*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: Thu, 19 Nov 2009 02:52:29 -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/19/t1258625050egaa3x6j5xz8o7k.htm/, Retrieved Thu, 19 Nov 2009 11:04:26 +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/19/t1258625050egaa3x6j5xz8o7k.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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8.4 420 8.4 418 8.4 410 8.6 418 8.9 426 8.8 428 8.3 430 7.5 424 7.2 423 7.4 427 8.8 441 9.3 449 9.3 452 8.7 462 8.2 455 8.3 461 8.5 461 8.6 463 8.5 462 8.2 456 8.1 455 7.9 456 8.6 472 8.7 472 8.7 471 8.5 465 8.4 459 8.5 465 8.7 468 8.7 467 8.6 463 8.5 460 8.3 462 8.00 461 8.2 476 8.1 476 8.1 471 8.00 453 7.9 443 7.9 442 8.00 444 8.00 438 7.9 427 8.00 424 7.7 416 7.2 406 7.5 431 7.3 434 7.00 418 7.00 412 7.00 404 7.2 409 7.3 412 7.1 406 6.8 398 6.4 397 6.1 385 6.5 390 7.7 413 7.9 413 7.5 401 6.9 397 6.6 397 6.9 409 7.7 419 8.00 424 8.00 428 7.7 430 7.3 424 7.4 433 8.1 456 8.3 459 8.2 446

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 12 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation wgb[t] = + 2.16390037135749 + 0.0147986395386048nwwz[t] -0.00488362405726619M1[t] -0.247512438056681M2[t] -0.304558880654981M3[t] -0.229921650819173M4[t] + 0.00271331158097471M5[t] + 0.0426748050074802M6[t] -0.0828335426426028M7[t] -0.327474996882453M8[t] -0.516585158481063M9[t] -0.572887610798434M10[t] -0.0955655748106912M11[t] -0.0134290670674358t + 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) 2.16390037135749 0.987587 2.1911 0.032406 0.016203 nwwz 0.0147986395386048 0.002065 7.1668 0 0 M1 -0.00488362405726619 0.211738 -0.0231 0.981677 0.490838 M2 -0.247512438056681 0.222861 -1.1106 0.27124 0.13562 M3 -0.304558880654981 0.225395 -1.3512 0.181783 0.090891 M4 -0.229921650819173 0.222321 -1.0342 0.30527 0.152635 M5 0.00271331158097471 0.220512 0.0123 0.990224 0.495112 M6 0.0426748050074802 0.220438 0.1936 0.847161 0.423581 M7 -0.0828335426426028 0.221153 -0.3746 0.709335 0.354668 M8 -0.327474996882453 0.221942 -1.4755 0.145395 0.072698 M9 -0.516585158481063 0.223533 -2.311 0.024345 0.012173 M10 -0.572887610798434 0.222669 -2.5728 0.012623 0.006311 M11 -0.0955655748106912 0.217763 -0.4389 0.662372 0.331186 t -0.0134290670674358 0.002333 -5.7574 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.87110266697072 R-squared 0.7588198564035 Adjusted R-squared 0.70567846883139 F-TEST (value) 14.2792631331619 F-TEST (DF numerator) 13 F-TEST (DF denominator) 59 p-value 1.03916875104915e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.377025054892792 Sum Squared Residuals 8.38672562899786 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.4 8.36101628644682 0.0389837135531758 2 8.4 8.07536112630276 0.32463887369724 3 8.4 7.88649650032819 0.513503499671813 4 8.6 8.0660937794054 0.533906220594604 5 8.9 8.40368879104695 0.496311208953054 6 8.8 8.45981849648323 0.340181503516774 7 8.3 8.35047836084292 -0.0504783608429172 8 7.5 8.003616002304 -0.503616002304003 9 7.2 7.78627813409935 -0.586278134099351 10 7.4 7.77574117286897 -0.375741172868966 11 8.8 8.44681509532974 0.353184904670262 12 9.3 8.64734071938183 0.652659280618167 13 9.3 8.67342394687295 0.626576053127055 14 8.7 8.56535246119214 0.134647538807856 15 8.2 8.39128647475617 -0.191286474756175 16 8.3 8.54128647475618 -0.241286474756175 17 8.5 8.76049237008889 -0.260492370088887 18 8.6 8.81662207552517 -0.216622075525167 19 8.5 8.66288602126904 -0.162886021269043 20 8.2 8.31602366273013 -0.116023662730129 21 8.1 8.09868579452548 0.00131420547452215 22 7.9 8.04375291467928 -0.143752914679276 23 8.6 8.74442411621726 -0.14442411621726 24 8.7 8.82656062396052 -0.126560623960516 25 8.7 8.79344929329721 -0.093449293297209 26 8.5 8.44859957499873 0.0514004250012708 27 8.4 8.28933222810137 0.110667771898636 28 8.5 8.43933222810137 0.0606677718986348 29 8.7 8.70293404204989 -0.00293404204989235 30 8.7 8.71466782887036 -0.0146678288703573 31 8.6 8.51653585599842 0.0834641440015811 32 8.5 8.21406941607532 0.285930583924682 33 8.3 8.04112746648648 0.258872533513519 34 8 7.95659730756307 0.0434026924369294 35 8.2 8.64246986956245 -0.44246986956245 36 8.1 8.7246063773057 -0.624606377305706 37 8.1 8.63230048848798 -0.532300488487979 38 8 8.10986709572624 -0.109867095726242 39 7.9 7.89140519067446 0.0085948093255428 40 7.9 7.93781471390422 -0.0378147139042239 41 8 8.18661788831415 -0.186617888314146 42 8 8.12435847744159 -0.124358477441587 43 7.9 7.82263602779941 0.0773639722005855 44 8 7.52016958787631 0.479830412123686 45 7.7 7.19924124290143 0.50075875709857 46 7.2 6.98152332813057 0.218476671869425 47 7.5 7.815382285516 -0.315382285516002 48 7.3 7.94191471187507 -0.641914711875072 49 7 7.6868237881327 -0.686823788132693 50 7 7.34197406983421 -0.341974069834214 51 7 7.15310944385964 -0.153109443859639 52 7.2 7.28831080432103 -0.0883108043210348 53 7.3 7.55191261826956 -0.251912618269562 54 7.1 7.489653207397 -0.389653207397003 55 6.8 7.23232667637065 -0.432326676370645 56 6.4 6.95945751552475 -0.559457515524754 57 6.1 6.57933461239545 -0.47933461239545 58 6.5 6.58359629070367 -0.083596290703668 59 7.7 7.38785796901189 0.312142030988115 60 7.9 7.46999447675514 0.430005523244859 61 7.5 7.27409811116718 0.225901888832819 62 6.9 6.95884567194591 -0.0588456719459114 63 6.6 6.88837016228018 -0.288370162280176 64 6.9 7.1271619995118 -0.227161999511805 65 7.7 7.49435429023057 0.205645709769434 66 8 7.59487991428266 0.40512008571734 67 8 7.51513705771956 0.484862942280439 68 7.7 7.28666381548948 0.413336184510516 69 7.3 6.99533274959181 0.304667250408191 70 7.4 7.05878898605445 0.341211013945554 71 8.1 7.86305066436266 0.236949335637336 72 8.3 7.98958309072173 0.310416909278267 73 8.2 7.77888808559517 0.42111191440483 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.832575145516111 0.334849708967778 0.167424854483889 18 0.784382630836818 0.431234738326363 0.215617369163182 19 0.665670799421143 0.668658401157713 0.334329200578857 20 0.629881768161569 0.740236463676863 0.370118231838431 21 0.630410430289586 0.739179139420828 0.369589569710414 22 0.52117536844335 0.95764926311330 0.47882463155665 23 0.578357771457894 0.843284457084213 0.421642228542106 24 0.799750180797297 0.400499638405406 0.200249819202703 25 0.76444816717958 0.47110366564084 0.23555183282042 26 0.696336164103 0.607327671794 0.303663835897 27 0.628144901175871 0.743710197648257 0.371855098824129 28 0.549201983365055 0.901596033269891 0.450798016634946 29 0.463889046463111 0.927778092926223 0.536110953536889 30 0.378220199656325 0.756440399312649 0.621779800343675 31 0.311183720841028 0.622367441682057 0.688816279158972 32 0.348605225564319 0.697210451128638 0.651394774435681 33 0.351311317822415 0.70262263564483 0.648688682177585 34 0.279457321659225 0.55891464331845 0.720542678340775 35 0.344242460790344 0.688484921580688 0.655757539209656 36 0.54722389363674 0.905552212726519 0.452776106363260 37 0.63416389753946 0.731672204921079 0.365836102460540 38 0.563809754982837 0.872380490034326 0.436190245017163 39 0.480682953767731 0.961365907535462 0.519317046232269 40 0.398576703885737 0.797153407771473 0.601423296114263 41 0.328319444328554 0.656638888657108 0.671680555671446 42 0.256501658351055 0.513003316702111 0.743498341648945 43 0.199248310142508 0.398496620285016 0.800751689857492 44 0.298240786186327 0.596481572372655 0.701759213813673 45 0.545416192167147 0.909167615665705 0.454583807832853 46 0.656531590086785 0.68693681982643 0.343468409913215 47 0.599334500033846 0.801330999932308 0.400665499966154 48 0.62857343599714 0.742853128005721 0.371426564002861 49 0.6949245582475 0.610150883504999 0.305075441752499 50 0.6126579118887 0.7746841762226 0.3873420881113 51 0.582499907097593 0.835000185804814 0.417500092902407 52 0.746617585418802 0.506764829162397 0.253382414581198 53 0.85102915531106 0.297941689377880 0.148970844688940 54 0.946039073488914 0.107921853022172 0.0539609265110859 55 0.923978694327197 0.152042611345607 0.0760213056728034 56 0.869658337338271 0.260683325323458 0.130341662661729 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 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/10ealw1258624337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/10ealw1258624337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/1pgqd1258624337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/1pgqd1258624337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/2h00c1258624337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/2h00c1258624337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/3fpcv1258624337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/3fpcv1258624337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/4sodh1258624337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/4sodh1258624337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/5f7zb1258624337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/5f7zb1258624337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/64j3g1258624337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/64j3g1258624337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/7lsm51258624337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/7lsm51258624337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/8uh5v1258624337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/8uh5v1258624337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/9395n1258624337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625050egaa3x6j5xz8o7k/9395n1258624337.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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