Ws 7 (3)

*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 12:24:54 -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/t1258745160g74daxhesonvj5g.htm/, Retrieved Fri, 20 Nov 2009 20:26:12 +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/t1258745160g74daxhesonvj5g.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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18.0 16.4 19.6 17.8 23.3 22.3 23.7 22.8 20.3 18.3 22.8 22.4 24.3 23.9 21.5 21.3 23.5 23.0 22.2 21.4 20.9 21.2 22.2 20.9 19.5 17.9 21.1 20.7 22.0 22.2 19.2 19.8 17.8 17.7 19.2 19.6 19.9 20.8 19.6 19.8 18.1 18.6 20.4 21. 18.1 18.6 18.6 18.9 17.6 17.3 19.4 20.0 19.3 19.9 18.6 19.5 16.9 16.2 16.4 17.6 19.0 19.8 18.7 19.4 17.1 17.2 21.5 21.1 17.8 17.8 18.1 17.5 19.0 18.0 18.9 19.1 16.8 17.7 18.1 19.2 15.7 15.1 15.1 16.3 18.3 18.6 16.5 17.2 16.9 17.8 18.4 19.1 16.4 16.6 15.7 16.0 16.9 16.7 16.6 17.4 16.7 17.9 16.6 17.8 14.4 13.9 14.5 15.9 17.5 17.9 14.3 15.4 15.4 16.4 17.2 17.9 14.6 15.3 14.2 14.6 14.9 14.9 14.1 15.0 15.6 16.7 14.6 16.3 11.9 11.7 13.5 15.1 14.2 15.5 13.7 15.0 14.4 15.4 15.3 16.0 14.3 14.7 14.5 14.8

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 Y[t] = + 1.54167091765125 + 0.98059228033681X[t] + 0.387904499998967M1[t] -0.390548095699390M2[t] -0.792460059946535M3[t] -1.03691565041127M4[t] + 0.366721482980735M5[t] -1.14491108900953M6[t] -0.737442655419468M7[t] -0.821530714152305M8[t] -0.660810912706852M9[t] -0.358194409032587M10[t] -0.471564152213164M11[t] -0.0264160821289604t + 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) 1.54167091765125 1.233414 1.2499 0.216348 0.108174 X 0.98059228033681 0.057471 17.0624 0 0 M1 0.387904499998967 0.276379 1.4035 0.165792 0.082896 M2 -0.390548095699390 0.266241 -1.4669 0.147806 0.073903 M3 -0.792460059946535 0.278754 -2.8429 0.006163 0.003082 M4 -1.03691565041127 0.276503 -3.7501 0.00041 0.000205 M5 0.366721482980735 0.296309 1.2376 0.22084 0.11042 M6 -1.14491108900953 0.265079 -4.3191 6.2e-05 3.1e-05 M7 -0.737442655419468 0.284798 -2.5893 0.012137 0.006069 M8 -0.821530714152305 0.266386 -3.084 0.003126 0.001563 M9 -0.660810912706852 0.267358 -2.4716 0.016407 0.008203 M10 -0.358194409032587 0.291004 -1.2309 0.223332 0.111666 M11 -0.471564152213164 0.264668 -1.7817 0.080031 0.040015 t -0.0264160821289604 0.006086 -4.3406 5.8e-05 2.9e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.98920334433598 R-squared 0.978523256445487 Adjusted R-squared 0.97370950357982 F-TEST (value) 203.276587675413 F-TEST (DF numerator) 13 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.458137829416737 Sum Squared Residuals 12.1736357030754 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 18 17.984872733045 0.0151272669550136 2 19.6 18.5528332476892 1.04716675231082 3 23.3 22.5371704628287 0.762829537171278 4 23.7 22.7565949304034 0.943405069596563 5 20.3 19.7211507201508 0.578849279849173 6 22.8 22.2035304154125 0.596469584587477 7 24.3 24.0554711873788 0.244528812621157 8 21.5 21.3954271176413 0.104572882358660 9 23.5 23.1967377135304 0.303262286469589 10 22.2 21.9039904865368 0.296009513463182 11 20.9 21.5680862051599 -0.668086205159919 12 22.2 21.7190565911431 0.480943408856922 13 19.5 19.1387681680027 0.361231831997348 14 21.1 21.0795578751184 0.0204421248815975 15 22 22.1221182492475 -0.122118249247516 16 19.2 19.4978251038455 -0.297825103845481 17 17.8 18.8158023664012 -1.01580236640122 18 19.2 19.1408790449219 0.0591209550780661 19 19.9 20.6986421327872 -0.79864213278721 20 19.6 19.6075457115886 -0.0075457115885984 21 18.1 18.5651386945009 -0.465138694500919 22 20.4 21.1947605888546 -0.794760588854571 23 18.1 18.7015532907367 -0.601553290736686 24 18.6 19.4408790449219 -0.84087904492193 25 17.6 18.2334198142530 -0.633419814253042 26 19.4 20.0761502933351 -0.676150293335115 27 19.3 19.5497630189253 -0.249763018925326 28 18.6 18.8866544341969 -0.286654434196911 29 16.9 17.0279209603485 -0.127920960348478 30 16.4 16.8627014987008 -0.462701498700789 31 19 19.4010568669029 -0.401056866902873 32 18.7 18.8983158139064 -0.19831581390635 33 17.1 16.8753165164819 0.224683483518142 34 21.5 20.9758268313407 0.524173168659272 35 17.8 17.6000864809197 0.199913519080287 36 18.1 17.7510568669029 0.348943133097128 37 19 18.6028414249413 0.397158575058714 38 18.9 18.8766242554845 0.0233757445155378 39 16.8 17.0754670166368 -0.275467016636818 40 18.1 18.2754837645483 -0.175483764548342 41 15.7 15.6322764664305 0.0677235335695386 42 15.1 15.2709385487154 -0.170938548715409 43 18.3 17.9073531449512 0.392646855048824 44 16.5 16.4240198116178 0.075980188382158 45 16.9 17.1466788991364 -0.246678899136424 46 18.4 18.6976492851196 -0.297649285119583 47 16.4 16.1063827589680 0.293617241031981 48 15.7 15.9631754608501 -0.263175460850134 49 16.9 17.0110784749559 -0.111078474955908 50 16.6 16.8926243933644 -0.292624393364354 51 16.7 16.9545924871567 -0.254592487156657 52 16.6 16.5856615865293 0.0143384134707157 53 14.4 14.1385727444788 0.261427255521237 54 14.5 14.5617086510332 -0.0617086510331594 55 17.5 16.9039455631679 0.596054436832118 56 14.3 14.3419607214641 -0.0419607214640582 57 15.4 15.4568567211174 -0.0568567211173608 58 17.2 17.2039455631679 -0.00394556316788222 59 14.6 14.5146198089826 0.0853801910173616 60 14.2 14.2733532828311 -0.0733532828310736 61 14.9 14.9290193848021 -0.0290193848021241 62 14.1 14.2222099350085 -0.122209935008487 63 15.6 15.4608887652050 0.13911123479504 64 14.6 14.7977801804765 -0.197780180476546 65 11.9 11.6642767421903 0.235723257809746 66 13.5 13.4602418412162 0.0397581587838143 67 14.2 14.233531104812 -0.033531104812014 68 13.7 13.6327308237818 0.0672691762181886 69 14.4 14.1592714552330 0.240728544766973 70 15.3 15.0238272449804 0.276172755019582 71 14.3 13.6092714552330 0.690728544766975 72 14.5 14.1524787533509 0.347521246649088 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.969419796284642 0.061160407430716 0.030580203715358 18 0.973064147663213 0.0538717046735742 0.0269358523367871 19 0.959679575717558 0.0806408485648837 0.0403204242824419 20 0.974141172351366 0.0517176552972673 0.0258588276486336 21 0.95401102196195 0.0919779560760979 0.0459889780380489 22 0.950672927941168 0.0986541441176636 0.0493270720588318 23 0.981731697190842 0.0365366056183158 0.0182683028091579 24 0.992183450950955 0.0156330980980893 0.00781654904904467 25 0.990059551496419 0.0198808970071630 0.00994044850358148 26 0.989221567956466 0.021556864087067 0.0107784320435335 27 0.988251896256424 0.0234962074871514 0.0117481037435757 28 0.98418897485796 0.0316220502840807 0.0158110251420403 29 0.994950907063593 0.0100981858728132 0.00504909293640658 30 0.991882273241995 0.0162354535160091 0.00811772675800454 31 0.997859830745627 0.00428033850874624 0.00214016925437312 32 0.99806991215124 0.00386017569751857 0.00193008784875928 33 0.999744201365175 0.000511597269649726 0.000255798634824863 34 0.999977950883756 4.40982324873151e-05 2.20491162436576e-05 35 0.999989773203158 2.04535936849318e-05 1.02267968424659e-05 36 0.99999736921699 5.2615660195967e-06 2.63078300979835e-06 37 0.999999578742039 8.42515922650204e-07 4.21257961325102e-07 38 0.999999113394834 1.77321033240766e-06 8.8660516620383e-07 39 0.999997822990196 4.35401960696193e-06 2.17700980348097e-06 40 0.999993115379134 1.37692417315975e-05 6.88462086579875e-06 41 0.999984452375507 3.10952489858162e-05 1.55476244929081e-05 42 0.999969071414684 6.18571706325659e-05 3.09285853162829e-05 43 0.999976226822488 4.75463550246575e-05 2.37731775123287e-05 44 0.999963257938099 7.34841238029508e-05 3.67420619014754e-05 45 0.999891974218634 0.000216051562731430 0.000108025781365715 46 0.99981783901238 0.000364321975242393 0.000182160987621196 47 0.999687792692782 0.000624414614436992 0.000312207307218496 48 0.999063004926787 0.00187399014642539 0.000936995073212693 49 0.99736137059735 0.00527725880529884 0.00263862940264942 50 0.995850406008542 0.00829918798291619 0.00414959399145809 51 0.992294394148183 0.0154112117036349 0.00770560585181744 52 0.98435221770368 0.0312955645926384 0.0156477822963192 53 0.964582858053298 0.0708342838934042 0.0354171419467021 54 0.91734968438905 0.165300631221901 0.0826503156109505 55 0.969029732279413 0.0619405354411746 0.0309702677205873 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 20 0.512820512820513 NOK 5% type I error level 30 0.769230769230769 NOK 10% type I error level 38 0.974358974358974 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/10wdhk1258745090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/10wdhk1258745090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/14uyq1258745090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/14uyq1258745090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/23x5n1258745090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/23x5n1258745090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/306wh1258745090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/306wh1258745090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/4w44s1258745090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/4w44s1258745090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/5xacr1258745090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/5xacr1258745090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/6ph841258745090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/6ph841258745090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/7vkri1258745090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/7vkri1258745090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/85k961258745090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/85k961258745090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/9wniq1258745090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745160g74daxhesonvj5g/9wniq1258745090.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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