*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: Wed, 18 Nov 2009 11:01:24 -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/18/t1258567370cndndeoknz4y84a.htm/, Retrieved Wed, 18 Nov 2009 19:03:02 +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/18/t1258567370cndndeoknz4y84a.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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1.6 8.3 1.8 1.6 1.5 7.5 1.6 1.8 1.5 7.2 1.5 1.6 1.3 7.4 1.5 1.5 1.4 8.8 1.3 1.5 1.4 9.3 1.4 1.3 1.3 9.3 1.4 1.4 1.3 8.7 1.3 1.4 1.2 8.2 1.3 1.3 1.1 8.3 1.2 1.3 1.4 8.5 1.1 1.2 1.2 8.6 1.4 1.1 1.5 8.5 1.2 1.4 1.1 8.2 1.5 1.2 1.3 8.1 1.1 1.5 1.5 7.9 1.3 1.1 1.1 8.6 1.5 1.3 1.4 8.7 1.1 1.5 1.3 8.7 1.4 1.1 1.5 8.5 1.3 1.4 1.6 8.4 1.5 1.3 1.7 8.5 1.6 1.5 1.1 8.7 1.7 1.6 1.6 8.7 1.1 1.7 1.3 8.6 1.6 1.1 1.7 8.5 1.3 1.6 1.6 8.3 1.7 1.3 1.7 8 1.6 1.7 1.9 8.2 1.7 1.6 1.8 8.1 1.9 1.7 1.9 8.1 1.8 1.9 1.6 8 1.9 1.8 1.5 7.9 1.6 1.9 1.6 7.9 1.5 1.6 1.6 8 1.6 1.5 1.7 8 1.6 1.6 2 7.9 1.7 1.6 2 8 2 1.7 1.9 7.7 2 2 1.7 7.2 1.9 2 1.8 7.5 1.7 1.9 1.9 7.3 1.8 1.7 1.7 7 1.9 1.8 2 7 1.7 1.9 2.1 7 2 1.7 2.4 7.2 2.1 2 2.5 7.3 2.4 2.1 2.5 7.1 2.5 2.4 2.6 6.8 2.5 2.5 2.2 6.4 2.6 2.5 2.5 6.1 2.2 2.6 2.8 6.5 2.5 2.2 2.8 7.7 2.8 2.5 2.9 7.9 2.8 2.8 3 7.5 2.9 2.8 3.1 6.9 3 2.9 2.9 6.6 3.1 3 2.7 6.9 2.9 3.1

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 0.125897900427605 + 0.00944079300588357X[t] + 0.39313092210009Y1[t] + 0.35998595852647Y2[t] + 0.0090037694087649t + 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) 0.125897900427605 0.506992 0.2483 0.804844 0.402422 X 0.00944079300588357 0.052382 0.1802 0.857658 0.428829 Y1 0.39313092210009 0.123489 3.1835 0.002436 0.001218 Y2 0.35998595852647 0.125166 2.8761 0.005788 0.002894 t 0.0090037694087649 0.00288 3.1264 0.002871 0.001436 Multiple Linear Regression - Regression Statistics Multiple R 0.942776116416407 R-squared 0.888826805685203 Adjusted R-squared 0.880436375925596 F-TEST (value) 105.933406410735 F-TEST (DF numerator) 4 F-TEST (DF denominator) 53 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.188487886954718 Sum Squared Residuals 1.88296722701868 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.6 1.49687344520772 0.103126554792285 2 1.5 1.49169558749705 0.00830441250294869 3 1.5 1.38655683508875 0.113443164911251 4 1.3 1.36145016724604 -0.0614501672460424 5 1.4 1.30504486244303 0.0949551375569734 6 1.4 1.28608492885945 0.113915071140552 7 1.3 1.33108729412086 -0.0310872941208596 8 1.3 1.29511349551609 0.00488650448391445 9 1.2 1.26339827256926 -0.0633982725692618 10 1.1 1.23403302906861 -0.134033029068606 11 1.4 1.16961326901589 0.230386730984108 12 1.2 1.26150179850262 -0.0615017985026249 13 1.5 1.29893109174872 0.201068908251276 14 1.1 1.35104470818046 -0.251044708180457 15 1.3 1.30984781700654 -0.00984781700653888 16 1.5 1.25159522882356 0.248404771176443 17 1.1 1.41783092946175 -0.317830929461752 18 1.4 1.34252360103636 0.0574763989636363 19 1.3 1.32547226366457 -0.0254722636645674 20 1.5 1.40127056982009 0.0987294301799123 21 1.6 1.45195784849564 0.148042151504365 22 1.7 1.57321598112029 0.126784018879708 23 1.1 1.65941959719289 -0.559419597192889 24 1.6 1.46854340919425 0.131456590805753 25 1.3 1.45717698523659 -0.157176985236587 26 1.7 1.52729037797797 0.172709622022029 27 1.6 1.58366257006765 0.0163374299323458 28 1.7 1.69451539277523 0.00548460722476676 29 1.9 1.70872181714254 0.191278182857463 30 1.8 1.83140628752338 -0.0314062875233781 31 1.9 1.87309415642743 0.0269058435725718 32 1.6 1.88446834289297 -0.284468342892967 33 1.5 1.81058735222376 -0.310587352223763 34 1.6 1.67228224186458 -0.0722822418645782 35 1.6 1.68554458693129 -0.0855445869312934 36 1.7 1.73054695219271 -0.0305469521927055 37 2 1.77791973451089 0.222080265489109 38 2 1.94180545570292 0.0581945442970819 39 1.9 2.05597277476786 -0.155972774767859 40 1.7 2.02094305546367 -0.320943055463673 41 1.8 1.91815428250154 -0.118154282501538 42 1.9 1.89258579381384 0.00741420618615849 43 1.7 1.97406901338350 -0.274069013383497 44 2 1.94044519422489 0.0595548057751088 45 2.1 1.99539104855839 0.104608951441611 46 2.4 2.15359185633628 0.246408143663719 47 2.5 2.31747757752831 0.182522422471692 48 2.5 2.47190206810385 0.028097931896154 49 2.6 2.51407219546349 0.0859278045365071 50 2.2 2.55861273987991 -0.358612739879913 51 2.5 2.44353049839952 0.0564695016004754 52 2.8 2.43025547823008 0.369744521769918 53 2.8 2.67652326343387 0.123476736566125 54 2.9 2.79541097900176 0.104589020998243 55 3 2.83995152341818 0.160048476581822 56 3.1 2.91860250508607 0.181397494913932 57 2.9 3.00008572465572 -0.100085724655725 58 2.7 2.96929414339888 -0.269294143398883 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.0627955954478451 0.125591190895690 0.937204404552155 9 0.0192078981272032 0.0384157962544064 0.980792101872797 10 0.00608235804448552 0.0121647160889710 0.993917641955514 11 0.0858155998254255 0.171631199650851 0.914184400174574 12 0.0454719297815119 0.0909438595630238 0.954528070218488 13 0.0932562808927185 0.186512561785437 0.906743719107282 14 0.0778926285407004 0.155785257081401 0.9221073714593 15 0.0458759925100906 0.0917519850201812 0.95412400748991 16 0.181575604052821 0.363151208105643 0.818424395947179 17 0.175923852753216 0.351847705506432 0.824076147246784 18 0.138740739351622 0.277481478703245 0.861259260648378 19 0.105288661714443 0.210577323428887 0.894711338285556 20 0.106601909719025 0.213203819438050 0.893398090280975 21 0.145802692918965 0.291605385837929 0.854197307081035 22 0.154422880218274 0.308845760436547 0.845577119781726 23 0.521539274738019 0.956921450523962 0.478460725261981 24 0.506116836535814 0.987766326928372 0.493883163464186 25 0.473733346586997 0.947466693173995 0.526266653413003 26 0.553275664027776 0.893448671944448 0.446724335972224 27 0.514065968829683 0.971868062340634 0.485934031170317 28 0.477315537830808 0.954631075661617 0.522684462169192 29 0.608318460762233 0.783363078475534 0.391681539237767 30 0.553347267273295 0.89330546545341 0.446652732726705 31 0.659809334834905 0.68038133033019 0.340190665165095 32 0.635837445155578 0.728325109688843 0.364162554844422 33 0.652592122045238 0.694815755909523 0.347407877954762 34 0.582355544541346 0.835288910917309 0.417644455458654 35 0.504918789840074 0.990162420319852 0.495081210159926 36 0.420676020510547 0.841352041021095 0.579323979489453 37 0.526553618565518 0.946892762868964 0.473446381434482 38 0.493642966815098 0.987285933630196 0.506357033184902 39 0.431446044314813 0.862892088629626 0.568553955685187 40 0.391939226203284 0.783878452406568 0.608060773796716 41 0.307878399925071 0.615756799850142 0.692121600074929 42 0.239855130925645 0.479710261851289 0.760144869074355 43 0.425076284655705 0.85015256931141 0.574923715344295 44 0.358011704803248 0.716023409606496 0.641988295196752 45 0.450867411134635 0.90173482226927 0.549132588865365 46 0.414719684644353 0.829439369288707 0.585280315355647 47 0.346651849714245 0.693303699428491 0.653348150285755 48 0.240585722005056 0.481171444010112 0.759414277994944 49 0.225725089585705 0.45145017917141 0.774274910414295 50 0.606282762219178 0.787434475561643 0.393717237780822 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 0.0465116279069767 OK 10% type I error level 4 0.0930232558139535 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/102bhe1258567277.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/102bhe1258567277.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/1mw3j1258567276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/1mw3j1258567276.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/2ce7z1258567276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/2ce7z1258567276.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/3cljx1258567276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/3cljx1258567276.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/45upn1258567276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/45upn1258567276.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/57g7v1258567276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/57g7v1258567276.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/6dwj71258567276.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/6dwj71258567276.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/7auk01258567277.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/7auk01258567277.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/83jl41258567277.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/83jl41258567277.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/9xjfh1258567277.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567370cndndeoknz4y84a/9xjfh1258567277.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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