*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: Fri, 20 Nov 2009 06:07:55 -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/20/t1258722797sbsvubhs068jrjs.htm/, Retrieved Fri, 20 Nov 2009 14:13:36 +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/2009/Nov/20/t1258722797sbsvubhs068jrjs.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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8.3 10 8.2 7 8 5 7.9 9 7.6 10 7.6 9 8.3 8 8.4 7 8.4 10 8.4 9 8.4 11 8.6 12 8.9 12 8.8 12 8.3 12 7.5 12 7.2 11 7.4 12 8.8 11 9.3 12 9.3 11 8.7 13 8.2 10 8.3 11 8.5 12 8.6 12 8.5 11 8.2 9 8.1 8 7.9 9 8.6 9 8.7 8 8.7 6 8.5 10 8.4 10 8.5 11 8.7 12 8.7 12 8.6 11 8.5 11 8.3 9 8 11 8.2 11 8.1 11 8.1 9 8 12 7.9 12 7.9 10 8 12 8 11 7.9 10 8 11 7.7 11 7.2 10 7.5 9 7.3 8 7 9 7 8 7 5 7.2 6 7.3 4 7.1 7 6.8 4 6.4 4 6.1 4 6.5 0 7.7 2 7.9 4 7.5 6 6.9 1 6.6 2 6.9 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] = + 6.66044393743112 + 0.145004737137222X[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) 6.66044393743112 0.185533 35.899 0 0 X 0.145004737137222 0.01964 7.383 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.661656111589323 R-squared 0.437788810003502 Adjusted R-squared 0.429757221574981 F-TEST (value) 54.5083720237515 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 2.49030684962293e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.526392140220193 Sum Squared Residuals 19.3962079699917 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.3 8.11049130880333 0.189508691196674 2 8.2 7.67547709739168 0.524522902608324 3 8 7.38546762311723 0.61453237688277 4 7.9 7.96548657166612 -0.0654865716661187 5 7.6 8.11049130880334 -0.510491308803342 6 7.6 7.96548657166612 -0.365486571666119 7 8.3 7.8204818345289 0.479518165471104 8 8.4 7.67547709739167 0.724522902608326 9 8.4 8.11049130880334 0.289508691196659 10 8.4 7.96548657166612 0.434513428333881 11 8.4 8.25549604594056 0.144503954059437 12 8.6 8.40050078307779 0.199499216922214 13 8.9 8.40050078307779 0.499499216922214 14 8.8 8.40050078307779 0.399499216922215 15 8.3 8.40050078307779 -0.100500783077785 16 7.5 8.40050078307779 -0.900500783077786 17 7.2 8.25549604594056 -1.05549604594056 18 7.4 8.40050078307779 -1.00050078307779 19 8.8 8.25549604594056 0.544503954059437 20 9.3 8.40050078307779 0.899499216922215 21 9.3 8.25549604594056 1.04450395405944 22 8.7 8.54550552021501 0.154494479784991 23 8.2 8.11049130880334 0.0895086911966579 24 8.3 8.25549604594056 0.044503954059437 25 8.5 8.40050078307779 0.099499216922214 26 8.6 8.40050078307779 0.199499216922214 27 8.5 8.25549604594056 0.244503954059436 28 8.2 7.96548657166612 0.23451342833388 29 8.1 7.8204818345289 0.279518165471103 30 7.9 7.96548657166612 -0.0654865716661187 31 8.6 7.96548657166612 0.634513428333881 32 8.7 7.8204818345289 0.879518165471103 33 8.7 7.53047236025445 1.16952763974555 34 8.5 8.11049130880334 0.389508691196659 35 8.4 8.11049130880334 0.289508691196659 36 8.5 8.25549604594056 0.244503954059436 37 8.7 8.40050078307779 0.299499216922213 38 8.7 8.40050078307779 0.299499216922213 39 8.6 8.25549604594056 0.344503954059436 40 8.5 8.25549604594056 0.244503954059436 41 8.3 7.96548657166612 0.334513428333882 42 8 8.25549604594056 -0.255496045940564 43 8.2 8.25549604594056 -0.0554960459405645 44 8.1 8.25549604594056 -0.155496045940564 45 8.1 7.96548657166612 0.134513428333881 46 8 8.40050078307779 -0.400500783077786 47 7.9 8.40050078307779 -0.500500783077786 48 7.9 8.11049130880334 -0.210491308803341 49 8 8.40050078307779 -0.400500783077786 50 8 8.25549604594056 -0.255496045940564 51 7.9 8.11049130880334 -0.210491308803341 52 8 8.25549604594056 -0.255496045940564 53 7.7 8.25549604594056 -0.555496045940564 54 7.2 8.11049130880334 -0.910491308803341 55 7.5 7.96548657166612 -0.465486571666119 56 7.3 7.8204818345289 -0.520481834528897 57 7 7.96548657166612 -0.965486571666119 58 7 7.8204818345289 -0.820481834528897 59 7 7.38546762311723 -0.38546762311723 60 7.2 7.53047236025445 -0.330472360254452 61 7.3 7.24046288598001 0.0595371140199923 62 7.1 7.67547709739167 -0.575477097391675 63 6.8 7.24046288598001 -0.440462885980008 64 6.4 7.24046288598001 -0.840462885980007 65 6.1 7.24046288598001 -1.14046288598001 66 6.5 6.66044393743112 -0.160443937431118 67 7.7 6.95045341170556 0.749546588294437 68 7.9 7.24046288598001 0.659537114019993 69 7.5 7.53047236025445 -0.0304723602544521 70 6.9 6.80544867456834 0.09455132543166 71 6.6 6.95045341170556 -0.350453411705563 72 6.9 6.80544867456834 0.09455132543166 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.201634931661317 0.403269863322634 0.798365068338683 6 0.158680686646084 0.317361373292168 0.841319313353916 7 0.123378916740595 0.246757833481191 0.876621083259405 8 0.100275141591751 0.200550283183502 0.899724858408249 9 0.100018571552418 0.200037143104836 0.899981428447582 10 0.0794261745575863 0.158852349115173 0.920573825442414 11 0.0583718492516754 0.116743698503351 0.941628150748325 12 0.0499758852399392 0.0999517704798785 0.95002411476006 13 0.0650402193122202 0.130080438624440 0.93495978068778 14 0.0523920457599798 0.104784091519960 0.94760795424002 15 0.0337859294207408 0.0675718588414816 0.96621407057926 16 0.127123665630304 0.254247331260609 0.872876334369696 17 0.354887190760272 0.709774381520544 0.645112809239728 18 0.49254786714204 0.98509573428408 0.50745213285796 19 0.527498302243903 0.945003395512193 0.472501697756097 20 0.723851034925295 0.55229793014941 0.276148965074705 21 0.866935713272604 0.266128573454791 0.133064286727396 22 0.831601228539993 0.336797542920013 0.168398771460007 23 0.782815682817092 0.434368634365815 0.217184317182908 24 0.725836239374492 0.548327521251015 0.274163760625508 25 0.665731572908055 0.66853685418389 0.334268427091945 26 0.610434143690936 0.779131712618129 0.389565856309064 27 0.554465045123061 0.891069909753877 0.445534954876939 28 0.493809414030442 0.987618828060883 0.506190585969558 29 0.437573228193504 0.875146456387008 0.562426771806496 30 0.38352280216428 0.76704560432856 0.61647719783572 31 0.397782321866463 0.795564643732925 0.602217678133537 32 0.490744827381155 0.98148965476231 0.509255172618845 33 0.702294077844172 0.595411844311656 0.297705922155828 34 0.691276605191057 0.617446789617885 0.308723394808943 35 0.665764939704677 0.668470120590645 0.334235060295323 36 0.639417883149273 0.721164233701455 0.360582116850727 37 0.63870386642472 0.72259226715056 0.36129613357528 38 0.647659279552814 0.704681440894372 0.352340720447186 39 0.672061869582135 0.65587626083573 0.327938130417865 40 0.682312990557057 0.635374018885886 0.317687009442943 41 0.712298243100874 0.575403513798252 0.287701756899126 42 0.676144482275835 0.64771103544833 0.323855517724165 43 0.649654165532517 0.700691668934966 0.350345834467483 44 0.616244601276595 0.76751079744681 0.383755398723405 45 0.626590229424199 0.746819541151603 0.373409770575801 46 0.58606269177445 0.8278746164511 0.41393730822555 47 0.546744741600744 0.906510516798511 0.453255258399256 48 0.517798934429409 0.964402131141182 0.482201065570591 49 0.476008378673189 0.952016757346378 0.523991621326811 50 0.453317430924227 0.906634861848453 0.546682569075773 51 0.443673750357212 0.887347500714424 0.556326249642788 52 0.457426313878433 0.914852627756866 0.542573686121567 53 0.447457284311792 0.894914568623584 0.552542715688208 54 0.477882628320142 0.955765256640283 0.522117371679858 55 0.459589790892676 0.919179581785353 0.540410209107324 56 0.439058752885508 0.878117505771016 0.560941247114492 57 0.460423949973459 0.920847899946919 0.539576050026541 58 0.457134691176783 0.914269382353566 0.542865308823217 59 0.400867044330790 0.801734088661581 0.59913295566921 60 0.327674745806351 0.655349491612702 0.672325254193649 61 0.264108729655594 0.528217459311188 0.735891270344406 62 0.206313250807009 0.412626501614019 0.79368674919299 63 0.156153685838297 0.312307371676595 0.843846314161703 64 0.197857373104450 0.395714746208901 0.80214262689555 65 0.646915582436826 0.706168835126348 0.353084417563174 66 0.538000476943639 0.923999046112722 0.461999523056361 67 0.624180258619905 0.75163948276019 0.375819741380095 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 0 0 OK 10% type I error level 2 0.0317460317460317 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/109d4u1258722468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/109d4u1258722468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/13klw1258722468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/13klw1258722468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/2bhzq1258722468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/2bhzq1258722468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/356r51258722468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/356r51258722468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/40b761258722468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/40b761258722468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/5qhkj1258722468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/5qhkj1258722468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/6yyru1258722468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/6yyru1258722468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/704t81258722468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/704t81258722468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/89z761258722468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/89z761258722468.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/9muq21258722468.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722797sbsvubhs068jrjs/9muq21258722468.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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