MRLM 3

*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, 17 Dec 2010 19:06:06 +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/17/t1292613407uzd3mzevfertwat.htm/, Retrieved Fri, 17 Dec 2010 20:16:50 +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/17/t1292613407uzd3mzevfertwat.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 «

216234,00 627 213586,00 696 209465,00 825 204045,00 677 200237,00 656 203666,00 785 241476,00 412 260307,00 352 243324,00 839 244460,00 729 233575,00 696 237217,00 641 235243,00 695 230354,00 638 227184,00 762 221678,00 635 217142,00 721 219452,00 854 256446,00 418 265845,00 367 248624,00 824 241114,00 687 229245,00 601 231805,00 676 219277,00 740 219313,00 691 212610,00 683 214771,00 594 211142,00 729 211457,00 731 240048,00 386 240636,00 331 230580,00 707 208795,00 715 197922,00 657 194596,00 653 194581,00 642 185686,00 643 178106,00 718 172608,00 654 167302,00 632 168053,00 731 202300,00 392 202388,00 344 182516,00 792 173476,00 852 166444,00 649 171297,00 629 169701,00 685 164182,00 617 161914,00 715 159612,00 715 151001,00 629 158114,00 916 186530,00 531 187069,00 357 174330,00 917 169362,00 828 166827,00 708 178037,00 858 186413,00 775 189226,00 785 191563,00 1006 188906,00 789 186005,00 734 195309,00 906 223532,00 532 226899,00 387 214 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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation werklozen[t] = + 247782.126363658 -59.7701717815407faillissementen[t] -2726.79381393584M1[t] -6846.8598385134M2[t] -4065.50321044598M3[t] -13694.4633436283M4[t] -18124.3806176421M5[t] -6065.53375023769M6[t] + 3880.89510775736M7[t] + 4039.97818116383M8[t] + 18307.0021250767M9[t] + 5911.34682429606M10[t] -6170.62103068924M11[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) 247782.126363658 33440.839086 7.4096 0 0 faillissementen -59.7701717815407 44.65583 -1.3385 0.185881 0.09294 M1 -2726.79381393584 15689.768279 -0.1738 0.862622 0.431311 M2 -6846.8598385134 15730.204049 -0.4353 0.664956 0.332478 M3 -4065.50321044598 16065.289531 -0.2531 0.801101 0.40055 M4 -13694.4633436283 15733.837748 -0.8704 0.387619 0.193809 M5 -18124.3806176421 15713.438373 -1.1534 0.253385 0.126692 M6 -6065.53375023769 16485.543003 -0.3679 0.714242 0.357121 M7 3880.89510775736 19546.034676 0.1986 0.843296 0.421648 M8 4039.97818116383 22144.355593 0.1824 0.855864 0.427932 M9 18307.0021250767 16855.544968 1.0861 0.281847 0.140924 M10 5911.34682429606 15978.283682 0.37 0.712736 0.356368 M11 -6170.62103068924 15682.124631 -0.3935 0.695382 0.347691 Multiple Linear Regression - Regression Statistics Multiple R 0.46977367438293 R-squared 0.220687305143239 Adjusted R-squared 0.0621830282232196 F-TEST (value) 1.39231135860515 F-TEST (DF numerator) 12 F-TEST (DF denominator) 59 p-value 0.195425950974637 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 27158.2718774539 Sum Squared Residuals 43516732150.8125 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 216234 207579.434842696 8654.56515730385 2 213586 199335.226965193 14250.7730348072 3 209465 194406.231433441 15058.7685665586 4 204045 193623.256723927 10421.7432760728 5 200237 190448.513057326 9788.4869426743 6 203666 194797.007764911 8868.99223508865 7 241476 227037.710697421 14438.2893025789 8 260307 230783.00407772 29523.99592228 9 243324 215941.954364023 27382.0456359774 10 244460 210121.017959211 34338.9820407886 11 233575 200011.465773017 33563.5342269831 12 237217 209469.446251691 27747.5537483091 13 235243 203515.063161552 31727.9368384481 14 230354 202801.896928522 27552.1030714779 15 227184 198171.752255679 29012.2477443215 16 221678 196133.603938752 25544.3960612481 17 217142 186563.451891526 30578.5481084744 18 219452 190672.865911985 28779.1340880149 19 256446 226679.089666732 29766.9103332681 20 265845 229886.451500997 35958.5484990031 21 248624 216838.506940746 31785.4930592543 22 241114 212631.365174036 28482.6348259639 23 229245 205689.632092263 23555.3679077367 24 231805 207377.490239337 24427.509760663 25 219277 200825.405431383 18451.5945686175 26 219313 199634.0778241 19678.9221758995 27 212610 202893.59582642 9716.40417357977 28 214771 198584.180981795 16186.8190182049 29 211142 186085.290517273 25056.7094827268 30 211457 198024.597041115 13432.4029588854 31 240048 228591.735163741 11456.2648362588 32 240636 232038.177685132 8597.82231486764 33 230580 223831.617039186 6748.38296081405 34 208795 210957.800364153 -2162.80036415296 35 197922 202342.502472497 -4420.50247249702 36 194596 208752.204190312 -14156.2041903124 37 194581 206682.882265974 -12101.8822659735 38 185686 202503.046069614 -16817.0460696144 39 178106 200801.639814066 -22695.6398140663 40 172608 194997.970674903 -22389.9706749026 41 167302 191882.997180083 -24580.9971800827 42 168053 198024.597041115 -29971.5970411146 43 202300 228233.114133052 -25933.1141330519 44 202388 231261.165451972 -28873.1654519723 45 182516 218751.152437755 -36235.152437755 46 173476 202769.286830082 -29293.2868300819 47 166444 202820.663846749 -36376.6638467493 48 171297 210186.688313069 -38889.6883130694 49 169701 204112.764879367 -34411.7648793673 50 164182 204057.070535935 -39875.0705359345 51 161914 200980.950329411 -39066.9503294109 52 159612 191351.990196229 -31739.9901962286 53 151001 192062.307695427 -41061.3076954273 54 158114 186967.11526153 -28853.1152615295 55 186530 219925.060255418 -33395.0602554177 56 187069 230484.153218812 -43415.1532188123 57 174330 211279.880965062 -36949.8809650624 58 169362 204203.770952839 -34841.7709528389 59 166827 199294.223711638 -32467.2237116385 60 178037 196499.318975097 -18462.3189750966 61 186413 198733.449419029 -12320.4494190286 62 189226 194015.681676636 -4789.68167663566 63 191563 183587.830340983 7975.16965901742 64 188906 186928.997484395 1977.00251560538 65 186005 185786.439658366 218.560341634473 66 195309 187564.816979345 7744.18302065506 67 223532 219865.290083636 3666.70991636379 68 226899 228691.048065366 -1792.04806536608 69 214126 206856.888253228 7269.11174677161 70 206903 203426.758719679 3476.24128032117 71 204442 188296.512103835 16145.4878961651 72 220375 201041.852030494 19333.1479695063 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.137804559096631 0.275609118193262 0.862195440903369 17 0.102232854311918 0.204465708623836 0.897767145688082 18 0.062585737158113 0.125171474316226 0.937414262841887 19 0.0393037754396392 0.0786075508792783 0.96069622456036 20 0.0215192066901272 0.0430384133802544 0.978480793309873 21 0.0117533731722594 0.0235067463445188 0.98824662682774 22 0.00638187988574387 0.0127637597714877 0.993618120114256 23 0.0033655569790357 0.0067311139580714 0.996634443020964 24 0.00191562392319877 0.00383124784639753 0.998084376076801 25 0.00113815038482444 0.00227630076964889 0.998861849615176 26 0.000627377665276422 0.00125475533055284 0.999372622334724 27 0.000342907287936036 0.000685814575872071 0.999657092712064 28 0.000223597034770272 0.000447194069540544 0.99977640296523 29 0.000151935736130595 0.00030387147226119 0.99984806426387 30 0.000106459390249125 0.00021291878049825 0.99989354060975 31 0.000105990204368756 0.000211980408737511 0.999894009795631 32 0.000437355383976168 0.000874710767952337 0.999562644616024 33 0.00116368945177799 0.00232737890355599 0.998836310548222 34 0.0155388775844838 0.0310777551689676 0.984461122415516 35 0.0585204074361881 0.117040814872376 0.941479592563812 36 0.146310973718261 0.292621947436521 0.85368902628174 37 0.205719640260788 0.411439280521575 0.794280359739212 38 0.277703375148034 0.555406750296068 0.722296624851966 39 0.360167552385175 0.72033510477035 0.639832447614825 40 0.4623319526107 0.9246639052214 0.5376680473893 41 0.482250073012532 0.964500146025064 0.517749926987468 42 0.511080167078778 0.977839665842444 0.488919832921222 43 0.564380915132649 0.871238169734701 0.435619084867351 44 0.623006822185 0.753986355629999 0.376993177815 45 0.692339544171639 0.615320911656721 0.307660455828361 46 0.785405218408202 0.429189563183596 0.214594781591798 47 0.774226731166613 0.451546537666774 0.225773268833387 48 0.741323860757751 0.517352278484498 0.258676139242249 49 0.6988178004813 0.6023643990374 0.3011821995187 50 0.640387229746425 0.71922554050715 0.359612770253575 51 0.600723027586926 0.798553944826148 0.399276972413074 52 0.539339929625805 0.92132014074839 0.460660070374195 53 0.461846301003664 0.923692602007327 0.538153698996336 54 0.470062999414794 0.940125998829588 0.529937000585206 55 0.454947179305737 0.909894358611474 0.545052820694263 56 0.419599551410073 0.839199102820145 0.580400448589927 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.268292682926829 NOK 5% type I error level 15 0.365853658536585 NOK 10% type I error level 16 0.390243902439024 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/107z081292612757.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/107z081292612757.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/1jg3x1292612757.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/1jg3x1292612757.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/2jg3x1292612757.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/2jg3x1292612757.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/3t8301292612757.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/3t8301292612757.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/4t8301292612757.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/4t8301292612757.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/5t8301292612757.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/5t8301292612757.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/64z2l1292612757.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/64z2l1292612757.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/7w8jo1292612757.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/7w8jo1292612757.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/8w8jo1292612757.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/8w8jo1292612757.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/9w8jo1292612757.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292613407uzd3mzevfertwat/9w8jo1292612757.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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