*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, 21 Dec 2010 15:18:04 +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/Dec/21/t1292944815dmaq6a9j507q3kg.htm/, Retrieved Tue, 21 Dec 2010 16:20:18 +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/Dec/21/t1292944815dmaq6a9j507q3kg.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:

» Textbox « » Textfile « » CSV «

4,031636 0,5215052 9,166456 1,303763 3,702076 0,4248284 7,970589 1,416094 3,056176 0,4250311 7,104091 1,052458 3,280707 0,4771938 6,621064 1,312283 2,984728 0,8280212 7,529215 1,309429 3,693712 0,6156186 8,170938 1,492409 3,226317 0,366627 8,15745 1,026556 2,190349 0,4308883 7,378962 1,005406 2,599515 0,2810287 7,921496 1,334886 3,080288 0,4646245 8,15674 1,393873 2,929672 0,2693951 8,856365 1,128092 2,922548 0,5779049 8,817177 1,122787 3,234943 0,5661151 8,734347 1,213104 2,983081 0,5077584 9,345927 1,253528 3,284389 0,7507175 8,99297 1,094796 3,806511 0,6808395 10,78512 0,9129438 3,784579 0,7661091 8,886867 1,19513 2,645654 0,4561473 8,818847 0,9274994 3,092081 0,4977496 8,823744 0,9653326 3,204859 0,4193273 9,165298 1,198078 3,107225 0,6095514 8,652657 0,966362 3,466909 0,457337 8,173054 0,9736851 2,984404 0,5705478 7,563416 0,9948013 3,218072 0,3478996 7,595809 0,8262616 2,82731 0,3874993 8,381467 0,6888877 3,182049 0,5824285 7,216432 0,7813066 2,236319 0,2391033 6,540178 0,60479 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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation firearmsuicide[t] = + 1.71199639506215 + 3.03670572133133firearmhomicide[t] -0.0130633968862607nonfirearmsuicide[t] + 0.158490113952911nonfirearmhomicide[t] -0.00960612631308565t + 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) 1.71199639506215 0.318443 5.3762 1e-06 0 firearmhomicide 3.03670572133133 0.300588 10.1026 0 0 nonfirearmsuicide -0.0130633968862607 0.032495 -0.402 0.688686 0.344343 nonfirearmhomicide 0.158490113952911 0.205871 0.7699 0.443521 0.221761 t -0.00960612631308565 0.002101 -4.5718 1.6e-05 8e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.793536557853685 R-squared 0.629700268650275 Adjusted R-squared 0.6122743989397 F-TEST (value) 36.1359449547662 F-TEST (DF numerator) 4 F-TEST (DF denominator) 85 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.423902038319824 Sum Squared Residuals 15.2738997377946 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4.031636 3.37293658696225 0.65869941303775 2 3.702076 3.10317690720379 0.598899092796214 3 3.056176 3.04787301733818 0.00830298266181571 4 3.280707 3.24415932782078 0.036547672179217 5 2.984728 4.28759690655662 -1.30286890655662 6 3.693712 3.65360402840894 0.040107971591058 7 3.226317 2.81422678985431 0.412090210145688 8 2.190349 3.0065809527165 -0.816231952716502 9 2.599515 2.58702730746591 0.012487692534093 10 3.080288 3.14122336803985 -0.060935368039852 11 2.929672 2.48749986575162 0.442172134248385 12 2.922548 3.41441835252797 -0.491870352527972 13 3.234943 3.38440646588751 -0.149463465887508 14 2.983081 3.19600570690514 -0.21292470690514 15 3.284389 3.90364823421837 -0.619259234218375 16 3.806511 3.6296098428798 0.176901157120201 17 3.784579 3.94846375406582 -0.163884754065821 18 2.645654 2.9560666242635 -0.310412624263496 19 3.092081 3.0787266571056 0.0133543428943958 20 3.204859 2.86340107321048 0.341457926789517 21 3.107225 3.40142169730096 -0.294196697300956 22 3.466909 2.93701111492919 0.529897885070814 23 2.984404 3.2825035247878 -0.298099524787795 24 3.218072 2.56964529681666 0.648426703183338 25 2.82731 2.64825603872056 0.179053961279442 26 3.182049 3.2604593258858 -0.0784103258857988 27 2.236319 2.18913374974791 0.0471852502520882 28 2.033218 2.25260512759844 -0.219387127598441 29 1.644804 2.28189840307892 -0.637094403078917 30 1.627971 2.79903725047044 -1.17106625047044 31 1.677559 2.55235569162686 -0.874796691626857 32 2.330828 2.57757987037303 -0.246751870373027 33 2.493615 2.6664867297555 -0.172871729755496 34 2.257172 2.59101512718635 -0.333843127186351 35 2.655517 2.11860186273048 0.536915137269525 36 2.298655 1.89702102074704 0.401633979252958 37 2.600402 2.74471767206868 -0.144315672068676 38 3.04523 2.94715823910389 0.0980717608961076 39 2.790583 2.51590549090073 0.274677509099269 40 3.227052 2.68448957674478 0.54256242325522 41 2.967479 2.63368143669193 0.333797563308075 42 2.938817 2.36223504059837 0.576581959401631 43 3.277961 2.88483236728918 0.393128632710816 44 3.423985 2.99453233624501 0.42945266375499 45 3.072646 2.98397314539265 0.0886728546073463 46 2.754253 2.82877543266636 -0.0745224326663612 47 2.910431 2.88522986430416 0.0252011356958406 48 3.174369 3.07194003461006 0.102428965389942 49 3.068387 2.89408967619416 0.174297323805842 50 3.089543 2.97057688410901 0.118966115890993 51 2.906654 2.73312230179828 0.173531698201724 52 2.931161 2.84164065105412 0.0895203489458812 53 3.02566 2.87446860517032 0.151191394829684 54 2.939551 3.30565040098829 -0.366099400988293 55 2.691019 2.52396879536994 0.167050204630056 56 3.19812 2.92846536243766 0.269654637562337 57 3.07639 3.02546293892844 0.0509270610715593 58 2.863873 2.65657793003519 0.207295069964811 59 3.013802 3.03875547125176 -0.024953471251759 60 3.053364 3.02573728451561 0.0276267154843931 61 2.864753 3.09840593296446 -0.233652932964459 62 3.057062 3.0793341660037 -0.022272166003703 63 2.959365 3.3308664358065 -0.371501435806499 64 3.252258 2.65808552900681 0.594172470993186 65 3.602988 3.29166106254232 0.311326937457679 66 3.497704 3.29961248017486 0.198091519825142 67 3.296867 3.00260712004901 0.294259879950987 68 3.602417 3.26282742001544 0.339589579984557 69 3.3001 2.96680398185742 0.333296018142576 70 3.40193 3.55072480013786 -0.148794800137863 71 3.502591 2.97320441558394 0.529386584416058 72 3.402348 2.94432456200323 0.458023437996767 73 3.498551 2.92819966817301 0.570351331826995 74 3.199823 3.26931375219066 -0.0694907521906572 75 2.700064 2.59032837554032 0.109735624459678 76 2.801034 2.60555492929209 0.195479070707907 77 2.898628 2.57716929942544 0.321458700574556 78 2.800854 2.86485436154323 -0.0640003615432274 79 2.399942 2.04009362834654 0.359848371653462 80 2.402724 1.86758870196781 0.535135298032187 81 2.202331 2.00772030908078 0.194610690919225 82 2.102594 2.60810458211481 -0.505510582114813 83 1.798293 2.18663182972079 -0.388338829720795 84 1.202484 1.80600896335049 -0.603524963350491 85 1.400201 1.97278033561879 -0.572579335618785 86 1.200832 1.90398233924564 -0.703150339245641 87 1.298083 1.72301343927642 -0.424930439276422 88 1.099742 1.59893053950116 -0.499188539501157 89 1.001377 1.50185587568484 -0.500478875684836 90 0.8361743 1.38803492437419 -0.551860624374186 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.400822109036709 0.801644218073418 0.599177890963291 9 0.432325817147636 0.864651634295271 0.567674182852364 10 0.328921831309569 0.657843662619137 0.671078168690431 11 0.24228870315743 0.48457740631486 0.75771129684257 12 0.186820010577495 0.373640021154989 0.813179989422505 13 0.210664917133697 0.421329834267395 0.789335082866303 14 0.14216432289632 0.28432864579264 0.85783567710368 15 0.188011941389334 0.376023882778668 0.811988058610666 16 0.162608899836193 0.325217799672385 0.837391100163807 17 0.351675975095469 0.703351950190937 0.648324024904531 18 0.285782692938843 0.571565385877686 0.714217307061157 19 0.290230159556658 0.580460319113316 0.709769840443342 20 0.253282706927829 0.506565413855659 0.74671729307217 21 0.249095924420917 0.498191848841835 0.750904075579083 22 0.468396082183404 0.936792164366808 0.531603917816596 23 0.444956603231645 0.88991320646329 0.555043396768355 24 0.551196231502434 0.897607536995131 0.448803768497566 25 0.479760080469389 0.959520160938778 0.520239919530611 26 0.454292701385494 0.908585402770989 0.545707298614506 27 0.397800327731411 0.795600655462821 0.602199672268589 28 0.390209888183589 0.780419776367178 0.609790111816411 29 0.425029838290003 0.850059676580007 0.574970161709997 30 0.768412879204533 0.463174241590935 0.231587120795467 31 0.887156223347081 0.225687553305837 0.112843776652919 32 0.89050426857983 0.21899146284034 0.10949573142017 33 0.891056961752104 0.217886076495791 0.108943038247896 34 0.913967010307553 0.172065979384894 0.0860329896924468 35 0.930354895670062 0.139290208659875 0.0696451043299376 36 0.911403818720171 0.177192362559658 0.0885961812798291 37 0.933240619221705 0.133518761556591 0.0667593807782954 38 0.945185538300404 0.109628923399191 0.0548144616995955 39 0.930374500996416 0.139250998007167 0.0696254990035836 40 0.946346638390752 0.107306723218496 0.053653361609248 41 0.939413268068858 0.121173463862284 0.060586731931142 42 0.92160206907634 0.15679586184732 0.0783979309236601 43 0.903065491579444 0.193869016841112 0.0969345084205562 44 0.877643096032509 0.244713807934982 0.122356903967491 45 0.85357122138099 0.292857557238021 0.146428778619011 46 0.86960850469909 0.260782990601821 0.13039149530091 47 0.85301185811048 0.29397628377904 0.14698814188952 48 0.85320887144442 0.29358225711116 0.14679112855558 49 0.895591083748815 0.208817832502371 0.104408916251185 50 0.881477693865649 0.237044612268702 0.118522306134351 51 0.874747190090173 0.250505619819654 0.125252809909827 52 0.849867511094938 0.300264977810123 0.150132488905062 53 0.852101758858103 0.295796482283795 0.147898241141897 54 0.839078582433605 0.321842835132789 0.160921417566395 55 0.810012215836566 0.379975568326868 0.189987784163434 56 0.796560727735869 0.406878544528262 0.203439272264131 57 0.748660473059176 0.502679053881647 0.251339526940824 58 0.695293643939821 0.609412712120357 0.304706356060179 59 0.687836026290889 0.624327947418223 0.312163973709111 60 0.649487191024007 0.701025617951985 0.350512808975993 61 0.640995664856847 0.718008670286307 0.359004335143153 62 0.806122079833012 0.387755840333977 0.193877920166988 63 0.970332730553334 0.0593345388933319 0.029667269446666 64 0.978875751977932 0.0422484960441366 0.0211242480220683 65 0.97647200042916 0.0470559991416806 0.0235279995708403 66 0.980504167394198 0.0389916652116049 0.0194958326058025 67 0.992805004207145 0.0143899915857104 0.0071949957928552 68 0.990049718106269 0.019900563787463 0.00995028189373152 69 0.99608899219362 0.00782201561275927 0.00391100780637963 70 0.99872923790038 0.00254152419923865 0.00127076209961932 71 0.99882861930789 0.00234276138422012 0.00117138069211006 72 0.998583618558482 0.00283276288303582 0.00141638144151791 73 0.99785899521616 0.00428200956767893 0.00214100478383947 74 0.997033525776622 0.00593294844675595 0.00296647422337797 75 0.998749965741036 0.00250006851792769 0.00125003425896384 76 0.999592729934534 0.000814540130932014 0.000407270065466007 77 0.998842595139618 0.00231480972076446 0.00115740486038223 78 0.996660164160105 0.00667967167978924 0.00333983583989462 79 0.990304205110888 0.0193915897782246 0.0096957948891123 80 0.973096110280135 0.0538077794397298 0.0269038897198649 81 0.983308116722417 0.033383766555165 0.0166918832775825 82 0.985413993276258 0.0291720134474834 0.0145860067237417 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 10 0.133333333333333 NOK 5% type I error level 18 0.24 NOK 10% type I error level 20 0.266666666666667 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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