Seatbelt Law part 5

*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: Fri, 20 Nov 2009 11:31:53 -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/20/t1258742529jdz224wjierd0jw.htm/, Retrieved Fri, 20 Nov 2009 19:42:21 +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/20/t1258742529jdz224wjierd0jw.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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500857 1,1 509127 509933 506971 1,6 500857 509127 569323 1,5 506971 500857 579714 1,6 569323 506971 577992 1,7 579714 569323 565464 1,6 577992 579714 547344 1,7 565464 577992 554788 1,6 547344 565464 562325 1,6 554788 547344 560854 1,3 562325 554788 555332 1,1 560854 562325 543599 1,6 555332 560854 536662 1,9 543599 555332 542722 1,6 536662 543599 593530 1,7 542722 536662 610763 1,6 593530 542722 612613 1,4 610763 593530 611324 2,1 612613 610763 594167 1,9 611324 612613 595454 1,7 594167 611324 590865 1,8 595454 594167 589379 2 590865 595454 584428 2,5 589379 590865 573100 2,1 584428 589379 567456 2,1 573100 584428 569028 2,3 567456 573100 620735 2,4 569028 567456 628884 2,4 620735 569028 628232 2,3 628884 620735 612117 1,7 628232 628884 595404 2 612117 628232 597141 2,3 595404 612117 593408 2 597141 595404 590072 2 593408 597141 579799 1,3 590072 593408 574205 1,7 579799 590072 572775 1,9 574205 579799 572942 1,7 572775 574205 619567 1,6 572942 572775 625809 1,7 619567 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation TWIB[t] = + 17449.4878192846 + 1308.17632173413GI[t] + 0.99428281596959TWIB1[t] -0.0329312886467168`TWIB2 `[t] -3736.91327370935M1[t] + 7320.3659768742M2[t] + 58676.1756270611M3[t] + 15535.1254686653M4[t] + 557.825793580474M5[t] -6334.55395907056M6[t] -7567.63201795877M7[t] + 11374.1603938179M8[t] + 8282.24218097304M9[t] + 1910.72164777117M10[t] -3066.56991435644M11[t] -170.214815449318t + 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) 17449.4878192846 17152.637454 1.0173 0.313718 0.156859 GI 1308.17632173413 1078.72132 1.2127 0.230721 0.115361 TWIB1 0.99428281596959 0.143548 6.9265 0 0 `TWIB2 ` -0.0329312886467168 0.140787 -0.2339 0.815975 0.407988 M1 -3736.91327370935 3956.796504 -0.9444 0.349318 0.174659 M2 7320.3659768742 3956.301952 1.8503 0.069955 0.034977 M3 58676.1756270611 4268.734679 13.7456 0 0 M4 15535.1254686653 9776.578552 1.589 0.118119 0.05906 M5 557.825793580474 4928.716726 0.1132 0.910325 0.455162 M6 -6334.55395907056 4043.672772 -1.5665 0.123289 0.061645 M7 -7567.63201795877 3979.157759 -1.9018 0.062741 0.031371 M8 11374.1603938179 3961.407036 2.8712 0.005902 0.002951 M9 8282.24218097304 4393.45407 1.8851 0.065005 0.032503 M10 1910.72164777117 4389.124588 0.4353 0.665124 0.332562 M11 -3066.56991435644 4134.419778 -0.7417 0.461596 0.230798 t -170.214815449318 58.669167 -2.9013 0.005437 0.002719 Multiple Linear Regression - Regression Statistics Multiple R 0.990297707438577 R-squared 0.980689549358102 Adjusted R-squared 0.975119227057554 F-TEST (value) 176.056159131347 F-TEST (DF numerator) 15 F-TEST (DF denominator) 52 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6427.4395375485 Sum Squared Residuals 2148222908.45976 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 500857 504404.830116698 -3547.83011669796 2 506971 507749.806443279 -778.8064432789 3 569323 565155.97053979 4167.02946021052 4 579714 583769.703440668 -4055.7034406678 5 577992 577031.267613347 960.732386653092 6 565464 567783.511383645 -2319.51138364549 7 547344 554111.368702064 -6767.36870206405 8 554788 555148.287225015 -360.287225015 9 562325 559884.310429077 2440.68957092303 10 560854 560198.891255182 655.108744817821 11 555332 553078.956468437 2253.04353156315 12 543599 551187.411944026 -7588.4119440263 13 536662 536188.663047524 473.336952476172 14 542722 540172.317501449 2549.68249855130 15 593530 597742.528182478 -4212.52818247766 16 610763 604618.403281043 6144.59671895693 17 612613 604670.556380204 7942.44361979637 18 611324 599795.603549612 11528.3964503880 19 594167 596788.121977146 -2621.12197714644 20 595454 598281.602466602 -2827.60246660231 21 590865 596994.931173946 -6129.93117394614 22 589379 586109.684678669 3269.31532133100 23 584428 580289.883881028 4138.11611897189 24 573100 577789.210124305 -4689.21012430516 25 567456 562781.889105933 4674.11089406713 26 569028 568691.902229872 336.097770128443 27 620735 621757.191476609 -1022.19147660879 28 628884 629805.54008235 -921.540082350759 29 628232 620926.840484924 7305.15951507644 30 612117 612162.710656588 -45.7106565884505 31 595404 595150.474299619 253.525700381135 32 597141 598227.743805708 -1086.7438057085 33 593408 596850.607759386 -3442.60775938586 34 590072 586540.013010341 3531.98698965915 35 579799 577282.788233994 2516.21176600635 36 574205 570598.005272064 3606.99472793573 37 572775 561728.797502986 11046.2024970137 38 572942 571116.619875627 1825.38012437311 39 619567 622384.534051223 -2817.53405122274 40 625809 625557.023478929 251.976521070690 41 619916 615211.218624698 4704.78137530246 42 587625 602214.575950529 -14589.575950529 43 565742 568898.960749713 -3156.96074971251 44 557274 566976.031725868 -9702.03172586842 45 560576 556145.764833573 4430.23516642672 46 548854 553296.831127687 -4442.8311276875 47 531673 536123.967201857 -4450.96720185673 48 525919 522323.569805107 3595.43019489287 49 511038 512476.225070058 -1438.2250700582 50 498662 508756.853555622 -10094.8535556222 51 555362 548258.072398445 7103.92760155475 52 564591 561468.565454021 3122.43454597903 53 541657 553760.900637975 -12103.9006379747 54 527070 524245.589266374 2824.41073362574 55 509846 509617.209658006 228.79034199423 56 514258 512266.899268256 1991.10073174381 57 516922 514220.385804018 2701.61419598226 58 507561 510574.57992812 -3013.57992812047 59 492622 497078.404214685 -4456.40421468465 60 490243 485167.802854497 5075.19714550285 61 469357 480564.595156801 -11207.5951568009 62 477580 471417.500394152 6162.49960584819 63 528379 531597.703351456 -3218.70335145607 64 533590 538131.764262988 -4541.76426298808 65 517945 526754.216258854 -8809.21625885367 66 506174 503572.009193251 2601.99080674921 67 501866 489802.864613452 12063.1353865476 68 516141 504155.43550855 11985.5644914504 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.394236688023268 0.788473376046535 0.605763311976732 20 0.277816185168378 0.555632370336757 0.722183814831622 21 0.196706716012789 0.393413432025578 0.80329328398721 22 0.124075749764129 0.248151499528258 0.875924250235871 23 0.134113604111581 0.268227208223163 0.865886395888419 24 0.0963496738431998 0.192699347686400 0.9036503261568 25 0.0537142318478846 0.107428463695769 0.946285768152115 26 0.0465547232685474 0.0931094465370949 0.953445276731453 27 0.037932369174464 0.075864738348928 0.962067630825536 28 0.0350744192217013 0.0701488384434025 0.96492558077830 29 0.026144627606742 0.052289255213484 0.973855372393258 30 0.028803860559177 0.057607721118354 0.971196139440823 31 0.0167266531467957 0.0334533062935913 0.983273346853204 32 0.00931700933892444 0.0186340186778489 0.990682990661076 33 0.00586057592914463 0.0117211518582893 0.994139424070855 34 0.00339660670568067 0.00679321341136133 0.99660339329432 35 0.00205673235564202 0.00411346471128404 0.997943267644358 36 0.00203518492023581 0.00407036984047161 0.997964815079764 37 0.0107939358059513 0.0215878716119027 0.989206064194049 38 0.0123204320531501 0.0246408641063003 0.98767956794685 39 0.00840116894779004 0.0168023378955801 0.99159883105221 40 0.00501993494564298 0.0100398698912860 0.994980065054357 41 0.134450777770978 0.268901555541956 0.865549222229022 42 0.325274228940437 0.650548457880874 0.674725771059563 43 0.258524734077681 0.517049468155363 0.741475265922318 44 0.315604326918517 0.631208653837034 0.684395673081483 45 0.271008937926646 0.542017875853292 0.728991062073354 46 0.215372496149584 0.430744992299168 0.784627503850416 47 0.151371393367972 0.302742786735944 0.848628606632028 48 0.179832645692258 0.359665291384516 0.820167354307742 49 0.888596439153102 0.222807121693797 0.111403560846898 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.0967741935483871 NOK 5% type I error level 10 0.32258064516129 NOK 10% type I error level 15 0.483870967741935 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/10ccyi1258741909.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/10ccyi1258741909.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/110tb1258741909.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/110tb1258741909.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/2fxo41258741909.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/2fxo41258741909.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/302ki1258741909.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/302ki1258741909.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/4it6o1258741909.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/4it6o1258741909.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/56s7f1258741909.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/56s7f1258741909.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/6lkag1258741909.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/6lkag1258741909.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/706v81258741909.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/706v81258741909.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/85tk41258741909.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/85tk41258741909.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/9u8cx1258741909.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742529jdz224wjierd0jw/9u8cx1258741909.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 = Include Monthly Dummies ; par3 = 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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