paper

*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: Tue, 28 Dec 2010 19:47:09 +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/Dec/28/t1293565978agnwwjnt2txq3de.htm/, Retrieved Tue, 28 Dec 2010 20:52:58 +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/28/t1293565978agnwwjnt2txq3de.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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921365 18919 48873 137852 987921 19147 52118 145224 1132614 21518 60530 163575 1332224 20941 55644 190761 1418133 22401 57121 196562 1411549 22181 55697 204493 1695920 22494 56483 259479 1636173 21479 51541 259479 1539653 22322 56328 223164 1395314 21829 54349 194886 1127575 20370 59885 160407 1036076 18467 55806 151747 989236 18780 54559 152448 1008380 18815 55590 148388 1207763 20881 63442 168510 1368839 21443 61258 188041 1469798 22333 55829 192020 1498721 22944 58023 205250 1761769 22536 58887 261642 1653214 21658 51510 251614 1599104 23035 60006 222726 1421179 21969 60831 179039 1163995 20297 61559 151462 1037735 18564 61325 143653 1015407 18844 55222 143762 1039210 18762 56370 134580 1258049 21757 66063 165273 1469445 20501 60864 181016 1552346 23181 57596 189079 1549144 23015 57650 199266 1785895 22828 55324 248742 1662335 21597 54203 244139 1629440 23005 61155 219777 1467430 22243 63908 180679 1202209 20729 67466 156369 1076982 18310 63739 149176 1039367 19427 56602 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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation bewegingen[t] = + 8650.42436770292 + 0.00580420016496377passagiers[t] + 0.0857171609177784cargo[t] -0.00293593909601016auto[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) 8650.42436770292 759.800383 11.3851 0 0 passagiers 0.00580420016496377 0.000943 6.1565 0 0 cargo 0.0857171609177784 0.009401 9.1178 0 0 auto -0.00293593909601016 0.006804 -0.4315 0.667454 0.333727 Multiple Linear Regression - Regression Statistics Multiple R 0.899279511857937 R-squared 0.808703640447449 Adjusted R-squared 0.800264095173072 F-TEST (value) 95.8231295829055 F-TEST (DF numerator) 3 F-TEST (DF denominator) 68 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 831.41292487375 Sum Squared Residuals 47004826.7120045 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 18919 17782.7409819661 1136.25901803386 2 19147 18425.5537723079 721.446227692107 3 21518 19932.5562460665 1585.44375393354 4 20941 20592.5021524865 348.497847513514 5 22401 21200.7080484380 1200.29195156204 6 22181 21017.1470244345 1163.85297556553 7 22494 22573.6313708935 -79.6313708935403 8 21479 21803.2336143818 -324.233614381789 9 22322 21759.9588920445 562.0411079555 10 21829 20835.5746687345 993.425331265515 11 20370 19857.3223676994 512.677632300595 12 18467 19002.0287899932 -535.028789993215 13 18780 18621.2126612955 158.787338704461 14 18815 18832.6225748896 -17.6225748896364 15 20881 20603.8555974171 277.144402582912 16 21443 21294.2248372602 148.77516273981 17 22333 21403.1705134291 929.829486570876 18 22944 21720.2663716138 1223.73362838624 19 22536 23155.5457661379 -619.545766137909 20 21658 21922.5769183946 -264.576918394605 21 23035 22421.5780552314 613.421944768598 22 21969 21587.8447699248 381.155230075214 23 20297 20238.4638402976 58.5361597024423 24 18564 19508.4944602152 -944.494460215215 25 18844 18855.4464284892 -11.4464284892372 26 18762 19118.9648985290 -356.964898529045 27 21757 21129.8939205317 627.106079468262 28 20501 21865.0146098044 -1364.01460980440 29 23181 22042.3924488696 1138.60755113037 30 23015 21998.5277150599 1016.47228494008 31 22828 23028.0412693063 -200.041269306311 32 21597 22228.2994871935 -631.299487193493 33 23005 22704.8013737244 300.198626275595 34 22243 22115.2315957811 127.768404218927 35 20729 20952.1901617987 -223.190161798678 36 18310 19926.9979389178 -1616.9979389178 37 19427 19102.5729987587 324.427001241292 38 18849 19346.6526981843 -497.652698184289 39 21817 21890.9565687611 -73.9565687610673 40 21101 21983.9324573532 -882.932457353157 41 23546 22477.6183173241 1068.38168267594 42 23456 23130.374288379 325.625711620978 43 23649 24418.3584332168 -769.358433216772 44 22432 23692.4535416175 -1260.45354161749 45 23745 23993.5246642741 -248.5246642741 46 23874 23530.9659176217 343.034082378254 47 22327 22565.3720483289 -238.372048328859 48 20143 21673.6195892778 -1530.61958927781 49 21252 20840.7340354881 411.265964511864 50 21094 21506.8154382875 -412.815438287461 51 21800 23173.1965867395 -1373.19658673951 52 22480 22358.7210107259 121.278989274102 53 23055 22776.9834281942 278.016571805777 54 23352 22554.0843791709 797.91562082907 55 23171 23132.4486984251 38.5513015749356 56 20691 22526.9611669726 -1835.96116697258 57 23183 22399.4420172848 783.557982715187 58 22412 21606.0931157503 805.906884249725 59 18958 19709.8384717453 -751.838471745263 60 17347 18580.5557362545 -1233.55573625446 61 17353 17300.8190649193 52.1809350806783 62 17153 17519.9476274078 -366.947627407826 63 20141 19132.281253019 1008.71874698101 64 19699 20083.3279558204 -384.32795582037 65 20780 20124.8236742848 655.176325715155 66 21101 20292.9050713577 808.094928642293 67 20871 21618.7056567799 -747.705656779926 68 19574 20239.8620421468 -665.86204214681 69 21002 20514.1432314818 487.856768518228 70 20105 20150.3115235848 -45.311523584763 71 17772 18789.8590567933 -1017.85905679328 72 16117 18126.1220907098 -2009.12209070978 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.231968237748975 0.463936475497951 0.768031762251025 8 0.113701425034541 0.227402850069081 0.88629857496546 9 0.0514425731733788 0.102885146346758 0.948557426826621 10 0.0239918039687801 0.0479836079375603 0.97600819603122 11 0.0467272115478025 0.093454423095605 0.953272788452198 12 0.227353013937551 0.454706027875102 0.772646986062449 13 0.157836805464755 0.31567361092951 0.842163194535245 14 0.134438054202568 0.268876108405136 0.865561945797432 15 0.101662219592079 0.203324439184158 0.898337780407921 16 0.098244397732677 0.196488795465354 0.901755602267323 17 0.0843185046042436 0.168637009208487 0.915681495395756 18 0.0798675921588242 0.159735184317648 0.920132407841176 19 0.0775624508741231 0.155124901748246 0.922437549125877 20 0.0578823539679545 0.115764707935909 0.942117646032046 21 0.0436602542690680 0.0873205085381359 0.956339745730932 22 0.0536739001188255 0.107347800237651 0.946326099881175 23 0.0539428124038628 0.107885624807726 0.946057187596137 24 0.103910908902323 0.207821817804646 0.896089091097677 25 0.0931427898388888 0.186285579677778 0.906857210161111 26 0.122806676909924 0.245613353819848 0.877193323090076 27 0.114755694402456 0.229511388804912 0.885244305597544 28 0.388579187815333 0.777158375630666 0.611420812184667 29 0.386735393459922 0.773470786919845 0.613264606540078 30 0.397013727186267 0.794027454372534 0.602986272813733 31 0.364917552878686 0.729835105757371 0.635082447121314 32 0.355442960874143 0.710885921748286 0.644557039125857 33 0.319179889994313 0.638359779988626 0.680820110005687 34 0.263163166871704 0.526326333743409 0.736836833128296 35 0.215539150284052 0.431078300568104 0.784460849715948 36 0.324304410217052 0.648608820434104 0.675695589782948 37 0.344488569726822 0.688977139453644 0.655511430273178 38 0.323419938674555 0.646839877349111 0.676580061325445 39 0.277145236989512 0.554290473979025 0.722854763010488 40 0.302158098453160 0.604316196906319 0.69784190154684 41 0.36224528986408 0.72449057972816 0.63775471013592 42 0.359540149898821 0.719080299797641 0.640459850101179 43 0.317521252822061 0.635042505644121 0.68247874717794 44 0.300916219017074 0.601832438034147 0.699083780982926 45 0.241043731542508 0.482087463085017 0.758956268457492 46 0.200692077135279 0.401384154270558 0.799307922864721 47 0.165066076342867 0.330132152685735 0.834933923657132 48 0.163555518001719 0.327111036003438 0.836444481998281 49 0.205116737072232 0.410233474144464 0.794883262927768 50 0.159946113185379 0.319892226370759 0.84005388681462 51 0.192363279843737 0.384726559687475 0.807636720156263 52 0.151570636295789 0.303141272591578 0.848429363704211 53 0.121157427698475 0.242314855396949 0.878842572301525 54 0.100314644491739 0.200629288983479 0.89968535550826 55 0.0953034718800017 0.190606943760003 0.904696528119998 56 0.355265450183737 0.710530900367475 0.644734549816263 57 0.279384145071889 0.558768290143777 0.720615854928111 58 0.230848684133917 0.461697368267833 0.769151315866083 59 0.199855730423384 0.399711460846768 0.800144269576616 60 0.227607365070658 0.455214730141316 0.772392634929342 61 0.222100840527121 0.444201681054242 0.777899159472879 62 0.194880886382961 0.389761772765922 0.805119113617039 63 0.365332927445117 0.730665854890234 0.634667072554883 64 0.304595600082816 0.609191200165632 0.695404399917184 65 0.208768127149027 0.417536254298054 0.791231872850973 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 1 0.0169491525423729 OK 10% type I error level 3 0.0508474576271186 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/10wzq11293565620.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/10wzq11293565620.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/1phb71293565620.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/1phb71293565620.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/2i8as1293565620.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/2i8as1293565620.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/3i8as1293565620.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/3i8as1293565620.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/4i8as1293565620.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/4i8as1293565620.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/5i8as1293565620.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/5i8as1293565620.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/6shrd1293565620.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/6shrd1293565620.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/73qry1293565620.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/73qry1293565620.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/83qry1293565620.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/83qry1293565620.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/93qry1293565620.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/28/t1293565978agnwwjnt2txq3de/93qry1293565620.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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