*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, 23 Nov 2009 07:12:14 -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/23/t1258986242fr4bv7qt8mhhp94.htm/, Retrieved Mon, 23 Nov 2009 15:24:17 +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/23/t1258986242fr4bv7qt8mhhp94.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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7.0 519 6.9 517 6.7 510 6.7 509 6.5 501 6.4 507 6.5 569 6.5 580 6.5 578 6.7 565 6.8 547 7.2 555 7.6 562 7.6 561 7.2 555 6.4 544 6.1 537 6.3 543 7.1 594 7.5 611 7.4 613 7.1 611 6.8 594 6.9 595 7.2 591 7.4 589 7.3 584 6.9 573 6.9 567 6.8 569 7.1 621 7.2 629 7.1 628 7.0 612 6.9 595 7.1 597 7.3 593 7.5 590 7.5 580 7.5 574 7.3 573 7.0 573 6.7 620 6.5 626 6.5 620 6.5 588 6.6 566 6.8 557 6.9 561 6.9 549 6.8 532 6.8 526 6.5 511 6.1 499 6.1 555 5.9 565 5.7 542 5.9 527 5.9 510 6.1 514 6.3 517 6.2 508 5.9 493 5.7 490 5.4 469 5.6 478 6.2 528 6.3 534 6.0 518 5.6 506 5.5 502 5.9 516

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation wkgo[t] = + 1.23093674535242 + 0.0103435238594171werkl[t] + 0.286150510982614M1[t] + 0.376901864475398M2[t] + 0.304428091241834M3[t] + 0.144028063857075M4[t] + 0.0347731160037059M5[t] -0.0600990228996263M6[t] -0.350881465943132M7[t] -0.410111208411898M8[t] -0.4400532039841M9[t] -0.344142691253912M10[t] -0.222945908640876M11[t] -0.0074243215055993t + 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) 1.23093674535242 0.607698 2.0256 0.047418 0.023709 werkl 0.0103435238594171 0.001013 10.2136 0 0 M1 0.286150510982614 0.158771 1.8023 0.076698 0.038349 M2 0.376901864475398 0.158835 2.3729 0.020984 0.010492 M3 0.304428091241834 0.159599 1.9075 0.061417 0.030709 M4 0.144028063857075 0.160283 0.8986 0.37259 0.186295 M5 0.0347731160037059 0.161874 0.2148 0.830664 0.415332 M6 -0.0600990228996263 0.161235 -0.3727 0.7107 0.35535 M7 -0.350881465943132 0.15943 -2.2009 0.031741 0.015871 M8 -0.410111208411898 0.16116 -2.5447 0.013619 0.00681 M9 -0.4400532039841 0.15983 -2.7533 0.007865 0.003933 M10 -0.344142691253912 0.158118 -2.1765 0.033603 0.016802 M11 -0.222945908640876 0.157752 -1.4133 0.162921 0.08146 t -0.0074243215055993 0.001735 -4.2784 7.1e-05 3.6e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.89956015219401 R-squared 0.80920846741531 Adjusted R-squared 0.76644484804288 F-TEST (value) 18.922824571229 F-TEST (DF numerator) 13 F-TEST (DF denominator) 58 p-value 2.22044604925031e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.273127732459692 Sum Squared Residuals 4.32672797783724 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7 6.8779518178669 0.122048182133101 2 6.9 6.94059180213524 -0.0405918021352414 3 6.7 6.78828904038016 -0.0882890403801597 4 6.7 6.61012116763039 0.089878832369615 5 6.5 6.41069370739608 0.0893062926039205 6 6.4 6.37045839014365 0.0295416098563496 7 6.5 6.7135501048784 -0.213550104878404 8 6.5 6.76067480335763 -0.260674803357628 9 6.5 6.70262143856099 -0.202621438560991 10 6.7 6.65664181961316 0.0433581803868414 11 6.8 6.58423085125109 0.215769148748911 12 7.2 6.8825006292617 0.317499370738300 13 7.6 7.23363148575464 0.366368514245364 14 7.6 7.3066149938824 0.293385006117596 15 7.2 7.16465575598674 0.0353442440132631 16 6.4 6.88305264464279 -0.48305264464279 17 6.1 6.6939687082679 -0.593968708267903 18 6.3 6.65373339101547 -0.353733391015474 19 7.1 6.88304634329664 0.21695365670336 20 7.5 6.99223218493236 0.507767815067635 21 7.4 6.9755529155734 0.424447084426604 22 7.1 7.04335205907915 0.0566479409208477 23 6.8 6.9812846145765 -0.181284614576499 24 6.9 7.20714972557119 -0.307149725571192 25 7.2 7.44450181961054 -0.244501819610539 26 7.4 7.50714180387889 -0.107141803878888 27 7.3 7.37552608984264 -0.0755260898426409 28 6.9 7.0939229784987 -0.193922978498694 29 6.9 6.91518256598322 -0.0151825659832232 30 6.8 6.83357315329313 -0.0335731532931262 31 7.1 7.07322962943371 0.0267703705662906 32 7.2 7.08932375633468 0.110676243665319 33 7.1 7.04161391539746 0.0583860846025384 34 7 6.96460372487138 0.0353962751286226 35 6.9 6.90253628036872 -0.00253628036872376 36 7.1 7.13874491522284 -0.0387449152228354 37 7.3 7.37609700926218 -0.076097009262182 38 7.5 7.42839346967111 0.071606530328886 39 7.5 7.24506013633778 0.254939863662219 40 7.5 7.01517464429092 0.48482535570908 41 7.3 6.88815185107253 0.411848148927465 42 7 6.7858553906636 0.214144609336397 43 6.7 6.9737942475071 -0.2737942475071 44 6.5 6.96920132668924 -0.469201326689238 45 6.5 6.86977386645493 -0.369773866454933 46 6.5 6.62726729417818 -0.127267294178176 47 6.6 6.51348223037844 0.0865177696215623 48 6.8 6.63591210277896 0.164087897221039 49 6.9 6.95601238769364 -0.0560123876936435 50 6.9 6.91521713336782 -0.0152171333678220 51 6.8 6.65947913301857 0.140520866981430 52 6.8 6.42959364097171 0.370406359028291 53 6.5 6.15776151372148 0.342238486278516 54 6.1 5.93134276699955 0.168657233000452 55 6.1 6.2123733385778 -0.112373338577799 56 5.9 6.2491545131976 -0.349154513197605 57 5.7 5.97388714735321 -0.273887147353210 58 5.9 5.90722048068654 -0.00722048068654271 59 5.9 5.84515303618389 0.0548469638161106 60 6.1 6.10204871875683 -0.00204871875683488 61 6.3 6.4118054798121 -0.111805479812101 62 6.2 6.40204079706453 -0.202040797064531 63 5.9 6.16698984443411 -0.266989844434112 64 5.7 5.9681349239655 -0.268134923965502 65 5.4 5.63424165355878 -0.234241653558775 66 5.6 5.6250369078846 -0.0250369078845979 67 6.2 5.84400633630635 0.355993663693654 68 6.3 5.83941341548848 0.460586584511516 69 6 5.63655071666001 0.363449283339992 70 5.6 5.60091462157159 -0.00091462157159313 71 5.5 5.67331298724136 -0.173312987241362 72 5.9 6.03364390840848 -0.133643908408477 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.107416202016229 0.214832404032458 0.892583797983771 18 0.062789152012863 0.125578304025726 0.937210847987137 19 0.867698460364325 0.264603079271349 0.132301539635675 20 0.953153166819076 0.0936936663618488 0.0468468331809244 21 0.969700677834979 0.0605986443300425 0.0302993221650212 22 0.948257678382213 0.103484643235574 0.051742321617787 23 0.930341355415893 0.139317289168214 0.0696586445841072 24 0.944482275961874 0.111035448076252 0.0555177240381258 25 0.949454947343487 0.101090105313027 0.0505450526565135 26 0.923660303522809 0.152679392954382 0.076339696477191 27 0.88636677984389 0.227266440312222 0.113633220156111 28 0.867635381655599 0.264729236688802 0.132364618344401 29 0.843982349556589 0.312035300886823 0.156017650443411 30 0.801273438984535 0.39745312203093 0.198726561015465 31 0.73755191631972 0.52489616736056 0.26244808368028 32 0.672872110234208 0.654255779531584 0.327127889765792 33 0.600440935006994 0.799118129986013 0.399559064993006 34 0.51977153058403 0.960456938831939 0.480228469415969 35 0.436841512134296 0.873683024268592 0.563158487865704 36 0.361730029128506 0.723460058257012 0.638269970871494 37 0.294304213259035 0.58860842651807 0.705695786740965 38 0.229327559093004 0.458655118186007 0.770672440906996 39 0.209367031476425 0.418734062952849 0.790632968523575 40 0.294818524255143 0.589637048510286 0.705181475744857 41 0.354095084190116 0.708190168380232 0.645904915809884 42 0.321884723353432 0.643769446706865 0.678115276646568 43 0.331617482320269 0.663234964640537 0.668382517679731 44 0.452925408939562 0.905850817879124 0.547074591060438 45 0.49541439320942 0.99082878641884 0.50458560679058 46 0.443450130522417 0.886900261044835 0.556549869477583 47 0.351974635905671 0.703949271811342 0.648025364094329 48 0.277475722361558 0.554951444723116 0.722524277638442 49 0.201076878217661 0.402153756435323 0.798923121782339 50 0.135782382785872 0.271564765571745 0.864217617214128 51 0.103089494726042 0.206178989452085 0.896910505273958 52 0.160885161979411 0.321770323958822 0.839114838020589 53 0.677362378677927 0.645275242644146 0.322637621322073 54 0.793419126946915 0.413161746106171 0.206580873053086 55 0.64391069836218 0.712178603275639 0.356089301637819 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 0.0512820512820513 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/10kb571258985527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/10kb571258985527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/1rw6a1258985527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/1rw6a1258985527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/2n9uc1258985527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/2n9uc1258985527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/3ciee1258985527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/3ciee1258985527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/4kae51258985527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/4kae51258985527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/5ugua1258985527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/5ugua1258985527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/62cu41258985527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/62cu41258985527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/7v2op1258985527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/7v2op1258985527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/8172h1258985527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/8172h1258985527.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/91j9o1258985527.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258986242fr4bv7qt8mhhp94/91j9o1258985527.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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