paper multiple regressie

*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: Mon, 28 Dec 2009 08:24:16 -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/Dec/28/t12620139070hpsi63xvflvlvw.htm/, Retrieved Mon, 28 Dec 2009 16:25:19 +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/Dec/28/t12620139070hpsi63xvflvlvw.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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111.6 0 104.6 0 91.6 0 98.3 0 97.7 0 106.3 0 102.3 0 106.6 0 108.1 0 93.8 0 88.2 0 108.9 0 114.2 0 102.5 0 94.2 0 97.4 0 98.5 0 106.5 0 102.9 0 97.1 0 103.7 0 93.4 0 85.8 0 108.6 0 110.2 0 101.2 0 101.2 0 96.9 0 99.4 0 118.7 0 108.0 0 101.2 0 119.9 0 94.8 0 95.3 0 118.0 0 115.9 0 111.4 0 108.2 0 108.8 0 109.5 0 124.8 0 115.3 0 109.5 0 124.2 0 92.9 0 98.4 0 120.9 0 111.7 0 116.1 0 109.4 0 111.7 0 114.3 0 133.7 0 114.3 0 126.5 0 131.0 0 104.0 0 108.9 0 128.5 0 132.4 0 128.0 0 116.4 0 120.9 0 118.6 0 133.1 0 121.1 0 127.6 0 135.4 0 114.9 0 114.3 0 128.9 0 138.9 0 129.4 0 115.0 0 128.0 0 127.0 0 128.8 0 137.9 0 128.4 0 135.9 0 122.2 0 113.1 0 136.2 1 138.0 1 115.2 1 111.0 1 99.2 1 102.4 1 112.7 1 105.5 1 98.3 1 116.4 1 97.4 1 93.3 1 117.4 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation y[t] = + 104.027876197698 -22.2016888398704dummy[t] + 2.48493715677481M1[t] -5.99325813974861M2[t] -14.0839534362722M3[t] -12.7246487327957M4[t] -12.3653440293192M5[t] -0.6310393258427M6[t] -8.20923462236621M7[t] -10.1374299188897M8[t] -0.62812521541325M9[t] -21.1938205119368M10[t] -23.6220158084603M11[t] + 0.415695296523518t + 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) 104.027876197698 2.375712 43.7881 0 0 dummy -22.2016888398704 2.159455 -10.2812 0 0 M1 2.48493715677481 2.907594 0.8546 0.395242 0.197621 M2 -5.99325813974861 2.906488 -2.062 0.042372 0.021186 M3 -14.0839534362722 2.905627 -4.8471 6e-06 3e-06 M4 -12.7246487327957 2.905012 -4.3802 3.5e-05 1.7e-05 M5 -12.3653440293192 2.904643 -4.2571 5.5e-05 2.7e-05 M6 -0.6310393258427 2.904519 -0.2173 0.828544 0.414272 M7 -8.20923462236621 2.904643 -2.8262 0.005914 0.002957 M8 -10.1374299188897 2.905012 -3.4896 0.000781 0.000391 M9 -0.62812521541325 2.905627 -0.2162 0.829388 0.414694 M10 -21.1938205119368 2.906488 -7.2919 0 0 M11 -23.6220158084603 2.907594 -8.1242 0 0 t 0.415695296523518 0.026736 15.5481 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.911752520921038 R-squared 0.831292659405868 Adjusted R-squared 0.80454637370192 F-TEST (value) 31.0806767192788 F-TEST (DF numerator) 13 F-TEST (DF denominator) 82 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.79278986918573 Sum Squared Residuals 2751.62598642034 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 111.6 106.928508650996 4.67149134900355 2 104.6 98.866008650996 5.73399134900393 3 91.6 91.191008650996 0.408991349003984 4 98.3 92.9660086509961 5.33399134900389 5 97.7 93.741008650996 3.95899134900399 6 106.3 105.891008650996 0.408991349003931 7 102.3 98.728508650996 3.57149134900394 8 106.6 97.216008650996 9.38399134900394 9 108.1 107.141008650996 0.95899134900393 10 93.8 86.991008650996 6.80899134900393 11 88.2 84.978508650996 3.22149134900394 12 108.9 109.016219755980 -0.116219755979855 13 114.2 111.916852209278 2.28314779072180 14 102.5 103.854352209278 -1.35435220927828 15 94.2 96.1793522092783 -1.97935220927827 16 97.4 97.9543522092783 -0.554352209278254 17 98.5 98.7293522092783 -0.229352209278277 18 106.5 110.879352209278 -4.37935220927827 19 102.9 103.716852209278 -0.81685220927826 20 97.1 102.204352209278 -5.10435220927827 21 103.7 112.129352209278 -8.42935220927827 22 93.4 91.9793522092783 1.42064779072174 23 85.8 89.9668522092783 -4.16685220927826 24 108.6 114.004563314262 -5.40456331426208 25 110.2 116.905195767560 -6.70519576756042 26 101.2 108.842695767560 -7.64269576756048 27 101.2 101.167695767560 0.0323042324395135 28 96.9 102.942695767560 -6.04269576756047 29 99.4 103.717695767560 -4.31769576756049 30 118.7 115.867695767560 2.83230423243952 31 108 108.705195767560 -0.705195767560483 32 101.2 107.192695767560 -5.99269576756048 33 119.9 117.117695767560 2.78230423243952 34 94.8 96.9676957675605 -2.16769576756049 35 95.3 94.9551957675605 0.344804232439518 36 118 118.992906872544 -0.992906872544286 37 115.9 121.893539325843 -5.99353932584263 38 111.4 113.831039325843 -2.43103932584268 39 108.2 106.156039325843 2.0439606741573 40 108.8 107.931039325843 0.868960674157311 41 109.5 108.706039325843 0.793960674157299 42 124.8 120.856039325843 3.9439606741573 43 115.3 113.693539325843 1.6064606741573 44 109.5 112.181039325843 -2.68103932584270 45 124.2 122.106039325843 2.09396067415730 46 92.9 101.956039325843 -9.05603932584268 47 98.4 99.9435393258427 -1.54353932584269 48 120.9 123.981250430826 -3.08125043082649 49 111.7 126.881882884125 -15.1818828841249 50 116.1 118.819382884125 -2.71938288412491 51 109.4 111.144382884125 -1.74438288412491 52 111.7 112.919382884125 -1.21938288412490 53 114.3 113.694382884125 0.60561711587508 54 133.7 125.844382884125 7.85561711587508 55 114.3 118.681882884125 -4.38188288412491 56 126.5 117.169382884125 9.3306171158751 57 131 127.094382884125 3.90561711587509 58 104 106.944382884125 -2.94438288412491 59 108.9 104.931882884125 3.96811711587510 60 128.5 128.969593989109 -0.46959398910871 61 132.4 131.870226442407 0.529773557592938 62 128 123.807726442407 4.19227355759288 63 116.4 116.132726442407 0.267273557592871 64 120.9 117.907726442407 2.99227355759289 65 118.6 118.682726442407 -0.0827264424071377 66 133.1 130.832726442407 2.26727355759287 67 121.1 123.670226442407 -2.57022644240713 68 127.6 122.157726442407 5.44227355759288 69 135.4 132.082726442407 3.31727355759288 70 114.9 111.932726442407 2.96727355759288 71 114.3 109.920226442407 4.37977355759287 72 128.9 133.957937547391 -5.05793754739092 73 138.9 136.858570000689 2.04142999931072 74 129.4 128.796070000689 0.603929999310673 75 115 121.121070000689 -6.12107000068935 76 128 122.896070000689 5.10392999931067 77 127 123.671070000689 3.32892999931065 78 128.8 135.821070000689 -7.02107000068932 79 137.9 128.658570000689 9.24142999931067 80 128.4 127.146070000689 1.25392999931067 81 135.9 137.071070000689 -1.17107000068933 82 122.2 116.921070000689 5.27892999931066 83 113.1 114.908570000689 -1.80857000068934 84 136.2 116.744592265803 19.4554077341973 85 138 119.645224719101 18.3547752808989 86 115.2 111.582724719101 3.61727528089888 87 111 103.907724719101 7.09227528089886 88 99.2 105.682724719101 -6.48272471910111 89 102.4 106.457724719101 -4.05772471910113 90 112.7 118.607724719101 -5.90772471910113 91 105.5 111.445224719101 -5.94522471910113 92 98.3 109.932724719101 -11.6327247191011 93 116.4 119.857724719101 -3.45772471910112 94 97.4 99.7077247191011 -2.30772471910112 95 93.3 97.6952247191011 -4.39522471910113 96 117.4 121.732935824085 -4.33293582408493 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0311181134277341 0.0622362268554682 0.968881886572266 18 0.0072937961982188 0.0145875923964376 0.992706203801781 19 0.00152896039034983 0.00305792078069966 0.99847103960965 20 0.038780071915194 0.077560143830388 0.961219928084806 21 0.0236179621741697 0.0472359243483395 0.97638203782583 22 0.0104565723810792 0.0209131447621584 0.98954342761892 23 0.00441524713117402 0.00883049426234805 0.995584752868826 24 0.00177068167781521 0.00354136335563041 0.998229318322185 25 0.000701963789412308 0.00140392757882462 0.999298036210588 26 0.000271339887506164 0.000542679775012327 0.999728660112494 27 0.00356566816957861 0.00713133633915721 0.996434331830421 28 0.00169695165331692 0.00339390330663383 0.998303048346683 29 0.0008311015843362 0.0016622031686724 0.999168898415664 30 0.0103844198368912 0.0207688396737823 0.98961558016311 31 0.00755470595342012 0.0151094119068402 0.99244529404658 32 0.00455122271788162 0.00910244543576324 0.995448777282118 33 0.0204497910325862 0.0408995820651725 0.979550208967414 34 0.0122844722888364 0.0245689445776728 0.987715527711164 35 0.0109163477117444 0.0218326954234888 0.989083652288256 36 0.0102923979110515 0.0205847958221031 0.989707602088948 37 0.00672496624980122 0.0134499324996024 0.993275033750199 38 0.00516397223440304 0.0103279444688061 0.994836027765597 39 0.00636614191034566 0.0127322838206913 0.993633858089654 40 0.00555257042550102 0.0111051408510020 0.994447429574499 41 0.00436498040815331 0.00872996081630662 0.995635019591847 42 0.0055085992513166 0.0110171985026332 0.994491400748683 43 0.00402034125235582 0.00804068250471164 0.995979658747644 44 0.00237621791694382 0.00475243583388764 0.997623782083056 45 0.00217095167037915 0.0043419033407583 0.99782904832962 46 0.00321274495467807 0.00642548990935613 0.996787255045322 47 0.00191695403274498 0.00383390806548997 0.998083045967255 48 0.00127075040606488 0.00254150081212976 0.998729249593935 49 0.0137770118310192 0.0275540236620383 0.98622298816898 50 0.0118801435342150 0.0237602870684299 0.988119856465785 51 0.0085721408477242 0.0171442816954484 0.991427859152276 52 0.00627452161142717 0.0125490432228543 0.993725478388573 53 0.00460117623750388 0.00920235247500777 0.995398823762496 54 0.00916243758548624 0.0183248751709725 0.990837562414514 55 0.00782604778349404 0.0156520955669881 0.992173952216506 56 0.0203014818132545 0.040602963626509 0.979698518186745 57 0.0173971074231769 0.0347942148463537 0.982602892576823 58 0.0149513248581461 0.0299026497162922 0.985048675141854 59 0.0120588681121655 0.0241177362243311 0.987941131887834 60 0.0106316168893587 0.0212632337787174 0.98936838311064 61 0.0190140981810713 0.0380281963621426 0.980985901818929 62 0.0163602144196977 0.0327204288393953 0.983639785580302 63 0.0116796763151060 0.0233593526302120 0.988320323684894 64 0.00793466525243108 0.0158693305048622 0.992065334747569 65 0.00543728479004968 0.0108745695800994 0.99456271520995 66 0.00329409470442836 0.00658818940885673 0.996705905295572 67 0.00363892293229379 0.00727784586458758 0.996361077067706 68 0.00255977238516969 0.00511954477033937 0.99744022761483 69 0.00148190251679454 0.00296380503358907 0.998518097483206 70 0.00115608008474736 0.00231216016949472 0.998843919915253 71 0.000677114103348961 0.00135422820669792 0.99932288589665 72 0.0159823436116269 0.0319646872232538 0.984017656388373 73 0.104239074443504 0.208478148887007 0.895760925556496 74 0.09592662960427 0.19185325920854 0.90407337039573 75 0.609818126169313 0.780363747661373 0.390181873830687 76 0.556916438100399 0.886167123799202 0.443083561899601 77 0.428982483611379 0.857964967222758 0.571017516388621 78 0.535763837424574 0.928472325150853 0.464236162575426 79 0.649928469999853 0.700143060000294 0.350071530000147 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 23 0.365079365079365 NOK 5% type I error level 54 0.857142857142857 NOK 10% type I error level 56 0.888888888888889 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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