*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, 29 Dec 2010 14:34:13 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl.htm/, Retrieved Wed, 29 Dec 2010 15:32:00 +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/2010/Dec/29/t12936331183yhrkphtp8323pl.htm/}, year = {2010}, } @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 = {2010}, 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 «

9 2 3 3 2 14 9 2 5 4 1 18 9 4 3 2 2 11 9 3 3 2 2 12 9 3 4 4 1 16 9 2 5 4 1 18 9 4 4 4 2 14 9 3 4 4 3 14 9 2 4 3 2 15 9 2 4 3 2 15 9 2 4 5 2 17 9 1 5 4 1 19 9 2 2 2 4 10 9 1 4 3 2 16 9 2 5 5 2 18 9 3 4 4 3 14 9 2 4 3 3 14 9 2 4 4 1 17 9 3 4 2 1 14 9 2 5 3 2 16 9 1 4 4 1 18 9 3 3 2 3 11 9 4 3 5 2 14 9 3 3 3 3 12 9 2 5 4 2 17 9 4 2 3 4 9 9 2 4 4 2 16 9 4 4 4 2 14 9 3 4 4 2 15 9 4 3 2 2 11 9 2 4 4 2 16 9 3 3 4 3 13 9 1 4 4 2 17 9 2 4 3 2 15 9 3 4 4 3 14 9 2 4 4 2 16 9 4 2 3 4 9 9 2 4 3 2 15 9 2 5 4 2 17 9 2 3 4 4 13 9 2 4 4 3 15 9 2 4 4 2 16 9 2 5 4 3 16 9 3 3 4 4 12 9 2 4 2 12 9 4 3 3 3 11 9 2 4 4 3 15 9 2 4 3 2 15 9 3 5 4 1 17 9 4 4 3 2 13 9 2 3 4 1 16 9 2 3 3 2 14 9 4 4 2 3 11 9 2 3 3 4 12 9 3 4 4 5 12 9 2 4 4 3 15 9 2 4 4 2 16 9 2 3 4 2 15 9 3 3 3 3 12 9 4 3 3 2 12 9 5 3 2 4 8 9 3 4 3 3 13 9 5 4 2 2 11 9 3 4 3 2 14 9 3 4 4 2 15 10 4 3 2 3 10

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 5 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation PPS [t] = + 12.3921250945773 -0.270748679771877month[t] -0.70634116694064IDT[t] + 1.6294703266687HPP[t] + 0.354910328804295TGYW[t] -0.456467813010273POP[t] -0.00177826582113197t + 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) 12.3921250945773 1.703364 7.2751 0 0 month -0.270748679771877 0.130847 -2.0692 0.042916 0.021458 IDT -0.70634116694064 0.150916 -4.6804 1.7e-05 9e-06 HPP 1.6294703266687 0.154682 10.5343 0 0 TGYW 0.354910328804295 0.149059 2.381 0.020512 0.010256 POP -0.456467813010273 0.084163 -5.4236 1e-06 1e-06 t -0.00177826582113197 0.01024 -0.1737 0.86273 0.431365 Multiple Linear Regression - Regression Statistics Multiple R 0.969262536820557 R-squared 0.939469865283822 Adjusted R-squared 0.933314258363532 F-TEST (value) 152.620184727401 F-TEST (DF numerator) 6 F-TEST (DF denominator) 59 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.858338696061553 Sum Squared Residuals 43.4679737122422 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 14 13.5811327173264 0.41886728267358 2 18 17.6496732466572 0.350326753342769 3 11 11.8099835229986 -0.809983522998559 4 12 12.5145464241181 -0.514546424118067 5 16 15.3085269555845 0.691473044415505 6 18 17.6425601833727 0.357439816627299 7 14 14.1421614439913 -0.142161443991317 8 14 14.3902565321006 -0.390256532100552 9 15 15.196376917426 -0.196376917426039 10 15 15.1945986516049 -0.194598651604907 11 17 15.9026410433924 1.09735895660764 12 19 18.3382317553865 0.661768244613451 13 10 10.6624772459793 -0.662477245979277 14 16 15.893826755261 0.10617324473898 15 18 17.5249983067765 0.475001693223467 16 14 14.3760304055315 -0.376030405531496 17 14 14.7256829778467 -0.72568297784671 18 17 15.9917506668504 1.00824933314958 19 14 14.5738105764801 -0.573810576480059 20 16 16.8062863200623 -0.806286320062285 21 18 16.6927570363277 1.30724296367234 22 11 12.0260698263274 -1.02606982632742 23 14 12.8391491929888 1.1608508070112 24 12 12.3774236234894 -0.377423623489449 25 17 17.1523053197609 -0.152305319760919 26 9 9.58158778522758 -0.581587785227575 27 16 15.51927846145 0.480721538550041 28 14 14.1048178617475 -0.104817861747546 29 15 14.8093807628671 0.190619237132946 30 11 11.761970345828 -0.761970345827996 31 16 15.5121653981654 0.487834601834569 32 13 12.7181078257247 0.281892174275312 33 17 16.2149500334638 0.785049966536193 34 15 15.1519202718977 -0.15192027189774 35 14 14.34224335493 -0.342243354929989 36 16 15.5032740690598 0.496725930940229 37 9 9.56202686119512 -0.562026861195124 38 15 15.1448072086132 -0.144807208613212 39 17 17.1274095982651 -0.127409598265071 40 13 12.953755053086 0.0462449469140006 41 15 15.0379149269438 -0.0379149269438377 42 16 15.492604474133 0.507395525867021 43 16 16.6638287219703 -0.66382872197027 44 12 12.2403008228608 -0.240300822860831 45 9 10.2127708889583 -1.21277088895826 46 9 10.0403026702017 -1.04030267020174 47 9 9.67727967161132 -0.677279671611324 48 9 7.6911207503172 1.3088792496828 49 9 7.07387700962741 1.92612299037259 50 9 8.05900248515173 0.940997514848269 51 9 9.21021930464857 -0.210219304648574 52 9 8.84681666698359 0.153183333016413 53 9 7.69204331584448 1.30795668415552 54 9 10.4660164189705 -1.46601641897046 55 9 11.4715289619098 -2.4715289619098 56 9 9.66127527922114 -0.661275279221136 57 9 8.84811887158544 0.151881128414564 58 9 10.0091495857152 -1.00914958571522 59 9 9.83146608128863 -0.831466081288628 60 9 9.20402880689132 -0.204028806891325 61 9 9.8377234442793 -0.837723444279303 62 9 8.66332230387432 0.336677696125681 63 9 7.04860164905699 1.95139835094301 64 9 7.84838763041749 1.15161236958251 65 10 9.01961187825478 0.980388121745222 66 9 8.83173484012068 0.168265159879322 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 9.87547010642142e-46 1.97509402128428e-45 1 11 3.68832641222742e-60 7.37665282445484e-60 1 12 6.20738896648861e-74 1.24147779329772e-73 1 13 7.14676073082291e-92 1.42935214616458e-91 1 14 1.80137628372807e-107 3.60275256745614e-107 1 15 6.1367723749857e-120 1.22735447499714e-119 1 16 2.61408240513385e-138 5.22816481026769e-138 1 17 2.04696493114567e-146 4.09392986229134e-146 1 18 2.77069308898969e-159 5.54138617797939e-159 1 19 2.04521913394736e-171 4.09043826789473e-171 1 20 1.88625001363939e-195 3.77250002727878e-195 1 21 2.14205249068436e-211 4.28410498136872e-211 1 22 4.62033321151078e-219 9.24066642302155e-219 1 23 3.67884829099447e-234 7.35769658198895e-234 1 24 4.58497099916721e-251 9.16994199833441e-251 1 25 2.57503423158562e-273 5.15006846317125e-273 1 26 2.29742522172458e-276 4.59485044344917e-276 1 27 1.05377776936405e-296 2.1075555387281e-296 1 28 5.36052820769423e-309 1.07210564153885e-308 1 29 0 0 1 30 0 0 1 31 0 0 1 32 0 0 1 33 0 0 1 34 0 0 1 35 0 0 1 36 0 0 1 37 0 0 1 38 0 0 1 39 0 0 1 40 0 0 1 41 0 0 1 42 0 0 1 43 0 0 1 44 0 0 1 45 0.90844298567508 0.183114028649841 0.0915570143249203 46 0.873988030381475 0.252023939237049 0.126011969618525 47 0.880481375805699 0.239037248388603 0.119518624194301 48 0.986611977713672 0.026776044572655 0.0133880222863275 49 0.998380493547057 0.00323901290588689 0.00161950645294345 50 0.996284118089228 0.00743176382154309 0.00371588191077154 51 0.990934680975449 0.0181306380491027 0.00906531902455135 52 0.982302734792122 0.0353945304157558 0.0176972652078779 53 0.984450951975175 0.0310980960496493 0.0155490480248247 54 0.998129387698434 0.00374122460313188 0.00187061230156594 55 0.998709472515105 0.00258105496979 0.001290527484895 56 0.991549205192476 0.0169015896150476 0.0084507948075238 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 39 0.829787234042553 NOK 5% type I error level 44 0.936170212765957 NOK 10% type I error level 44 0.936170212765957 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/10i9p91293633243.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/10i9p91293633243.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/14z901293633243.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/14z901293633243.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/24z901293633243.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/24z901293633243.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/34z901293633243.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/34z901293633243.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/4xrq31293633243.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/4xrq31293633243.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/5xrq31293633243.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/5xrq31293633243.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/6xrq31293633243.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/6xrq31293633243.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/7piqo1293633243.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/7piqo1293633243.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/8i9p91293633243.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/8i9p91293633243.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/9i9p91293633243.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t12936331183yhrkphtp8323pl/9i9p91293633243.ps (open in new window)

Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 6 ; 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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