Ws7.3 Liniair trend

*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 14:07:19 -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/19/t1258664962ksvs4mnjw56t2k7.htm/, Retrieved Thu, 19 Nov 2009 22:09:34 +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/t1258664962ksvs4mnjw56t2k7.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:

Ws7.3 Liniair trend

Dataseries X:

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1.4 8.2 1.2 8.0 1.0 7.5 1.7 6.8 2.4 6.5 2.0 6.6 2.1 7.6 2.0 8.0 1.8 8.1 2.7 7.7 2.3 7.5 1.9 7.6 2.0 7.8 2.3 7.8 2.8 7.8 2.4 7.5 2.3 7.5 2.7 7.1 2.7 7.5 2.9 7.5 3.0 7.6 2.2 7.7 2.3 7.7 2.8 7.9 2.8 8.1 2.8 8.2 2.2 8.2 2.6 8.2 2.8 7.9 2.5 7.3 2.4 6.9 2.3 6.6 1.9 6.7 1.7 6.9 2.0 7.0 2.1 7.1 1.7 7.2 1.8 7.1 1.8 6.9 1.8 7.0 1.3 6.8 1.3 6.4 1.3 6.7 1.2 6.6 1.4 6.4 2.2 6.3 2.9 6.2 3.1 6.5 3.5 6.8 3.6 6.8 4.4 6.4 4.1 6.1 5.1 5.8 5.8 6.1 5.9 7.2 5.4 7.3 5.5 6.9 4.8 6.1 3.2 5.8 2.7 6.2 2.1 7.1 1.9 7.7 0.6 7.9 0.7 7.7

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] = + 3.19853175831125 -0.200223337414399X[t] -0.0731380364246286M1[t] -0.0635410640368461M2[t] -0.247325814755517M3[t] -0.231129176925388M4[t] + 0.370889683425942M5[t] + 0.390427099169884M6[t] + 0.486116384355618M7[t] + 0.349702934330727M8[t] + 0.277271617312685M9[t] + 0.216809033056628M10[t] -0.0036312174579902M11[t] + 0.020417916773178t + 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) 3.19853175831125 2.465007 1.2976 0.200388 0.100194 X -0.200223337414399 0.308151 -0.6498 0.518823 0.259412 M1 -0.0731380364246286 0.729866 -0.1002 0.92058 0.46029 M2 -0.0635410640368461 0.734145 -0.0866 0.931374 0.465687 M3 -0.247325814755517 0.727078 -0.3402 0.735158 0.367579 M4 -0.231129176925388 0.72053 -0.3208 0.749717 0.374858 M5 0.370889683425942 0.759008 0.4887 0.627226 0.313613 M6 0.390427099169884 0.767117 0.509 0.613023 0.306512 M7 0.486116384355618 0.75254 0.646 0.521254 0.260627 M8 0.349702934330727 0.752364 0.4648 0.644089 0.322044 M9 0.277271617312685 0.751841 0.3688 0.713841 0.356921 M10 0.216809033056628 0.753179 0.2879 0.774645 0.387322 M11 -0.0036312174579902 0.755071 -0.0048 0.996182 0.498091 t 0.020417916773178 0.010032 2.0353 0.047139 0.02357 Multiple Linear Regression - Regression Statistics Multiple R 0.449208059996124 R-squared 0.201787881165481 Adjusted R-squared -0.00574726973149353 F-TEST (value) 0.972307005793218 F-TEST (DF numerator) 13 F-TEST (DF denominator) 50 p-value 0.490542412898064 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.18809754306499 Sum Squared Residuals 70.5787885918531 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.4 1.50398027186173 -0.103980271861729 2 1.2 1.57403982850557 -0.374039828505567 3 1 1.51078466326727 -0.510784663267274 4 1.7 1.68755555406066 0.0124444459393401 5 2.4 2.37005933240949 0.0299406675905115 6 2 2.38999233118517 -0.389992331185169 7 2.1 2.30587619572968 -0.205876195729681 8 2 2.10979132751221 -0.109791327512209 9 1.8 2.03775559352591 -0.237755593525906 10 2.7 2.07780026100879 0.622199738991215 11 2.3 1.91782259475023 0.382177405249775 12 1.9 1.92184939523995 -0.0218493952399534 13 2 1.82908460810562 0.170915391894377 14 2.3 1.85909949726658 0.440900502733417 15 2.8 1.69573266332109 1.10426733667891 16 2.4 1.79241421914872 0.607585780851283 17 2.3 2.41485099627322 -0.114850996273225 18 2.7 2.53489566375610 0.165104336243896 19 2.7 2.57091353074926 0.129086469250744 20 2.9 2.45491799749754 0.445082002502455 21 3 2.38288226351124 0.617117736488759 22 2.2 2.32281526228692 -0.122815262286921 23 2.3 2.12279292854548 0.177207071454519 24 2.8 2.10679739529377 0.69320260470623 25 2.8 2.01403260815944 0.78596739184056 26 2.8 2.02402516357896 0.77597483642104 27 2.2 1.86065832963347 0.339341670366534 28 2.6 1.89727288423677 0.702727115763227 29 2.8 2.5797766625856 0.220223337414399 30 2.5 2.73986599755136 -0.239865997551361 31 2.4 2.93606253447603 -0.536062534476032 32 2.3 2.88013400244864 -0.58013400244864 33 1.9 2.80809826846234 -0.908098268462335 34 1.7 2.72800893349658 -1.02800893349658 35 2 2.50796426601370 -0.507964266013696 36 2.1 2.51199106650342 -0.411991066503424 37 1.7 2.43924861311053 -0.739248613110534 38 1.8 2.48928583601293 -0.689285836012934 39 1.8 2.36596366955032 -0.56596366955032 40 1.8 2.38255589041219 -0.582555890412187 41 1.3 3.04503733501958 -1.74503733501958 42 1.3 3.16508200250246 -1.86508200250245 43 1.3 3.22112220323705 -1.92112220323705 44 1.2 3.12514900372677 -1.92514900372678 45 1.4 3.11318027096479 -1.71318027096479 46 2.2 3.09315793722335 -0.893157937223351 47 2.9 2.91315793722335 -0.0131579372233512 48 3.1 2.8771400702302 0.222859929769801 49 3.5 2.76435294935443 0.73564705064557 50 3.6 2.79436783851539 0.805632161484611 51 4.4 2.71109033953566 1.68890966046434 52 4.1 2.80777189536328 1.29222810463672 53 5.1 3.49027567371211 1.60972432628789 54 5.8 3.47016400500491 2.32983599499509 55 5.9 3.36602553580798 2.53397446419202 56 5.4 3.23000766881483 2.16999233118517 57 5.5 3.25808360353573 2.24191639646427 58 4.8 3.37821760598437 1.42178239401563 59 3.2 3.23826227346725 -0.0382622734672462 60 2.7 3.18222207273265 -0.482222072732654 61 2.1 2.94930094940824 -0.849300949408245 62 1.9 2.85918183612057 -0.959181836120567 63 0.6 2.65577033469219 -2.05577033469219 64 0.7 2.73242955677838 -2.03242955677838 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0600373187173287 0.120074637434657 0.939962681282671 18 0.0171496024352501 0.0342992048705002 0.98285039756475 19 0.00523332764629523 0.0104666552925905 0.994766672353705 20 0.00131956573917313 0.00263913147834625 0.998680434260827 21 0.00031787983766048 0.00063575967532096 0.99968212016234 22 0.000783736742613174 0.00156747348522635 0.999216263257387 23 0.000332817470904682 0.000665634941809364 0.999667182529095 24 0.000109057872006419 0.000218115744012838 0.999890942127994 25 3.26511036663507e-05 6.53022073327015e-05 0.999967348896334 26 9.92324251880837e-06 1.98464850376167e-05 0.999990076757481 27 4.83146507837474e-06 9.66293015674947e-06 0.999995168534922 28 1.99254256937857e-06 3.98508513875715e-06 0.99999800745743 29 7.5952923354468e-07 1.51905846708936e-06 0.999999240470766 30 4.62514090971134e-07 9.25028181942269e-07 0.999999537485909 31 7.47901855831481e-07 1.49580371166296e-06 0.999999252098144 32 5.74943995003683e-07 1.14988799000737e-06 0.999999425056005 33 4.20206737185008e-07 8.40413474370015e-07 0.999999579793263 34 3.74734427182479e-07 7.49468854364957e-07 0.999999625265573 35 1.90317590760419e-07 3.80635181520837e-07 0.99999980968241 36 1.04633919411434e-07 2.09267838822868e-07 0.99999989536608 37 4.48920499259087e-08 8.97840998518173e-08 0.99999995510795 38 1.34617346537585e-08 2.6923469307517e-08 0.999999986538265 39 3.63797319526439e-09 7.27594639052878e-09 0.999999996362027 40 2.81727894498358e-09 5.63455788996715e-09 0.99999999718272 41 5.31720823324365e-09 1.06344164664873e-08 0.999999994682792 42 7.49651005109034e-09 1.49930201021807e-08 0.99999999250349 43 1.01025409233999e-07 2.02050818467999e-07 0.99999989897459 44 1.29035586785711e-05 2.58071173571422e-05 0.999987096441321 45 0.0539955911259929 0.107991182251986 0.946004408874007 46 0.861571711324773 0.276856577350453 0.138428288675227 47 0.795204949780818 0.409590100438364 0.204795050219182 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 25 0.806451612903226 NOK 5% type I error level 27 0.870967741935484 NOK 10% type I error level 27 0.870967741935484 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/10n4g21258664835.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/10n4g21258664835.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/1k6rs1258664835.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/1k6rs1258664835.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/2b8fz1258664835.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/2b8fz1258664835.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/3t0w31258664835.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/3t0w31258664835.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/4bsi91258664835.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/4bsi91258664835.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/5c2yk1258664835.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/5c2yk1258664835.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/6lrws1258664835.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/6lrws1258664835.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/7h1ta1258664835.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/7h1ta1258664835.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/8wv1n1258664835.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/8wv1n1258664835.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/9m2i31258664835.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258664962ksvs4mnjw56t2k7/9m2i31258664835.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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