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*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 05:08:05 -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/t12587189766g4odercltn0t1v.htm/, Retrieved Fri, 20 Nov 2009 13:09:51 +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/t12587189766g4odercltn0t1v.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:

WS7 link4

Dataseries X:

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1.4 8.2 1,7 1 1,2 1,4 1.2 8.0 1.4 1,7 1 1,2 1.0 7.5 1.2 1.4 1,7 1 1.7 6.8 1.0 1.2 1.4 1,7 2.4 6.5 1.7 1.0 1.2 1.4 2.0 6.6 2.4 1.7 1.0 1.2 2.1 7.6 2.0 2.4 1.7 1.0 2.0 8.0 2.1 2.0 2.4 1.7 1.8 8.1 2.0 2.1 2.0 2.4 2.7 7.7 1.8 2.0 2.1 2.0 2.3 7.5 2.7 1.8 2.0 2.1 1.9 7.6 2.3 2.7 1.8 2.0 2.0 7.8 1.9 2.3 2.7 1.8 2.3 7.8 2.0 1.9 2.3 2.7 2.8 7.8 2.3 2.0 1.9 2.3 2.4 7.5 2.8 2.3 2.0 1.9 2.3 7.5 2.4 2.8 2.3 2.0 2.7 7.1 2.3 2.4 2.8 2.3 2.7 7.5 2.7 2.3 2.4 2.8 2.9 7.5 2.7 2.7 2.3 2.4 3.0 7.6 2.9 2.7 2.7 2.3 2.2 7.7 3.0 2.9 2.7 2.7 2.3 7.7 2.2 3.0 2.9 2.7 2.8 7.9 2.3 2.2 3.0 2.9 2.8 8.1 2.8 2.3 2.2 3.0 2.8 8.2 2.8 2.8 2.3 2.2 2.2 8.2 2.8 2.8 2.8 2.3 2.6 8.2 2.2 2.8 2.8 2.8 2.8 7.9 2.6 2.2 2.8 2.8 2.5 7.3 2.8 2.6 2.2 2.8 2.4 6.9 2.5 2.8 2.6 2.2 2.3 6.6 2.4 2.5 2.8 2.6 1.9 6.7 2.3 2.4 2.5 2.8 1.7 6.9 1.9 2.3 2.4 2.5 2.0 7.0 1.7 1.9 2.3 2.4 2.1 7.1 2.0 1.7 1.9 2.3 1.7 7.2 2.1 2.0 1.7 1.9 1.8 7.1 1.7 2.1 2.0 1.7 1.8 6.9 1.8 1.7 2.1 2.0 1.8 7.0 1.8 1.8 1.7 2.1 1.3 6.8 1.8 1.8 1.8 1.7 1.3 6 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 Y[t] = + 1.11682006097517 -0.102335980692679X[t] + 1.09224433596786Y1[t] -0.180220978992785Y2[t] + 0.214398874249468Y3[t] -0.272382037219519Y4[t] -0.144229979838938M1[t] + 0.072857990135528M2[t] -0.189718701153859M3[t] + 0.0739143876135337M4[t] + 0.167717263342514M5[t] -0.0476955774918094M6[t] -0.00929293578842435M7[t] -0.168909399373597M8[t] -0.0269912290538317M9[t] -0.00817541421200658M10[t] -0.17472517625845M11[t] + 0.000282830471790956t + 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) 1.11682006097517 1.078162 1.0359 0.305684 0.152842 X -0.102335980692679 0.136555 -0.7494 0.457425 0.228713 Y1 1.09224433596786 0.140797 7.7576 0 0 Y2 -0.180220978992785 0.218295 -0.8256 0.413301 0.20665 Y3 0.214398874249468 0.223337 0.96 0.342085 0.171043 Y4 -0.272382037219519 0.155436 -1.7524 0.086373 0.043187 M1 -0.144229979838938 0.312788 -0.4611 0.646892 0.323446 M2 0.072857990135528 0.31116 0.2341 0.815908 0.407954 M3 -0.189718701153859 0.312082 -0.6079 0.546235 0.273118 M4 0.0739143876135337 0.305004 0.2423 0.809595 0.404797 M5 0.167717263342514 0.320694 0.523 0.603497 0.301748 M6 -0.0476955774918094 0.326547 -0.1461 0.884512 0.442256 M7 -0.00929293578842435 0.320428 -0.029 0.976989 0.488494 M8 -0.168909399373597 0.321012 -0.5262 0.601292 0.300646 M9 -0.0269912290538317 0.317548 -0.085 0.932631 0.466316 M10 -0.00817541421200658 0.318987 -0.0256 0.979664 0.489832 M11 -0.17472517625845 0.319805 -0.5463 0.587466 0.293733 t 0.000282830471790956 0.00537 0.0527 0.958223 0.479112 Multiple Linear Regression - Regression Statistics Multiple R 0.932553912003 R-squared 0.869656798792099 Adjusted R-squared 0.821486485302223 F-TEST (value) 18.0537915530665 F-TEST (DF numerator) 17 F-TEST (DF denominator) 46 p-value 6.66133814775094e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.500545103159913 Sum Squared Residuals 11.5250884136789 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.4 1.68625605907266 -0.286256059072665 2 1.2 1.48186270216616 -0.281862702166161 3 1 1.3109098776177 -0.310909877617701 4 1.7 1.20906922361824 0.49093077638176 5 2.4 2.17330579131884 0.226694208681163 6 2 2.5979551653636 -0.5979551653636 7 2.1 2.17580785658253 -0.075807856582534 8 2 2.11626444230694 -0.116264442306945 9 1.8 1.84455833777972 -0.0445583377797183 10 2.7 1.83455730838887 0.865442691611132 11 2.3 2.65914357997548 -0.359143579975483 12 1.9 2.20917980202786 -0.309179802027864 13 2 1.92739150800058 0.0726084919994249 14 2.3 1.99517175044338 0.304828249556622 15 2.8 2.06572235770488 0.734277642295118 16 2.4 2.98278764775072 -0.582787647750718 17 2.3 2.58694658862084 -0.286946588620842 18 2.7 2.40109975449459 0.298900245505413 19 2.7 2.63182009836957 0.0681799016304323 20 2.9 2.48791100112193 0.412088998878067 21 3 2.95132502445953 0.0486749755404684 22 2.2 2.9244174946143 -0.724417494614301 23 2.3 1.90921277121598 0.390787228784024 24 2.8 2.28411827857974 0.515881721420262 25 2.8 2.44904670003718 0.350953299962819 26 2.8 2.80541893011834 -0.0054189301183389 27 2.2 2.62308630270352 -0.423086302703524 28 2.6 2.09546460175223 0.504535398247767 29 2.8 2.76528142394362 0.0347185760563767 30 2.5 2.62927415304348 -0.129274153043476 31 2.4 2.59436529293831 -0.194365292938307 32 2.3 2.34450127409586 -0.044501274095865 33 1.9 2.2664702714019 -0.366470271401901 34 1.7 1.90650080783002 -0.206500807830025 35 2 1.58943811888665 0.410561881113349 36 2.1 2.05940867815870 0.0405913218412957 37 1.7 2.02645911065915 -0.326459110659154 38 1.8 1.917939746607 -0.117939746607 39 1.8 1.79715118338093 0.00284881661906928 40 1.8 1.91981365322983 -0.119813653229829 41 1.3 2.16475925788189 -0.86475925788189 42 1.3 1.41720326809055 -0.117203268090548 43 1.3 1.51529843555431 -0.215298435554313 44 1.2 1.25899896338547 -0.0589989633854662 45 1.4 1.44863374532853 -0.0486337453285312 46 2.2 1.71443695380426 0.485563046195735 47 2.9 2.37471500584967 0.525284994150334 48 3.1 3.20953444892722 -0.109534448927222 49 3.5 3.24422337920656 0.255776620793435 50 3.6 3.79462130044042 -0.194621300440420 51 4.4 3.4626102226958 0.937389777304202 52 4.1 4.64428344927368 -0.544283449273679 53 5.1 4.20970693823481 0.890293061765193 54 5.8 5.25446765900779 0.545532340992211 55 5.9 5.48270831655528 0.417291683444722 56 5.4 5.59232431908979 -0.19232431908979 57 5.5 5.08901262103032 0.410987378969682 58 4.8 5.22008743536254 -0.42008743536254 59 3.2 4.16749052407222 -0.967490524072224 60 2.7 2.83775879230647 -0.137758792306473 61 2.1 2.16662324302386 -0.0666232430238606 62 1.9 1.6049855702247 0.295014429775299 63 0.6 1.54052005589716 -0.940520055897164 64 0.7 0.448581424375302 0.251418575624698 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.410667670906671 0.821335341813341 0.58933232909333 22 0.610673704696258 0.778652590607483 0.389326295303742 23 0.529099174464118 0.941801651071765 0.470900825535882 24 0.436768861996008 0.873537723992017 0.563231138003992 25 0.321971614704178 0.643943229408355 0.678028385295822 26 0.215106034191501 0.430212068383002 0.784893965808499 27 0.188093088897153 0.376186177794306 0.811906911102847 28 0.140835863316996 0.281671726633992 0.859164136683004 29 0.107396247290345 0.214792494580690 0.892603752709655 30 0.0900938788783673 0.180187757756735 0.909906121121633 31 0.0825825509635048 0.165165101927010 0.917417449036495 32 0.0690200116366719 0.138040023273344 0.930979988363328 33 0.053440412065848 0.106880824131696 0.946559587934152 34 0.0318513672972287 0.0637027345944574 0.968148632702771 35 0.0374816973233203 0.0749633946466406 0.96251830267668 36 0.0281776424261646 0.0563552848523293 0.971822357573835 37 0.0154209939904499 0.0308419879808997 0.98457900600955 38 0.00752328929618727 0.0150465785923745 0.992476710703813 39 0.0041309440165405 0.008261888033081 0.99586905598346 40 0.00316576578305560 0.00633153156611119 0.996834234216944 41 0.00218988817321958 0.00437977634643917 0.99781011182678 42 0.0103289777901020 0.0206579555802039 0.989671022209898 43 0.0433062535639294 0.0866125071278589 0.95669374643607 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.130434782608696 NOK 5% type I error level 6 0.260869565217391 NOK 10% type I error level 10 0.434782608695652 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/10s4hd1258718880.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/10s4hd1258718880.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/11wrs1258718880.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/11wrs1258718880.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/2q5q41258718880.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/2q5q41258718880.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/3iz931258718880.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/3iz931258718880.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/4o1ve1258718880.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/4o1ve1258718880.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/5f01l1258718880.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/5f01l1258718880.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/6lsns1258718880.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/6lsns1258718880.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/7wxiw1258718880.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/7wxiw1258718880.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/841ny1258718880.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/841ny1258718880.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/9vuh31258718880.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587189766g4odercltn0t1v/9vuh31258718880.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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