Seatbelt Law part 4

*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 15:43:19 -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/t12587570272tl98ttc1t0ougg.htm/, Retrieved Fri, 20 Nov 2009 23:43:59 +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/t12587570272tl98ttc1t0ougg.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:

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Dataseries X:

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7.2 1,9 7.5 8.3 8.8 8.9 7.4 1,6 7.2 7.5 8.3 8.8 8.8 1,7 7.4 7.2 7.5 8.3 9.3 1,6 8.8 7.4 7.2 7.5 9.3 1,4 9.3 8.8 7.4 7.2 8.7 2,1 9.3 9.3 8.8 7.4 8.2 1,9 8.7 9.3 9.3 8.8 8.3 1,7 8.2 8.7 9.3 9.3 8.5 1,8 8.3 8.2 8.7 9.3 8.6 2 8.5 8.3 8.2 8.7 8.5 2,5 8.6 8.5 8.3 8.2 8.2 2,1 8.5 8.6 8.5 8.3 8.1 2,1 8.2 8.5 8.6 8.5 7.9 2,3 8.1 8.2 8.5 8.6 8.6 2,4 7.9 8.1 8.2 8.5 8.7 2,4 8.6 7.9 8.1 8.2 8.7 2,3 8.7 8.6 7.9 8.1 8.5 1,7 8.7 8.7 8.6 7.9 8.4 2 8.5 8.7 8.7 8.6 8.5 2,3 8.4 8.5 8.7 8.7 8.7 2 8.5 8.4 8.5 8.7 8.7 2 8.7 8.5 8.4 8.5 8.6 1,3 8.7 8.7 8.5 8.4 8.5 1,7 8.6 8.7 8.7 8.5 8.3 1,9 8.5 8.6 8.7 8.7 8 1,7 8.3 8.5 8.6 8.7 8.2 1,6 8 8.3 8.5 8.6 8.1 1,7 8.2 8 8.3 8.5 8.1 1,8 8.1 8.2 8 8.3 8 1,9 8.1 8.1 8.2 8 7.9 1,9 8 8.1 8.1 8.2 7.9 1,9 7.9 8 8.1 8.1 8 2 7.9 7.9 8 8.1 8 2,1 8 7.9 7.9 8 7.9 1,9 8 8 7.9 7.9 8 1,9 7.9 8 8 7.9 7.7 1,3 8 7.9 8 8 7.2 1,3 7.7 8 7.9 8 7.5 1,4 7.2 7.7 8 7.9 7.3 1,2 7.5 7.2 7.7 8 7 1,3 7.3 7.5 7.2 7.7 7 1,8 7 7.3 7.5 7.2 7 2,2 7 7 7.3 7.5 7.2 2,6 7 7 7 7.3 7. 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 Multiple Linear Regression - Estimated Regression Equation TWIB[t] = + 0.86793374916331 + 0.0509126058020017GI[t] + 1.47563767426721TWIB1[t] -0.787676888382591TWIB2[t] -0.140644079346224TWIB3[t] + 0.349848893647999TWIB4[t] -0.144827600397996M1[t] -0.118927642048376M2[t] + 0.608833924114353M3[t] -0.392339280785223M4[t] -0.00254042804679102M5[t] + 0.120655167502287M6[t] + 0.0123581916177904M7[t] + 0.162523576349744M8[t] + 0.0094586184244671M9[t] -0.104618718579842M10[t] -0.0223453107403499M11[t] -0.00703422898260113t + 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) 0.86793374916331 0.677724 1.2807 0.208075 0.104037 GI 0.0509126058020017 0.028725 1.7724 0.084341 0.042171 TWIB1 1.47563767426721 0.136411 10.8176 0 0 TWIB2 -0.787676888382591 0.261456 -3.0127 0.00459 0.002295 TWIB3 -0.140644079346224 0.262464 -0.5359 0.595177 0.297589 TWIB4 0.349848893647999 0.144111 2.4276 0.020045 0.010022 M1 -0.144827600397996 0.102505 -1.4129 0.16583 0.082915 M2 -0.118927642048376 0.105678 -1.1254 0.267487 0.133743 M3 0.608833924114353 0.107385 5.6697 2e-06 1e-06 M4 -0.392339280785223 0.140127 -2.7999 0.007991 0.003996 M5 -0.00254042804679102 0.154355 -0.0165 0.986955 0.493477 M6 0.120655167502287 0.12274 0.983 0.33182 0.16591 M7 0.0123581916177904 0.10025 0.1233 0.90254 0.45127 M8 0.162523576349744 0.103143 1.5757 0.123384 0.061692 M9 0.0094586184244671 0.11174 0.0846 0.932985 0.466493 M10 -0.104618718579842 0.11305 -0.9254 0.360586 0.180293 M11 -0.0223453107403499 0.106781 -0.2093 0.83536 0.41768 t -0.00703422898260113 0.002396 -2.9355 0.005625 0.002812 Multiple Linear Regression - Regression Statistics Multiple R 0.986273371796106 R-squared 0.97273516391406 Adjusted R-squared 0.960537737244034 F-TEST (value) 79.7492118812622 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.147760839247470 Sum Squared Residuals 0.829664093374437 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.2 7.21835750945546 -0.0183575094554583 2 7.4 7.44473681581612 -0.0447368158161163 3 8.8 8.63957683159764 0.160423168402361 4 9.3 9.2969496123183 0.00305038768170033 5 9.3 9.17151942434806 0.128480575651940 6 8.7 8.80254923842953 -0.102549238429532 7 8.2 8.2111193192758 -0.0111193192757963 8 8.3 8.2537796965847 0.0462203034153002 9 8.5 8.72456042948277 -0.224560429482775 10 8.6 8.69040393415576 -0.0904039341557564 11 8.5 8.59213895090523 -0.0921389509052281 12 8.2 8.36760960757275 -0.167609607572754 13 8.1 7.90772953554523 0.19227046445477 14 7.9 8.07456638246013 -0.174566382460129 15 8.6 8.59123346864434 0.00876653135565657 16 8.7 8.68261752426595 0.0173824757340503 17 8.7 8.64962475950493 0.0503752404950684 18 8.5 8.48805023947999 0.0119497605200076 19 8.4 8.32369509911903 0.0763049008809687 20 8.5 8.52705653622358 -0.0270565362235826 21 8.7 8.60614383970933 0.0938561602906723 22 8.7 8.64548674894262 0.0545132510573787 23 8.6 8.47850242876217 0.121497571237829 24 8.5 8.37347085890956 0.126529141090445 25 8.3 8.2329652508305 0.0670347491695038 26 8 8.03935302095656 -0.039353020956557 27 8.2 8.44891269152266 -0.248912691522662 28 8.1 7.97037104609335 0.129628953906652 29 8.1 8.02535123040041 0.0746487695995913 30 8 8.0922880624217 -0.0922880624216998 31 7.9 7.9134272767921 -0.0134272767921033 32 7.9 7.9527774645882 -0.0527774645881952 33 8 7.8906016350334 0.109398364966601 34 8 7.90122461562323 0.098775384376769 35 7.9 7.85252869511666 0.0474713048833375 36 8 7.70621160151307 0.293788398486931 37 7.7 7.78511855428105 -0.0851185542810496 38 7.2 7.29658970046427 -0.096589700464271 39 7.5 7.47184323030635 0.0281567696936487 40 7.3 7.3671611349039 -0.067161134903898 41 7 7.18895378945042 -0.188953789450422 42 7 6.82829786368639 0.171702136313610 43 7 7.10271825161851 -0.102718251618515 44 7.2 7.23843789476294 -0.038437894762935 45 7.3 7.2786940957745 0.0213059042255011 46 7.1 7.16288470127839 -0.0628847012783912 47 6.8 6.87682992521594 -0.0768299252159383 48 6.4 6.65270793200462 -0.252707932004621 49 6.1 6.25582914988777 -0.155829149887766 50 6.5 6.14475408030293 0.355245919697073 51 7.7 7.648433777929 0.0515662220709958 52 7.9 7.9829006824185 -0.0829006824185043 53 7.5 7.56455079629618 -0.0645507962961778 54 6.9 6.88881459598239 0.0111854040176149 55 6.6 6.54904005319455 0.0509599468054454 56 6.9 6.82794840784059 0.0720515921594126 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.0139035117817499 0.0278070235634999 0.98609648821825 22 0.148972647864848 0.297945295729697 0.851027352135152 23 0.0795953210646793 0.159190642129359 0.92040467893532 24 0.0508452612096893 0.101690522419379 0.94915473879031 25 0.0349366492121456 0.0698732984242912 0.965063350787854 26 0.0186254335708494 0.0372508671416988 0.98137456642915 27 0.333318578890394 0.666637157780787 0.666681421109606 28 0.238143867425734 0.476287734851468 0.761856132574266 29 0.160672825485881 0.321345650971763 0.839327174514119 30 0.203686178917709 0.407372357835417 0.796313821082291 31 0.154623016112308 0.309246032224616 0.845376983887692 32 0.138215763811601 0.276431527623202 0.861784236188399 33 0.102779537436283 0.205559074872566 0.897220462563717 34 0.0972451230109546 0.194490246021909 0.902754876989045 35 0.0779957692425929 0.155991538485186 0.922004230757407 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 2 0.133333333333333 NOK 10% type I error level 3 0.2 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/109b7c1258756994.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/109b7c1258756994.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/1nvci1258756994.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/1nvci1258756994.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/26woa1258756994.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/26woa1258756994.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/3guen1258756994.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/3guen1258756994.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/4lp1f1258756994.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/4lp1f1258756994.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/5mzki1258756994.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/5mzki1258756994.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/67jfd1258756994.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/67jfd1258756994.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/7x57b1258756994.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/7x57b1258756994.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/811d41258756994.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/811d41258756994.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/9vux11258756994.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587570272tl98ttc1t0ougg/9vux11258756994.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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