*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 10:41:18 -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/t1258739719zply9go0510kk3c.htm/, Retrieved Fri, 20 Nov 2009 18:55:31 +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/t1258739719zply9go0510kk3c.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:

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No (this computation is public)

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Dataseries X:

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9.3 8.1 10.9 25.6 8.7 7.7 10 23.7 8.2 7.5 9.2 22 8.3 7.6 9.2 21.3 8.5 7.8 9.5 20.7 8.6 7.8 9.6 20.4 8.5 7.8 9.5 20.3 8.2 7.5 9.1 20.4 8.1 7.5 8.9 19.8 7.9 7.1 9 19.5 8.6 7.5 10.1 23.1 8.7 7.5 10.3 23.5 8.7 7.6 10.2 23.5 8.5 7.7 9.6 22.9 8.4 7.7 9.2 21.9 8.5 7.9 9.3 21.5 8.7 8.1 9.4 20.5 8.7 8.2 9.4 20.2 8.6 8.2 9.2 19.4 8.5 8.2 9 19.2 8.3 7.9 9 18.8 8 7.3 9 18.8 8.2 6.9 9.8 22.6 8.1 6.6 10 23.3 8.1 6.7 9.8 23 8 6.9 9.3 21.4 7.9 7 9 19.9 7.9 7.1 9 18.8 8 7.2 9.1 18.6 8 7.1 9.1 18.4 7.9 6.9 9.1 18.6 8 7 9.2 19.9 7.7 6.8 8.8 19.2 7.2 6.4 8.3 18.4 7.5 6.7 8.4 21.1 7.3 6.6 8.1 20.5 7 6.4 7.7 19.1 7 6.3 7.9 18.1 7 6.2 7.9 17 7.2 6.5 8 17.1 7.3 6.8 7.9 17.4 7.1 6.8 7.6 16.8 6.8 6.4 7.1 15.3 6.4 6.1 6.8 14.3 6.1 5.8 6.5 13.4 6.5 6.1 6.9 15.3 7.7 7.2 8.2 22.1 7.9 7.3 8.7 23.7 7.5 6.9 8.3 22.2 6.9 6.1 7.9 19.5 6.6 5.8 7.5 16.6 6.9 6.2 7.8 17.3 7.7 7.1 8.3 19.8 8 7.7 8.4 21.2 8 7.9 8.2 21.5 7.7 7.7 7.7 20.6 7.3 7.4 7.2 19.1 7.4 7.5 7.3 19.6 8.1 8 8.1 23.5 8.3 8. 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 TW[t] = + 0.198077214550677 + 0.533258589434412WM[t] + 0.42373357822937WV[t] + 0.0067473415919117WJ[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) 0.198077214550677 0.048873 4.0529 0.000155 7.7e-05 WM 0.533258589434412 0.008401 63.4794 0 0 WV 0.42373357822937 0.006563 64.5665 0 0 WJ 0.0067473415919117 0.002578 2.6176 0.011321 0.005661 Multiple Linear Regression - Regression Statistics Multiple R 0.99889653819086 R-squared 0.997794294009685 Adjusted R-squared 0.99767820422072 F-TEST (value) 8595.02203350076 F-TEST (DF numerator) 3 F-TEST (DF denominator) 57 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0328062112908612 Sum Squared Residuals 0.0613461074578558 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9.3 9.3088997364225 -0.00889973642249405 2 8.7 8.70141613121766 -0.00141613121766539 3 8.2 8.24430707004103 -0.0443070700410328 4 8.3 8.29290978987014 0.00709021012986453 5 8.5 8.52263317627068 -0.0226331762706832 6 8.6 8.56298233161605 0.0370176683839531 7 8.5 8.51993423963392 -0.0199342396339185 8 8.2 8.19113796567104 0.00886203432896158 9 8.1 8.10234284507002 -0.00234284507001730 10 7.9 7.92938856464161 -0.0293885646416147 11 8.6 8.63308936619857 -0.0330893661985699 12 8.7 8.72053501848121 -0.0205350184812094 13 8.7 8.73148751960171 -0.0314875196017129 14 8.5 8.52652482665238 -0.0265248266523847 15 8.4 8.35028405376872 0.0497159462312756 16 8.5 8.49661019284178 0.00338980715821969 17 8.7 8.63888792695969 0.0611120730403116 18 8.7 8.69018958342556 0.00981041657444402 19 8.6 8.60004499450615 -4.49945061518061e-05 20 8.5 8.5139488105419 -0.0139488105418954 21 8.3 8.3512722970748 -0.0512722970748066 22 8 8.03131714341416 -0.0313171434141595 23 8.2 8.18264046827316 0.0173595317268437 24 8.1 8.11213274620304 -0.0121327462030437 25 8.1 8.07868768702304 0.0213123129769619 26 8 7.96267686924818 0.0373231307518236 27 7.9 7.87876164233494 0.0212383576650617 28 7.9 7.92466542552728 -0.0246654255272765 29 8 8.01901517397527 -0.0190151739752730 30 8 7.96433984671345 0.0356601532865509 31 7.9 7.85903759714495 0.0409624028550512 32 8 7.96350835798081 0.0364916420191876 33 7.7 7.68264006968784 0.0173599303121561 34 7.2 7.25207197152587 -0.0520719715258647 35 7.5 7.47264072847729 0.0273592715227130 36 7.3 7.28814639110989 0.0118536088901127 37 7 7.00255496370258 -0.00255496370258068 38 7 7.0272284788131 -0.0272284788131016 39 7 6.96648054411856 0.0335194558814424 40 7.2 7.16950621293101 0.0304937870689908 41 7.3 7.28913463441597 0.0108653655840302 42 7.1 7.15796615599201 -0.0579661559920117 43 6.8 6.72267491871569 0.077325081284306 44 6.4 6.42882992682465 -0.0288299268246466 45 6.1 6.13565966909279 -0.035659669092792 46 6.5 6.4779506262395 0.0220493737605041 47 7.7 7.66127064914053 0.0387293508594697 48 7.9 7.93725904374572 -0.0372590437457151 49 7.5 7.54434116429234 -0.0443411642923356 50 6.9 6.9300230391549 -0.0300230391548949 51 6.6 6.58098474041628 0.0190152595837203 52 6.9 6.9261313887732 -0.0261313887731935 53 7.7 7.63479926235863 0.0652007376413705 54 8 8.00657405207089 -0.0065740520708908 55 8 8.03050325678947 -0.0305032567894724 56 7.7 7.70591214235518 -0.00591214235518432 57 7.3 7.32394676402231 -0.0239467640223084 58 7.4 7.42301965158464 -0.0230196515846415 59 8.1 8.0549504410938 0.0450495589061997 60 8.3 8.28114340212495 0.0188565978750555 61 8.2 8.18004631208504 0.0199536879149614 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.498730092702326 0.997460185404653 0.501269907297674 8 0.326459897047336 0.652919794094672 0.673540102952664 9 0.196539379185171 0.393078758370343 0.803460620814829 10 0.232745964840186 0.465491929680373 0.767254035159814 11 0.161076253593726 0.322152507187451 0.838923746406274 12 0.101409031032728 0.202818062065456 0.898590968967272 13 0.0689116774248963 0.137823354849793 0.931088322575104 14 0.0458970686612504 0.0917941373225009 0.95410293133875 15 0.145909869359920 0.291819738719839 0.85409013064008 16 0.101004090751638 0.202008181503277 0.898995909248361 17 0.119296535650744 0.238593071301488 0.880703464349256 18 0.113947295131692 0.227894590263384 0.886052704868308 19 0.113457320290465 0.226914640580930 0.886542679709535 20 0.130834655947332 0.261669311894664 0.869165344052668 21 0.248003483752508 0.496006967505016 0.751996516247492 22 0.219391240093874 0.438782480187748 0.780608759906126 23 0.265375751734047 0.530751503468095 0.734624248265953 24 0.224598585655924 0.449197171311849 0.775401414344076 25 0.216067515172772 0.432135030345545 0.783932484827228 26 0.23228146240509 0.46456292481018 0.76771853759491 27 0.193139575468280 0.386279150936559 0.80686042453172 28 0.180886958761925 0.361773917523849 0.819113041238075 29 0.172982246097573 0.345964492195146 0.827017753902427 30 0.183392883884443 0.366785767768886 0.816607116115557 31 0.180719420009153 0.361438840018305 0.819280579990847 32 0.161025847961048 0.322051695922096 0.838974152038952 33 0.118707525513881 0.237415051027761 0.881292474486119 34 0.227312294916693 0.454624589833385 0.772687705083307 35 0.201856976938399 0.403713953876797 0.798143023061601 36 0.156424971906000 0.312849943811999 0.843575028094 37 0.116334665974012 0.232669331948025 0.883665334025988 38 0.106548985472596 0.213097970945191 0.893451014527404 39 0.0944315208645749 0.188863041729150 0.905568479135425 40 0.0766661377188412 0.153332275437682 0.923333862281159 41 0.0514942415075011 0.102988483015002 0.948505758492499 42 0.135089646094665 0.270179292189330 0.864910353905335 43 0.373637796539132 0.747275593078264 0.626362203460868 44 0.340104917552508 0.680209835105017 0.659895082447492 45 0.321863815198244 0.643727630396488 0.678136184801756 46 0.294512460133649 0.589024920267298 0.705487539866351 47 0.350024834595000 0.700049669189999 0.649975165405 48 0.358350903706078 0.716701807412156 0.641649096293922 49 0.455362108841121 0.910724217682243 0.544637891158879 50 0.630440624591269 0.739118750817462 0.369559375408731 51 0.514265959541928 0.971468080916143 0.485734040458072 52 0.990266816482488 0.0194663670350236 0.0097331835175118 53 0.979082883247285 0.0418342335054293 0.0209171167527146 54 0.960492553885537 0.0790148922289258 0.0395074461144629 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 0.0416666666666667 OK 10% type I error level 4 0.0833333333333333 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/10km431258738873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/10km431258738873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/1wykt1258738873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/1wykt1258738873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/2wd221258738873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/2wd221258738873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/39odb1258738873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/39odb1258738873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/4ddq51258738873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/4ddq51258738873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/5cshi1258738873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/5cshi1258738873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/6rsm91258738873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/6rsm91258738873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/760za1258738873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/760za1258738873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/89me61258738873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/89me61258738873.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/9cjyr1258738873.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739719zply9go0510kk3c/9cjyr1258738873.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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