Model 3

*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, 28 Dec 2010 21:02:14 +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/28/t12935700561819ebdku3zaob7.htm/, Retrieved Tue, 28 Dec 2010 22:00:58 +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/28/t12935700561819ebdku3zaob7.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:

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4.24 0 4.15 0 3.93 0 3.7 0 3.7 0 3.65 0 3.55 0 3.43 0 3.47 0 3.58 0 3.67 0 3.72 0 3.8 0 3.76 0 3.63 0 3.48 0 3.41 0 3.43 0 3.5 0 3.62 0 3.58 0 3.52 0 3.45 0 3.36 0 3.27 0 3.21 0 3.19 0 3.16 0 3.12 0 3.06 0 3.01 0 2.98 0 2.97 0 3.02 0 3.07 0 3.18 0 3.29 1 3.43 1 3.61 1 3.74 1 3.87 1 3.88 1 4.09 1 4.19 1 4.2 1 4.29 1 4.37 1 4.47 1 4.61 1 4.65 1 4.69 1 4.82 1 4.86 1 4.87 1 5.01 1 5.03 1 5.13 1 5.18 1 5.21 1 5.26 1 5.25 1 5.2 1 5.16 1 5.19 1 5.39 1 5.58 1 5.76 1 5.89 1 5.98 1 6.02 1 5.62 1 4.87 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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Rente[t] = + 3.02 + 0.652708333333334Dummy[t] + 0.142065972222224M1[t] + 0.113090277777778M2[t] + 0.0624479166666668M3[t] + 0.0234722222222226M4[t] + 0.0478298611111114M5[t] + 0.0488541666666669M6[t] + 0.104878472222222M7[t] + 0.122569444444445M8[t] + 0.135260416666667M9[t] + 0.162951388888889M10[t] + 0.107309027777778M11[t] + 0.0189756944444444t + 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) 3.02 0.292716 10.3172 0 0 Dummy 0.652708333333334 0.280923 2.3234 0.023682 0.011841 M1 0.142065972222224 0.337512 0.4209 0.675368 0.337684 M2 0.113090277777778 0.336047 0.3365 0.737685 0.368843 M3 0.0624479166666668 0.334717 0.1866 0.852649 0.426325 M4 0.0234722222222226 0.333522 0.0704 0.944136 0.472068 M5 0.0478298611111114 0.332464 0.1439 0.886106 0.443053 M6 0.0488541666666669 0.331544 0.1474 0.883364 0.441682 M7 0.104878472222222 0.330764 0.3171 0.752322 0.376161 M8 0.122569444444445 0.330124 0.3713 0.71178 0.35589 M9 0.135260416666667 0.329626 0.4103 0.683065 0.341532 M10 0.162951388888889 0.329269 0.4949 0.622549 0.311274 M11 0.107309027777778 0.329055 0.3261 0.745513 0.372757 t 0.0189756944444444 0.006854 2.7686 0.007546 0.003773 Multiple Linear Regression - Regression Statistics Multiple R 0.806666523468836 R-squared 0.650710880085298 Adjusted R-squared 0.572421939414761 F-TEST (value) 8.31165774516843 F-TEST (DF numerator) 13 F-TEST (DF denominator) 58 p-value 4.02587418957268e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.569816749379626 Sum Squared Residuals 18.8320854166667 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4.24 3.18104166666666 1.05895833333334 2 4.15 3.17104166666667 0.978958333333333 3 3.93 3.139375 0.790625 4 3.7 3.119375 0.580625 5 3.7 3.16270833333333 0.537291666666666 6 3.65 3.18270833333333 0.467291666666666 7 3.55 3.25770833333333 0.292291666666666 8 3.43 3.294375 0.135625 9 3.47 3.32604166666667 0.143958333333333 10 3.58 3.37270833333333 0.207291666666667 11 3.67 3.33604166666667 0.333958333333333 12 3.72 3.24770833333333 0.472291666666667 13 3.8 3.40875 0.391249999999998 14 3.76 3.39875 0.361249999999999 15 3.63 3.36708333333333 0.262916666666666 16 3.48 3.34708333333333 0.132916666666666 17 3.41 3.39041666666667 0.0195833333333332 18 3.43 3.41041666666667 0.0195833333333333 19 3.5 3.48541666666667 0.0145833333333334 20 3.62 3.52208333333333 0.0979166666666666 21 3.58 3.55375 0.0262499999999998 22 3.52 3.60041666666667 -0.0804166666666667 23 3.45 3.56375 -0.11375 24 3.36 3.47541666666667 -0.115416666666667 25 3.27 3.63645833333333 -0.366458333333335 26 3.21 3.62645833333333 -0.416458333333333 27 3.19 3.59479166666667 -0.404791666666667 28 3.16 3.57479166666667 -0.414791666666667 29 3.12 3.618125 -0.498125 30 3.06 3.638125 -0.578125 31 3.01 3.713125 -0.703125 32 2.98 3.74979166666667 -0.769791666666666 33 2.97 3.78145833333333 -0.811458333333333 34 3.02 3.828125 -0.808125 35 3.07 3.79145833333333 -0.721458333333333 36 3.18 3.703125 -0.523124999999999 37 3.29 4.516875 -1.226875 38 3.43 4.506875 -1.076875 39 3.61 4.47520833333333 -0.865208333333334 40 3.74 4.45520833333333 -0.715208333333333 41 3.87 4.49854166666667 -0.628541666666667 42 3.88 4.51854166666667 -0.638541666666667 43 4.09 4.59354166666667 -0.503541666666667 44 4.19 4.63020833333333 -0.440208333333333 45 4.2 4.661875 -0.461875 46 4.29 4.70854166666667 -0.418541666666667 47 4.37 4.671875 -0.301875 48 4.47 4.58354166666667 -0.113541666666667 49 4.61 4.74458333333333 -0.134583333333335 50 4.65 4.73458333333333 -0.0845833333333329 51 4.69 4.70291666666667 -0.0129166666666662 52 4.82 4.68291666666667 0.137083333333333 53 4.86 4.72625 0.13375 54 4.87 4.74625 0.12375 55 5.01 4.82125 0.18875 56 5.03 4.85791666666667 0.172083333333334 57 5.13 4.88958333333333 0.240416666666667 58 5.18 4.93625 0.24375 59 5.21 4.89958333333333 0.310416666666667 60 5.26 4.81125 0.44875 61 5.25 4.97229166666667 0.277708333333332 62 5.2 4.96229166666667 0.237708333333334 63 5.16 4.930625 0.229375000000001 64 5.19 4.910625 0.279375000000001 65 5.39 4.95395833333333 0.436041666666667 66 5.58 4.97395833333333 0.606041666666667 67 5.76 5.04895833333333 0.711041666666667 68 5.89 5.085625 0.804375 69 5.98 5.11729166666667 0.862708333333334 70 6.02 5.16395833333333 0.856041666666667 71 5.62 5.12729166666667 0.492708333333333 72 4.87 5.03895833333333 -0.168958333333333 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00794050411734323 0.0158810082346865 0.992059495882657 18 0.00227643241623275 0.0045528648324655 0.997723567583767 19 0.0036598404906696 0.00731968098133919 0.99634015950933 20 0.0209682344950465 0.0419364689900931 0.979031765504953 21 0.02541564414336 0.05083128828672 0.97458435585664 22 0.0172594690357531 0.0345189380715061 0.982740530964247 23 0.0127538678295161 0.0255077356590323 0.987246132170484 24 0.0177155672787734 0.0354311345575468 0.982284432721227 25 0.0648321543424658 0.129664308684932 0.935167845657534 26 0.108785477474452 0.217570954948904 0.891214522525548 27 0.108856417929988 0.217712835859977 0.891143582070012 28 0.0944995458158617 0.188999091631723 0.905500454184138 29 0.0730937227123498 0.1461874454247 0.92690627728765 30 0.0516011123842665 0.103202224768533 0.948398887615734 31 0.0320295458383089 0.0640590916766178 0.967970454161691 32 0.0196882900227498 0.0393765800454996 0.98031170997725 33 0.0124998030864314 0.0249996061728627 0.987500196913569 34 0.00820941216663278 0.0164188243332656 0.991790587833367 35 0.00500948304378698 0.010018966087574 0.994990516956213 36 0.00292179266107847 0.00584358532215695 0.997078207338921 37 0.0032047112113414 0.0064094224226828 0.996795288788659 38 0.00342600709829082 0.00685201419658164 0.99657399290171 39 0.0063011628950347 0.0126023257900694 0.993698837104965 40 0.0191882786374856 0.0383765572749711 0.980811721362514 41 0.0469622871343096 0.0939245742686192 0.95303771286569 42 0.0812495368822417 0.162499073764483 0.918750463117758 43 0.166016843572936 0.332033687145872 0.833983156427064 44 0.269691995014896 0.539383990029793 0.730308004985104 45 0.401829072747773 0.803658145495546 0.598170927252227 46 0.54811602098519 0.90376795802962 0.45188397901481 47 0.577519113434554 0.844961773130891 0.422480886565446 48 0.585628640886128 0.828742718227744 0.414371359113872 49 0.634223953390982 0.731552093218036 0.365776046609018 50 0.636314830438485 0.727370339123031 0.363685169561515 51 0.618604763422782 0.762790473154436 0.381395236577218 52 0.612570306157094 0.774859387685812 0.387429693842906 53 0.554710787526257 0.890578424947486 0.445289212473743 54 0.473004808037441 0.946009616074882 0.526995191962559 55 0.379015092327707 0.758030184655415 0.620984907672293 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.128205128205128 NOK 5% type I error level 16 0.41025641025641 NOK 10% type I error level 19 0.487179487179487 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/102eon1293570125.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/102eon1293570125.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/1l4981293570125.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/1l4981293570125.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/2wvrb1293570125.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/2wvrb1293570125.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/3wvrb1293570125.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/3wvrb1293570125.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/4wvrb1293570125.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/4wvrb1293570125.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/56m8w1293570125.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/56m8w1293570125.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/66m8w1293570125.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/66m8w1293570125.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/7hw7h1293570125.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/7hw7h1293570125.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/8hw7h1293570125.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/8hw7h1293570125.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/9snok1293570125.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t12935700561819ebdku3zaob7/9snok1293570125.ps (open in new window)

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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