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R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 24 Dec 2010 13:15:50 +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/24/t12931967273kcdm08zdji21fe.htm/, Retrieved Fri, 24 Dec 2010 14:18:47 +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/24/t12931967273kcdm08zdji21fe.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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1,5 508643 493 797 1,6 527568 514 840 1,8 520008 522 988 1,5 498484 490 819 1,3 523917 484 831 1,6 553522 506 904 1,6 558901 501 814 1,8 548933 462 798 1,8 567013 465 828 1,6 551085 454 789 1,8 588245 464 930 2 605010 427 744 1,3 631572 460 832 1,1 639180 473 826 1 653847 465 907 1,2 657073 422 776 1,2 626291 415 835 1,3 625616 413 715 1,3 633352 420 729 1,4 672820 363 733 1,1 691369 376 736 0,9 702595 380 712 1 692241 384 711 1,1 718722 346 667 1,4 732297 389 799 1,5 721798 407 661 1,8 766192 393 692 1,8 788456 346 649 1,8 806132 348 729 1,7 813944 353 622 1,5 788025 364 671 1,1 765985 305 635 1,3 702684 307 648 1,6 730159 312 745 1,9 678942 312 624 1,9 672527 286 477 2 594783 324 710 2,2 594575 336 515 2,2 576299 327 461 2 530770 302 590 2,3 524491 299 415 2,6 456590 311 554 3,2 428448 315 585 3,2 444937 264 513 3,1 372206 278 591 2,8 317272 278 561 2,3 297604 287 684 1,9 288561 279 668 1,9 289287 324 795 2 258923 354 776 2 255493 354 1043 1,8 277992 360 96 etc...

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation inflatie[t] = + 4.35891569656023 -1.24880250097788e-06beurswaarde[t] + 1.08573074824315e-05werkloosheid[t] -0.00277272710599615failliet[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) 4.35891569656023 0.490685 8.8833 0 0 beurswaarde -1.24880250097788e-06 0 -2.7034 0.00907 0.004535 werkloosheid 1.08573074824315e-05 0.001406 0.0077 0.993864 0.496932 failliet -0.00277272710599615 0.000733 -3.7808 0.000382 0.000191 Multiple Linear Regression - Regression Statistics Multiple R 0.61098302178297 R-squared 0.373300252907049 Adjusted R-squared 0.339727052169926 F-TEST (value) 11.1189950529288 F-TEST (DF numerator) 3 F-TEST (DF denominator) 56 p-value 7.89864764538795e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.503152512180858 Sum Squared Residuals 14.1770972287789 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.5 1.51921019516525 -0.0192101951652503 2 1.6 1.37657734573353 0.223422654266465 3 1.8 0.975741539413358 0.824258460586642 4 1.5 1.47086421151832 0.0291357884816838 5 1.3 1.40576554839410 -0.105765548394098 6 1.6 1.16662453237954 0.433375467620458 7 1.6 1.40939837672902 0.190601623270977 8 1.8 1.46578663876289 0.334213361237106 9 1.8 1.36005904828778 0.439940951712223 10 1.6 1.48796690127490 0.112033098725104 11 1.8 1.05071545146793 0.749284548532075 12 2 1.54510479887746 0.454895201122535 13 1.3 1.26829241266575 0.0317075873342502 14 1.1 1.27556903087156 -0.175569030871559 15 1 1.03257509054417 -0.0325750905441687 16 1.2 1.39130684033976 -0.191306840339765 17 1.2 1.26608057851872 -0.0660805785187162 18 1.3 1.59962905831145 -0.299629058311449 19 1.3 1.55122614383232 -0.251226143832315 20 1.4 1.49022863177324 -0.0902286317732372 21 1.1 1.45888755786188 -0.358887557861882 22 0.9 1.51145738075974 -0.611457380759741 23 1 1.52720363819079 -0.527203638190792 24 1.1 1.61572151414190 -0.515721514141895 25 1.4 1.23323590642137 0.166764093578627 26 1.5 1.62917885604129 -0.129178856041292 27 1.8 1.48763297522225 0.312367024777754 28 1.8 1.57854660844663 0.221453391553366 29 1.8 1.33467632157462 0.465323678425378 30 1.7 1.62165676331598 0.0783432366840166 31 1.5 1.51828027752732 -0.0182802775273244 32 1.1 1.64498147932327 -0.544981479323275 33 1.3 1.68800818867469 -0.38800818867469 34 1.6 1.38479709721611 0.215202902783891 35 1.9 1.78425699473423 0.115743005265773 36 1.9 2.19957665736489 -0.29957665736489 37 2 1.65103072098814 0.348969279011856 38 2.2 2.19210254526739 0.00789745473261452 39 2.2 2.36455520773171 -0.164555207731707 40 2 2.06345870743817 -0.0634587074381653 41 2.3 2.55649460996868 -0.256494609968684 42 2.6 2.25601076854391 0.343989231456093 43 3.2 2.20524345747048 0.994756542529524 44 3.2 2.38373458198197 0.81626541801803 45 3.1 2.25844052471765 0.841559475282353 46 2.8 2.41022405448625 0.38977594551375 47 2.3 2.0938377838053 0.206162216194701 48 1.9 2.14940748005772 -0.249407480057721 49 1.9 1.79685308581721 0.103146914182791 50 2 1.8877792591953 0.112220740804699 51 2 1.15174451447268 0.848255485527316 52 1.8 1.34275829222177 0.457241707778227 53 1.6 1.88105017423335 -0.281050174233347 54 1.4 1.14293612727970 0.257063872720297 55 0.2 1.36175984076092 -1.16175984076092 56 0.3 1.78030504386925 -1.48030504386925 57 0.4 1.37824343121475 -0.978243431214752 58 0.7 1.60325211064546 -0.90325211064546 59 1 1.50664408229786 -0.506644082297864 60 1.1 1.55531300385636 -0.455313003856362 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.00593480620108487 0.0118696124021697 0.994065193798915 8 0.026199816137618 0.052399632275236 0.973800183862382 9 0.008842169520049 0.017684339040098 0.99115783047995 10 0.00277045068352916 0.00554090136705831 0.99722954931647 11 0.00124841927522679 0.00249683855045358 0.998751580724773 12 0.000840800305516568 0.00168160061103314 0.999159199694483 13 0.00722858026937127 0.0144571605387425 0.992771419730629 14 0.00849066562683718 0.0169813312536744 0.991509334373163 15 0.0137473826774069 0.0274947653548138 0.986252617322593 16 0.00930310344131337 0.0186062068826267 0.990696896558687 17 0.0107879574703232 0.0215759149406465 0.989212042529677 18 0.00589551671868799 0.0117910334373760 0.994104483281312 19 0.00309012456908617 0.00618024913817234 0.996909875430914 20 0.00142754452992982 0.00285508905985965 0.99857245547007 21 0.000730310427390314 0.00146062085478063 0.99926968957261 22 0.000468881933397531 0.000937763866795062 0.999531118066602 23 0.000227758291824409 0.000455516583648817 0.999772241708176 24 0.000120680714106821 0.000241361428213642 0.999879319285893 25 0.000150237494828878 0.000300474989657757 0.999849762505171 26 0.000355090918231983 0.000710181836463966 0.999644909081768 27 0.00246347098795560 0.00492694197591119 0.997536529012044 28 0.00490170921490538 0.00980341842981076 0.995098290785095 29 0.00835029871856296 0.0167005974371259 0.991649701281437 30 0.00746519336294888 0.0149303867258978 0.992534806637051 31 0.00619813032028444 0.0123962606405689 0.993801869679716 32 0.0055331155276128 0.0110662310552256 0.994466884472387 33 0.00480665521841378 0.00961331043682757 0.995193344781586 34 0.00401528018759582 0.00803056037519164 0.995984719812404 35 0.00471331428517476 0.00942662857034951 0.995286685714825 36 0.00717403037166518 0.0143480607433304 0.992825969628335 37 0.00628973917807802 0.0125794783561560 0.993710260821922 38 0.00509430088626126 0.0101886017725225 0.994905699113739 39 0.0030848164336854 0.0061696328673708 0.996915183566315 40 0.00294513452615564 0.00589026905231128 0.997054865473844 41 0.00218331958646158 0.00436663917292316 0.997816680413538 42 0.0015229677806907 0.0030459355613814 0.99847703221931 43 0.0132028518466318 0.0264057036932635 0.986797148153368 44 0.0143472289655099 0.0286944579310198 0.98565277103449 45 0.0375817455756027 0.0751634911512054 0.962418254424397 46 0.0666011221565276 0.133202244313055 0.933398877843472 47 0.0736946609068492 0.147389321813698 0.92630533909315 48 0.0868201853472538 0.173640370694508 0.913179814652746 49 0.0602862508245389 0.120572501649078 0.939713749175461 50 0.0423979375236004 0.0847958750472008 0.9576020624764 51 0.0248768924971189 0.0497537849942379 0.975123107502881 52 0.0195417243328471 0.0390834486656943 0.980458275667153 53 0.072660553163273 0.145321106326546 0.927339446836727 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 20 0.425531914893617 NOK 5% type I error level 39 0.829787234042553 NOK 10% type I error level 42 0.893617021276596 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/10ek1a1293196541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/10ek1a1293196541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/1pjny1293196541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/1pjny1293196541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/2pjny1293196541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/2pjny1293196541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/30b411293196541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/30b411293196541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/40b411293196541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/40b411293196541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/50b411293196541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/50b411293196541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/6b2341293196541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/6b2341293196541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/7b2341293196541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/7b2341293196541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/84b2p1293196541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/84b2p1293196541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/94b2p1293196541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12931967273kcdm08zdji21fe/94b2p1293196541.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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