*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, 30 Nov 2010 15:53:20 +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/30/t1291132317edygsccmc4l2qe3.htm/, Retrieved Tue, 30 Nov 2010 16:52:08 +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/30/t1291132317edygsccmc4l2qe3.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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98.60 627 98.97 696 99.11 825 99.64 677 100.03 656 99.98 785 100.32 412 100.44 352 100.51 839 101.00 729 100.88 696 100.55 641 100.83 695 101.51 638 102.16 762 102.39 635 102.54 721 102.85 854 103.47 418 103.57 367 103.69 824 103.50 687 103.47 601 103.45 676 103.48 740 103.93 691 103.89 683 104.40 594 104.79 729 104.77 731 105.13 386 105.26 331 104.96 707 104.75 715 105.01 657 105.15 653 105.20 642 105.77 643 105.78 718 106.26 654 106.13 632 106.12 731 106.57 392 106.44 344 106.54 792 107.10 852 108.10 649 108.40 629 108.84 685 109.62 617 110.42 715 110.67 715 111.66 629 112.28 916 112.87 531 112.18 357 112.36 917 112.16 828 111.49 708 111.25 858 111.36 775 111.74 785 111.10 1006 111.33 789 111.25 734 111.04 906 110.97 532 111.31 387 111.02 991 111.07 841 111.36 892 111.54 782

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 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Faillissementen[t] = -239.725864032335 + 8.866157329222CPI[t] + 5.27664544508994M1[t] -15.1629692504744M2[t] + 89.9775532923783M3[t] -20.8177018483159M4[t] -17.1778900204776M5[t] + 118.876386531072M6[t] -259.840863516247M7[t] -348.482096774114M8[t] + 140.361893039137M9[t] + 69.9563799283681M10[t] -5.95566921335386M11[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) -239.725864032335 204.542789 -1.172 0.245905 0.122952 CPI 8.866157329222 1.899015 4.6688 1.8e-05 9e-06 M1 5.27664544508994 39.247925 0.1344 0.893509 0.446755 M2 -15.1629692504744 39.161969 -0.3872 0.700012 0.350006 M3 89.9775532923783 39.142337 2.2987 0.02508 0.01254 M4 -20.8177018483159 39.10371 -0.5324 0.596468 0.298234 M5 -17.1778900204776 39.082699 -0.4395 0.661886 0.330943 M6 118.876386531072 39.076761 3.0421 0.003503 0.001751 M7 -259.840863516247 39.064108 -6.6517 0 0 M8 -348.482096774114 39.064466 -8.9207 0 0 M9 140.361893039137 39.064836 3.593 0.000667 0.000334 M10 69.9563799283681 39.063541 1.7908 0.07845 0.039225 M11 -5.95566921335386 39.062801 -0.1525 0.879341 0.439671 Multiple Linear Regression - Regression Statistics Multiple R 0.919098600199838 R-squared 0.844742236889301 Adjusted R-squared 0.813164386765091 F-TEST (value) 26.7511003303439 F-TEST (DF numerator) 12 F-TEST (DF denominator) 59 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 67.6587541936089 Sum Squared Residuals 270084.71412284 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 627 639.753894074044 -12.7538940740442 2 696 622.594757590291 73.4052424097091 3 825 728.976542159235 96.0234578407654 4 677 622.880350403028 54.1196495969718 5 656 629.977963589263 26.0220364107366 6 785 765.588932274352 19.4110677256481 7 412 389.886175718967 22.1138242810327 8 352 302.308881340607 49.691118659393 9 839 791.773502166904 47.2264978330957 10 729 725.712406147454 3.28759385254566 11 696 648.736418126226 47.2635818737744 12 641 651.766255420936 -10.7662554209362 13 695 659.525424918208 35.4745750817917 14 638 645.114797206515 -7.11479720651497 15 762 756.018322013362 5.98167798663819 16 635 647.262283058389 -12.2622830583887 17 721 652.23201848561 68.7679815143896 18 854 791.034803809219 62.9651961907812 19 418 417.814571306017 0.185428693983157 20 367 330.059953781072 36.9400462189279 21 824 819.96788247383 4.03211752617015 22 687 747.877799470509 -60.8777994705092 23 601 671.69976560891 -70.6997656089105 24 676 677.47811167568 -1.47811167567999 25 740 683.020741840647 56.9792581593534 26 691 666.570897943232 24.4291020567678 27 683 771.356774192916 -88.3567741929159 28 594 665.083259290125 -71.083259290125 29 729 672.18087247636 56.8191275236401 30 731 808.057825881325 -77.0578258813251 31 386 432.532392472525 -46.5323924725253 32 331 345.043759667457 -14.0437596674573 33 707 831.227902281942 -124.227902281942 34 715 758.960496132037 -43.9604961320367 35 657 685.353647895913 -28.3536478959125 36 653 692.550579135357 -39.5505791353575 37 642 698.270532446908 -56.2705324469085 38 643 682.884627429001 -39.8846274290006 39 718 788.113811545145 -70.1138115451455 40 654 681.574311922478 -27.5743119224779 41 632 684.061523297517 -52.0615232975173 42 731 820.027138275775 -89.0271382757748 43 392 445.299659026605 -53.299659026605 44 344 355.505825315939 -11.5058253159393 45 792 845.236430862113 -53.2364308621126 46 852 779.795965855708 72.2040341442916 47 649 712.750074043208 -63.7500740432084 48 629 721.365590455329 -92.365590455329 49 685 730.543345125277 -45.5433451252765 50 617 717.019333146505 -100.019333146505 51 715 829.252781552736 -114.252781552736 52 715 720.674065744347 -5.67406574434687 53 629 733.091373328115 -104.091373328115 54 916 874.642667423782 41.3573325762177 55 531 501.156450200704 29.8435497992963 56 357 406.397568385674 -49.3975683856738 57 917 896.837466518185 20.1625334818154 58 828 824.658721941572 3.34127805842829 59 708 742.806347389271 -34.8063473892709 60 858 746.634138843611 111.365861156388 61 775 752.886061594916 22.1139384050841 62 785 735.815586684456 49.1844133155441 63 1006 835.281768536606 170.718231463394 64 789 726.525729581633 62.4742704183667 65 734 729.456248823134 4.54375117686601 66 906 863.648632335547 42.3513676644530 67 532 484.310751275182 47.6892487248182 68 387 398.684011509250 -11.6840115092504 69 991 884.956815697027 106.043184302973 70 841 814.99461045272 26.0053895472803 71 892 741.653746936472 150.346253063528 72 782 749.205324469086 32.794675530914 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.207539701664320 0.415079403328640 0.79246029833568 17 0.226397338449660 0.452794676899321 0.77360266155034 18 0.213967349392421 0.427934698784842 0.786032650607579 19 0.123740723759558 0.247481447519116 0.876259276240442 20 0.0741583411576403 0.148316682315281 0.92584165884236 21 0.0415461497039534 0.0830922994079068 0.958453850296047 22 0.0262539141539626 0.0525078283079252 0.973746085846037 23 0.034976573335027 0.069953146670054 0.965023426664973 24 0.0227825468855303 0.0455650937710607 0.97721745311447 25 0.0366859417642549 0.0733718835285097 0.963314058235745 26 0.0263256786382589 0.0526513572765178 0.973674321361741 27 0.0580041783993413 0.116008356798683 0.94199582160066 28 0.0465339519572764 0.0930679039145528 0.953466048042724 29 0.0620678628992184 0.124135725798437 0.937932137100782 30 0.064731773007286 0.129463546014572 0.935268226992714 31 0.0416813743113331 0.0833627486226661 0.958318625688667 32 0.0298058274008465 0.059611654801693 0.970194172599153 33 0.0551863193060922 0.110372638612184 0.944813680693908 34 0.0359974598102194 0.0719949196204387 0.96400254018978 35 0.0230981348673078 0.0461962697346156 0.976901865132692 36 0.0137112819053458 0.0274225638106916 0.986288718094654 37 0.00823835759005514 0.0164767151801103 0.991761642409945 38 0.0049735205709833 0.0099470411419666 0.995026479429017 39 0.00277748355352566 0.00555496710705133 0.997222516446474 40 0.00171918925782514 0.00343837851565028 0.998280810742175 41 0.00142837987038362 0.00285675974076724 0.998571620129616 42 0.000900542263055139 0.00180108452611028 0.999099457736945 43 0.000436296038528507 0.000872592077057015 0.999563703961472 44 0.000349349291650330 0.000698698583300659 0.99965065070835 45 0.000170096635035995 0.000340193270071991 0.999829903364964 46 0.0045321816708876 0.0090643633417752 0.995467818329112 47 0.00235578942551838 0.00471157885103677 0.997644210574482 48 0.00170112502243286 0.00340225004486573 0.998298874977567 49 0.00087671692003394 0.00175343384006788 0.999123283079966 50 0.00102428067180743 0.00204856134361487 0.998975719328193 51 0.0851072852332293 0.170214570466459 0.91489271476677 52 0.118754215401499 0.237508430802999 0.8812457845985 53 0.138222147205093 0.276444294410186 0.861777852794907 54 0.163812682095216 0.327625364190432 0.836187317904784 55 0.168301826947525 0.336603653895049 0.831698173052476 56 0.0899578355714843 0.179915671142969 0.910042164428516 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 13 0.317073170731707 NOK 5% type I error level 17 0.414634146341463 NOK 10% type I error level 26 0.634146341463415 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/10y5j31291132391.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/10y5j31291132391.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/1r4mr1291132391.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/1r4mr1291132391.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/22d4u1291132391.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/22d4u1291132391.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/32d4u1291132391.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/32d4u1291132391.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/42d4u1291132391.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/42d4u1291132391.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/5vm3x1291132391.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/5vm3x1291132391.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/6vm3x1291132391.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/6vm3x1291132391.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/7ne201291132391.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/7ne201291132391.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/8ne201291132391.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/8ne201291132391.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/9y5j31291132391.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291132317edygsccmc4l2qe3/9y5j31291132391.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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