WS8

*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 13:36:49 +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/t129112466949204ogl95fk8jr.htm/, Retrieved Tue, 30 Nov 2010 14:45:04 +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/t129112466949204ogl95fk8jr.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 «

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] = -150.168524850830 + 7.83109990590287CPI[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) -150.168524850830 453.796083 -0.3309 0.741696 0.370848 CPI 7.83109990590287 4.274153 1.8322 0.071176 0.035588 Multiple Linear Regression - Regression Statistics Multiple R 0.2139203175466 R-squared 0.0457619022592382 Adjusted R-squared 0.0321299294343702 F-TEST (value) 3.35695374742512 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 0.071175591307595 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 153.993608598224 Sum Squared Residuals 1659982.20423721 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 627 621.977925871194 5.02207412880633 2 696 624.875432836377 71.1245671636233 3 825 625.971786823203 199.028213176797 4 677 630.122269773332 46.8777302266682 5 656 633.176398736634 22.8236012633661 6 785 632.784843741339 152.215156258661 7 412 635.447417709346 -223.447417709346 8 352 636.387149698054 -284.387149698054 9 839 636.935326691467 202.064673308533 10 729 640.77256564536 88.2274343546403 11 696 639.832833656651 56.1671663433487 12 641 637.248570687703 3.75142931229665 13 695 639.441278661356 55.5587213386438 14 638 644.76642659737 -6.76642659737017 15 762 649.856641536207 112.143358463793 16 635 651.657794514565 -16.6577945145647 17 721 652.83245950045 68.1675404995499 18 854 655.26010047128 198.73989952872 19 418 660.11538241294 -242.11538241294 20 367 660.89849240353 -293.89849240353 21 824 661.838224392238 162.161775607762 22 687 660.350315410117 26.6496845898832 23 601 660.11538241294 -59.1153824129397 24 676 659.958760414822 16.0412395851783 25 740 660.193693411999 79.8063065880012 26 691 663.717688369655 27.2823116303449 27 683 663.404444373419 19.5955556265810 28 594 667.39830532543 -73.3983053254295 29 729 670.452434288732 58.5475657112684 30 731 670.295812290613 60.7041877093866 31 386 673.115008256739 -287.115008256738 32 331 674.133051244506 -343.133051244506 33 707 671.783721272735 35.216278727265 34 715 670.139190292495 44.8608097075046 35 657 672.17527626803 -15.1752762680302 36 653 673.271630254857 -20.2716302548566 37 642 673.663185250152 -31.6631852501517 38 643 678.126912196516 -35.1269121965163 39 718 678.205223195575 39.7947768044246 40 654 681.964151150409 -27.9641511504088 41 632 680.946108162641 -48.9461081626413 42 731 680.867797163582 50.1322028364176 43 392 684.391792121239 -292.391792121239 44 344 683.373749133471 -339.373749133471 45 792 684.156859124062 107.843140875938 46 852 688.542275071367 163.457724928633 47 649 696.37337497727 -47.37337497727 48 629 698.722704949041 -69.722704949041 49 685 702.168388907638 -17.1683889076382 50 617 708.276646834242 -91.2766468342424 51 715 714.541526758965 0.458473241035302 52 715 716.49930173544 -1.49930173544042 53 629 724.252090642284 -95.2520906422842 54 916 729.107372583944 186.892627416056 55 531 733.727721528427 -202.727721528427 56 357 728.324262593354 -371.324262593354 57 917 729.733860576416 187.266139423584 58 828 728.167640595236 99.8323594047643 59 708 722.92080365828 -14.9208036582807 60 858 721.041339680864 136.958660319136 61 775 721.902760670513 53.0972393294866 62 785 724.878578634756 60.1214213652436 63 1006 719.866674694979 286.133325305021 64 789 721.667827673336 67.3321723266637 65 734 721.041339680864 12.9586603191359 66 906 719.396808700625 186.603191299375 67 532 718.848631707211 -186.848631707211 68 387 721.511205675218 -334.511205675218 69 991 719.240186702506 271.759813297494 70 841 719.631741697802 121.368258302198 71 892 721.902760670513 170.097239329487 72 782 723.312358653576 58.6876413464241 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.184799533569330 0.369599067138661 0.81520046643067 6 0.119575058514962 0.239150117029924 0.880424941485038 7 0.406336237420098 0.812672474840197 0.593663762579902 8 0.512903198571293 0.974193602857414 0.487096801428707 9 0.736686378135761 0.526627243728479 0.263313621864239 10 0.705420209778591 0.589159580442819 0.294579790221409 11 0.626578601899202 0.746842796201595 0.373421398100798 12 0.528222194755099 0.943555610489801 0.471777805244901 13 0.444079759593997 0.888159519187993 0.555920240406003 14 0.352518815491764 0.705037630983528 0.647481184508236 15 0.321942611460695 0.643885222921391 0.678057388539305 16 0.247882025991931 0.495764051983862 0.752117974008069 17 0.195724145156816 0.391448290313633 0.804275854843184 18 0.224557197396625 0.449114394793249 0.775442802603375 19 0.357537310717394 0.715074621434787 0.642462689282606 20 0.4866046833133 0.9732093666266 0.5133953166867 21 0.55404930474323 0.89190139051354 0.44595069525677 22 0.487988137450697 0.975976274901394 0.512011862549303 23 0.415596237089237 0.831192474178475 0.584403762910763 24 0.35049027894558 0.70098055789116 0.64950972105442 25 0.316993810288469 0.633987620576939 0.68300618971153 26 0.263181701948566 0.526363403897132 0.736818298051434 27 0.213375601879739 0.426751203759477 0.786624398120261 28 0.168538993958513 0.337077987917027 0.831461006041487 29 0.142539292922434 0.285078585844868 0.857460707077566 30 0.119900034798776 0.239800069597552 0.880099965201224 31 0.193900576532663 0.387801153065326 0.806099423467337 32 0.343931210745724 0.687862421491448 0.656068789254276 33 0.302915449338445 0.605830898676891 0.697084550661555 34 0.266452102566609 0.532904205133217 0.733547897433391 35 0.215334103361962 0.430668206723925 0.784665896638038 36 0.169917109466141 0.339834218932283 0.830082890533859 37 0.130266498048077 0.260532996096153 0.869733501951923 38 0.0975650204399094 0.195130040879819 0.90243497956009 39 0.0806211418503719 0.161242283700744 0.919378858149628 40 0.0584266140801642 0.116853228160328 0.941573385919836 41 0.0407942184913522 0.0815884369827044 0.959205781508648 42 0.0342519015381133 0.0685038030762265 0.965748098461887 43 0.0552508278817586 0.110501655763517 0.944749172118241 44 0.144764683946770 0.289529367893539 0.85523531605323 45 0.136703634628356 0.273407269256711 0.863296365371644 46 0.161658629034516 0.323317258069033 0.838341370965484 47 0.123738392789934 0.247476785579867 0.876261607210066 48 0.0964523527832184 0.192904705566437 0.903547647216782 49 0.0740222568635826 0.148044513727165 0.925977743136417 50 0.0699294109145902 0.139858821829180 0.93007058908541 51 0.0564502932033749 0.112900586406750 0.943549706796625 52 0.0447666781156209 0.0895333562312417 0.95523332188438 53 0.0345425190623266 0.0690850381246533 0.965457480937673 54 0.0591263365377701 0.118252673075540 0.94087366346223 55 0.0469733045989618 0.0939466091979236 0.953026695401038 56 0.225957021771099 0.451914043542198 0.774042978228901 57 0.241733585633053 0.483467171266106 0.758266414366947 58 0.213311119810217 0.426622239620434 0.786688880189783 59 0.159412509470647 0.318825018941295 0.840587490529353 60 0.129137330534468 0.258274661068936 0.870862669465532 61 0.0879759149818907 0.175951829963781 0.91202408501811 62 0.0603008753306398 0.120601750661280 0.93969912466936 63 0.0910957716614457 0.182191543322891 0.908904228338554 64 0.0562886538633476 0.112577307726695 0.943711346136652 65 0.0303407672059227 0.0606815344118453 0.969659232794077 66 0.0229629325101944 0.0459258650203887 0.977037067489806 67 0.0430855183497256 0.0861710366994513 0.956914481650274 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 0.0158730158730159 OK 10% type I error level 8 0.126984126984127 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/10z7od1291124201.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/10z7od1291124201.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/1aor11291124201.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/1aor11291124201.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/2aor11291124201.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/2aor11291124201.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/3lf841291124201.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/3lf841291124201.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/4lf841291124201.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/4lf841291124201.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/5lf841291124201.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/5lf841291124201.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/6w6771291124201.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/6w6771291124201.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/7og7a1291124201.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/7og7a1291124201.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/8og7a1291124201.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/8og7a1291124201.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/9og7a1291124201.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t129112466949204ogl95fk8jr/9og7a1291124201.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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