VFD

*Unverified author*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Mon, 22 Dec 2008 02:33:54 -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/22/t1229938553v80x2ad4ril1f2b.htm/, Retrieved Mon, 22 Dec 2008 10:35:54 +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/2008/Dec/22/t1229938553v80x2ad4ril1f2b.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}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

485 0 464 0 460 0 467 0 460 0 448 0 443 0 436 0 431 0 484 0 510 0 513 0 503 0 471 0 471 0 476 0 475 0 470 0 461 0 455 0 456 0 517 0 525 0 523 0 519 0 509 0 512 0 519 0 517 0 510 0 509 0 501 0 507 0 569 0 580 0 578 0 565 1 547 1 555 1 562 1 561 1 555 1 544 1 537 1 543 1 594 1 611 1 613 1 611 1 594 1 595 1 591 1 589 1 584 1 573 1 567 1 569 1 621 1 629 1 628 1 612 1 595 1 597 1 593 1 590 1 580 1 574 1 573 1 573 1 620 1 626 1 620 1 588 1 566 1 557 1 561 1 549 1 532 1 526 1 511 1 499 1 555 1 565 1 542 1 527 1 510 1 514 1 517 1 508 1 493 1 490 1 469 1 478 1 528 1

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 8 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 544.98245362402 + 99.8637884872825D[t] -30.8934816647542M1[t] -49.5561830655957M2[t] -48.3438844664372M3[t] -44.6315858672786M4[t] -48.6692872681201M5[t] -57.7069886689615M6[t] -63.619690069803M7[t] -71.9073914706445M8[t] -70.445092871486M9[t] -15.8577942723274M10[t] + 3.55555854369861M11[t] -0.58729859915854t + 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) 544.98245362402 13.569928 40.161 0 0 D 99.8637884872825 12.747178 7.8342 0 0 M1 -30.8934816647542 16.622932 -1.8585 0.066778 0.033389 M2 -49.5561830655957 16.600708 -2.9852 0.003759 0.001879 M3 -48.3438844664372 16.581625 -2.9155 0.004605 0.002303 M4 -44.6315858672786 16.565695 -2.6942 0.008595 0.004297 M5 -48.6692872681201 16.552926 -2.9402 0.004287 0.002143 M6 -57.7069886689615 16.543325 -3.4882 0.000793 0.000397 M7 -63.619690069803 16.536898 -3.8471 0.000239 0.00012 M8 -71.9073914706445 16.53365 -4.3492 4e-05 2e-05 M9 -70.445092871486 16.53358 -4.2607 5.5e-05 2.8e-05 M10 -15.8577942723274 16.536691 -0.9589 0.340475 0.170238 M11 3.55555854369861 17.07285 0.2083 0.835557 0.417778 t -0.58729859915854 0.229296 -2.5613 0.012306 0.006153 Multiple Linear Regression - Regression Statistics Multiple R 0.822840737610512 R-squared 0.677066879471412 Adjusted R-squared 0.624590247385517 F-TEST (value) 12.9022548238836 F-TEST (DF numerator) 13 F-TEST (DF denominator) 80 p-value 9.9920072216264e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 31.937496325344 Sum Squared Residuals 81600.293722509 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 485 513.501673360107 -28.5016733601068 2 464 494.251673360107 -30.2516733601070 3 460 494.876673360107 -34.8766733601071 4 467 498.001673360107 -31.0016733601071 5 460 493.376673360107 -33.3766733601071 6 448 483.751673360107 -35.7516733601071 7 443 477.251673360107 -34.2516733601071 8 436 468.376673360107 -32.3766733601071 9 431 469.251673360107 -38.2516733601071 10 484 523.251673360107 -39.2516733601071 11 510 542.077727576975 -32.0777275769745 12 513 537.934870434117 -24.9348704341174 13 503 506.454090170205 -3.45409017020467 14 471 487.204090170205 -16.2040901702046 15 471 487.829090170205 -16.8290901702046 16 476 490.954090170205 -14.9540901702046 17 475 486.329090170205 -11.3290901702046 18 470 476.704090170205 -6.70409017020463 19 461 470.204090170205 -9.20409017020463 20 455 461.329090170205 -6.32909017020463 21 456 462.204090170205 -6.20409017020463 22 517 516.204090170205 0.795909829795377 23 525 535.030144387072 -10.0301443870721 24 523 530.887287244215 -7.88728724421495 25 519 499.406506980302 19.5934930196978 26 509 480.156506980302 28.8434930196978 27 512 480.781506980302 31.2184930196978 28 519 483.906506980302 35.0934930196978 29 517 479.281506980302 37.7184930196978 30 510 469.656506980302 40.3434930196979 31 509 463.156506980302 45.8434930196978 32 501 454.281506980302 46.7184930196978 33 507 455.156506980302 51.8434930196978 34 569 509.156506980302 59.8434930196978 35 580 527.98256119717 52.0174388028304 36 578 523.839704054313 54.1602959456875 37 565 592.222712277682 -27.2227122776822 38 547 572.972712277682 -25.9727122776822 39 555 573.597712277682 -18.5977122776822 40 562 576.722712277682 -14.7227122776822 41 561 572.097712277682 -11.0977122776822 42 555 562.472712277682 -7.47271227768217 43 544 555.972712277682 -11.9727122776822 44 537 547.097712277682 -10.0977122776822 45 543 547.972712277682 -4.97271227768217 46 594 601.972712277682 -7.97271227768215 47 611 620.79876649455 -9.79876649454962 48 613 616.655909351692 -3.65590935169247 49 611 585.17512908778 25.8248709122203 50 594 565.92512908778 28.0748709122203 51 595 566.55012908778 28.4498709122203 52 591 569.67512908778 21.3248709122203 53 589 565.05012908778 23.9498709122203 54 584 555.42512908778 28.5748709122203 55 573 548.92512908778 24.0748709122203 56 567 540.05012908778 26.9498709122203 57 569 540.92512908778 28.0748709122203 58 621 594.92512908778 26.0748709122203 59 629 613.751183304647 15.2488166953528 60 628 609.60832616179 18.3916738382100 61 612 578.127545897877 33.8724541021227 62 595 558.877545897877 36.1224541021228 63 597 559.502545897877 37.4974541021228 64 593 562.627545897877 30.3724541021228 65 590 558.002545897877 31.9974541021228 66 580 548.377545897877 31.6224541021228 67 574 541.877545897877 32.1224541021228 68 573 533.002545897877 39.9974541021228 69 573 533.877545897877 39.1224541021228 70 620 587.877545897877 32.1224541021228 71 626 606.703600114745 19.2963998852553 72 620 602.560742971888 17.4392570281125 73 588 571.079962707975 16.9200372920252 74 566 551.829962707975 14.1700372920252 75 557 552.454962707975 4.54503729202525 76 561 555.579962707975 5.42003729202525 77 549 550.954962707975 -1.95496270797475 78 532 541.329962707975 -9.32996270797476 79 526 534.829962707975 -8.82996270797473 80 511 525.954962707975 -14.9549627079747 81 499 526.829962707975 -27.8299627079747 82 555 580.829962707975 -25.8299627079747 83 565 599.656016924842 -34.6560169248422 84 542 595.513159781985 -53.5131597819851 85 527 564.032379518072 -37.0323795180723 86 510 544.782379518072 -34.7823795180723 87 514 545.407379518072 -31.4073795180723 88 517 548.532379518072 -31.5323795180723 89 508 543.907379518072 -35.9073795180723 90 493 534.282379518072 -41.2823795180723 91 490 527.782379518072 -37.7823795180723 92 469 518.907379518072 -49.9073795180723 93 478 519.782379518072 -41.7823795180723 94 528 573.782379518072 -45.7823795180723 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00287092487207398 0.00574184974414797 0.997129075127926 18 0.00127347421862568 0.00254694843725135 0.998726525781374 19 0.00023364972948472 0.00046729945896944 0.999766350270515 20 4.55901323410935e-05 9.1180264682187e-05 0.999954409867659 21 2.47129659273825e-05 4.9425931854765e-05 0.999975287034073 22 4.99231254720826e-05 9.98462509441652e-05 0.999950076874528 23 1.29737333201065e-05 2.59474666402129e-05 0.99998702626668 24 4.90112228308848e-06 9.80224456617697e-06 0.999995098877717 25 1.02356455573328e-06 2.04712911146656e-06 0.999998976435444 26 1.92062457441306e-06 3.84124914882612e-06 0.999998079375426 27 3.99188016013903e-06 7.98376032027806e-06 0.99999600811984 28 4.51861666447420e-06 9.03723332894839e-06 0.999995481383335 29 4.46644802782184e-06 8.93289605564369e-06 0.999995533551972 30 3.89324469774223e-06 7.78648939548445e-06 0.999996106755302 31 5.45526778271429e-06 1.09105355654286e-05 0.999994544732217 32 4.57322431709617e-06 9.14644863419234e-06 0.999995426775683 33 7.55127198684428e-06 1.51025439736886e-05 0.999992448728013 34 1.48757179013733e-05 2.97514358027466e-05 0.999985124282099 35 1.19086323786157e-05 2.38172647572313e-05 0.999988091367621 36 7.19153434555186e-06 1.43830686911037e-05 0.999992808465654 37 5.57625592987495e-06 1.11525118597499e-05 0.99999442374407 38 5.23054832999914e-06 1.04610966599983e-05 0.99999476945167 39 5.48936424168459e-06 1.09787284833692e-05 0.999994510635758 40 5.26054053045984e-06 1.05210810609197e-05 0.99999473945947 41 5.22852973653325e-06 1.04570594730665e-05 0.999994771470263 42 5.28533688165397e-06 1.05706737633079e-05 0.999994714663118 43 6.9750443927071e-06 1.39500887854142e-05 0.999993024955607 44 1.09680612180486e-05 2.19361224360973e-05 0.999989031938782 45 2.02161313938931e-05 4.04322627877861e-05 0.999979783868606 46 5.82709489698477e-05 0.000116541897939695 0.99994172905103 47 0.000179622700072342 0.000359245400144684 0.999820377299928 48 0.000388032068632805 0.00077606413726561 0.999611967931367 49 0.000426930585638487 0.000853861171276973 0.999573069414362 50 0.000550449989817654 0.00110089997963531 0.999449550010182 51 0.000685164405965075 0.00137032881193015 0.999314835594035 52 0.00116597938368851 0.00233195876737703 0.998834020616311 53 0.00173310558054062 0.00346621116108124 0.99826689441946 54 0.00183850618209862 0.00367701236419723 0.998161493817901 55 0.00372505865821503 0.00745011731643005 0.996274941341785 56 0.00636255122071879 0.0127251024414376 0.993637448779281 57 0.0118777431476233 0.0237554862952467 0.988122256852377 58 0.0320840590338745 0.0641681180677489 0.967915940966126 59 0.107442171275702 0.214884342551403 0.892557828724298 60 0.160502608907609 0.321005217815219 0.83949739109239 61 0.230868183824885 0.46173636764977 0.769131816175115 62 0.263409677225093 0.526819354450186 0.736590322774907 63 0.261980773542459 0.523961547084918 0.738019226457541 64 0.370338318646104 0.740676637292208 0.629661681353896 65 0.409339005053465 0.81867801010693 0.590660994946535 66 0.418233502219938 0.836467004439876 0.581766497780062 67 0.413201885340556 0.826403770681112 0.586798114659444 68 0.383351969730245 0.76670393946049 0.616648030269755 69 0.382345406652684 0.764690813305367 0.617654593347316 70 0.376025707878868 0.752051415757736 0.623974292121132 71 0.435198247834966 0.870396495669932 0.564801752165034 72 0.836553878243794 0.326892243512412 0.163446121756206 73 0.945729214232102 0.108541571535795 0.0542707857678977 74 0.98031034026229 0.0393793194754214 0.0196896597377107 75 0.973671748756206 0.052656502487589 0.0263282512437945 76 0.961635239010658 0.0767295219786838 0.0383647609893419 77 0.928742821094928 0.142514357810143 0.0712571789050716 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 39 0.639344262295082 NOK 5% type I error level 42 0.688524590163934 NOK 10% type I error level 45 0.737704918032787 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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