W8 - Geboortecijfers -Lineaire trend

*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: Sat, 27 Nov 2010 10:41:09 +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/27/t12908544900hzfrk3t0k3vkcl.htm/, Retrieved Sat, 27 Nov 2010 11:41:40 +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/27/t12908544900hzfrk3t0k3vkcl.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 «

9700 9081 9084 9743 8587 9731 9563 9998 9437 10038 9918 9252 9737 9035 9133 9487 8700 9627 8947 9283 8829 9947 9628 9318 9605 8640 9214 9567 8547 9185 9470 9123 9278 10170 9434 9655 9429 8739 9552 9687 9019 9672 9206 9069 9788 10312 10105 9863 9656 9295 9946 9701 9049 10190 9706 9765 9893 9994 10433 10073 10112 9266 9820 10097 9115 10411 9678 10408 10153 10368 10581 10597 10680 9738 9556

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 9 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Gebcijf[t] = + 9164.15747747747 + 11.6144523470840t + 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) 9164.15747747747 102.10434 89.7529 0 0 t 11.6144523470840 2.334665 4.9748 4e-06 2e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.503175160490125 R-squared 0.253185242134263 Adjusted R-squared 0.242954902985417 F-TEST (value) 24.7484700605287 F-TEST (DF numerator) 1 F-TEST (DF denominator) 73 p-value 4.20387907296149e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 437.710760269624 Sum Squared Residuals 13986121.8048743 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9700 9175.77192982462 524.228070175382 2 9081 9187.38638217164 -106.386382171642 3 9084 9199.00083451873 -115.000834518726 4 9743 9210.61528686581 532.38471313419 5 8587 9222.2297392129 -635.229739212894 6 9731 9233.84419155998 497.155808440022 7 9563 9245.45864390706 317.541356092938 8 9998 9257.07309625415 740.926903745854 9 9437 9268.68754860123 168.312451398770 10 10038 9280.30200094831 757.697999051686 11 9918 9291.9164532954 626.083546704602 12 9252 9303.53090564248 -51.5309056424822 13 9737 9315.14535798957 421.854642010434 14 9035 9326.75981033665 -291.75981033665 15 9133 9338.37426268373 -205.374262683734 16 9487 9349.98871503082 137.011284969182 17 8700 9361.6031673779 -661.603167377902 18 9627 9373.21761972499 253.782380275014 19 8947 9384.83207207207 -437.83207207207 20 9283 9396.44652441915 -113.446524419154 21 8829 9408.06097676624 -579.060976766238 22 9947 9419.67542911332 527.324570886678 23 9628 9431.2898814604 196.710118539594 24 9318 9442.9043338075 -124.90433380749 25 9605 9454.51878615457 150.481213845426 26 8640 9466.13323850166 -826.133238501658 27 9214 9477.74769084874 -263.747690848742 28 9567 9489.36214319583 77.637856804174 29 8547 9500.9765955429 -953.97659554291 30 9185 9512.59104789 -327.591047889994 31 9470 9524.20550023708 -54.205500237078 32 9123 9535.81995258416 -412.819952584162 33 9278 9547.43440493125 -269.434404931246 34 10170 9559.04885727833 610.95114272167 35 9434 9570.66330962541 -136.663309625414 36 9655 9582.2777619725 72.7222380275021 37 9429 9593.89221431958 -164.892214319582 38 8739 9605.50666666667 -866.506666666666 39 9552 9617.12111901375 -65.1211190137498 40 9687 9628.73557136083 58.2644286391662 41 9019 9640.35002370792 -621.350023707918 42 9672 9651.964476055 20.0355239449982 43 9206 9663.57892840209 -457.578928402086 44 9069 9675.19338074917 -606.19338074917 45 9788 9686.80783309625 101.192166903746 46 10312 9698.42228544334 613.577714556662 47 10105 9710.03673779042 394.963262209578 48 9863 9721.6511901375 141.348809862494 49 9656 9733.2656424846 -77.2656424845897 50 9295 9744.88009483167 -449.880094831674 51 9946 9756.49454717876 189.505452821242 52 9701 9768.10899952584 -67.1089995258417 53 9049 9779.72345187293 -730.723451872926 54 10190 9791.33790422 398.66209577999 55 9706 9802.9523565671 -96.9523565670936 56 9765 9814.56680891418 -49.5668089141776 57 9893 9826.18126126126 66.8187387387384 58 9994 9837.79571360835 156.204286391654 59 10433 9849.41016595543 583.58983404457 60 10073 9861.02461830251 211.975381697486 61 10112 9872.6390706496 239.360929350402 62 9266 9884.25352299668 -618.253522996682 63 9820 9895.86797534377 -75.8679753437655 64 10097 9907.48242769085 189.517572309150 65 9115 9919.09688003793 -804.096880037933 66 10411 9930.71133238502 480.288667614983 67 9678 9942.3257847321 -264.325784732101 68 10408 9953.94023707918 454.059762920815 69 10153 9965.55468942627 187.445310573731 70 10368 9977.16914177335 390.830858226647 71 10581 9988.78359412044 592.216405879563 72 10597 10000.3980464675 596.601953532479 73 10680 10012.0124988146 667.987501185395 74 9738 10023.6269511617 -285.626951161689 75 9556 10035.2414035088 -479.241403508773 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.688634033539313 0.622731932921375 0.311365966460687 6 0.786660966062836 0.426678067874329 0.213339033937164 7 0.701829417804344 0.596341164391311 0.298170582195656 8 0.713607743354493 0.572784513291013 0.286392256645507 9 0.633517907865681 0.732964184268638 0.366482092134319 10 0.623665166422377 0.752669667155245 0.376334833577623 11 0.570697286155062 0.858605427689875 0.429302713844938 12 0.631151046668469 0.737697906663062 0.368848953331531 13 0.575572080850766 0.848855838298467 0.424427919149234 14 0.66697803014617 0.666043939707661 0.333021969853830 15 0.650941420966366 0.698117158067269 0.349058579033634 16 0.58352781228383 0.83294437543234 0.41647218771617 17 0.691295349836029 0.617409300327943 0.308704650163971 18 0.664145629613711 0.671708740772577 0.335854370386289 19 0.638811981778236 0.722376036443529 0.361188018221764 20 0.564970896257414 0.870058207485172 0.435029103742586 21 0.551797400388875 0.89640519922225 0.448202599611125 22 0.685982711590795 0.62803457681841 0.314017288409205 23 0.668874420221326 0.662251159557348 0.331125579778674 24 0.603771667988433 0.792456664023133 0.396228332011567 25 0.577977092043577 0.844045815912847 0.422022907956423 26 0.661846231502987 0.676307536994026 0.338153768497013 27 0.595023687825903 0.809952624348193 0.404976312174097 28 0.566339021249006 0.867321957501987 0.433660978750994 29 0.678809738161429 0.642380523677142 0.321190261838571 30 0.616873378551196 0.766253242897608 0.383126621448804 31 0.571791239029687 0.856417521940625 0.428208760970313 32 0.51312690790671 0.97374618418658 0.48687309209329 33 0.449307055351734 0.898614110703468 0.550692944648266 34 0.671782081295746 0.656435837408509 0.328217918704254 35 0.613348681893997 0.773302636212007 0.386651318106003 36 0.585522768181493 0.828954463637014 0.414477231818507 37 0.520989567591777 0.958020864816446 0.479010432408223 38 0.601978571386129 0.796042857227742 0.398021428613871 39 0.549362286194049 0.901275427611903 0.450637713805951 40 0.515063656950505 0.96987268609899 0.484936343049495 41 0.514195602735869 0.971608794528263 0.485804397264131 42 0.470473372175228 0.940946744350456 0.529526627824772 43 0.437856220921439 0.875712441842878 0.562143779078561 44 0.462231739471294 0.924463478942589 0.537768260528706 45 0.431126285342165 0.86225257068433 0.568873714657835 46 0.582305004612788 0.835389990774423 0.417694995387212 47 0.623154284540717 0.753691430918565 0.376845715459283 48 0.587141338628864 0.825717322742272 0.412858661371136 49 0.519130660938041 0.961738678123919 0.480869339061959 50 0.48951348353655 0.9790269670731 0.51048651646345 51 0.454283838396826 0.908567676793652 0.545716161603174 52 0.384811486423858 0.769622972847716 0.615188513576142 53 0.491548593911952 0.983097187823905 0.508451406088048 54 0.496113784862523 0.992227569725045 0.503886215137477 55 0.426199960928072 0.852399921856145 0.573800039071928 56 0.357594267905408 0.715188535810817 0.642405732094592 57 0.292877552664560 0.585755105329121 0.70712244733544 58 0.237622060786382 0.475244121572765 0.762377939213618 59 0.292671882743466 0.585343765486932 0.707328117256534 60 0.251237969518459 0.502475939036918 0.748762030481541 61 0.226043750669063 0.452087501338126 0.773956249330937 62 0.249256694695164 0.498513389390329 0.750743305304836 63 0.184496929199064 0.368993858398127 0.815503070800936 64 0.134659494854206 0.269318989708413 0.865340505145794 65 0.425176561752996 0.850353123505992 0.574823438247004 66 0.34541081228523 0.69082162457046 0.65458918771477 67 0.550628480753971 0.898743038492058 0.449371519246029 68 0.458377684921744 0.916755369843487 0.541622315078256 69 0.543941113410423 0.912117773179154 0.456058886589577 70 0.639894368994486 0.720211262011028 0.360105631005514 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/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/10g88n1290854459.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/10g88n1290854459.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/197tc1290854459.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/197tc1290854459.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/297tc1290854459.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/297tc1290854459.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/397tc1290854459.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/397tc1290854459.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/42gsw1290854459.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/42gsw1290854459.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/52gsw1290854459.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/52gsw1290854459.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/6d79h1290854459.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/6d79h1290854459.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/7d79h1290854459.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/7d79h1290854459.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/85y821290854459.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/85y821290854459.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/95y821290854459.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/27/t12908544900hzfrk3t0k3vkcl/95y821290854459.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 = 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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