WS8

*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: Tue, 30 Nov 2010 17:31:09 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no.htm/, Retrieved Tue, 30 Nov 2010 18:29:50 +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/2010/Nov/30/t1291138179z63evnujf3r12no.htm/}, year = {2010}, } @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 = {2010}, 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:

Dataseries X:

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98.60 627 98.97 696 99.11 825 99.64 677 100.03 656 99.98 785 100.32 412 100.44 352 100.51 839 101.00 729 100.88 696 100.55 641 100.83 695 101.51 638 102.16 762 102.39 635 102.54 721 102.85 854 103.47 418 103.57 367 103.69 824 103.50 687 103.47 601 103.45 676 103.48 740 103.93 691 103.89 683 104.40 594 104.79 729 104.77 731 105.13 386 105.26 331 104.96 707 104.75 715 105.01 657 105.15 653 105.20 642 105.77 643 105.78 718 106.26 654 106.13 632 106.12 731 106.57 392 106.44 344 106.54 792 107.10 852 108.10 649 108.40 629 108.84 685 109.62 617 110.42 715 110.67 715 111.66 629 112.28 916 112.87 531 112.18 357 112.36 917 112.16 828 111.49 708 111.25 858 111.36 775 111.74 785 111.10 1006 111.33 789 111.25 734 111.04 906 110.97 532 111.31 387 111.02 991 111.07 841 111.36 892 111.54 782

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 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Faillissementen[t] = + 408.95908717141 + 2.23911448031443CPI[t] + 7.33053571206581M1[t] -10.9361673873247M2[t] + 93.8258552548724M3[t] -15.9009954311326M4[t] -11.7671228621771M5[t] + 123.599391789101M6[t] -253.98318337504M7[t] -344.162649032121M8[t] + 143.154153453330M9[t] + 71.9062474424826M10[t] -4.59415795677702M11[t] + 1.39464647082141t + 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) 408.95908717141 820.446718 0.4985 0.620046 0.310023 CPI 2.23911448031443 8.336044 0.2686 0.789186 0.394593 M1 7.33053571206581 39.439505 0.1859 0.853197 0.426598 M2 -10.9361673873247 39.612667 -0.2761 0.78347 0.391735 M3 93.8258552548724 39.535221 2.3732 0.020968 0.010484 M4 -15.9009954311326 39.674149 -0.4008 0.690048 0.345024 M5 -11.7671228621771 39.749692 -0.296 0.768264 0.384132 M6 123.599391789101 39.612106 3.1202 0.002815 0.001407 M7 -253.98318337504 39.826239 -6.3773 0 0 M8 -344.162649032121 39.53075 -8.7062 0 0 M9 143.154153453330 39.324563 3.6403 0.000581 0.000291 M10 71.9062474424826 39.24702 1.8321 0.072066 0.036033 M11 -4.59415795677702 39.209039 -0.1172 0.907129 0.453565 t 1.39464647082141 1.707909 0.8166 0.417509 0.208755 Multiple Linear Regression - Regression Statistics Multiple R 0.920058091199456 R-squared 0.846506891181586 Adjusted R-squared 0.812103263342976 F-TEST (value) 24.6051635935784 F-TEST (DF numerator) 13 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 67.8506144015972 Sum Squared Residuals 267014.940731106 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 627 638.460957113301 -11.4609571133011 2 696 622.417372842448 73.5826271575524 3 825 728.88751798271 96.11248201729 4 677 621.742044442093 55.2579555579069 5 656 628.143818129193 27.8561818708069 6 785 764.793023527277 20.2069764727229 7 412 389.366393757264 22.6336062427363 8 352 300.850268308642 51.1497316913585 9 839 789.718455278537 49.2815447214629 10 729 720.962361833865 8.0376381661353 11 696 645.587909167789 50.4120908322114 12 641 650.837805816883 -9.83780581688329 13 695 660.189940054259 34.8100599457414 14 638 644.840481272303 -6.84048127230322 15 762 752.452574797526 9.54742520247396 16 635 644.635366912815 -9.63536691281483 17 721 650.499753124639 70.500246875361 18 854 787.955039735636 66.0449602643637 19 418 413.155362020111 4.84463797988871 20 367 324.594454281883 42.4055457181171 21 824 813.574596975793 10.4254030242065 22 687 743.295905684508 -56.2959056845076 23 601 668.12297332166 -67.1229733216599 24 676 674.066995459652 1.93300454034796 25 740 682.859351076949 57.1406489230513 26 691 666.994895964521 24.0051040354789 27 683 773.062000498327 -90.062000498327 28 594 665.871744668104 -71.8717446681038 29 729 672.273518355203 56.7264816447966 30 731 808.989897187697 -77.9898971876969 31 386 433.60804970729 -47.6080497072901 32 331 345.114315403471 -14.1143154034712 33 707 833.15403001565 -126.154030015650 34 715 762.830556434758 -47.8305564347575 35 657 688.306967271201 -31.3069672712011 36 653 694.609247726044 -41.6092477260435 37 642 703.446385632946 -61.4463856329465 38 643 687.850624258157 -44.8506242581565 39 718 794.029684515978 -76.0296845159781 40 654 686.772255251346 -32.7722552513456 41 632 692.009689408682 -60.0096894086816 42 731 828.748459385978 -97.7484593859784 43 392 453.5681322088 -61.5681322087999 44 344 364.492228140099 -20.4922281400992 45 792 853.427588544403 -61.4275885444035 46 852 784.828233113353 67.1717668866466 47 649 711.96158866523 -62.9615886652296 48 629 718.622127436922 -89.6221274369223 49 685 728.332519991148 -43.3325199911479 50 617 713.206972657224 -96.206972657224 51 715 821.154933354494 -106.154933354494 52 715 713.38250775939 1.61749224061092 53 629 721.127750134677 -92.1277501346774 54 916 859.277162234572 56.7228377654278 55 531 484.410311084638 46.5896889153623 56 357 394.080502906961 -37.080502906961 57 917 883.19499246969 33.8050075303097 58 828 812.893910033601 15.1060899663987 59 708 736.287944403352 -28.2879444033524 60 858 741.739361355675 116.260638644325 61 775 750.710846131397 24.2891538686028 62 785 734.689653005348 50.3103469946525 63 1006 839.413288850965 166.586711149035 64 789 731.596080966254 57.4039190337465 65 734 736.945470847605 -2.9454708476054 66 906 873.236417928839 32.7635820711609 67 532 496.891751221897 35.1082487781028 68 387 408.868230958944 -21.8682309589442 69 991 896.930336715926 94.069663284074 70 841 827.189032899915 13.8109671000845 71 892 752.732617170768 139.267382829232 72 782 759.124462204823 22.8755377951765 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.149205321997006 0.298410643994012 0.850794678002994 18 0.253624662133675 0.507249324267349 0.746375337866325 19 0.168289288950372 0.336578577900744 0.831710711049628 20 0.116721696046893 0.233443392093786 0.883278303953107 21 0.067604368639685 0.13520873727937 0.932395631360315 22 0.053292267853691 0.106584535707382 0.94670773214631 23 0.0749611431852895 0.149922286370579 0.92503885681471 24 0.054226317809821 0.108452635619642 0.945773682190179 25 0.0744801514460085 0.148960302892017 0.925519848553991 26 0.0594316167063397 0.118863233412679 0.94056838329366 27 0.131364670361864 0.262729340723727 0.868635329638136 28 0.096331759845679 0.192663519691358 0.903668240154321 29 0.176111034926723 0.352222069853447 0.823888965073277 30 0.159762043320385 0.319524086640771 0.840237956679615 31 0.113219805563506 0.226439611127012 0.886780194436494 32 0.104413307837514 0.208826615675029 0.895586692162486 33 0.105628777421686 0.211257554843372 0.894371222578314 34 0.104793504242500 0.209587008485000 0.8952064957575 35 0.0885221135537612 0.177044227107522 0.911477886446239 36 0.0641950641618797 0.128390128323759 0.93580493583812 37 0.0421572785951483 0.0843145571902967 0.957842721404852 38 0.0297516117867906 0.0595032235735812 0.97024838821321 39 0.0182331154683073 0.0364662309366147 0.981766884531693 40 0.0141672366606608 0.0283344733213216 0.98583276333934 41 0.0113080216973767 0.0226160433947533 0.988691978302623 42 0.00729227238777909 0.0145845447755582 0.99270772761222 43 0.00447997707805712 0.00895995415611424 0.995520022921943 44 0.00520612512933131 0.0104122502586626 0.994793874870669 45 0.00323053409898548 0.00646106819797096 0.996769465901014 46 0.108452086714887 0.216904173429775 0.891547913285113 47 0.0826711665440701 0.165342333088140 0.91732883345593 48 0.0535767491673835 0.107153498334767 0.946423250832616 49 0.0508001577789021 0.101600315557804 0.949199842221098 50 0.0335644299147566 0.0671288598295132 0.966435570085243 51 0.173234938606203 0.346469877212406 0.826765061393797 52 0.135835153108941 0.271670306217882 0.864164846891059 53 0.117830331163163 0.235660662326327 0.882169668836837 54 0.112766623801230 0.225533247602460 0.88723337619877 55 0.0811550068936111 0.162310013787222 0.918844993106389 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0512820512820513 NOK 5% type I error level 7 0.179487179487179 NOK 10% type I error level 10 0.256410256410256 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/10bree1291138261.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/10bree1291138261.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/1m8hk1291138261.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/1m8hk1291138261.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/2m8hk1291138261.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/2m8hk1291138261.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/3fhyn1291138261.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/3fhyn1291138261.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/4fhyn1291138261.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/4fhyn1291138261.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/5fhyn1291138261.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/5fhyn1291138261.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/6pqf81291138261.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/6pqf81291138261.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/700wt1291138261.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/700wt1291138261.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/800wt1291138261.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/800wt1291138261.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/900wt1291138261.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291138179z63evnujf3r12no/900wt1291138261.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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