*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: Wed, 18 Nov 2009 08:51:49 -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/18/t1258559557cmpgnyd47wn6kyk.htm/, Retrieved Wed, 18 Nov 2009 16:52:49 +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/18/t1258559557cmpgnyd47wn6kyk.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:

WS7multipleregression

Dataseries X:

» Textbox « » Textfile « » CSV «

7291 4071 6820 4351 8031 4871 7862 4649 7357 4922 7213 4879 7079 4853 7012 4545 7319 4733 8148 5191 7599 4983 6908 4593 7878 4656 7407 4513 7911 4857 7323 4681 7179 4897 6758 4547 6934 4692 6696 4390 7688 5341 8296 5415 7697 4890 7907 5120 7592 4422 7710 4797 9011 5689 8225 5171 7733 4265 8062 5215 7859 4874 8221 4590 8330 4994 8868 4988 9053 5110 8811 5141 8120 4395 7953 4523 8878 5306 8601 5365 8361 5496 9116 5647 9310 5443 9891 5546 10147 5912 10317 5665 10682 5963 10276 5861 10614 5366 9413 5619 11068 6721 9772 6054 10350 6619 10541 6856 10049 6193 10714 6317 10759 6618 11684 6585 11462 6852 10485 6586 11056 6154 10184 6193 11082 7606 10554 6588 11315 7143 10847 7629 11104 7041 11026 7146 11073 7200 12073 7739 12328 7953 11172 7082

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 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation UivEU[t] = + 803.320507273808 + 1.47757153916225InvnietEU[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) 803.320507273808 414.630008 1.9374 0.056727 0.028364 InvnietEU 1.47757153916225 0.073338 20.1475 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.923534989572788 R-squared 0.85291687696521 Adjusted R-squared 0.850815689493285 F-TEST (value) 405.921360355144 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 611.1596633404 Sum Squared Residuals 26146129.3866046 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7291 6818.51424320335 472.485756796653 2 6820 7232.23427416876 -412.234274168764 3 8031 8000.57147453314 30.4285254668565 4 7862 7672.55059283912 189.449407160877 5 7357 8075.92762303042 -718.927623030419 6 7213 8012.39204684644 -799.392046846442 7 7079 7973.97518682822 -894.975186828223 8 7012 7518.88315276625 -506.883152766249 9 7319 7796.66660212875 -477.666602128752 10 8148 8473.39436706506 -325.394367065065 11 7599 8166.05948691932 -567.059486919316 12 6908 7589.80658664604 -681.806586646037 13 7878 7682.89359361326 195.106406386741 14 7407 7471.60086351306 -64.6008635130567 15 7911 7979.88547298487 -68.8854729848721 16 7323 7719.83288209232 -396.832882092315 17 7179 8038.98833455136 -859.988334551362 18 6758 7521.83829584457 -763.838295844573 19 6934 7736.0861690231 -802.0861690231 20 6696 7289.8595641961 -593.859564196099 21 7688 8695.0300979394 -1007.03009793940 22 8296 8804.37039183741 -508.37039183741 23 7697 8028.64533377723 -331.645333777226 24 7907 8368.48678778454 -461.486787784545 25 7592 7337.1418534493 254.858146550709 26 7710 7891.23118063514 -181.231180635137 27 9011 9209.22499356787 -198.224993567868 28 8225 8443.84293628182 -218.84293628182 29 7733 7105.16312180082 627.836878199183 30 8062 8508.85608400496 -446.856084004959 31 7859 8005.00418915063 -146.004189150630 32 8221 7585.37387202855 635.62612797145 33 8330 8182.3127738501 147.687226149899 34 8868 8173.44734461513 694.552655384873 35 9053 8353.71107239292 699.288927607078 36 8811 8399.51579010695 411.484209893048 37 8120 7297.24742189191 822.75257810809 38 7953 7486.37657890468 466.623421095321 39 8878 8643.31509406872 234.684905931276 40 8601 8730.4918148793 -129.491814879297 41 8361 8924.05368650955 -563.053686509553 42 9116 9147.16698892305 -31.1669889230531 43 9310 8845.74239493395 464.257605066047 44 9891 8997.93226346766 893.067736532335 45 10147 9538.72344680105 608.276553198950 46 10317 9173.76327662797 1143.23672337203 47 10682 9614.07959529833 1067.92040470167 48 10276 9463.36729830378 812.632701696225 49 10614 8731.96938641846 1882.03061358154 50 9413 9105.7949858265 307.20501417349 51 11068 10734.0788219833 333.921178016686 52 9772 9748.5386053621 23.4613946379094 53 10350 10583.3665249888 -233.366524988764 54 10541 10933.5509797702 -392.550979770219 55 10049 9953.92104930564 95.078950694356 56 10714 10137.1399201618 576.860079838236 57 10759 10581.8889534496 177.111046550398 58 11684 10533.1290926572 1150.87090734275 59 11462 10927.6406936136 534.35930638643 60 10485 10534.6066641964 -49.60666419641 61 11056 9896.29575927832 1159.70424072168 62 10184 9953.92104930564 230.078950694356 63 11082 12041.7296341419 -959.72963414191 64 10554 10537.5618072747 16.4381927252653 65 11315 11357.6140115098 -42.6140115097856 66 10847 12075.7137795426 -1228.71377954264 67 11104 11206.9017145152 -102.901714515236 68 11026 11362.0467261273 -336.046726127273 69 11073 11441.8355892420 -368.835589242034 70 12073 12238.2466488505 -165.246648850489 71 12328 12554.4469582312 -226.446958231212 72 11172 11267.4821476209 -95.4821476208884 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.349886655604426 0.699773311208853 0.650113344395574 6 0.286504922258079 0.573009844516158 0.713495077741921 7 0.246780253462222 0.493560506924444 0.753219746537778 8 0.179194948031286 0.358389896062571 0.820805051968714 9 0.108239178304248 0.216478356608496 0.891760821695752 10 0.0997897596899658 0.199579519379932 0.900210240310034 11 0.0602160490247399 0.120432098049480 0.93978395097526 12 0.0538641559660431 0.107728311932086 0.946135844033957 13 0.0553963814674786 0.110792762934957 0.944603618532521 14 0.0333165979700343 0.0666331959400685 0.966683402029966 15 0.0251558241086116 0.0503116482172232 0.974844175891388 16 0.0152654994835191 0.0305309989670382 0.98473450051648 17 0.0159012654792789 0.0318025309585578 0.984098734520721 18 0.0219150390481325 0.0438300780962650 0.978084960951867 19 0.0263262114570717 0.0526524229141433 0.973673788542928 20 0.0286047951192505 0.057209590238501 0.97139520488075 21 0.0323821554566790 0.0647643109133579 0.96761784454332 22 0.0310001104987271 0.0620002209974543 0.968999889501273 23 0.0256921453683884 0.0513842907367769 0.974307854631612 24 0.0230278569907104 0.0460557139814209 0.97697214300929 25 0.0242068951043027 0.0484137902086053 0.975793104895697 26 0.0218070012332284 0.0436140024664567 0.978192998766772 27 0.0309764370562159 0.0619528741124319 0.969023562943784 28 0.0299738177056743 0.0599476354113485 0.970026182294326 29 0.0494991882850446 0.0989983765700893 0.950500811714955 30 0.0548362212044138 0.109672442408828 0.945163778795586 31 0.0591576274671434 0.118315254934287 0.940842372532857 32 0.0994235435581726 0.198847087116345 0.900576456441827 33 0.113227056583833 0.226454113167667 0.886772943416167 34 0.199548600376303 0.399097200752607 0.800451399623697 35 0.290590313085516 0.581180626171031 0.709409686914484 36 0.308522985146780 0.617045970293561 0.69147701485322 37 0.348184513071758 0.696369026143517 0.651815486928242 38 0.356451340220347 0.712902680440693 0.643548659779653 39 0.36369127564135 0.7273825512827 0.63630872435865 40 0.412979693692688 0.825959387385376 0.587020306307312 41 0.655051897216928 0.689896205566144 0.344948102783072 42 0.736797046452477 0.526405907095046 0.263202953547523 43 0.789213716619423 0.421572566761154 0.210786283380577 44 0.83034329637657 0.339313407246859 0.169656703623430 45 0.821369895962407 0.357260208075187 0.178630104037593 46 0.853516159283916 0.292967681432169 0.146483840716084 47 0.86706080379906 0.26587839240188 0.13293919620094 48 0.840236192007033 0.319527615985935 0.159763807992967 49 0.958603250312966 0.0827934993740671 0.0413967496870336 50 0.951149238248412 0.097701523503176 0.048850761751588 51 0.933045251269582 0.133909497460836 0.0669547487304182 52 0.931177121990727 0.137645756018545 0.0688228780092725 53 0.927249427188322 0.145501145623356 0.072750572811678 54 0.925481974136343 0.149036051727315 0.0745180258636574 55 0.921453291717126 0.157093416565748 0.0785467082828742 56 0.885401551640525 0.229196896718950 0.114598448359475 57 0.837385615339915 0.32522876932017 0.162614384660085 58 0.919688860611222 0.160622278777556 0.080311139388778 59 0.9187918969043 0.162416206191402 0.0812081030957008 60 0.886525828695105 0.226948342609789 0.113474171304895 61 0.947647041951964 0.104705916096072 0.052352958048036 62 0.906839556572279 0.186320886855442 0.093160443427721 63 0.929745122815423 0.140509754369154 0.0702548771845772 64 0.87121959594848 0.25756080810304 0.12878040405152 65 0.797705681402 0.404588637196001 0.202294318598001 66 0.99652977730945 0.00694044538110047 0.00347022269055023 67 0.988258502944204 0.023482994111591 0.0117414970557955 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0158730158730159 NOK 5% type I error level 8 0.126984126984127 NOK 10% type I error level 20 0.317460317460317 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/1072lr1258559504.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/1072lr1258559504.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/142fp1258559504.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/142fp1258559504.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/22jtq1258559504.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/22jtq1258559504.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/38e5l1258559504.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/38e5l1258559504.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/4qs2q1258559504.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/4qs2q1258559504.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/5n1sb1258559504.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/5n1sb1258559504.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/6r61d1258559504.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/6r61d1258559504.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/78c0l1258559504.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/78c0l1258559504.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/8p6j11258559504.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/8p6j11258559504.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/99pe41258559504.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258559557cmpgnyd47wn6kyk/99pe41258559504.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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