MLRM 1

*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: Fri, 10 Dec 2010 14:33:44 +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/10/t129199173923ovah38002h662.htm/, Retrieved Fri, 10 Dec 2010 15:35:53 +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/10/t129199173923ovah38002h662.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 «

216.234 627 213.586 696 209.465 825 204.045 677 200.237 656 203.666 785 241.476 412 260.307 352 243.324 839 244.460 729 233.575 696 237.217 641 235.243 695 230.354 638 227.184 762 221.678 635 217.142 721 219.452 854 256.446 418 265.845 367 248.624 824 241.114 687 229.245 601 231.805 676 219.277 740 219.313 691 212.610 683 214.771 594 211.142 729 211.457 731 240.048 386 240.636 331 230.580 707 208.795 715 197.922 657 194.596 653 194.581 642 185.686 643 178.106 718 172.608 654 167.302 632 168.053 731 202.300 392 202.388 344 182.516 792 173.476 852 166.444 649 171.297 629 169.701 685 164.182 617 161.914 715 159.612 715 151.001 629 158.114 916 186.530 531 187.069 357 174.330 917 169.362 828 166.827 708 178.037 858 186.413 775 189.226 785 191.563 1006 188.906 789 186.005 734 195.309 906 223.532 532 226.899 387 214.126 991 206.903 841 204.442 892 220.375 782

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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation faillissementen[t] = + 1063.98774129240 -1.87038258237956werlozen[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) 1063.98774129240 130.01803 8.1834 0 0 werlozen -1.87038258237956 0.628545 -2.9757 0.00401 0.002005 Multiple Linear Regression - Regression Statistics Multiple R 0.335103814391781 R-squared 0.112294566419921 Adjusted R-squared 0.09961306022592 F-TEST (value) 8.8549865214781 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 0.00400991597917932 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 148.528145775716 Sum Squared Residuals 1544242.70613007 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 627 659.54743397414 -32.5474339741402 2 696 664.500207052283 31.4997929477172 3 825 672.208053674269 152.791946325731 4 677 682.345527270766 -5.34552727076606 5 656 689.467944144467 -33.4679441444674 6 785 683.054402269488 101.945597730512 7 412 612.335236829717 -200.335236829717 8 352 577.114062420927 -225.114062420927 9 839 608.878769817479 230.121230182521 10 729 606.754015203896 122.245984796104 11 696 627.113129613098 68.8868703869023 12 641 620.301196248071 20.6988037519287 13 695 623.993331465689 71.0066685343115 14 638 633.137631910942 4.86236808905781 15 762 639.066744697085 122.933255302915 16 635 649.365071195667 -14.3650711956673 17 721 657.849126589341 63.150873410659 18 854 653.528542824044 200.471457175956 19 418 584.335609571495 -166.335609571495 20 367 566.755883679709 -199.755883679709 21 824 598.965742130868 225.034257869132 22 687 613.012315324538 73.9876846754619 23 601 635.211886194801 -34.2118861948011 24 676 630.42370678391 45.5762932160905 25 740 653.85585977596 86.1441402240394 26 691 653.788526002995 37.2114739970051 27 683 666.325700452685 16.6742995473149 28 594 662.283803692163 -68.2838036921629 29 729 669.071422083618 59.9285779163817 30 731 668.482251570169 62.5177484298312 31 386 615.006143157355 -229.006143157355 32 331 613.906358198916 -282.906358198916 33 707 632.714925447324 74.2850745526756 34 715 673.461210004463 41.5387899955368 35 657 693.797879822676 -36.7978798226761 36 653 700.01877229167 -47.0187722916705 37 642 700.046828030406 -58.0468280304062 38 643 716.683881100672 -73.6838811006724 39 718 730.86138107511 -12.8613810751094 40 654 741.144744513032 -87.1447445130323 41 632 751.068994495138 -119.068994495138 42 731 749.664337175771 -18.6643371757711 43 392 685.609344877018 -293.609344877018 44 344 685.444751209769 -341.444751209769 45 792 722.612993886816 69.3870061131844 46 852 739.521252431527 112.478747568473 47 649 752.67378275082 -103.673782750820 48 629 743.596816078532 -114.596816078532 49 685 746.58194668001 -61.5819466800096 50 617 756.904588152162 -139.904588152162 51 715 761.146615848999 -46.1466158489993 52 715 765.452236553637 -50.452236553637 53 629 781.558100970507 -152.558100970507 54 916 768.254069662042 147.745930337958 55 531 715.105278201144 -184.105278201144 56 357 714.097141989241 -357.097141989241 57 917 737.923945706175 179.076054293825 58 828 747.216006375436 80.7839936245637 59 708 751.957426221769 -43.9574262217685 60 858 730.990437473294 127.009562526706 61 775 715.324112963282 59.6758870367176 62 785 710.062726759049 74.9372732409513 63 1006 705.691642664028 300.308357335972 64 789 710.66124918541 78.3387508145898 65 734 716.087229056893 17.9127709431067 66 906 698.685189510434 207.314810489566 67 532 645.897381887936 -113.897381887936 68 387 639.599803733064 -252.599803733064 69 991 663.490200457798 327.509799542202 70 841 676.999973850325 164.000026149675 71 892 681.602985385561 210.397014614439 72 782 651.802179700508 130.197820299492 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.198908571693278 0.397817143386556 0.801091428306722 6 0.130098235248855 0.260196470497711 0.869901764751145 7 0.100968621871976 0.201937243743952 0.899031378128024 8 0.0555317247312669 0.111063449462534 0.944468275268733 9 0.531693668951404 0.936612662097192 0.468306331048596 10 0.546250008590724 0.907499982818553 0.453749991409276 11 0.461473937900309 0.922947875800617 0.538526062099691 12 0.362805237037516 0.725610474075031 0.637194762962484 13 0.291140316441312 0.582280632882625 0.708859683558688 14 0.213859876165718 0.427719752331435 0.786140123834282 15 0.183450656435303 0.366901312870606 0.816549343564697 16 0.133870633726274 0.267741267452547 0.866129366273726 17 0.0932480662319745 0.186496132463949 0.906751933768026 18 0.112528441292653 0.225056882585306 0.887471558707347 19 0.110925459719977 0.221850919439954 0.889074540280023 20 0.107325778878974 0.214651557757948 0.892674221121026 21 0.217749319606236 0.435498639212473 0.782250680393764 22 0.179197060643222 0.358394121286444 0.820802939356778 23 0.13951711227915 0.2790342245583 0.86048288772085 24 0.103409961238199 0.206819922476398 0.8965900387618 25 0.0778326645273656 0.155665329054731 0.922167335472634 26 0.0550442819384774 0.110088563876955 0.944955718061523 27 0.0391417534010793 0.0782835068021586 0.96085824659892 28 0.0341336688821570 0.0682673377643141 0.965866331117843 29 0.0232818025600168 0.0465636051200336 0.976718197439983 30 0.0156619370466856 0.0313238740933712 0.984338062953314 31 0.0294735723919536 0.0589471447839072 0.970526427608046 32 0.0798533631529987 0.159706726305997 0.920146636847001 33 0.0609483123357114 0.121896624671423 0.939051687664289 34 0.0430647639305741 0.0861295278611483 0.956935236069426 35 0.0358889253318836 0.0717778506637672 0.964111074668116 36 0.0300537639171909 0.0601075278343819 0.96994623608281 37 0.0248688351259365 0.0497376702518729 0.975131164874063 38 0.0215411972045984 0.0430823944091968 0.978458802795402 39 0.0149543115693898 0.0299086231387795 0.98504568843061 40 0.0126058306660347 0.0252116613320694 0.987394169333965 41 0.0114980064506142 0.0229960129012285 0.988501993549386 42 0.00727903230279727 0.0145580646055945 0.992720967697203 43 0.0274015369993161 0.0548030739986321 0.972598463000684 44 0.128843708410598 0.257687416821196 0.871156291589402 45 0.102009624203382 0.204019248406764 0.897990375796618 46 0.0894266723717907 0.178853344743581 0.91057332762821 47 0.0729192659129782 0.145838531825956 0.927080734087022 48 0.0615989097003169 0.123197819400634 0.938401090299683 49 0.0448896074180404 0.0897792148360807 0.95511039258196 50 0.0409940563586634 0.0819881127173267 0.959005943641337 51 0.0284971636655973 0.0569943273311945 0.971502836334403 52 0.0196762021737055 0.0393524043474111 0.980323797826294 53 0.0223485778796041 0.0446971557592083 0.977651422120396 54 0.019650523788725 0.03930104757745 0.980349476211275 55 0.03044981133075 0.0608996226615 0.96955018866925 56 0.290633379159772 0.581266758319544 0.709366620840228 57 0.265381356877396 0.530762713754792 0.734618643122604 58 0.211442873219079 0.422885746438158 0.788557126780921 59 0.254250725746741 0.508501451493482 0.745749274253259 60 0.206552692356775 0.413105384713549 0.793447307643225 61 0.173928207655503 0.347856415311005 0.826071792344497 62 0.141915096852232 0.283830193704465 0.858084903147768 63 0.155385717628948 0.310771435257896 0.844614282371052 64 0.117974993458502 0.235949986917005 0.882025006541498 65 0.279320983650652 0.558641967301304 0.720679016349348 66 0.311518977761252 0.623037955522504 0.688481022238748 67 0.206361205949823 0.412722411899647 0.793638794050177 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 11 0.174603174603175 NOK 10% type I error level 22 0.349206349206349 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/10tb2j1291991616.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/10tb2j1291991616.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/14a571291991616.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/14a571291991616.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/2ej5a1291991616.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/2ej5a1291991616.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/3ej5a1291991616.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/3ej5a1291991616.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/4ej5a1291991616.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/4ej5a1291991616.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/5pa4d1291991616.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/5pa4d1291991616.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/6pa4d1291991616.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/6pa4d1291991616.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/702ly1291991616.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/702ly1291991616.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/802ly1291991616.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/802ly1291991616.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/9tb2j1291991616.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t129199173923ovah38002h662/9tb2j1291991616.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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