Seatbelt Law Q3

*Unverified author*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Mon, 24 Nov 2008 05:04:39 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/24/t12275283620p39ig0alstughs.htm/, Retrieved Mon, 24 Nov 2008 12:06:16 +0000

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/2008/Nov/24/t12275283620p39ig0alstughs.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Van Dooren Leen

Dataseries X:

» Textbox « » Textfile « » CSV «

519 0 517 0 510 0 509 0 501 0 507 0 569 0 580 0 578 0 565 0 547 0 555 0 562 0 561 0 555 0 544 0 537 0 543 0 594 0 611 0 613 0 611 0 594 0 595 0 591 0 589 0 584 0 573 0 567 0 569 0 621 0 629 0 628 0 612 0 595 0 597 0 593 0 590 0 580 0 574 0 573 0 573 0 620 0 626 0 620 0 588 1 566 1 557 1 561 1 549 1 532 1 526 1 511 1 499 1 555 1 565 1 542 1 527 1 510 1 514 1 517 1 508 1 493 1 490 1 469 1 478 1 528 1 534 1 518 1 506 1

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation W[t] = + 575.133333333333 -49.4133333333334D[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) 575.133333333333 4.94718 116.2548 0 0 D -49.4133333333334 8.278215 -5.9691 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.58636069678184 R-squared 0.343818866730485 Adjusted R-squared 0.334169144182404 F-TEST (value) 35.6299225202962 F-TEST (DF numerator) 1 F-TEST (DF denominator) 68 p-value 9.62911737012462e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 33.1866911666143 Sum Squared Residuals 74892.24 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 519 575.133333333333 -56.1333333333329 2 517 575.133333333333 -58.1333333333333 3 510 575.133333333333 -65.1333333333333 4 509 575.133333333333 -66.1333333333333 5 501 575.133333333333 -74.1333333333334 6 507 575.133333333333 -68.1333333333333 7 569 575.133333333333 -6.13333333333334 8 580 575.133333333333 4.86666666666666 9 578 575.133333333333 2.86666666666666 10 565 575.133333333333 -10.1333333333333 11 547 575.133333333333 -28.1333333333333 12 555 575.133333333333 -20.1333333333333 13 562 575.133333333333 -13.1333333333333 14 561 575.133333333333 -14.1333333333333 15 555 575.133333333333 -20.1333333333333 16 544 575.133333333333 -31.1333333333333 17 537 575.133333333333 -38.1333333333333 18 543 575.133333333333 -32.1333333333333 19 594 575.133333333333 18.8666666666667 20 611 575.133333333333 35.8666666666667 21 613 575.133333333333 37.8666666666667 22 611 575.133333333333 35.8666666666667 23 594 575.133333333333 18.8666666666667 24 595 575.133333333333 19.8666666666667 25 591 575.133333333333 15.8666666666667 26 589 575.133333333333 13.8666666666667 27 584 575.133333333333 8.86666666666666 28 573 575.133333333333 -2.13333333333334 29 567 575.133333333333 -8.13333333333334 30 569 575.133333333333 -6.13333333333334 31 621 575.133333333333 45.8666666666667 32 629 575.133333333333 53.8666666666667 33 628 575.133333333333 52.8666666666667 34 612 575.133333333333 36.8666666666667 35 595 575.133333333333 19.8666666666667 36 597 575.133333333333 21.8666666666667 37 593 575.133333333333 17.8666666666667 38 590 575.133333333333 14.8666666666667 39 580 575.133333333333 4.86666666666666 40 574 575.133333333333 -1.13333333333334 41 573 575.133333333333 -2.13333333333334 42 573 575.133333333333 -2.13333333333334 43 620 575.133333333333 44.8666666666667 44 626 575.133333333333 50.8666666666667 45 620 575.133333333333 44.8666666666667 46 588 525.72 62.28 47 566 525.72 40.28 48 557 525.72 31.28 49 561 525.72 35.28 50 549 525.72 23.28 51 532 525.72 6.28 52 526 525.72 0.280000000000006 53 511 525.72 -14.72 54 499 525.72 -26.72 55 555 525.72 29.28 56 565 525.72 39.28 57 542 525.72 16.28 58 527 525.72 1.28000000000001 59 510 525.72 -15.72 60 514 525.72 -11.72 61 517 525.72 -8.72 62 508 525.72 -17.72 63 493 525.72 -32.72 64 490 525.72 -35.72 65 469 525.72 -56.72 66 478 525.72 -47.72 67 528 525.72 2.28000000000001 68 534 525.72 8.28 69 518 525.72 -7.72 70 506 525.72 -19.72 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0310454939754052 0.0620909879508105 0.968954506024595 6 0.0102820659948718 0.0205641319897436 0.989717934005128 7 0.477193387505754 0.954386775011508 0.522806612494246 8 0.775490274817697 0.449019450364607 0.224509725182303 9 0.855340652213299 0.289318695573402 0.144659347786701 10 0.846835160063037 0.306329679873925 0.153164839936963 11 0.807633762340266 0.384732475319467 0.192366237659734 12 0.772041208044313 0.455917583911374 0.227958791955687 13 0.744948331349244 0.510103337301512 0.255051668650756 14 0.712478214280523 0.575043571438954 0.287521785719477 15 0.672724861103676 0.654550277792647 0.327275138896324 16 0.646037882084227 0.707924235831546 0.353962117915773 17 0.652167889589037 0.695664220821925 0.347832110410963 18 0.655347147987642 0.689305704024715 0.344652852012358 19 0.762550901027877 0.474898197944245 0.237449098972123 20 0.890089616169335 0.219820767661331 0.109910383830665 21 0.945909531735559 0.108180936528883 0.0540904682644414 22 0.967046466034328 0.0659070679313447 0.0329535339656724 23 0.965470692909326 0.069058614181349 0.0345293070906745 24 0.9628717166456 0.0742565667088007 0.0371282833544004 25 0.956466225627292 0.0870675487454165 0.0435337743727082 26 0.947198369601562 0.105603260796876 0.0528016303984379 27 0.93382366233693 0.132352675326142 0.0661763376630708 28 0.918654617497183 0.162690765005634 0.081345382502817 29 0.906894112034449 0.186211775931103 0.0931058879655514 30 0.895109345192523 0.209781309614954 0.104890654807477 31 0.921087506103588 0.157824987792825 0.0789124938964123 32 0.950417395422414 0.0991652091551722 0.0495826045775861 33 0.966936295304402 0.0661274093911963 0.0330637046955981 34 0.96525734106228 0.0694853178754396 0.0347426589377198 35 0.953462401243249 0.0930751975135023 0.0465375987567511 36 0.939091237261523 0.121817525476953 0.0609087627384766 37 0.91915076695459 0.161698466090818 0.080849233045409 38 0.893312527622616 0.213374944754767 0.106687472377384 39 0.864142409332088 0.271715181335824 0.135857590667912 40 0.840261466915267 0.319477066169466 0.159738533084733 41 0.826600301948104 0.346799396103791 0.173399698051896 42 0.836628667654943 0.326742664690114 0.163371332345057 43 0.822386967340257 0.355226065319486 0.177613032659743 44 0.812230252331198 0.375539495337604 0.187769747668802 45 0.788438972347637 0.423122055304725 0.211561027652363 46 0.876795170151682 0.246409659696636 0.123204829848318 47 0.898760114335672 0.202479771328656 0.101239885664328 48 0.90559670934519 0.188806581309621 0.0944032906548103 49 0.925874742711462 0.148250514577076 0.074125257288538 50 0.927643977407308 0.144712045185384 0.0723560225926921 51 0.909555673842518 0.180888652314965 0.0904443261574824 52 0.881947060447229 0.236105879105542 0.118052939552771 53 0.848557700219818 0.302884599560363 0.151442299780182 54 0.826530895002095 0.34693820999581 0.173469104997905 55 0.85797180124597 0.284056397508061 0.142028198754030 56 0.946479333120875 0.107041333758250 0.0535206668791252 57 0.958664440105697 0.0826711197886062 0.0413355598943031 58 0.948734083750628 0.102531832498743 0.0512659162493715 59 0.91699338328285 0.166013233434302 0.0830066167171508 60 0.872822459558041 0.254355080883918 0.127177540441959 61 0.818402795170627 0.363194409658746 0.181597204829373 62 0.729490648945615 0.54101870210877 0.270509351054385 63 0.629682777836075 0.740634444327849 0.370317222163925 64 0.522897222999264 0.954205554001473 0.477102777000736 65 0.634220003290275 0.73155999341945 0.365779996709725 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 0.0163934426229508 OK 10% type I error level 11 0.180327868852459 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/10cddo1227528275.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/10cddo1227528275.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/14yjx1227528274.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/14yjx1227528274.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/2gbcp1227528274.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/2gbcp1227528274.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/32cwz1227528274.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/32cwz1227528274.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/4gn3v1227528274.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/4gn3v1227528274.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/5flqs1227528274.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/5flqs1227528274.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/6ztye1227528275.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/6ztye1227528275.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/7v3kx1227528275.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/7v3kx1227528275.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/8dzq31227528275.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/8dzq31227528275.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/9vtcw1227528275.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t12275283620p39ig0alstughs/9vtcw1227528275.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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