Model 1 - Werkzoekende Mannen & Werkzoekende mannen <25

*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: Wed, 18 Nov 2009 09:00:25 -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/18/t1258560224ma6b2cnfdiwmrla.htm/, Retrieved Wed, 18 Nov 2009 17:03:56 +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/18/t1258560224ma6b2cnfdiwmrla.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:

» Textbox « » Textfile « » CSV «

8.2 20.3 8 20.3 7.5 20.3 6.8 15.8 6.5 15.8 6.6 15.8 7.6 23.2 8 23.2 8.1 23.2 7.7 20.9 7.5 20.9 7.6 20.9 7.8 19.8 7.8 19.8 7.8 19.8 7.5 20.6 7.5 20.6 7.1 20.6 7.5 21.1 7.5 21.1 7.6 21.1 7.7 22.4 7.7 22.4 7.9 22.4 8.1 20.5 8.2 20.5 8.2 20.5 8.2 18.4 7.9 18.4 7.3 18.4 6.9 17.6 6.6 17.6 6.7 17.6 6.9 18.5 7 18.5 7.1 18.5 7.2 17.3 7.1 17.3 6.9 17.3 7 16.2 6.8 16.2 6.4 16.2 6.7 18.5 6.6 18.5 6.4 18.5 6.3 16.3 6.2 16.3 6.5 16.3 6.8 16.8 6.8 16.8 6.4 16.8 6.1 14.8 5.8 14.8 6.1 14.8 7.2 21.4 7.3 21.4 6.9 21.4 6.1 16.1 5.8 16.1 6.2 16.1 7.1 19.6 7.7 19.6 7.9 19.6 7.7 18.9 7.4 18.9 7.5 18.9 8 21.9 8.1 21.9 8 21.9

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 2.83141091127098 + 0.230588729016787X[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) 2.83141091127098 0.400232 7.0744 0 0 X 0.230588729016787 0.020918 11.0235 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.8028671994604 R-squared 0.644595739969386 Adjusted R-squared 0.6392911987749 F-TEST (value) 121.517717807403 F-TEST (DF numerator) 1 F-TEST (DF denominator) 67 p-value 1.11022302462516e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.398324831007973 Sum Squared Residuals 10.6303989568345 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.2 7.51236211031176 0.687637889688239 2 8 7.51236211031175 0.487637889688249 3 7.5 7.51236211031175 -0.0123621103117507 4 6.8 6.47471282973621 0.325287170263789 5 6.5 6.47471282973621 0.0252871702637894 6 6.6 6.47471282973621 0.125287170263789 7 7.6 8.18106942446043 -0.581069424460432 8 8 8.18106942446043 -0.181069424460432 9 8.1 8.18106942446043 -0.081069424460432 10 7.7 7.65071534772182 0.049284652278178 11 7.5 7.65071534772182 -0.150715347721822 12 7.6 7.65071534772182 -0.0507153477218226 13 7.8 7.39706774580336 0.402932254196643 14 7.8 7.39706774580336 0.402932254196643 15 7.8 7.39706774580336 0.402932254196643 16 7.5 7.58153872901679 -0.0815387290167869 17 7.5 7.58153872901679 -0.0815387290167869 18 7.1 7.58153872901679 -0.481538729016787 19 7.5 7.69683309352518 -0.19683309352518 20 7.5 7.69683309352518 -0.19683309352518 21 7.6 7.69683309352518 -0.0968330935251805 22 7.7 7.996598441247 -0.296598441247002 23 7.7 7.996598441247 -0.296598441247002 24 7.9 7.996598441247 -0.096598441247002 25 8.1 7.55847985611511 0.541520143884892 26 8.2 7.55847985611511 0.641520143884891 27 8.2 7.55847985611511 0.641520143884891 28 8.2 7.07424352517986 1.12575647482014 29 7.9 7.07424352517986 0.825756474820145 30 7.3 7.07424352517986 0.225756474820144 31 6.9 6.88977254196643 0.0102274580335736 32 6.6 6.88977254196643 -0.289772541966427 33 6.7 6.88977254196643 -0.189772541966427 34 6.9 7.09730239808153 -0.197302398081534 35 7 7.09730239808153 -0.0973023980815344 36 7.1 7.09730239808153 0.00269760191846521 37 7.2 6.82059592326139 0.379404076738610 38 7.1 6.82059592326139 0.279404076738609 39 6.9 6.82059592326139 0.0794040767386097 40 7 6.56694832134292 0.433051678657075 41 6.8 6.56694832134292 0.233051678657075 42 6.4 6.56694832134292 -0.166948321342924 43 6.7 7.09730239808153 -0.397302398081534 44 6.6 7.09730239808153 -0.497302398081535 45 6.4 7.09730239808153 -0.697302398081534 46 6.3 6.5900071942446 -0.290007194244604 47 6.2 6.5900071942446 -0.390007194244604 48 6.5 6.5900071942446 -0.0900071942446039 49 6.8 6.705301558753 0.0946984412470026 50 6.8 6.705301558753 0.0946984412470026 51 6.4 6.705301558753 -0.305301558752997 52 6.1 6.24412410071942 -0.144124100719424 53 5.8 6.24412410071942 -0.444124100719424 54 6.1 6.24412410071942 -0.144124100719424 55 7.2 7.76600971223022 -0.566009712230215 56 7.3 7.76600971223022 -0.466009712230216 57 6.9 7.76600971223022 -0.866009712230215 58 6.1 6.54388944844125 -0.443889448441247 59 5.8 6.54388944844125 -0.743889448441247 60 6.2 6.54388944844125 -0.343889448441246 61 7.1 7.35095 -0.250950000000001 62 7.7 7.35095 0.34905 63 7.9 7.35095 0.54905 64 7.7 7.18953788968825 0.510462110311751 65 7.4 7.18953788968825 0.210462110311752 66 7.5 7.18953788968825 0.310462110311751 67 8 7.88130407673861 0.118695923261391 68 8.1 7.88130407673861 0.218695923261391 69 8 7.88130407673861 0.118695923261391 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.410779322406094 0.821558644812188 0.589220677593906 6 0.249308135825926 0.498616271651852 0.750691864174074 7 0.664821767623725 0.670356464752549 0.335178232376275 8 0.551070329572687 0.897859340854626 0.448929670427313 9 0.428858746873263 0.857717493746526 0.571141253126737 10 0.317806993429051 0.635613986858103 0.682193006570949 11 0.243276224372036 0.486552448744072 0.756723775627964 12 0.168652273645345 0.33730454729069 0.831347726354655 13 0.158178368203229 0.316356736406458 0.841821631796771 14 0.143419546057798 0.286839092115595 0.856580453942202 15 0.126945023237601 0.253890046475202 0.8730549767624 16 0.091656871391938 0.183313742783876 0.908343128608062 17 0.0641826134958258 0.128365226991652 0.935817386504174 18 0.103372298195755 0.206744596391510 0.896627701804245 19 0.0787449753703466 0.157489950740693 0.921255024629653 20 0.0586680497171225 0.117336099434245 0.941331950282877 21 0.0389889198471232 0.0779778396942464 0.961011080152877 22 0.0297545684708475 0.0595091369416949 0.970245431529153 23 0.0227119970539866 0.0454239941079733 0.977288002946013 24 0.0146109295024639 0.0292218590049277 0.985389070497536 25 0.0254160245127901 0.0508320490255803 0.97458397548721 26 0.0532808192990594 0.106561638598119 0.94671918070094 27 0.0933878036191835 0.186775607238367 0.906612196380816 28 0.397437004886946 0.794874009773891 0.602562995113054 29 0.576155738524188 0.847688522951625 0.423844261475812 30 0.531107966096581 0.937784067806837 0.468892033903419 31 0.503030646348662 0.993938707302676 0.496969353651338 32 0.547342160103699 0.905315679792603 0.452657839896301 33 0.537333542615282 0.925332914769437 0.462666457384718 34 0.508402144569951 0.983195710860097 0.491597855430049 35 0.456935159774739 0.913870319549477 0.543064840225261 36 0.396404615735285 0.792809231470569 0.603595384264715 37 0.389274996491767 0.778549992983533 0.610725003508233 38 0.362407786413580 0.724815572827159 0.63759221358642 39 0.314822224343188 0.629644448686375 0.685177775656812 40 0.346315332308344 0.692630664616688 0.653684667691656 41 0.334680625551125 0.66936125110225 0.665319374448875 42 0.313099603286194 0.626199206572388 0.686900396713806 43 0.320531824773139 0.641063649546278 0.679468175226861 44 0.360200258353042 0.720400516706085 0.639799741646958 45 0.504438971883416 0.991122056233168 0.495561028116584 46 0.46840147067835 0.9368029413567 0.53159852932165 47 0.45260894713281 0.90521789426562 0.54739105286719 48 0.385163445505860 0.770326891011719 0.614836554494140 49 0.33215543706978 0.66431087413956 0.66784456293022 50 0.284687053643325 0.56937410728665 0.715312946356675 51 0.241658295716038 0.483316591432076 0.758341704283962 52 0.191801515154857 0.383603030309715 0.808198484845143 53 0.169891090436399 0.339782180872799 0.830108909563601 54 0.125523004224611 0.251046008449222 0.874476995775389 55 0.165538024848352 0.331076049696704 0.834461975151648 56 0.198664064418893 0.397328128837786 0.801335935581107 57 0.718280571731045 0.563438856537909 0.281719428268955 58 0.657775758294092 0.684448483411816 0.342224241705908 59 0.829339540995622 0.341320918008756 0.170660459004378 60 0.923272012081324 0.153455975837351 0.0767279879186757 61 0.995299124134247 0.00940175173150685 0.00470087586575342 62 0.985306605161027 0.0293867896779467 0.0146933948389733 63 0.988067572763725 0.0238648544725498 0.0119324272362749 64 0.995164576872942 0.00967084625411643 0.00483542312705821 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0333333333333333 NOK 5% type I error level 6 0.1 NOK 10% type I error level 9 0.15 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/10gjdc1258560021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/10gjdc1258560021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/19fl11258560020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/19fl11258560020.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/2yojv1258560020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/2yojv1258560020.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/3flkj1258560020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/3flkj1258560020.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/4pbfl1258560020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/4pbfl1258560020.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/5s7a81258560020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/5s7a81258560020.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/6vino1258560020.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/6vino1258560020.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/7cgxs1258560021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/7cgxs1258560021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/8fd251258560021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/8fd251258560021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/92mcp1258560021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560224ma6b2cnfdiwmrla/92mcp1258560021.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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