Multiple Regression - Totale Uitvoer van Belgie (C)

*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, 11 Dec 2008 10:25:54 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/11/t1229016392jud56fsnr176pxz.htm/, Retrieved Thu, 11 Dec 2008 17:26:43 +0000

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/2008/Dec/11/t1229016392jud56fsnr176pxz.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

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Dataseries X:

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15044.5 1 14944.2 1 16754.8 1 14254 1 15454.9 1 15644.8 1 14568.3 1 12520.2 1 14803 1 15873.2 1 14755.3 1 12875.1 1 14291.1 1 14205.3 1 15859.4 1 15258.9 1 15498.6 1 15106.5 1 15023.6 1 12083 1 15761.3 1 16943 1 15070.3 1 13659.6 1 14768.9 0 14725.1 0 15998.1 0 15370.6 0 14956.9 0 15469.7 0 15101.8 0 11703.7 0 16283.6 0 16726.5 0 14968.9 0 14861 0 14583.3 0 15305.8 0 17903.9 0 16379.4 0 15420.3 0 17870.5 0 15912.8 0 13866.5 0 17823.2 0 17872 0 17420.4 0 16704.4 0 15991.2 0 16583.6 0 19123.5 0 17838.7 0 17209.4 0 18586.5 0 16258.1 0 15141.6 0 19202.1 0 17746.5 0 19090.1 0 18040.3 0 17515.5 0 17751.8 0 21072.4 0 17170 0 19439.5 0 19795.4 0 17574.9 0 16165.4 0 19464.6 0 19932.1 0 19961.2 0 17343.4 0 18924.2 0 18574.1 0 21350.6 0 18594.6 0 19823.1 0 20844.4 0 19640.2 0 17735.4 0 19813.6 0 22160 0 20664.3 0 17877.4 0 21211.2 0 21423.1 0 21688.7 0 23243.2 0 21490.2 0 22925.8 0 23184.8 0 18562.2 0

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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 10563.7854727398 + 1566.02977053141X[t] + 1198.58264284277M1[t] + 1244.43846977800M2[t] + 3172.20679671325M3[t] + 1614.92512364849M4[t] + 1660.83095058374M5[t] + 2427.63677751898M6[t] + 1203.21760445422M7[t] -1334.62656861053M8[t] + 1990.40930490856M9[t] + 2474.36334612951M10[t] + 1611.93167306476M11[t] + 102.031673064757t + 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) 10563.7854727398 439.52362 24.0346 0 0 X 1566.02977053141 302.055124 5.1846 2e-06 1e-06 M1 1198.58264284277 426.410188 2.8109 0.006245 0.003122 M2 1244.43846977800 426.04899 2.9209 0.004563 0.002281 M3 3172.20679671325 425.746334 7.4509 0 0 M4 1614.92512364849 425.502344 3.7953 0.00029 0.000145 M5 1660.83095058374 425.317123 3.9049 0.000199 1e-04 M6 2427.63677751898 425.190746 5.7095 0 0 M7 1203.21760445422 425.123267 2.8303 0.005912 0.002956 M8 -1334.62656861053 425.114712 -3.1395 0.00239 0.001195 M9 1990.40930490856 439.250488 4.5314 2.1e-05 1e-05 M10 2474.36334612951 439.107877 5.635 0 0 M11 1611.93167306476 439.022288 3.6716 0.000439 0.00022 t 102.031673064757 5.005273 20.3848 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.955855226880187 R-squared 0.913659214754173 Adjusted R-squared 0.899269083879869 F-TEST (value) 63.4920712490278 F-TEST (DF numerator) 13 F-TEST (DF denominator) 78 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 821.282112099488 Sum Squared Residuals 52611335.9970585 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15044.5 13430.4295591787 1614.07044082129 2 14944.2 13578.3170591787 1365.88294082125 3 16754.8 15608.1170591787 1146.68294082125 4 14254 14152.8670591787 101.132940821263 5 15454.9 14300.8045591787 1154.09544082127 6 15644.8 15169.6420591787 475.157940821257 7 14568.3 14047.2545591787 521.045440821251 8 12520.2 11611.4420591787 908.757940821255 9 14803 15038.5096057626 -235.509605762597 10 15873.2 15624.4953200483 248.704679951683 11 14755.3 14864.0953200483 -108.795320048315 12 12875.1 13354.1953200483 -479.095320048315 13 14291.1 14654.8096359558 -363.709635955839 14 14205.3 14802.6971359558 -597.397135955826 15 15859.4 16832.4971359558 -973.097135955834 16 15258.9 15377.2471359558 -118.347135955836 17 15498.6 15525.1846359558 -26.5846359558347 18 15106.5 16394.0221359558 -1287.52213595583 19 15023.6 15271.6346359558 -248.034635955833 20 12083 12835.8221359558 -752.822135955834 21 15761.3 16262.8896825397 -501.589682539684 22 16943 16848.8753968254 94.1246031746026 23 15070.3 16088.4753968254 -1018.17539682540 24 13659.6 14578.5753968254 -918.9753968254 25 14768.9 14313.1599422015 455.740057798475 26 14725.1 14461.0474422015 264.052557798482 27 15998.1 16490.8474422015 -492.747442201518 28 15370.6 15035.5974422015 335.002557798481 29 14956.9 15183.5349422015 -226.634942201520 30 15469.7 16052.3724422015 -582.672442201518 31 15101.8 14929.9849422015 171.815057798481 32 11703.7 12494.1724422015 -790.472442201519 33 16283.6 15921.2399887854 362.360011214632 34 16726.5 16507.2257030711 219.274296928919 35 14968.9 15746.8257030711 -777.925703071082 36 14861 14236.9257030711 624.074296928917 37 14583.3 15537.5400189786 -954.240018978612 38 15305.8 15685.4275189786 -379.627518978607 39 17903.9 17715.2275189786 188.672481021396 40 16379.4 16259.9775189786 119.422481021393 41 15420.3 16407.9150189786 -987.615018978608 42 17870.5 17276.7525189786 593.747481021394 43 15912.8 16154.3650189786 -241.565018978606 44 13866.5 13718.5525189786 147.947481021394 45 17823.2 17145.6200655625 677.579934437545 46 17872 17731.6057798482 140.394220151831 47 17420.4 16971.2057798482 449.194220151832 48 16704.4 15461.3057798482 1243.09422015183 49 15991.2 16761.9200957557 -770.720095755698 50 16583.6 16909.8075957557 -326.207595755695 51 19123.5 18939.6075957557 183.892404244307 52 17838.7 17484.3575957557 354.342404244306 53 17209.4 17632.2950957557 -422.895095755694 54 18586.5 18501.1325957557 85.3674042443063 55 16258.1 17378.7450957557 -1120.64509575569 56 15141.6 14942.9325957557 198.667404244307 57 19202.1 18370.0001423395 832.099857660456 58 17746.5 18955.9858566253 -1209.48585662526 59 19090.1 18195.5858566253 894.514143374741 60 18040.3 16685.6858566253 1354.61414337474 61 17515.5 17986.3001725328 -470.800172532786 62 17751.8 18134.1876725328 -382.387672532781 63 21072.4 20163.9876725328 908.412327467222 64 17170 18708.7376725328 -1538.73767253278 65 19439.5 18856.6751725328 582.824827467218 66 19795.4 19725.5126725328 69.8873274672203 67 17574.9 18603.1251725328 -1028.22517253278 68 16165.4 16167.3126725328 -1.91267253278096 69 19464.6 19594.3802191166 -129.780219116632 70 19932.1 20180.3659334023 -248.265933402346 71 19961.2 19419.9659334023 541.234066597656 72 17343.4 17910.0659334023 -566.665933402345 73 18924.2 19210.6802493099 -286.480249309872 74 18574.1 19358.5677493099 -784.46774930987 75 21350.6 21388.3677493099 -37.7677493098687 76 18594.6 19933.1177493099 -1338.51774930987 77 19823.1 20081.0552493099 -257.955249309872 78 20844.4 20949.8927493099 -105.492749309867 79 19640.2 19827.5052493099 -187.305249309867 80 17735.4 17391.6927493099 343.707250690133 81 19813.6 20818.7602958937 -1005.16029589372 82 22160 21404.7460101794 755.253989820568 83 20664.3 20644.3460101794 19.9539898205674 84 17877.4 19134.4460101794 -1257.04601017943 85 21211.2 20435.0603260870 776.139673913039 86 21423.1 20582.9478260870 840.152173913043 87 21688.7 22612.7478260870 -924.047826086955 88 23243.2 21157.4978260870 2085.70217391305 89 21490.2 21305.4353260870 184.764673913044 90 22925.8 22174.2728260870 751.527173913042 91 23184.8 21051.8853260870 2132.91467391304 92 18562.2 18616.0728260870 -53.8728260869546 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.438955227059444 0.877910454118887 0.561044772940556 18 0.284355518690831 0.568711037381661 0.71564448130917 19 0.223503272372044 0.447006544744089 0.776496727627956 20 0.133335399396372 0.266670798792744 0.866664600603628 21 0.159987709284272 0.319975418568544 0.840012290715728 22 0.186867990036565 0.373735980073130 0.813132009963435 23 0.123268960786708 0.246537921573416 0.876731039213292 24 0.09694508749919 0.19389017499838 0.90305491250081 25 0.0627930320097903 0.125586064019581 0.93720696799021 26 0.0380462019608389 0.0760924039216777 0.96195379803916 27 0.0241741182570358 0.0483482365140716 0.975825881742964 28 0.0181676887121782 0.0363353774243565 0.981832311287822 29 0.0136642263247229 0.0273284526494459 0.986335773675277 30 0.00779843228722813 0.0155968645744563 0.992201567712772 31 0.00449886622741541 0.00899773245483082 0.995501133772585 32 0.00370700438624347 0.00741400877248694 0.996292995613757 33 0.00484767340006221 0.00969534680012443 0.995152326599938 34 0.00278916223405966 0.00557832446811932 0.99721083776594 35 0.00175732815491731 0.00351465630983461 0.998242671845083 36 0.00561746237973574 0.0112349247594715 0.994382537620264 37 0.00369259092360761 0.00738518184721522 0.996307409076392 38 0.00238999344098494 0.00477998688196987 0.997610006559015 39 0.00562085696325638 0.0112417139265128 0.994379143036744 40 0.00601755272421131 0.0120351054484226 0.993982447275789 41 0.00440684125162349 0.00881368250324699 0.995593158748377 42 0.0189950076148883 0.0379900152297766 0.981004992385112 43 0.0125953609845853 0.0251907219691706 0.987404639015415 44 0.0127747412778454 0.0255494825556908 0.987225258722155 45 0.0203098140841280 0.0406196281682559 0.979690185915872 46 0.0149468344417464 0.0298936688834927 0.985053165558254 47 0.0222820983958124 0.0445641967916248 0.977717901604188 48 0.0602751553994512 0.120550310798902 0.93972484460055 49 0.043642091607959 0.087284183215918 0.956357908392041 50 0.0300090396871124 0.0600180793742247 0.969990960312888 51 0.0260123021049670 0.0520246042099341 0.973987697895033 52 0.0246462486955313 0.0492924973910625 0.975353751304469 53 0.0161118235506821 0.0322236471013641 0.983888176449318 54 0.0122844554663542 0.0245689109327085 0.987715544533646 55 0.0111036879562718 0.0222073759125436 0.988896312043728 56 0.0085839653423333 0.0171679306846666 0.991416034657667 57 0.0139955233139416 0.0279910466278833 0.986004476686058 58 0.0144057022154742 0.0288114044309485 0.985594297784526 59 0.0203945084586282 0.0407890169172565 0.979605491541372 60 0.0911951155893556 0.182390231178711 0.908804884410644 61 0.0634864057542592 0.126972811508518 0.93651359424574 62 0.0427472280289847 0.0854944560579694 0.957252771971015 63 0.0967457162124623 0.193491432424925 0.903254283787538 64 0.113569205390781 0.227138410781561 0.88643079460922 65 0.138484378812575 0.276968757625149 0.861515621187425 66 0.110090764151518 0.220181528303036 0.889909235848482 67 0.105484554553917 0.210969109107834 0.894515445446083 68 0.0803555968848858 0.160711193769772 0.919644403115114 69 0.0867057243985756 0.173411448797151 0.913294275601424 70 0.0547907119312748 0.109581423862550 0.945209288068725 71 0.0550006958733298 0.110001391746660 0.94499930412667 72 0.0684974524361495 0.136994904872299 0.93150254756385 73 0.0374456218420821 0.0748912436841642 0.962554378157918 74 0.0208154005192624 0.0416308010385248 0.979184599480738 75 0.039430379805159 0.078860759610318 0.960569620194841 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.135593220338983 NOK 5% type I error level 30 0.508474576271186 NOK 10% type I error level 37 0.627118644067797 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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