WS7 Celebrity aanpassing

*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, 22 Nov 2010 11:35:59 +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/22/t1290425886ytaqo2nonoousup.htm/, Retrieved Mon, 22 Nov 2010 12:38:36 +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/22/t1290425886ytaqo2nonoousup.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 «

13 14 13 3 12 8 13 5 15 12 16 6 12 7 12 6 10 10 11 5 12 7 12 3 15 16 18 8 9 11 11 4 12 14 14 4 11 6 9 4 11 16 14 6 11 11 12 6 15 16 11 5 7 12 12 4 11 7 13 6 11 13 11 4 10 11 12 6 14 15 16 6 10 7 9 4 6 9 11 4 11 7 13 2 15 14 15 7 11 15 10 5 12 7 11 4 14 15 13 6 15 17 16 6 9 15 15 7 13 14 14 5 13 14 14 6 16 8 14 4 13 8 8 4 12 14 13 7 14 14 15 7 11 8 13 4 9 11 11 4 16 16 15 6 12 10 15 6 10 8 9 5 13 14 13 6 16 16 16 7 14 13 13 6 15 5 11 3 5 8 12 3 8 10 12 4 11 8 12 6 16 13 14 7 17 15 14 5 9 6 8 4 9 12 13 5 13 16 16 6 10 5 13 6 6 15 11 6 12 12 14 5 8 8 13 4 14 13 13 5 12 14 13 5 11 12 12 4 16 16 16 6 8 10 15 2 15 15 15 8 7 8 12 3 16 16 14 6 14 19 12 6 16 14 15 6 9 6 12 5 14 13 13 5 11 15 12 6 13 7 12 5 15 13 13 6 5 4 5 2 15 14 13 5 13 13 13 5 11 11 14 5 11 14 17 6 12 12 13 6 12 15 13 6 12 14 12 5 12 13 13 5 14 8 14 4 6 6 11 2 7 7 12 4 14 13 12 6 14 13 16 6 10 11 12 5 13 5 12 3 12 12 12 6 9 8 10 4 12 11 15 5 16 14 15 8 10 9 12 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 9 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Celebrity[t] = + 0.228869730297837 + 0.152948770159782Popularity[t] + 0.104099951420784KnowingPeople[t] + 0.146565226481132Liked[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) 0.228869730297837 0.517949 0.4419 0.659207 0.329603 Popularity 0.152948770159782 0.038134 4.0109 9.5e-05 4.7e-05 KnowingPeople 0.104099951420784 0.030709 3.3898 0.000891 0.000446 Liked 0.146565226481132 0.048174 3.0424 0.002766 0.001383 Multiple Linear Regression - Regression Statistics Multiple R 0.677006667554951 R-squared 0.45833802791386 Adjusted R-squared 0.447647331096371 F-TEST (value) 42.8726055689874 F-TEST (DF numerator) 3 F-TEST (DF denominator) 152 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.04086140973719 Sum Squared Residuals 164.675656090573 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3 5.57995100652068 -2.57995100652068 2 5 4.8024025278362 0.197597472163795 3 6 6.11734432344208 -0.117344323442084 4 6 4.55173734993429 1.44826265006571 5 5 4.41157443739595 0.588425562604054 6 3 4.55173734993429 -1.55173734993429 7 8 6.82687458208748 1.17312541791252 8 4 4.36272561865695 -0.362725618656947 9 4 5.57356746284204 -1.57356746284204 10 4 3.85499294891033 0.145007051089670 11 6 5.62881859552383 0.371181404476174 12 6 4.81518838545764 1.18481161454236 13 5 5.80091799671956 -0.80091799671956 14 4 4.3074932562393 -0.307493256239298 15 6 4.54535380625564 1.45464619374436 16 4 4.87682306181808 -0.87682306181808 17 6 4.66223961529786 1.33776038470214 18 6 6.27669540754465 -0.276695407544653 19 4 3.80614413017133 0.193855869828669 20 4 3.69567940533603 0.304320594663967 21 2 4.54535380625564 -2.54535380625564 22 7 6.17897899980252 0.82102100019748 23 5 4.93845773817852 0.0615422618214848 24 4 4.40517212345316 -0.405172123453159 25 6 5.83699972810126 0.163000271898742 26 6 6.637844080546 -0.637844080546003 27 7 5.36538633026461 1.63461366973539 28 5 5.72651623300182 -0.726516233001823 29 6 5.72651623300182 0.273483766998176 30 4 5.56076283495647 -1.56076283495647 31 4 4.22252516559033 -0.22252516559033 32 7 5.42700223636091 1.57299776363909 33 7 6.02603022964274 0.973969770357262 34 4 4.64945375767642 -0.649453757676424 35 4 4.36272561865695 -0.362725618656947 36 6 6.54012767280387 -0.54012767280387 37 6 5.30373288364004 0.696267116359962 38 5 3.91024408159211 1.08975591840789 39 6 5.57995100652069 0.420048993479308 40 7 6.686692899285 0.313307100714998 41 6 5.62879982525969 0.37120017474031 42 3 4.65581853109094 -1.65581853109094 43 3 3.5851959102366 -0.585195910236598 44 4 4.25224212355751 -0.252242123557513 45 6 4.50288853119529 1.49711146880471 46 7 6.08126259206039 0.918737407939613 47 5 6.44241126506174 -1.44241126506174 48 4 3.40253018210963 0.597469817890367 49 5 4.7599560230400 0.240043976960005 50 6 6.22784658880565 -0.227846588805655 51 6 4.18420513325429 1.81579486674571 52 6 4.32027911386074 1.67972088613926 53 5 5.36536756000047 -0.365367560000474 54 4 4.19060744719708 -0.190607447197077 55 5 5.62879982525969 -0.62879982525969 56 5 5.42700223636091 -0.427002236360909 57 4 4.91928833687843 -0.919288336878428 58 6 6.686692899285 -0.686692899285002 59 2 4.69193780300091 -2.69193780300091 60 8 6.2830789512233 1.71692104877670 61 3 3.89109345055616 -0.891093450556163 62 6 6.39356244632274 -0.393562446322738 63 6 6.10683430730326 -0.106834307303261 64 6 6.3319277699623 -0.331927769962302 65 5 3.98879108803416 1.01120891196584 66 5 5.62879982525969 -0.62879982525969 67 6 5.23158819114078 0.768411808859221 68 5 4.70468612009407 0.295313879905927 69 6 5.78174859541947 0.218251404580527 70 2 2.14283951918554 -0.142839519185540 71 5 5.88584854684026 -0.885848546840256 72 5 5.47585105509991 -0.475851055099908 73 5 5.10831883841991 -0.108318838419907 74 6 5.86031437212565 0.139685627874346 75 6 5.21880233351934 0.781197666480658 76 6 5.53110218778169 0.468897812218307 77 5 5.28043700987978 -0.280437009879777 78 5 5.32290228494013 -0.322902284940125 79 4 5.2548652946369 -1.25486529463690 80 2 3.38337955107368 -1.38337955107368 81 4 3.78699349913538 0.213006500864621 82 6 5.48223459877856 0.517765401221442 83 6 6.06849550470309 -0.0684955047030857 84 5 4.66223961529786 0.337760384702139 85 3 4.49648621725251 -1.49648621725251 86 6 5.07223710703821 0.92776289296179 87 4 3.90386053791346 0.096139462086536 88 5 5.40783283506082 -0.407832835060822 89 8 6.3319277699623 1.66807223003770 90 4 4.45403971245629 -0.454039712456294 91 6 5.75619565044073 0.243804349559266 92 6 5.31013519758282 0.689864802417176 93 7 6.686692899285 0.313307100714998 94 6 6.09404844968182 -0.094048449681824 95 5 4.96813715561743 0.0318628443825739 96 4 4.20337453455438 -0.203374534554378 97 6 3.77420764151394 2.22579235848606 98 3 3.6468118163329 -0.646811816332898 99 5 5.56718391916339 -0.567183919163391 100 6 5.26765115225834 0.73234884774166 101 7 6.79079285070579 0.209207149294215 102 7 6.43602772138309 0.563972278616914 103 6 6.77802576334848 -0.778025763348484 104 3 4.33715390341407 -1.33715390341407 105 2 2.72271688143142 -0.722716881431417 106 8 5.75619565044073 2.24380434955927 107 3 4.65585607161921 -1.65585607161921 108 8 6.13013018106352 1.86986981893648 109 3 4.54537257651978 -1.54537257651978 110 4 4.58783785158012 -0.587837851580125 111 5 5.21241878984069 -0.212418789840691 112 7 5.53110218778169 1.46889781221831 113 6 4.54535380625564 1.45464619374436 114 6 5.92193027822195 0.0780697217780461 115 7 6.11734432344208 0.882655676557916 116 6 6.18536254348117 -0.185362543481170 117 6 6.18536254348117 -0.185362543481170 118 6 5.08914943711982 0.91085056288018 119 6 5.98356495458239 0.0164350454176105 120 4 5.37813464735777 -1.37813464735777 121 4 5.21880233351934 -1.21880233351934 122 5 5.79453445304091 -0.79453445304091 123 4 4.14814217213673 -0.148142172136729 124 6 5.83061618442261 0.169383815577393 125 6 6.28946249490195 -0.289462494901954 126 5 4.74076785147577 0.259232148524229 127 8 6.47849299644343 1.52150700355657 128 6 5.53110218778169 0.468897812218307 129 5 5.00421888699912 -0.00421888699912362 130 4 2.05152542538619 1.94847457461381 131 8 6.47849299644343 1.52150700355657 132 6 5.67764864399869 0.322351356001311 133 4 4.93620066696004 -0.936200666960037 134 6 5.5607816052206 0.439218394779396 135 6 5.9283138219006 0.0716861780993956 136 4 4.90650247925699 -0.90650247925699 137 6 5.98356495458239 0.0164350454176105 138 3 4.41159320766008 -1.41159320766008 139 6 6.89487403186243 -0.894874031862433 140 5 5.57356746284204 -0.573567462842041 141 4 5.63520213920248 -1.63520213920248 142 6 5.72651623300182 0.273483766998176 143 4 6.32554422628365 -2.32554422628365 144 4 3.37285076467072 0.627149235329278 145 4 5.01060243067777 -1.01060243067777 146 6 4.08010518183351 1.91989481816649 147 5 4.33717267367821 0.662827326321791 148 6 5.51833510042439 0.481664899575608 149 6 6.24061367616296 -0.240613676162956 150 8 6.12372786712073 1.87627213287926 151 7 5.37175110367912 1.62824889632088 152 7 6.58259294786422 0.417407052135782 153 4 4.30109094229651 -0.301090942296511 154 6 5.81783032680117 0.18216967319883 155 6 5.58633455019934 0.413665449800658 156 2 5.26126760857969 -3.26126760857969 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.822113740480269 0.355772519039462 0.177886259519731 8 0.787696930494068 0.424606139011864 0.212303069505932 9 0.79697303978785 0.4060539204243 0.20302696021215 10 0.844814093246948 0.310371813506104 0.155185906753052 11 0.821251656578976 0.357496686842048 0.178748343421024 12 0.852567641812129 0.294864716375742 0.147432358187871 13 0.882458443833406 0.235083112333187 0.117541556166594 14 0.840396504171915 0.319206991656169 0.159603495828085 15 0.824241714157715 0.351516571684570 0.175758285842285 16 0.770684269407245 0.458631461185510 0.229315730592755 17 0.795431699225557 0.409136601548886 0.204568300774443 18 0.736844628872018 0.526310742255963 0.263155371127982 19 0.672178436173532 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0.0198848657865605 0.99005756710672 141 0.00939983230568245 0.0187996646113649 0.990600167694317 142 0.00579467851341887 0.0115893570268377 0.994205321486581 143 0.0211662838828577 0.0423325677657154 0.978833716117142 144 0.0143939258700069 0.0287878517400137 0.985606074129993 145 0.0158570271019589 0.0317140542039177 0.98414297289804 146 0.0153342027739372 0.0306684055478744 0.984665797226063 147 0.0138921838179649 0.0277843676359297 0.986107816182035 148 0.056576468602217 0.113152937204434 0.943423531397783 149 0.0265475866121463 0.0530951732242926 0.973452413387854 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 11 0.0769230769230769 NOK 10% type I error level 16 0.111888111888112 NOK

Charts produced by software:

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Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; par2 = Do not include Seasonal 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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Software written by Ed van Stee & Patrick Wessa

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