*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, 19 Nov 2009 03:28:54 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm.htm/, Retrieved Thu, 19 Nov 2009 11:29:59 +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/19/t1258626586dfbtid1axz4swcm.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:

Dataseries X:

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501 98.1 509 510 517 519 507 104.5 501 509 510 517 569 87.4 507 501 509 510 580 89.9 569 507 501 509 578 109.8 580 569 507 501 565 111.7 578 580 569 507 547 98.6 565 578 580 569 555 96.9 547 565 578 580 562 95.1 555 547 565 578 561 97 562 555 547 565 555 112.7 561 562 555 547 544 102.9 555 561 562 555 537 97.4 544 555 561 562 543 111.4 537 544 555 561 594 87.4 543 537 544 555 611 96.8 594 543 537 544 613 114.1 611 594 543 537 611 110.3 613 611 594 543 594 103.9 611 613 611 594 595 101.6 594 611 613 611 591 94.6 595 594 611 613 589 95.9 591 595 594 611 584 104.7 589 591 595 594 573 102.8 584 589 591 595 567 98.1 573 584 589 591 569 113.9 567 573 584 589 621 80.9 569 567 573 584 629 95.7 621 569 567 573 628 113.2 629 621 569 567 612 105.9 628 629 621 569 595 108.8 612 628 629 621 597 102.3 595 612 628 629 593 99 597 595 612 628 590 100.7 593 597 595 612 580 115.5 590 593 597 595 574 100.7 580 590 593 597 573 109.9 574 580 590 593 573 114.6 573 574 580 590 620 85.4 573 573 5 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] = + 47.8128014249776 -0.324121971901887X[t] + 1.04159402162681Y1[t] -0.0115728999770883Y2[t] + 0.120744435018517Y3[t] -0.187044380770622Y4[t] -2.37853399760623M1[t] + 12.7571732943496M2[t] + 54.6403772282848M3[t] + 12.2017243573166M4[t] -1.85566430621556M5[t] -14.7445527046213M6[t] -8.75987229083388M7[t] + 11.4755668209098M8[t] + 10.6272966968030M9[t] + 3.67932141273498M10[t] -1.60302960241247M11[t] + 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) 47.8128014249776 26.165866 1.8273 0.073509 0.036755 X -0.324121971901887 0.188693 -1.7177 0.091914 0.045957 Y1 1.04159402162681 0.145148 7.1761 0 0 Y2 -0.0115728999770883 0.20495 -0.0565 0.955191 0.477595 Y3 0.120744435018517 0.211135 0.5719 0.569913 0.284957 Y4 -0.187044380770622 0.142708 -1.3107 0.195837 0.097919 M1 -2.37853399760623 4.191388 -0.5675 0.572877 0.286439 M2 12.7571732943496 4.415046 2.8895 0.005653 0.002827 M3 54.6403772282848 5.502544 9.93 0 0 M4 12.2017243573166 9.896135 1.233 0.223237 0.111618 M5 -1.85566430621556 10.661253 -0.1741 0.86251 0.431255 M6 -14.7445527046213 8.80015 -1.6755 0.09996 0.04998 M7 -8.75987229083388 4.229563 -2.0711 0.043427 0.021714 M8 11.4755668209098 4.796649 2.3924 0.02046 0.01023 M9 10.6272966968030 5.58233 1.9037 0.062594 0.031297 M10 3.67932141273498 5.43817 0.6766 0.501734 0.250867 M11 -1.60302960241247 4.650111 -0.3447 0.731715 0.365858 Multiple Linear Regression - Regression Statistics Multiple R 0.99011755108764 R-squared 0.980332764971788 Adjusted R-squared 0.97416265202176 F-TEST (value) 158.884087359767 F-TEST (DF numerator) 16 F-TEST (DF denominator) 51 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.54603616320942 Sum Squared Residuals 2185.38006195232 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 501 503.25591928815 -2.25591928814988 2 507 507.524944403306 -0.524944403306282 3 569 562.481347616717 6.51865238328289 4 580 582.962870657616 -2.96287065761648 5 578 575.416290848828 2.58370915117167 6 565 566.064969447332 -1.06496944733168 7 547 552.509508389265 -5.50950838926462 8 555 552.398733105147 2.60126689485288 9 562 559.479358009366 2.52064199063367 10 561 559.37230304994 1.62769695005948 11 555 552.211387088486 2.78861291151368 12 544 550.101676784718 -6.10167678471817 13 537 536.687661694127 0.312338305872941 14 543 539.584382898476 3.41561710152369 15 594 595.371166087077 -1.37116608707733 16 611 604.149901526683 6.85009847331661 17 613 603.635860493578 9.36413950642165 18 611 598.900784233364 12.0992157666365 19 594 597.366903400128 -3.36690340012829 20 595 597.725604876481 -2.72560487648112 21 591 599.768944245347 -8.76894424534657 22 589 586.543094777548 2.45690522245175 23 584 579.672072874438 4.32792712556199 24 573 576.03608779444 -3.03608779443951 25 567 564.287945979808 2.71205402019188 26 569 567.95063047215 1.04936952785008 27 621 622.289518040613 -1.28951804061298 28 629 630.526624888503 -1.52662488850253 29 628 619.891818245554 8.10818175444596 30 612 614.13946488001 -2.13946488001017 31 595 593.769907809306 1.23009219069397 32 597 596.973108289206 0.0268917107939254 33 593 597.729501435714 -4.72950143571398 34 590 586.981051609966 3.01894839003347 35 580 577.244448288837 2.75555171116333 36 574 572.406195057445 1.59380494255507 37 573 561.297848006378 11.7021519936217 38 573 573.291714200758 -0.291714200757703 39 620 625.7968298118 -5.79682981180021 40 626 628.420376031179 -2.42037603117879 41 620 615.620725381058 4.379274618942 42 588 601.536816546666 -13.5368165466659 43 566 567.360145480997 -1.3601454809966 44 557 566.737045515213 -9.73704551521297 45 561 552.730989472384 8.26901052761555 46 549 553.609474369201 -4.60947436920137 47 532 534.434333335735 -2.43433333573538 48 526 524.751865580381 1.24813441961923 49 511 513.086205699233 -2.08620569923344 50 499 510.298753662557 -11.2987536625574 51 555 550.317523405593 4.68247659440668 52 565 561.865883233545 3.13411676645457 53 542 556.242872512116 -14.2428725121159 54 527 525.079006024978 1.92099397502165 55 510 509.388421785174 0.611578214825827 56 514 511.753612442081 2.24638755791865 57 517 514.291206837189 2.70879316281134 58 508 510.494076193343 -2.49407619334333 59 493 500.437758412504 -7.43775841250361 60 490 483.704174783017 6.29582521698337 61 469 479.384419332303 -10.3844193323032 62 478 470.349574362752 7.65042563724764 63 528 530.743615038199 -2.74361503819905 64 534 537.074343662473 -3.07434366247339 65 518 528.192432518865 -10.1924325188653 66 506 503.27895886765 2.72104113234962 67 502 493.60511313513 8.39488686486972 68 516 508.411895771871 7.58810422812863 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.626946598734633 0.746106802530733 0.373053401265367 21 0.51233834119257 0.97532331761486 0.48766165880743 22 0.366202291198321 0.732404582396641 0.63379770880168 23 0.255878374353227 0.511756748706455 0.744121625646773 24 0.17158102164555 0.3431620432911 0.82841897835445 25 0.114031506007924 0.228063012015847 0.885968493992076 26 0.0753069087766567 0.150613817553313 0.924693091223343 27 0.0420396572060157 0.0840793144120313 0.957960342793984 28 0.0265241006665830 0.0530482013331661 0.973475899333417 29 0.0224475041717274 0.0448950083434547 0.977552495828273 30 0.0174266815526796 0.0348533631053592 0.98257331844732 31 0.00957563312048926 0.0191512662409785 0.99042436687951 32 0.00479969732053865 0.0095993946410773 0.995200302679461 33 0.005810501518547 0.011621003037094 0.994189498481453 34 0.00298442816355333 0.00596885632710667 0.997015571836447 35 0.00257884379998193 0.00515768759996385 0.997421156200018 36 0.00168693624809591 0.00337387249619181 0.998313063751904 37 0.00625080632695103 0.0125016126539021 0.99374919367305 38 0.00592128921153452 0.0118425784230690 0.994078710788465 39 0.00372476141882594 0.00744952283765188 0.996275238581174 40 0.00215302022667656 0.00430604045335312 0.997846979773323 41 0.0694148499149473 0.138829699829895 0.930585150085053 42 0.261916919294390 0.523833838588779 0.73808308070561 43 0.183680294584735 0.367360589169471 0.816319705415265 44 0.233693174084284 0.467386348168567 0.766306825915716 45 0.167186000951117 0.334372001902233 0.832813999048883 46 0.119576515694294 0.239153031388588 0.880423484305706 47 0.158649687963686 0.317299375927372 0.841350312036314 48 0.171277409734488 0.342554819468976 0.828722590265512 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 6 0.206896551724138 NOK 5% type I error level 12 0.413793103448276 NOK 10% type I error level 14 0.482758620689655 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/10dmiz1258626530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/10dmiz1258626530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/14tff1258626530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/14tff1258626530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/2nxaw1258626530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/2nxaw1258626530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/3uclp1258626530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/3uclp1258626530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/4d1we1258626530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/4d1we1258626530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/5wsup1258626530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/5wsup1258626530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/61bsr1258626530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/61bsr1258626530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/7uimc1258626530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/7uimc1258626530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/82q551258626530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/82q551258626530.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/9xgek1258626530.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258626586dfbtid1axz4swcm/9xgek1258626530.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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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