Paper Multiple Regression met monthly dummies en lineaire 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, 17 Dec 2009 09:54:30 -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/Dec/17/t1261068931iuvhsobljxf84m8.htm/, Retrieved Thu, 17 Dec 2009 17:55:43 +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/Dec/17/t1261068931iuvhsobljxf84m8.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 «

7.6 1.62 8.3 1.49 8.4 1.79 8.4 1.8 8.4 1.58 8.4 1.86 8.6 1.74 8.9 1.59 8.8 1.26 8.3 1.13 7.5 1.92 7.2 2.61 7.4 2.26 8.8 2.41 9.3 2.26 9.3 2.03 8.7 2.86 8.2 2.55 8.3 2.27 8.5 2.26 8.6 2.57 8.5 3.07 8.2 2.76 8.1 2.51 7.9 2.87 8.6 3.14 8.7 3.11 8.7 3.16 8.5 2.47 8.4 2.57 8.5 2.89 8.7 2.63 8.7 2.38 8.6 1.69 8.5 1.96 8.3 2.19 8 1.87 8.2 1.6 8.1 1.63 8.1 1.22 8 1.21 7.9 1.49 7.9 1.64 8 1.66 8 1.77 7.9 1.82 8 1.78 7.7 1.28 7.2 1.29 7.5 1.37 7.3 1.12 7 1.51 7 2.24 7 2.94 7.2 3.09 7.3 3.46 7.1 3.64 6.8 4.39 6.4 4.15 6.1 5.21 6.5 5.8 7.7 5.91 7.9 5.39 7.5 5.46 6.9 4.72 6.6 3.14 6.9 2.63 7.7 2.32 8 1.93 8 0.62 7.7 0.6 7.3 -0.37 7.4 -1.1

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation TWG[t] = + 8.52999770283932 -0.117288081923750Infl[t] -0.136479017084508M1[t] + 0.587372677934864M2[t] + 0.694716430409092M3[t] + 0.595167523043634M4[t] + 0.36267624261792M5[t] + 0.205112649654338M6[t] + 0.368907246634373M7[t] + 0.665057776265042M8[t] + 0.693955198819427M9[t] + 0.513860535092991M10[t] + 0.242120662176955M11[t] -0.0194635209396826t + 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) 8.52999770283932 0.238943 35.699 0 0 Infl -0.117288081923750 0.04568 -2.5676 0.012795 0.006397 M1 -0.136479017084508 0.267113 -0.5109 0.611299 0.305649 M2 0.587372677934864 0.279226 2.1036 0.039688 0.019844 M3 0.694716430409092 0.278557 2.494 0.015454 0.007727 M4 0.595167523043634 0.278234 2.1391 0.036577 0.018289 M5 0.36267624261792 0.277955 1.3048 0.197026 0.098513 M6 0.205112649654338 0.277546 0.739 0.462824 0.231412 M7 0.368907246634373 0.277295 1.3304 0.188513 0.094257 M8 0.665057776265042 0.27709 2.4002 0.019562 0.009781 M9 0.693955198819427 0.276944 2.5058 0.014998 0.007499 M10 0.513860535092991 0.276898 1.8558 0.068482 0.034241 M11 0.242120662176955 0.27683 0.8746 0.385328 0.192664 t -0.0194635209396826 0.002754 -7.0665 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.782622929890878 R-squared 0.612498650390982 Adjusted R-squared 0.5271169970873 F-TEST (value) 7.17365647878089 F-TEST (DF numerator) 13 F-TEST (DF denominator) 59 p-value 4.03706177376506e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.479455191625504 Sum Squared Residuals 13.5627595658223 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.6 8.1840484720987 -0.584048472098692 2 8.3 8.90368409682844 -0.603684096828439 3 8.4 8.95637790378586 -0.556377903785859 4 8.4 8.83619259466148 -0.43619259466148 5 8.4 8.61004117131931 -0.210041171319309 6 8.4 8.4001733944774 -0.000173394477395415 7 8.6 8.5585790403486 0.0414209596514014 8 8.9 8.85285926132815 0.0471407386718526 9 8.8 8.90099822997769 -0.100998229977687 10 8.3 8.71668749596166 -0.416687495961655 11 7.5 8.33282651738617 -0.832826517386174 12 7.2 7.99031355774215 -0.790313557742149 13 7.4 7.87542184839127 -0.475421848391271 14 8.8 8.5622168101824 0.237783189817603 15 9.3 8.6676902540055 0.632309745994494 16 9.3 8.57565408454283 0.724345915457173 17 8.7 8.22635017518072 0.47364982481928 18 8.2 8.08568236667382 0.114317633326182 19 8.3 8.26285410565282 0.0371458943471811 20 8.5 8.54071399516304 -0.0407139951630443 21 8.6 8.51378859138138 0.0862114086186157 22 8.5 8.25558636575339 0.24441363424661 23 8.2 8.00074227729403 0.199257722705965 24 8.1 7.76848011465833 0.331519885341665 25 7.9 7.5703138671416 0.329686132858406 26 8.6 8.24303425910187 0.356965740898129 27 8.7 8.33443313309413 0.365566866905871 28 8.7 8.2095563006928 0.4904436993072 29 8.5 8.0385302758548 0.461469724145209 30 8.4 7.84977435375915 0.550225646240849 31 8.5 7.9565732435839 0.543426756416096 32 8.7 8.26375515357507 0.436244846424933 33 8.7 8.3025110756707 0.397488924329294 34 8.6 8.18388166753197 0.416118332468025 35 8.5 7.86101049155684 0.638989508443157 36 8.3 7.57245004959774 0.727549950402257 37 8 7.45403969778915 0.545960302210847 38 8.2 8.19009565398826 0.00990434601174413 39 8.1 8.27445724306509 -0.174457243065089 40 8.1 8.20353292834868 -0.103532928348685 41 8 7.95275100780253 0.0472489921974741 42 7.9 7.74288323096061 0.157116769039389 43 7.9 7.8696210947124 0.0303789052875994 44 8 8.14396234176491 -0.143962341764913 45 8 8.140494554368 -0.140494554368002 46 7.9 7.9350719656057 -0.0350719656056964 47 8 7.64856009502693 0.351439904973072 48 7.7 7.44561995287216 0.254380047127835 49 7.2 7.28850453402874 -0.088504534028738 50 7.5 7.98350966155453 -0.483509661554527 51 7.3 8.10071191357001 -0.80071191357001 52 7 7.9359571333146 -0.935957133314606 53 7 7.59838203214487 -0.598382032144872 54 7 7.33925326089498 -0.339253260894983 55 7.2 7.46599112464677 -0.265991124646772 56 7.3 7.69928154302597 -0.399281543025972 57 7.1 7.6876035898944 -0.587603589894399 58 6.8 7.40007934378547 -0.600079343785468 59 6.4 7.13702508959145 -0.737025089591448 60 6.1 6.75111553963564 -0.651115539635636 61 6.5 6.52597303327643 -0.0259730332764336 62 7.7 7.21745951834451 0.48254048165549 63 7.9 7.3663295524794 0.533670447520595 64 7.5 7.2391069584396 0.260893041560398 65 6.9 7.07394533769778 -0.173945337697781 66 6.6 7.08223339323404 -0.482233393234042 67 6.9 7.28638139105551 -0.386381391055506 68 7.7 7.59942770514286 0.100572294857144 69 8 7.65460395870782 0.345396041292179 70 8 7.60869316136182 0.391306838638184 71 7.7 7.31983552914457 0.380164470855429 72 7.3 7.17202078549397 0.127979214506028 73 7.4 7.10169854727412 0.29830145272588 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.389210429947418 0.778420859894836 0.610789570052582 18 0.416114410039407 0.832228820078814 0.583885589960593 19 0.455417944660807 0.910835889321614 0.544582055339193 20 0.458958025120436 0.917916050240872 0.541041974879564 21 0.343444557149994 0.686889114299987 0.656555442850006 22 0.273875223525977 0.547750447051955 0.726124776474023 23 0.250650859922854 0.501301719845708 0.749349140077146 24 0.207895045055858 0.415790090111716 0.792104954944142 25 0.142247606151174 0.284495212302349 0.857752393848826 26 0.105102353247342 0.210204706494684 0.894897646752658 27 0.0953886804972445 0.190777360994489 0.904611319502755 28 0.0798676610741896 0.159735322148379 0.92013233892581 29 0.0761720288366044 0.152344057673209 0.923827971163396 30 0.0579548999899335 0.115909799979867 0.942045100010066 31 0.0433000297727027 0.0866000595454055 0.956699970227297 32 0.0310398827872249 0.0620797655744498 0.968960117212775 33 0.0218545106180138 0.0437090212360276 0.978145489381986 34 0.0141373613478166 0.0282747226956333 0.985862638652183 35 0.0131828776767946 0.0263657553535891 0.986817122323205 36 0.015670040503764 0.031340081007528 0.984329959496236 37 0.0130298146330508 0.0260596292661016 0.98697018536695 38 0.0250053737687665 0.0500107475375331 0.974994626231233 39 0.0477283503929432 0.0954567007858864 0.952271649607057 40 0.0550336912314372 0.110067382462874 0.944966308768563 41 0.051164554007169 0.102329108014338 0.94883544599283 42 0.0547731596407504 0.109546319281501 0.94522684035925 43 0.0565318056264276 0.113063611252855 0.943468194373572 44 0.0511964912411387 0.102392982482277 0.948803508758861 45 0.0460379280575132 0.0920758561150264 0.953962071942487 46 0.0410525450118022 0.0821050900236045 0.958947454988198 47 0.0821667478052984 0.164333495610597 0.917833252194702 48 0.303182805388224 0.606365610776447 0.696817194611776 49 0.416622646646737 0.833245293293474 0.583377353353263 50 0.370407583361267 0.740815166722535 0.629592416638733 51 0.473242528185883 0.946485056371767 0.526757471814117 52 0.72390836117356 0.552183277652882 0.276091638826441 53 0.70568689685793 0.588626206284142 0.294313103142071 54 0.728994269793404 0.542011460413192 0.271005730206596 55 0.902100167151633 0.195799665696733 0.0978998328483667 56 0.953882569380055 0.0922348612398902 0.0461174306199451 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 5 0.125 NOK 10% type I error level 12 0.3 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/10z4nh1261068864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/10z4nh1261068864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/1dirh1261068863.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/1dirh1261068863.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/2uekb1261068863.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/2uekb1261068863.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/335mx1261068864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/335mx1261068864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/43f931261068864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/43f931261068864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/5x1yz1261068864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/5x1yz1261068864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/6q2y11261068864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/6q2y11261068864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/7etlg1261068864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/7etlg1261068864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/8kdo81261068864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/8kdo81261068864.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/94s3s1261068864.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/17/t1261068931iuvhsobljxf84m8/94s3s1261068864.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') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by