*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, 17 Nov 2009 12:05:28 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu.htm/, Retrieved Tue, 17 Nov 2009 20:11:36 +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/2009/Nov/17/t1258485084dbphva40sfuqztu.htm/}, year = {2009}, } @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 = {2009}, 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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344744 492865 338653 480961 327532 461935 326225 456608 318672 441977 317756 439148 337302 488180 349420 520564 336923 501492 330758 485025 321002 464196 320820 460170 327032 467037 324047 460070 316735 447988 315710 442867 313427 436087 310527 431328 330962 484015 339015 509673 341332 512927 339092 502831 323308 470984 325849 471067 330675 476049 332225 474605 331735 470439 328047 461251 326165 454724 327081 455626 346764 516847 344190 525192 343333 522975 345777 518585 344094 509239 348609 512238 354846 519164 356427 517009 353467 509933 355996 509127 352487 500857 355178 506971 374556 569323 375021 579714 375787 577992 372720 565464 364431 547344 370490 554788 376974 562325 377632 560854 378205 555332 370861 543599 369167 536662 371551 542722 382842 593530 381903 610763 384502 612613 392058 611324 384359 594167 388884 595454 386586 590865 387495 589379 385705 584428 378670 573100 377367 567456 376911 569028 389827 620735 387820 628884 3872 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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 129826.971818701 + 0.425980889008533X[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) 129826.971818701 6686.66185 19.4158 0 0 X 0.425980889008533 0.012678 33.6008 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.97037045137043 R-squared 0.941618812892852 Adjusted R-squared 0.940784795934178 F-TEST (value) 1129.01638641790 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6012.05901187784 Sum Squared Residuals 2530139749.36111 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 344744 339778.042679892 4965.95732010811 2 338653 334707.166177134 3945.83382286567 3 327532 326602.453782858 929.546217141979 4 326225 324333.253587110 1891.74641289044 5 318672 318100.727200026 571.272799974277 6 317756 316895.627265021 860.372734979423 7 337302 337782.322214887 -480.322214886969 8 349420 351577.287324539 -2157.2873245393 9 336923 343452.979809369 -6529.97980936856 10 330758 336438.352510065 -5680.35251006505 11 321002 327565.596572906 -6563.59657290631 12 320820 325850.597513758 -5030.59751375796 13 327032 328775.808278580 -1743.80827857956 14 324047 325807.999424857 -1760.99942485711 15 316735 320661.298323856 -3926.29832385601 16 315710 318479.850191243 -2769.85019124331 17 313427 315591.699763765 -2164.69976376547 18 310527 313564.456712974 -3037.45671297386 19 330962 336008.111812166 -5046.11181216643 20 339015 346937.929462347 -7922.92946234737 21 341332 348324.071275181 -6992.07127518114 22 339092 344023.368219751 -4931.36821975099 23 323308 330457.154847496 -7149.15484749624 24 325849 330492.511261284 -4643.51126128395 25 330675 332614.748050324 -1939.74805032445 26 332225 331999.631646596 225.368353403866 27 331735 330224.995262987 1510.00473701342 28 328047 326311.082854776 1735.91714522382 29 326165 323530.705592218 2634.29440778251 30 327081 323914.940354103 3166.05964589682 31 346764 349993.916360095 -3229.91636009458 32 344190 353548.726878871 -9358.72687887079 33 343333 352604.327247939 -9271.32724793887 34 345777 350734.271145191 -4957.27114519141 35 344094 346753.053756518 -2659.05375651767 36 348609 348030.570442654 578.429557345744 37 354846 350980.914079927 3865.08592007264 38 356427 350062.925264114 6364.07473588603 39 353467 347048.684493490 6418.31550651041 40 355996 346705.343896949 9290.65610305129 41 352487 343182.481944848 9304.51805515186 42 355178 345786.929100246 9391.07089975369 43 374556 372347.689491706 2208.31050829364 44 375021 376774.056909394 -1753.05690939403 45 375787 376040.517818521 -253.517818521334 46 372720 370703.829241022 2016.17075897757 47 364431 362985.055532188 1445.94446781218 48 370490 366156.057269967 4333.94273003267 49 376974 369366.675230425 7607.32476957535 50 377632 368740.057342693 8891.9426573069 51 378205 366387.790873588 11817.2091264120 52 370861 361389.757102851 9471.24289714914 53 369167 358434.727675799 10732.2723242013 54 371551 361016.17186319 10534.8281368096 55 382842 382659.408871936 182.591128064085 56 381903 390000.33753222 -8097.33753221997 57 384502 390788.402176886 -6286.40217688576 58 392058 390239.312810954 1818.68718904625 59 384359 382930.758698234 1428.24130176565 60 388884 383478.996102388 5405.00389761167 61 386586 381524.169802728 5061.83019727182 62 387495 380891.162201662 6603.8377983385 63 385705 378782.13082018 6922.86917981975 64 378670 373956.619309492 4713.38069050841 65 377367 371552.383171927 5814.61682807257 66 376911 372222.025129449 4688.97487055116 67 389827 394248.218957413 -4421.21895741306 68 387820 397719.537221944 -9899.5372219436 69 387267 397441.79768231 -10174.7976823100 70 380575 390577.115655938 -10002.1156559375 71 372402 383457.697057938 -11055.6970579379 72 376740 384197.625862146 -7457.62586214573 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00208023679487813 0.00416047358975625 0.997919763205122 6 0.000333890108903623 0.000667780217807247 0.999666109891096 7 0.00633547287071883 0.0126709457414377 0.993664527129281 8 0.0116436822598162 0.0232873645196324 0.988356317740184 9 0.0431254967328036 0.0862509934656072 0.956874503267196 10 0.0536514146732436 0.107302829346487 0.946348585326756 11 0.0839274352549989 0.167854870509998 0.916072564745001 12 0.0759808604817824 0.151961720963565 0.924019139518218 13 0.0451890953506926 0.0903781907013853 0.954810904649307 14 0.0259947167268942 0.0519894334537883 0.974005283273106 15 0.0184957160923067 0.0369914321846133 0.981504283907693 16 0.0109071677133686 0.0218143354267371 0.989092832286631 17 0.00595017235784623 0.0119003447156925 0.994049827642154 18 0.00342393897488477 0.00684787794976954 0.996576061025115 19 0.00265638274333633 0.00531276548667267 0.997343617256664 20 0.00384823810993956 0.00769647621987911 0.99615176189006 21 0.00345299275959636 0.00690598551919272 0.996547007240404 22 0.00222729105326101 0.00445458210652201 0.99777270894674 23 0.00308434916214896 0.00616869832429791 0.996915650837851 24 0.00246263051373635 0.0049252610274727 0.997537369486264 25 0.00159004585596344 0.00318009171192688 0.998409954144037 26 0.00117064726814150 0.00234129453628299 0.998829352731859 27 0.00100927645726862 0.00201855291453724 0.99899072354273 28 0.000848830267061147 0.00169766053412229 0.99915116973294 29 0.000790394576982058 0.00158078915396412 0.999209605423018 30 0.000809480664476595 0.00161896132895319 0.999190519335523 31 0.000634288451189815 0.00126857690237963 0.99936571154881 32 0.00191060090004638 0.00382120180009275 0.998089399099954 33 0.00706537162495687 0.0141307432499137 0.992934628375043 34 0.01296492442756 0.02592984885512 0.98703507557244 35 0.0278951272361137 0.0557902544722274 0.972104872763886 36 0.0586719259313989 0.117343851862798 0.941328074068601 37 0.124107319786497 0.248214639572994 0.875892680213503 38 0.239266854894346 0.478533709788691 0.760733145105654 39 0.369766087650988 0.739532175301975 0.630233912349012 40 0.534961122243909 0.930077755512182 0.465038877756091 41 0.694239198305907 0.611521603388186 0.305760801694093 42 0.816442171176073 0.367115657647853 0.183557828823927 43 0.778416219409487 0.443167561181027 0.221583780590513 44 0.749190994977414 0.501618010045171 0.250809005022586 45 0.703911851724032 0.592176296551936 0.296088148275968 46 0.667731155769878 0.664537688460244 0.332268844230122 47 0.754569256520842 0.490861486958317 0.245430743479158 48 0.753302127834498 0.493395744331003 0.246697872165502 49 0.730903192776263 0.538193614447474 0.269096807223737 50 0.716110367771457 0.567779264457086 0.283889632228543 51 0.745049606072205 0.509900787855589 0.254950393927795 52 0.724212185346469 0.551575629307063 0.275787814653531 53 0.721947833383018 0.556104333233964 0.278052166616982 54 0.704833545630086 0.590332908739829 0.295166454369914 55 0.638948761034149 0.722102477931702 0.361051238965851 56 0.668085439802943 0.663829120394113 0.331914560197057 57 0.63026334098976 0.739473318020479 0.369736659010239 58 0.652205692464106 0.695588615071787 0.347794307535894 59 0.569542354470185 0.860915291059631 0.430457645529816 60 0.597688405624748 0.804623188750505 0.402311594375252 61 0.589458046360012 0.821083907279976 0.410541953639988 62 0.66506006898777 0.669879862024461 0.334939931012231 63 0.753419878669147 0.493160242661706 0.246580121330853 64 0.683158035664303 0.633683928671394 0.316841964335697 65 0.646618494383395 0.70676301123321 0.353381505616605 66 0.899290789955163 0.201418420089675 0.100709210044838 67 0.972125815046466 0.055748369907069 0.0278741849535345 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 17 0.26984126984127 NOK 5% type I error level 24 0.380952380952381 NOK 10% type I error level 29 0.46031746031746 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/10bhz71258484720.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/10bhz71258484720.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/1zr841258484720.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/1zr841258484720.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/2lidu1258484720.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/2lidu1258484720.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/391qq1258484720.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/391qq1258484720.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/48xiu1258484720.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/48xiu1258484720.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/5pxk41258484720.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/5pxk41258484720.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/6fbjy1258484720.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/6fbjy1258484720.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/7k5es1258484720.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/7k5es1258484720.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/8glcc1258484720.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/8glcc1258484720.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/9pohb1258484720.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258485084dbphva40sfuqztu/9pohb1258484720.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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