paper multiple regressie 1 vertraging

*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: Mon, 28 Dec 2009 08:31:59 -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/Dec/28/t126201436533a7p5seutmdxys.htm/, Retrieved Mon, 28 Dec 2009 16:32:57 +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/Dec/28/t126201436533a7p5seutmdxys.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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User-defined keywords:

Dataseries X:

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104.6 0 111.6 91.6 0 104.6 98.3 0 91.6 97.7 0 98.3 106.3 0 97.7 102.3 0 106.3 106.6 0 102.3 108.1 0 106.6 93.8 0 108.1 88.2 0 93.8 108.9 0 88.2 114.2 0 108.9 102.5 0 114.2 94.2 0 102.5 97.4 0 94.2 98.5 0 97.4 106.5 0 98.5 102.9 0 106.5 97.1 0 102.9 103.7 0 97.1 93.4 0 103.7 85.8 0 93.4 108.6 0 85.8 110.2 0 108.6 101.2 0 110.2 101.2 0 101.2 96.9 0 101.2 99.4 0 96.9 118.7 0 99.4 108.0 0 118.7 101.2 0 108.0 119.9 0 101.2 94.8 0 119.9 95.3 0 94.8 118.0 0 95.3 115.9 0 118.0 111.4 0 115.9 108.2 0 111.4 108.8 0 108.2 109.5 0 108.8 124.8 0 109.5 115.3 0 124.8 109.5 0 115.3 124.2 0 109.5 92.9 0 124.2 98.4 0 92.9 120.9 0 98.4 111.7 0 120.9 116.1 0 111.7 109.4 0 116.1 111.7 0 109.4 114.3 0 111.7 133.7 0 114.3 114.3 0 133.7 126.5 0 114.3 131.0 0 126.5 104.0 0 131.0 108.9 0 104.0 128.5 0 108.9 132.4 0 128.5 128.0 0 132.4 116.4 0 128.0 120.9 0 116.4 118.6 0 120.9 133.1 0 118.6 121.1 0 133.1 127.6 0 121.1 135.4 0 127.6 114.9 0 135.4 114.3 0 114.9 128.9 0 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] = + 63.9736688660687 -14.0674255209321dummy[t] + 0.403238814946053y1[t] -8.55634213725512M1[t] -13.2326064261404M2[t] -8.61512575581727M3[t] -8.80825188673433M4[t] + 2.77686079729466M5[t] -9.53736803868769M6[t] -8.4140472610244M7[t] + 1.86847421219370M8[t] -22.5360482605337M9[t] -16.6756633737586M10[t] + 6.90440569704908M11[t] + 0.252377234387815t + 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) 63.9736688660687 10.232592 6.252 0 0 dummy -14.0674255209321 2.782781 -5.0552 3e-06 1e-06 y1 0.403238814946053 0.095595 4.2182 6.4e-05 3.2e-05 M1 -8.55634213725512 2.740486 -3.1222 0.002499 0.001249 M2 -13.2326064261404 2.805171 -4.7172 1e-05 5e-06 M3 -8.61512575581727 3.07202 -2.8044 0.006325 0.003163 M4 -8.80825188673433 3.014562 -2.9219 0.004521 0.00226 M5 2.77686079729466 3.000081 0.9256 0.35744 0.17872 M6 -9.53736803868769 2.735128 -3.487 0.000796 0.000398 M7 -8.4140472610244 2.85943 -2.9426 0.004258 0.002129 M8 1.86847421219370 2.919454 0.64 0.523996 0.261998 M9 -22.5360482605337 2.736693 -8.2348 0 0 M10 -16.6756633737586 3.437597 -4.851 6e-06 3e-06 M11 6.90440569704908 3.431645 2.012 0.047588 0.023794 t 0.252377234387815 0.046807 5.3919 1e-06 0 Multiple Linear Regression - Regression Statistics Multiple R 0.929128009262835 R-squared 0.863278857596718 Adjusted R-squared 0.839352657676144 F-TEST (value) 36.0809012907385 F-TEST (DF numerator) 14 F-TEST (DF denominator) 80 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.27952370992904 Sum Squared Residuals 2229.86964829623 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 104.6 100.671155711181 3.9288442888192 2 91.6 93.424596952061 -1.82459695206105 3 98.3 93.0523502624734 5.24764973752663 4 97.7 95.8133014260827 1.88669857391732 5 106.3 107.408848055532 -1.10884805553186 6 102.3 98.8148502624734 3.48514973752663 7 106.6 98.5775930147402 8.02240698525974 8 108.1 110.846418626614 -2.74641862661418 9 93.8 87.2991316106937 6.5008683893063 10 88.2 87.645578678128 0.554421321871909 11 108.9 109.219887619626 -0.319887619625647 12 114.2 110.914902626348 3.28509737365229 13 102.5 104.748103442694 -2.24810344269445 14 94.2 95.6063222533282 -1.40632225332818 15 97.4 97.129297993987 0.270702006013113 16 98.5 98.478913305285 0.0210866947149881 17 106.5 110.759965920142 -4.25996592014248 18 102.9 101.924024838116 0.975975161883644 19 97.1 101.848063116362 -4.74806311636168 20 103.7 110.044176697280 -6.34417669728047 21 93.4 88.5534076375849 4.84659236241516 22 85.8 90.5128099648035 -4.71280996480347 23 108.6 111.280641276409 -2.68064127640892 24 110.2 113.822457794518 -3.62245779451765 25 101.2 106.163674995564 -4.96367499556402 26 101.2 98.1106386065521 3.08936139344791 27 96.9 102.980496511263 -6.08049651126304 28 99.4 101.305820710466 -1.90582071046576 29 118.7 114.151407666248 4.54859233375229 30 108 109.872065193112 -1.87206519311199 31 101.2 106.933107885240 -5.73310788524032 32 119.9 114.725982651213 5.17401734878693 33 94.8 98.1144032523647 -3.31440325236468 34 95.3 94.1058711183817 1.19412888161827 35 118 118.139936831050 -0.139936831050195 36 115.9 120.641429467664 -4.74142946766433 37 111.4 111.490663053410 -0.0906630534103092 38 108.2 105.252201331656 2.94779866834439 39 108.8 108.831695028539 -0.0316950285391993 40 109.5 109.132889420978 0.367110579022422 41 124.8 121.252646509857 3.54735349014337 42 115.3 115.360348776937 -0.0603487769366968 43 109.5 112.905278047000 -3.40527804700029 44 124.2 121.101391627919 3.0986083720809 45 92.9 102.876856969287 -9.9768569692865 46 98.4 96.368244182638 2.03175581736200 47 120.9 122.418503970037 -1.51850397003673 48 111.7 124.839348843662 -13.1393488436617 49 116.1 112.825586843291 3.27441315670932 50 109.4 110.175950574556 -0.77595057455584 51 111.7 112.344108419128 -0.644108419128242 52 114.3 113.330808796975 0.96919120302508 53 133.7 126.216719634251 7.48328036574852 54 114.3 121.977701042610 -7.67770104261034 55 126.5 115.530566044708 10.9694339552920 56 131 130.984978294656 0.0150217053442220 57 104 108.647407723573 -4.64740772357345 58 108.9 103.872721841193 5.02727815880703 59 128.5 129.681038339624 -1.18103833962408 60 132.4 130.932490649905 1.46750935009455 61 128 124.201157125328 3.79884287467225 62 116.4 118.003019285068 -1.60301928506765 63 120.9 118.195306936404 2.70469306359561 64 118.6 120.069132707132 -1.46913270713240 65 133.1 130.979173351173 2.12082664882673 66 121.1 124.764284566296 -3.6642845662965 67 127.6 121.301116798995 6.29888320100503 68 135.4 134.457067803750 0.94293219624979 69 114.9 113.450185321990 1.44981467801015 70 114.3 111.296551736759 3.00344826324126 71 128.9 134.887054752987 -5.98705475298655 72 138.9 134.122312988538 4.77768701146235 73 129.4 129.850736235131 -0.450736235130868 74 115 121.596080438646 -6.59608043864591 75 128 120.659299408134 7.3407005918663 76 127 125.960655105903 1.03934489409686 77 128.8 137.394906209374 -8.5949062093739 78 137.9 126.058884474682 11.8411155253177 79 128.4 131.104055702742 -2.70405570274243 80 135.9 137.808185668361 -1.90818566836084 81 122.2 116.680331542117 5.51966845788334 82 113.1 117.268721898519 -4.16872189851871 83 136.2 123.364269466773 12.8357305332270 84 138 126.027057629366 11.9729423706345 85 115.2 118.448922593401 -3.24892259340111 86 111 104.831190558134 6.16880944186635 87 99.2 108.007445440071 -8.80744544007117 88 102.4 103.308478527179 -0.90847852717851 89 112.7 116.436332653423 -3.73633265342269 90 105.5 108.527840845772 -3.02784084577249 91 98.3 107.000219390212 -8.70021939021202 92 116.4 114.631798630206 1.76820136979365 93 97.4 97.7782759423903 -0.378275942390318 94 93.3 96.2295005795783 -2.92950057957829 95 117.4 118.408667743495 -1.00866774349490 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.000687394489422348 0.00137478897884470 0.999312605510578 19 0.136293464617780 0.272586929235561 0.86370653538222 20 0.100730628875767 0.201461257751534 0.899269371124233 21 0.0536173249067026 0.107234649813405 0.946382675093297 22 0.0252420767671698 0.0504841535343396 0.97475792323283 23 0.0113767220697915 0.0227534441395831 0.988623277930208 24 0.00577626761974871 0.0115525352394974 0.994223732380251 25 0.00238729593782835 0.00477459187565669 0.997612704062172 26 0.0248246362064822 0.0496492724129645 0.975175363793518 27 0.0136652788191595 0.0273305576383191 0.98633472118084 28 0.00749231167819276 0.0149846233563855 0.992507688321807 29 0.0549083645570806 0.109816729114161 0.94509163544292 30 0.0360181306213644 0.0720362612427287 0.963981869378636 31 0.0256774823533805 0.0513549647067609 0.97432251764662 32 0.0907920454767474 0.181584090953495 0.909207954523253 33 0.0630101299523772 0.126020259904754 0.936989870047623 34 0.0567297517668963 0.113459503533793 0.943270248233104 35 0.0507846522097389 0.101569304419478 0.949215347790261 36 0.0358179034406197 0.0716358068812393 0.96418209655938 37 0.0276051861952873 0.0552103723905747 0.972394813804713 38 0.0295802062216784 0.0591604124433569 0.970419793778322 39 0.0222116066062288 0.0444232132124576 0.977788393393771 40 0.0156769142270830 0.0313538284541660 0.984323085772917 41 0.0154430516623635 0.0308861033247269 0.984556948337637 42 0.00971401634533996 0.0194280326906799 0.99028598365466 43 0.00621197619461653 0.0124239523892331 0.993788023805383 44 0.0055949914719376 0.0111899829438752 0.994405008528062 45 0.0140938580574373 0.0281877161148745 0.985906141942563 46 0.00966835842429017 0.0193367168485803 0.99033164157571 47 0.00619532990378333 0.0123906598075667 0.993804670096217 48 0.0388916323624544 0.0777832647249087 0.961108367637546 49 0.0293736928962222 0.0587473857924443 0.970626307103778 50 0.019978374066901 0.039956748133802 0.9800216259331 51 0.0138965022997894 0.0277930045995789 0.98610349770021 52 0.00965175695168494 0.0193035139033699 0.990348243048315 53 0.0176406462531199 0.0352812925062397 0.98235935374688 54 0.0235341391428081 0.0470682782856162 0.976465860857192 55 0.0734051418714775 0.146810283742955 0.926594858128522 56 0.0575127837590879 0.115025567518176 0.942487216240912 57 0.0594654457026306 0.118930891405261 0.94053455429737 58 0.0535617629449351 0.107123525889870 0.946438237055065 59 0.0421129851583939 0.0842259703167877 0.957887014841606 60 0.0531610193175656 0.106322038635131 0.946838980682434 61 0.0436883663254936 0.0873767326509872 0.956311633674506 62 0.0311762028444302 0.0623524056888604 0.96882379715557 63 0.0213033738795522 0.0426067477591045 0.978696626120448 64 0.0148762056651547 0.0297524113303094 0.985123794334845 65 0.0108599070535188 0.0217198141070375 0.989140092946481 66 0.0162745707155353 0.0325491414310707 0.983725429284465 67 0.0205753345121873 0.0411506690243747 0.979424665487813 68 0.0129274979061789 0.0258549958123578 0.98707250209382 69 0.00947813703425877 0.0189562740685175 0.990521862965741 70 0.00576684435524568 0.0115336887104914 0.994233155644754 71 0.0324275742438275 0.064855148487655 0.967572425756173 72 0.0641183234857736 0.128236646971547 0.935881676514226 73 0.0480473306152563 0.0960946612305126 0.951952669384744 74 0.558544727836236 0.882910544327528 0.441455272163764 75 0.438923134905056 0.877846269810112 0.561076865094944 76 0.312049042513316 0.624098085026632 0.687950957486684 77 0.409014839002439 0.818029678004877 0.590985160997561 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0333333333333333 NOK 5% type I error level 29 0.483333333333333 NOK 10% type I error level 42 0.7 NOK

Charts produced by software:

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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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