0.999033139145377 0.00193372170924613 0.000966860854623063 54 0.996646245815013 0.00670750836997378 0.00335375418498689 55 0.992747936783641 0.0145041264327172 0.00725206321635859 56 0.98768159889554 0.0246368022089197 0.0123184011044599 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 30 0.576923076923077 NOK 5% type I error level 37 0.711538461538462 NOK 10% type I error level 42 0.807692307692308 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/109ygy1258655455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/109ygy1258655455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/1s3q61258655455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/1s3q61258655455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/2nn5l1258655455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/2nn5l1258655455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/36iko1258655455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/36iko1258655455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/4gdmo1258655455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/4gdmo1258655455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/5w2v71258655455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/5w2v71258655455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/6ua1x1258655455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/6ua1x1258655455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/7d5781258655455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/7d5781258655455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/823h11258655455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/823h11258655455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/9e8rj1258655455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258655672fx4vs250r56uqt8/9e8rj1258655455.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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