Model 1

*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 11:21:20 -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/t12586549367tkm1ocmhpbsd56.htm/, Retrieved Thu, 19 Nov 2009 19:22:28 +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/t12586549367tkm1ocmhpbsd56.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 «

7.2 102.9 7.4 97.4 8.8 111.4 9.3 87.4 9.3 96.8 8.7 114.1 8.2 110.3 8.3 103.9 8.5 101.6 8.6 94.6 8.5 95.9 8.2 104.7 8.1 102.8 7.9 98.1 8.6 113.9 8.7 80.9 8.7 95.7 8.5 113.2 8.4 105.9 8.5 108.8 8.7 102.3 8.7 99 8.6 100.7 8.5 115.5 8.3 100.7 8 109.9 8.2 114.6 8.1 85.4 8.1 100.5 8 114.8 7.9 116.5 7.9 112.9 8 102 8 106 7.9 105.3 8 118.8 7.7 106.1 7.2 109.3 7.5 117.2 7.3 92.5 7 104.2 7 112.5 7 122.4 7.2 113.3 7.3 100 7.1 110.7 6.8 112.8 6.4 109.8 6.1 117.3 6.5 109.1 7.7 115.9 7.9 96 7.5 99.8 6.9 116.8 6.6 115.7 6.9 99.4 7.7 94.3 8 91 8 93.2 7.7 103.1 7.3 94.1 7.4 91.8 8.1 102.7 8.3 82.6 8.2 89.1

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 Werkl.graad[t] = + 10.4398432435199 -0.0246737999717481Industr.prod.[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) 10.4398432435199 0.903781 11.5513 0 0 Industr.prod. -0.0246737999717481 0.008644 -2.8545 0.005828 0.002914 Multiple Linear Regression - Regression Statistics Multiple R 0.338411793864558 R-squared 0.114522542226628 Adjusted R-squared 0.100467344484193 F-TEST (value) 8.14805627962596 F-TEST (DF numerator) 1 F-TEST (DF denominator) 63 p-value 0.00582833248357506 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.664482105775001 Sum Squared Residuals 27.8167975403963 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.2 7.90090922642704 -0.700909226427037 2 7.4 8.03661512627164 -0.636615126271641 3 8.8 7.69118192666717 1.10881807333283 4 9.3 8.28335312598912 1.01664687401088 5 9.3 8.0514194062547 1.24858059374531 6 8.7 7.62456266674345 1.07543733325655 7 8.2 7.71832310663609 0.481676893363908 8 8.3 7.87623542645528 0.423764573544721 9 8.5 7.9329851663903 0.5670148336097 10 8.6 8.10570176619254 0.494298233807462 11 8.5 8.07362582622926 0.426374173770736 12 8.2 7.85649638647788 0.343503613522118 13 8.1 7.9033766064242 0.196623393575797 14 7.9 8.01934346629142 -0.119343466291418 15 8.6 7.6294974267378 0.970502573262201 16 8.7 8.44373282580549 0.256267174194513 17 8.7 8.07856058622361 0.621439413776385 18 8.5 7.64676908671802 0.853230913281978 19 8.4 7.82688782651178 0.573112173488217 20 8.5 7.75533380659371 0.744666193406286 21 8.7 7.91571350641008 0.784286493589923 22 8.7 7.99713704631684 0.702862953683154 23 8.6 7.95519158636487 0.644808413635126 24 8.5 7.590019346783 0.909980653216999 25 8.3 7.95519158636487 0.344808413635127 26 8 7.7281926266248 0.271807373375209 27 8.2 7.61222576675757 0.587774233242425 28 8.1 8.33270072593262 -0.23270072593262 29 8.1 7.96012634635922 0.139873653640777 30 8 7.60729100676322 0.392708993236775 31 7.9 7.56534554681125 0.334654453188747 32 7.9 7.65417122670955 0.245828773290454 33 8 7.9231156464016 0.076884353598399 34 8 7.8244204465146 0.175579553485392 35 7.9 7.84169210649483 0.0583078935051682 36 8 7.50859580687623 0.491404193123767 37 7.7 7.82195306651743 -0.121953066517434 38 7.2 7.74299690660784 -0.542996906607840 39 7.5 7.54807388683103 -0.0480738868310294 40 7.3 8.1575167461332 -0.857516746133208 41 7 7.86883328646375 -0.868833286463755 42 7 7.66404074669825 -0.664040746698246 43 7 7.41977012697794 -0.419770126977939 44 7.2 7.64430170672085 -0.444301706720847 45 7.3 7.9724632463451 -0.672463246345097 46 7.1 7.70845358664739 -0.608453586647392 47 6.8 7.65663860670672 -0.856638606706721 48 6.4 7.73066000662197 -1.33066000662197 49 6.1 7.54560650683385 -1.44560650683386 50 6.5 7.74793166660219 -1.24793166660219 51 7.7 7.5801498267943 0.119850173205698 52 7.9 8.07115844623209 -0.171158446232089 53 7.5 7.97739800633945 -0.477398006339447 54 6.9 7.55794340681973 -0.657943406819728 55 6.6 7.58508458678865 -0.985084586788652 56 6.9 7.98726752632815 -1.08726752632815 57 7.7 8.11310390618406 -0.413103906184061 58 8 8.19452744609083 -0.194527446090830 59 8 8.14024508615298 -0.140245086152984 60 7.7 7.89597446643268 -0.195974466432678 61 7.3 8.1180386661784 -0.818038666178411 62 7.4 8.17478840611343 -0.774788406113431 63 8.1 7.90584398642138 0.194156013578622 64 8.3 8.40178736585352 -0.101787365853514 65 8.2 8.24140766603715 -0.0414076660371526 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.971778566577711 0.0564428668445771 0.0282214334222886 6 0.963251119453557 0.0734977610928863 0.0367488805464432 7 0.93262842935286 0.134743141294282 0.0673715706471408 8 0.888348997616436 0.223302004767129 0.111651002383564 9 0.834408157729085 0.331183684541830 0.165591842270915 10 0.768208411787842 0.463583176424316 0.231791588212158 11 0.691333464333257 0.617333071333486 0.308666535666743 12 0.610831973901324 0.778336052197352 0.389168026098676 13 0.534250184445406 0.931499631109188 0.465749815554594 14 0.492760837633707 0.985521675267414 0.507239162366293 15 0.479291157583962 0.958582315167923 0.520708842416038 16 0.40173599561508 0.80347199123016 0.59826400438492 17 0.358917325788716 0.717834651577431 0.641082674211285 18 0.336356695054546 0.672713390109091 0.663643304945454 19 0.290683922818152 0.581367845636305 0.709316077181848 20 0.26968069520265 0.5393613904053 0.73031930479735 21 0.270750547563346 0.541501095126693 0.729249452436654 22 0.269230380012001 0.538460760024002 0.730769619987999 23 0.263718462881073 0.527436925762147 0.736281537118927 24 0.303866726528746 0.607733453057491 0.696133273471254 25 0.280138029013279 0.560276058026557 0.719861970986721 26 0.269985998261708 0.539971996523415 0.730014001738292 27 0.288117744075535 0.57623548815107 0.711882255924465 28 0.26277342657367 0.52554685314734 0.73722657342633 29 0.244497885695638 0.488995771391276 0.755502114304362 30 0.261436371081930 0.522872742163859 0.73856362891807 31 0.288470642693684 0.576941285387368 0.711529357306316 32 0.311979594564806 0.623959189129611 0.688020405435194 33 0.306219335809482 0.612438671618964 0.693780664190518 34 0.319552757432301 0.639105514864602 0.680447242567699 35 0.328554172731531 0.657108345463062 0.671445827268469 36 0.50131107137945 0.9973778572411 0.49868892862055 37 0.528464497567746 0.943071004864508 0.471535502432254 38 0.607104263472686 0.785791473054628 0.392895736527314 39 0.679827444414955 0.64034511117009 0.320172555585045 40 0.780519714225211 0.438960571549578 0.219480285774789 41 0.843563245127972 0.312873509744056 0.156436754872028 42 0.856215715524776 0.287568568950449 0.143784284475224 43 0.874411829310954 0.251176341378093 0.125588170689046 44 0.86970002733756 0.260599945324882 0.130299972662441 45 0.859316554266496 0.281366891467009 0.140683445733504 46 0.841322651996837 0.317354696006325 0.158677348003163 47 0.831888489146125 0.336223021707751 0.168111510853875 48 0.900585213462531 0.198829573074937 0.0994147865374686 49 0.951097841694941 0.0978043166101177 0.0489021583050588 50 0.977181143981445 0.0456377120371101 0.0228188560185550 51 0.986617375410639 0.0267652491787225 0.0133826245893613 52 0.977567344143363 0.0448653117132736 0.0224326558566368 53 0.960281550637752 0.0794368987244955 0.0397184493622478 54 0.934146823835998 0.131706352328004 0.0658531761640021 55 0.914880952006226 0.170238095987548 0.0851190479937742 56 0.965493578647901 0.0690128427041979 0.0345064213520990 57 0.933607774480789 0.132784451038423 0.0663922255192113 58 0.871457444700668 0.257085110598663 0.128542555299332 59 0.771835994547204 0.456328010905592 0.228164005452796 60 0.611727618777608 0.776544762444784 0.388272381222392 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 3 0.0535714285714286 NOK 10% type I error level 8 0.142857142857143 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/10lx5k1258654875.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/10lx5k1258654875.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/177iz1258654875.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/177iz1258654875.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/2ra5x1258654875.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/2ra5x1258654875.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/33hhl1258654875.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/33hhl1258654875.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/4vzal1258654875.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/4vzal1258654875.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/5itnx1258654875.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/5itnx1258654875.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/6vmmr1258654875.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/6vmmr1258654875.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/7pq6l1258654875.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/7pq6l1258654875.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/8frvm1258654875.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/8frvm1258654875.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/9jnnl1258654875.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586549367tkm1ocmhpbsd56/9jnnl1258654875.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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