Lineaire trend - Ws 8

*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, 30 Nov 2010 20:46:30 +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/30/t1291149908608m8edeypp0vl6.htm/, Retrieved Tue, 30 Nov 2010 21:45:18 +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/30/t1291149908608m8edeypp0vl6.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 «

37 30 47 35 30 43 82 40 47 19 52 136 80 42 54 66 81 63 137 72 107 58 36 52 79 77 54 84 48 96 83 66 61 53 30 74 69 59 42 65 70 100 63 105 82 81 75 102 121 98 76 77 63 37 35 23 40 29 37 51 20 28 13 22 25 13 16 13 16 17 9 17 25 14 8 7 10 7 10 3

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 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation [t] = + 99.3511904761905 -13.6846655328798M1[t] -24.8905895691610M2[t] -31.953656462585M3[t] -22.4452947845804M4[t] -25.9369331065760M5[t] -20.7142857142857M6[t] -10.4916383219955M7[t] -24.6975623582766M8[t] -15.1203231292517M9[t] -30.46910430839M10[t] -32.8178854875283M11[t] -0.651218820861678t + 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) 99.3511904761905 13.204773 7.5239 0 0 M1 -13.6846655328798 16.110628 -0.8494 0.398673 0.199337 M2 -24.8905895691610 16.105105 -1.5455 0.126934 0.063467 M3 -31.953656462585 16.100808 -1.9846 0.051286 0.025643 M4 -22.4452947845804 16.097738 -1.3943 0.16783 0.083915 M5 -25.9369331065760 16.095896 -1.6114 0.111794 0.055897 M6 -20.7142857142857 16.095281 -1.287 0.20253 0.101265 M7 -10.4916383219955 16.095896 -0.6518 0.516747 0.258373 M8 -24.6975623582766 16.097738 -1.5342 0.129683 0.064842 M9 -15.1203231292517 16.708188 -0.905 0.368727 0.184363 M10 -30.46910430839 16.70523 -1.8239 0.072623 0.036312 M11 -32.8178854875283 16.703455 -1.9647 0.053594 0.026797 t -0.651218820861678 0.140604 -4.6316 1.7e-05 9e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.55071598803982 R-squared 0.303288099482675 Adjusted R-squared 0.178503878494498 F-TEST (value) 2.43050040366409 F-TEST (DF numerator) 12 F-TEST (DF denominator) 67 p-value 0.0109600910241201 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 28.930207162859 Sum Squared Residuals 56076.1113945578 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 37 85.0153061224489 -48.0153061224489 2 30 73.158163265306 -43.158163265306 3 47 65.4438775510203 -18.4438775510203 4 35 74.3010204081633 -39.3010204081633 5 30 70.1581632653062 -40.1581632653062 6 43 74.7295918367347 -31.7295918367347 7 82 84.3010204081633 -2.30102040816328 8 40 69.4438775510204 -29.4438775510204 9 47 78.3698979591836 -31.3698979591836 10 19 62.3698979591837 -43.3698979591837 11 52 59.3698979591837 -7.36989795918367 12 136 91.5365646258503 44.4634353741497 13 80 77.2006802721089 2.79931972789114 14 42 65.343537414966 -23.3435374149660 15 54 57.6292517006803 -3.62925170068029 16 66 66.4863945578231 -0.486394557823147 17 81 62.343537414966 18.656462585034 18 63 66.9149659863946 -3.91496598639456 19 137 76.4863945578232 60.5136054421768 20 72 61.6292517006803 10.3707482993197 21 107 70.5552721088435 36.4447278911565 22 58 54.5552721088435 3.44472789115646 23 36 51.5552721088436 -15.5552721088436 24 52 83.7219387755102 -31.7219387755102 25 79 69.3860544217687 9.61394557823126 26 77 57.5289115646258 19.4710884353742 27 54 49.8146258503401 4.18537414965985 28 84 58.671768707483 25.328231292517 29 48 54.5289115646258 -6.52891156462585 30 96 59.1003401360544 36.8996598639456 31 83 68.671768707483 14.328231292517 32 66 53.8146258503401 12.1853741496599 33 61 62.7406462585034 -1.74064625850340 34 53 46.7406462585034 6.2593537414966 35 30 43.7406462585034 -13.7406462585034 36 74 75.90731292517 -1.90731292517005 37 69 61.5714285714286 7.4285714285714 38 59 49.7142857142857 9.2857142857143 39 42 42 -1.77635683940025e-14 40 65 50.8571428571429 14.1428571428571 41 70 46.7142857142857 23.2857142857143 42 100 51.2857142857143 48.7142857142857 43 63 60.8571428571429 2.14285714285714 44 105 46 59 45 82 54.9260204081633 27.0739795918367 46 81 38.9260204081633 42.0739795918367 47 75 35.9260204081633 39.0739795918367 48 102 68.0926870748299 33.9073129251701 49 121 53.7568027210885 67.2431972789115 50 98 41.8996598639456 56.1003401360544 51 76 34.1853741496599 41.8146258503401 52 77 43.0425170068027 33.9574829931973 53 63 38.8996598639456 24.1003401360544 54 37 43.4710884353742 -6.47108843537415 55 35 53.0425170068027 -18.0425170068027 56 23 38.1853741496599 -15.1853741496599 57 40 47.1113945578231 -7.11139455782312 58 29 31.1113945578231 -2.11139455782312 59 37 28.1113945578231 8.88860544217686 60 51 60.2780612244898 -9.27806122448977 61 20 45.9421768707483 -25.9421768707483 62 28 34.0850340136054 -6.08503401360543 63 13 26.3707482993197 -13.3707482993197 64 22 35.2278911564626 -13.2278911564626 65 25 31.0850340136054 -6.08503401360543 66 13 35.656462585034 -22.656462585034 67 16 45.2278911564626 -29.2278911564626 68 13 30.3707482993197 -17.3707482993197 69 16 39.296768707483 -23.296768707483 70 17 23.296768707483 -6.29676870748299 71 9 20.296768707483 -11.2967687074830 72 17 52.4634353741496 -35.4634353741496 73 25 38.1275510204082 -13.1275510204082 74 14 26.2704081632653 -12.2704081632653 75 8 18.5561224489796 -10.5561224489796 76 7 27.4132653061225 -20.4132653061225 77 10 23.2704081632653 -13.2704081632653 78 7 27.8418367346939 -20.8418367346939 79 10 37.4132653061224 -27.4132653061224 80 3 22.5561224489796 -19.5561224489796 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.136169946149592 0.272339892299184 0.863830053850408 17 0.132753383851534 0.265506767703068 0.867246616148466 18 0.0708729517833831 0.141745903566766 0.929127048216617 19 0.0848473096731656 0.169694619346331 0.915152690326834 20 0.0427249405702313 0.0854498811404627 0.957275059429769 21 0.0488266766892764 0.0976533533785528 0.951173323310724 22 0.0267617358900125 0.0535234717800251 0.973238264109987 23 0.115291540005817 0.230583080011633 0.884708459994183 24 0.859703579746627 0.280592840506747 0.140296420253373 25 0.819519034528943 0.360961930942115 0.180480965471057 26 0.769473990915924 0.461052018168153 0.230526009084076 27 0.757310809676689 0.485378380646622 0.242689190323311 28 0.687910463315897 0.624179073368205 0.312089536684103 29 0.742359235115735 0.515281529768531 0.257640764884265 30 0.690187308785498 0.619625382429004 0.309812691214502 31 0.746022773572955 0.507954452854090 0.253977226427045 32 0.699686453593894 0.600627092812213 0.300313546406106 33 0.714183990512346 0.571632018975308 0.285816009487654 34 0.688094359068353 0.623811281863293 0.311905640931647 35 0.791710675331737 0.416578649336527 0.208289324668263 36 0.816076572778662 0.367846854442676 0.183923427221338 37 0.825970527313897 0.348058945372206 0.174029472686103 38 0.84906902262965 0.301861954740701 0.150930977370350 39 0.908851830453418 0.182296339093165 0.0911481695465823 40 0.910545765741532 0.178908468516937 0.0894542342584684 41 0.901804430438164 0.196391139123671 0.0981955695618356 42 0.888523661261747 0.222952677476505 0.111476338738253 43 0.92019974418923 0.15960051162154 0.07980025581077 44 0.938904969083032 0.122190061833935 0.0610950309169676 45 0.913253652435199 0.173492695129602 0.0867463475648012 46 0.895878608596295 0.208242782807411 0.104121391403705 47 0.866770416036558 0.266459167926885 0.133229583963442 48 0.855117845129706 0.289764309740588 0.144882154870294 49 0.977103221980445 0.0457935560391096 0.0228967780195548 50 0.994899089061437 0.0102018218771264 0.00510091093856321 51 0.998686940581436 0.00262611883712692 0.00131305941856346 52 0.999924955270842 0.000150089458315595 7.50447291577977e-05 53 0.999983792078789 3.24158424226427e-05 1.62079212113213e-05 54 0.999980109908287 3.97801834259795e-05 1.98900917129897e-05 55 0.99997350745401 5.29850919806902e-05 2.64925459903451e-05 56 0.999950232967128 9.95340657445811e-05 4.97670328722906e-05 57 0.99990830776473 0.000183384470538571 9.16922352692856e-05 58 0.999699428778753 0.000601142442494757 0.000300571221247379 59 0.999590603348614 0.000818793302772121 0.000409396651386061 60 0.999959232244692 8.15355106162251e-05 4.07677553081125e-05 61 0.999989193630754 2.16127384916886e-05 1.08063692458443e-05 62 0.999935992459607 0.000128015080785379 6.40075403926894e-05 63 0.99967407895407 0.000651842091857805 0.000325921045928902 64 0.998355187116025 0.0032896257679506 0.0016448128839753 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 14 0.285714285714286 NOK 5% type I error level 16 0.326530612244898 NOK 10% type I error level 19 0.387755102040816 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/10pgrz1291149981.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/10pgrz1291149981.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/11xc61291149981.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/11xc61291149981.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/2b6tr1291149981.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/2b6tr1291149981.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/3b6tr1291149981.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/3b6tr1291149981.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/4b6tr1291149981.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/4b6tr1291149981.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/5b6tr1291149981.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/5b6tr1291149981.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/64ytu1291149981.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/64ytu1291149981.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/7x7sx1291149981.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/7x7sx1291149981.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/8x7sx1291149981.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/8x7sx1291149981.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/9pgrz1291149981.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149908608m8edeypp0vl6/9pgrz1291149981.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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