multiple regression, basic. totale industrie zonder bouwnijverheid, prijsindex van de industriele grondstoffen

*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: Thu, 19 Nov 2009 08:13:51 -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/19/t12586438219147a69tvvgltw9.htm/, Retrieved Thu, 19 Nov 2009 16:17:13 +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/19/t12586438219147a69tvvgltw9.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:

» Textbox « » Textfile « » CSV «

97.6 82.9 96.9 83.8 105.6 86.2 102.8 86.1 101.7 86.2 104.2 88.8 92.7 89.6 91.9 87.8 106.5 88.3 112.3 88.6 102.8 91 96.5 91.5 101 95.4 98.9 98.7 105.1 99.9 103 98.6 99 100.3 104.3 100.2 94.6 100.4 90.4 101.4 108.9 103 111.4 109.1 100.8 111.4 102.5 114.1 98.2 121.8 98.7 127.6 113.3 129.9 104.6 128 99.3 123.5 111.8 124 97.3 127.4 97.7 127.6 115.6 128.4 111.9 131.4 107 135.1 107.1 134 100.6 144.5 99.2 147.3 108.4 150.9 103 148.7 99.8 141.4 115 138.9 90.8 139.8 95.9 145.6 114.4 147.9 108.2 148.5 112.6 151.1 109.1 157.5 105 167.5 105 172.3 118.5 173.5 103.7 187.5 112.5 205.5 116.6 195.1 96.6 204.5 101.9 204.5 116.5 201.7 119.3 207 115.4 206.6 108.5 210.6 111.5 211.1 108.8 215 121.8 223.9 109.6 238.2 112.2 238.9 119.6 229.6 104.1 232.2 105.3 222.1 115 221.6 124.1 227.3 116.8 221 107.5 213.6 115.6 243.4

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation tot_indus[t] = + 92.7500732489502 + 0.0894748397840029prijsindex[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) 92.7500732489502 2.371316 39.1133 0 0 prijsindex 0.0894748397840029 0.015109 5.9221 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.575011290154962 R-squared 0.330637983805674 Adjusted R-squared 0.321210349774768 F-TEST (value) 35.071151756821 F-TEST (DF numerator) 1 F-TEST (DF denominator) 71 p-value 1.03454354749566e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.46580416981606 Sum Squared Residuals 2968.27027293117 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 97.6 100.167537467044 -2.56753746704385 2 96.9 100.248064822850 -3.34806482284963 3 105.6 100.462804438331 5.13719556166878 4 102.8 100.453856954353 2.34614304564719 5 101.7 100.462804438331 1.23719556166879 6 104.2 100.695439021770 3.50456097823038 7 92.7 100.767018893597 -8.06701889359682 8 91.9 100.605964181986 -8.70596418198561 9 106.5 100.650701601878 5.84929839812238 10 112.3 100.677544053813 11.6224559461872 11 102.8 100.892283669294 1.90771633070557 12 96.5 100.937021089186 -4.43702108918643 13 101 101.285972964344 -0.285972964344038 14 98.9 101.581239935631 -2.68123993563124 15 105.1 101.688609743372 3.41139025662794 16 103 101.572292451653 1.42770754834715 17 99 101.724399679286 -2.72439967928565 18 104.3 101.715452195307 2.58454780469275 19 94.6 101.733347163264 -7.13334716326406 20 90.4 101.822822003048 -11.4228220030480 21 108.9 101.965981746702 6.93401825329755 22 111.4 102.511778269385 8.88822173061513 23 100.8 102.717570400888 -1.91757040088809 24 102.5 102.959152468305 -0.45915246830489 25 98.2 103.648108734642 -5.44810873464171 26 98.7 104.167062805389 -5.46706280538892 27 113.3 104.372854936892 8.92714506310786 28 104.6 104.202852741303 0.397147258697465 29 99.3 103.800215962275 -4.50021596227452 30 111.8 103.844953382167 7.95504661783348 31 97.3 104.149167837432 -6.84916783743213 32 97.7 104.167062805389 -6.46706280538892 33 115.6 104.238642677216 11.3613573227839 34 111.9 104.507067196568 7.39293280343187 35 107 104.838124103769 2.16187589623105 36 107.1 104.739701780007 2.36029821999345 37 100.6 105.679187597739 -5.07918759773858 38 99.2 105.929717149134 -6.72971714913378 39 108.4 106.251826572356 2.14817342764381 40 103 106.054981924831 -3.05498192483139 41 99.8 105.401815594408 -5.60181559440817 42 115 105.178128494948 9.82187150505184 43 90.8 105.258655850754 -14.4586558507538 44 95.9 105.777609921501 -9.87760992150097 45 114.4 105.983402053004 8.41659794699582 46 108.2 106.037086956875 2.16291304312542 47 112.6 106.269721540313 6.330278459687 48 109.1 106.842360514931 2.25763948506938 49 105 107.737108912771 -2.73710891277064 50 105 108.166588143734 -3.16658814373385 51 118.5 108.273957951475 10.2260420485253 52 103.7 109.526605708451 -5.8266057084507 53 112.5 111.137152824563 1.36284717543725 54 116.6 110.206614490809 6.39338550919088 55 96.6 111.047677984779 -14.4476779847787 56 101.9 111.047677984779 -9.14767798477874 57 116.5 110.797148433384 5.70285156661647 58 119.3 111.271365084239 8.02863491576125 59 115.4 111.235575148325 4.16442485167486 60 108.5 111.593474507461 -3.09347450746116 61 111.5 111.638211927353 -0.138211927353162 62 108.8 111.987163802511 -3.18716380251078 63 121.8 112.783489876588 9.0165101234116 64 109.6 114.062980085500 -4.46298008549964 65 112.2 114.125612473348 -1.92561247334844 66 119.6 113.293496463357 6.30650353664278 67 104.1 113.526131046796 -9.42613104679563 68 105.3 112.622435164977 -7.3224351649772 69 115 112.577697745085 2.42230225491481 70 124.1 113.087704331854 11.0122956681460 71 116.8 112.524012841215 4.27598715878521 72 107.5 111.861899026813 -4.36189902681317 73 115.6 114.528249252376 1.07175074762354 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0319639094773833 0.0639278189547667 0.968036090522617 6 0.0197160748225516 0.0394321496451032 0.980283925177448 7 0.282727297028859 0.565454594057717 0.717272702971141 8 0.354741574244864 0.709483148489729 0.645258425755135 9 0.382676109212534 0.765352218425068 0.617323890787466 10 0.596172124215436 0.807655751569129 0.403827875784564 11 0.49232164049791 0.98464328099582 0.50767835950209 12 0.467329840723497 0.934659681446994 0.532670159276503 13 0.371800573129838 0.743601146259676 0.628199426870162 14 0.294548329632424 0.589096659264847 0.705451670367576 15 0.244476907461215 0.488953814922431 0.755523092538785 16 0.180340038590628 0.360680077181255 0.819659961409372 17 0.138558124751222 0.277116249502443 0.861441875248778 18 0.102250743980364 0.204501487960728 0.897749256019636 19 0.114148094166760 0.228296188333521 0.88585190583324 20 0.195051310543186 0.390102621086371 0.804948689456814 21 0.243458948061537 0.486917896123074 0.756541051938463 22 0.311189470800739 0.622378941601479 0.68881052919926 23 0.255090014838525 0.510180029677049 0.744909985161476 24 0.198471790994179 0.396943581988358 0.801528209005821 25 0.178115909718483 0.356231819436966 0.821884090281517 26 0.150179458737817 0.300358917475633 0.849820541262183 27 0.218833851905625 0.437667703811249 0.781166148094375 28 0.169247901987065 0.338495803974131 0.830752098012935 29 0.146202188369024 0.292404376738049 0.853797811630976 30 0.168912604926047 0.337825209852094 0.831087395073953 31 0.174921318865987 0.349842637731974 0.825078681134013 32 0.171298584079401 0.342597168158802 0.8287014159206 33 0.280666060740471 0.561332121480942 0.719333939259529 34 0.29278443092875 0.5855688618575 0.70721556907125 35 0.2415883245458 0.4831766490916 0.7584116754542 36 0.19745837138399 0.39491674276798 0.80254162861601 37 0.182993133432544 0.365986266865089 0.817006866567456 38 0.183760655942888 0.367521311885776 0.816239344057112 39 0.146602743117863 0.293205486235726 0.853397256882137 40 0.116615124542907 0.233230249085815 0.883384875457093 41 0.105719022947314 0.211438045894627 0.894280977052686 42 0.153695378275534 0.307390756551069 0.846304621724466 43 0.350903151530992 0.701806303061985 0.649096848469008 44 0.465994767268112 0.931989534536224 0.534005232731888 45 0.488683922484923 0.977367844969845 0.511316077515077 46 0.423560305930670 0.847120611861339 0.57643969406933 47 0.405017883294799 0.810035766589599 0.594982116705201 48 0.343017979784634 0.686035959569268 0.656982020215366 49 0.291229850082631 0.582459700165262 0.708770149917369 50 0.252533265549339 0.505066531098678 0.747466734450661 51 0.327315954736439 0.654631909472878 0.672684045263561 52 0.306576864075829 0.613153728151658 0.693423135924171 53 0.244960542882131 0.489921085764261 0.75503945711787 54 0.243279300662678 0.486558601325356 0.756720699337322 55 0.510379139432064 0.979241721135872 0.489620860567936 56 0.639446905117406 0.721106189765188 0.360553094882594 57 0.590727043183283 0.818545913633435 0.409272956816717 58 0.608776133699262 0.782447732601477 0.391223866300738 59 0.554441911140184 0.891116177719632 0.445558088859816 60 0.481036602794686 0.962073205589372 0.518963397205314 61 0.386479445973091 0.772958891946181 0.613520554026909 62 0.325933381738894 0.651866763477789 0.674066618261105 63 0.384375768608354 0.768751537216708 0.615624231391646 64 0.323583921151287 0.647167842302574 0.676416078848713 65 0.240000861981640 0.480001723963279 0.75999913801836 66 0.210900404627596 0.421800809255192 0.789099595372404 67 0.341144421200151 0.682288842400302 0.658855578799849 68 0.439256042860627 0.878512085721254 0.560743957139373 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 0.015625 OK 10% type I error level 2 0.03125 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/10yjg71258643624.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/10yjg71258643624.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/15ei81258643624.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/15ei81258643624.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/2jjvm1258643624.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/2jjvm1258643624.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/3a4nw1258643624.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/3a4nw1258643624.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/4jgdz1258643624.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/4jgdz1258643624.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/5s0ra1258643624.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/5s0ra1258643624.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/68tvd1258643624.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/68tvd1258643624.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/7gmdr1258643624.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/7gmdr1258643624.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/8zaxt1258643624.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/8zaxt1258643624.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/95ery1258643624.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586438219147a69tvvgltw9/95ery1258643624.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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