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central tendency of residuals

R Software Module: rwasp_centraltendency.wasp (opens new window with default values)

Title produced by software: Central Tendency

Date of computation: Fri, 30 Nov 2007 03:44:47 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Nov/30/t1196418935xljq63we7x6ts8c.htm/, Retrieved Fri, 30 Nov 2007 11:35:35 +0100

User-defined keywords:

central tendency, residuals, sarima

Dataseries X:

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0.00448570722258807 0.00161627008051144 -0.00148103604695934 0.00370662236561495 0.00365213590849957 -0.00111929007561087 0.00202894670871937 -0.00319537555574839 -0.000115297471801953 0.000207732752387937 -0.000193809964066644 -0.000617502855287235 0.00267917863791114 0.00101870086796636 0.00122816042981585 0.00164007142023998 -0.000425131732519528 -0.00136244733432715 0.000558705725005591 0.000315360633839379 0.00209902605197982 0.000918783548783637 0.00185127411835918 0.00296081256507189 0.000115071804741404 0.00224444271218977 0.00358967265745595 -0.000791569555137633 0.000820898909146944 0.0042654643382554 0.000352135463422806 0.00101319797453202 0.00577618519868283 -0.00317793724831984 0.00276463935659925 -0.00335592071342644 -0.00207518674583269 0.00250587076741912 0.00127015567990493 0.00582329478916602 0.00347779560993236 0.00106706078643245 -0.00208869630491994 0.00118711723817082 0.000678895704500574 -0.00133031152020092 4.66272759626643e-05 -0.00190522951870584 0.00492388039825642 -0.000982094706596713 0.00158700153242671 -0.00324114444069905 -0.00109834861765504 -0.00307957607000311 0.00246280425525008 0.000451168657026153 0.000557078701682656 -0.000667750757797388 -0.00189007898443563 0.00111563541279842 0.00235327038447162 0.00439708454751803 0.00118393590213247 -0.00548126638100145 -0.00487263830895669 0.00328381565008992 0.000385101134086802 0.00344659419526791 0.000301555401043752 -0.00262866719031730 0.00118629498011972 -0.000377642996183579 0.000626204256106537 0.0008049247836587 -0.000155132511933779 0.00321473719305704 0.00180526825813112 -0.00103547278097048 0.00202972923549924 0.00127992120343444 -0.00170430074604714 0.00588152522860069 -0.00257880934823165 -0.00270165345509185 0.000605358855440578 0.00366563922443009 0.0045409959925552 -0.000643791480355452 -0.000592834618556011 0.00277039361546545 0.00405977710356672 0.000665078000940488 -0.000969006282867427 -0.00158668951022226 -0.000794393348260039 0.000638022815746765 -0.00214771844939365 0.00105058419629655 -0.00254981462795891 0.00262015509706327 0.00153553233128507 -0.000947475648852895 0.00124325433079447 0.00104583455268845 -0.00480731405784928 -0.00122987289697014 0.00206882711674461 0.00183460657908581 -0.00177412254735189 0.00199191110992592 -0.00181600070846074 0.00190792404916493 -0.00352131241210541 -0.000333990880691547 0.00189578833611634 -0.00158666313146865 -0.00022224948193303

Text written by user:

Output produced by software:

Summary of compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 0.000548125298288 0.000216563038653109 2.53101961302819 Geometric Mean NaN Harmonic Mean 0.00379511592157654 Quadratic Mean 0.00239599438836171 Winsorized Mean ( 1 / 39 ) 0.000552829551558172 0.000215268130029340 2.56809752322753 Winsorized Mean ( 2 / 39 ) 0.000553140913278244 0.000214857374150678 2.57445626646415 Winsorized Mean ( 3 / 39 ) 0.000564261345209461 0.000204140160853216 2.76408788379071 Winsorized Mean ( 4 / 39 ) 0.000556825697106136 0.000200852605667161 2.77231004923516 Winsorized Mean ( 5 / 39 ) 0.000559367898078797 0.000199635902085378 2.80194039366512 Winsorized Mean ( 6 / 39 ) 0.000557170267816265 0.000198487765241668 2.80707612954323 Winsorized Mean ( 7 / 39 ) 0.000550338871980038 0.000197020892840187 2.79330209119722 Winsorized Mean ( 8 / 39 ) 0.000543000338211015 0.000193685897570362 2.80350993553237 Winsorized Mean ( 9 / 39 ) 0.000544905559515591 0.000185010055866002 2.94527536335782 Winsorized Mean ( 10 / 39 ) 0.000547640869224112 0.000183558410790189 2.98346922304789 Winsorized Mean ( 11 / 39 ) 0.000551058816127586 0.000182676757690134 3.0165786994223 Winsorized Mean ( 12 / 39 ) 0.000547626146304931 0.000181306033466583 3.02045186160816 Winsorized Mean ( 13 / 39 ) 0.000579872716420673 0.000173181495915735 3.34835262482559 Winsorized Mean ( 14 / 39 ) 0.000583201692637165 0.000171690596745371 3.39681790204324 Winsorized Mean ( 15 / 39 ) 0.000564064643138352 0.000168504960670757 3.34746609769242 Winsorized Mean ( 16 / 39 ) 0.00057786003050163 0.000164107810957551 3.52122197675956 Winsorized Mean ( 17 / 39 ) 0.000543166358765271 0.000158840896315473 3.41956241348886 Winsorized Mean ( 18 / 39 ) 0.000525267793591187 0.000153570142140254 3.42037707506624 Winsorized Mean ( 19 / 39 ) 0.000531134068314352 0.000152562442179166 3.48142085776656 Winsorized Mean ( 20 / 39 ) 0.000528460749103522 0.000149185262924979 3.54231201354834 Winsorized Mean ( 21 / 39 ) 0.0005389764892276 0.000145177846769741 3.71252571394341 Winsorized Mean ( 22 / 39 ) 0.000517492088205447 0.000142557104946540 3.63006872508746 Winsorized Mean ( 23 / 39 ) 0.000529790320374902 0.000138923502595429 3.81353990129194 Winsorized Mean ( 24 / 39 ) 0.000531647723832078 0.000133242652038907 3.99007161518248 Winsorized Mean ( 25 / 39 ) 0.000515260574653526 0.000129693590564375 3.97290700651679 Winsorized Mean ( 26 / 39 ) 0.000505265455324822 0.000123311665216052 4.0974668085096 Winsorized Mean ( 27 / 39 ) 0.000523815582891913 0.000119473886226561 4.38435209095475 Winsorized Mean ( 28 / 39 ) 0.000519470455950826 0.000117841767258651 4.40820320362846 Winsorized Mean ( 29 / 39 ) 0.000534861105414382 0.000115988491020276 4.61132911299691 Winsorized Mean ( 30 / 39 ) 0.000539051483768309 0.000113346017060772 4.75580437448709 Winsorized Mean ( 31 / 39 ) 0.000520266374811191 0.000110509764800306 4.70787695323869 Winsorized Mean ( 32 / 39 ) 0.000522835925844611 0.000109467687128453 4.77616673522204 Winsorized Mean ( 33 / 39 ) 0.000553457692798269 0.000103175277206834 5.36424720903596 Winsorized Mean ( 34 / 39 ) 0.000549434723831322 0.000102558590778988 5.35727645688254 Winsorized Mean ( 35 / 39 ) 0.000577698114203069 9.74796207474308e-05 5.92634757679128 Winsorized Mean ( 36 / 39 ) 0.000534240403295622 9.1245767820365e-05 5.85496090456905 Winsorized Mean ( 37 / 39 ) 0.000535026980710733 8.95368643399587e-05 5.97549383323624 Winsorized Mean ( 38 / 39 ) 0.000533532862493355 8.76613623535475e-05 6.08629444226022 Winsorized Mean ( 39 / 39 ) 0.00057227742412497 7.97670531908561e-05 7.17435835012859 Trimmed Mean ( 1 / 39 ) 0.000554177400453015 0.000208828368248387 2.65374577745999 Trimmed Mean ( 2 / 39 ) 0.000555572960813163 0.000201625742816275 2.75546640549472 Trimmed Mean ( 3 / 39 ) 0.00055685471559508 0.000193778768769879 2.87366216190778 Trimmed Mean ( 4 / 39 ) 0.000554204637109201 0.000189511092169065 2.92439155284266 Trimmed Mean ( 5 / 39 ) 0.000553488132390412 0.000185813493217589 2.97872949270845 Trimmed Mean ( 6 / 39 ) 0.00055217778460843 0.000182006407379315 3.03383706408560 Trimmed Mean ( 7 / 39 ) 0.000551232605748694 0.000178010664785076 3.09662685892575 Trimmed Mean ( 8 / 39 ) 0.000551380507942375 0.000173835521888327 3.17185177087446 Trimmed Mean ( 9 / 39 ) 0.000552618487561781 0.000169796500333067 3.25459291845112 Trimmed Mean ( 10 / 39 ) 0.000553652178949414 0.000166831573758425 3.31862948047899 Trimmed Mean ( 11 / 39 ) 0.000554392519199794 0.000163721802494012 3.386186266915 Trimmed Mean ( 12 / 39 ) 0.000554773793158199 0.000160335865464273 3.46007296341203 Trimmed Mean ( 13 / 39 ) 0.000555539612463906 0.000156700177300686 3.54523920798062 Trimmed Mean ( 14 / 39 ) 0.000553078961501986 0.000153798217998977 3.59613374392715 Trimmed Mean ( 15 / 39 ) 0.000553078961501986 0.000150693463256046 3.6702253007633 Trimmed Mean ( 16 / 39 ) 0.00054891177387143 0.000147594955678644 3.71904155767062 Trimmed Mean ( 17 / 39 ) 0.000546361362707474 0.000144651800548377 3.77707958446566 Trimmed Mean ( 18 / 39 ) 0.000546632833630668 0.000141989771981046 3.84980429226717 Trimmed Mean ( 19 / 39 ) 0.000548390716671891 0.000139617705224985 3.92780210638899 Trimmed Mean ( 20 / 39 ) 0.000549770776725091 0.000136983998491645 4.01339413930615 Trimmed Mean ( 21 / 39 ) 0.000551432958879574 0.00013436209543588 4.10408126704698 Trimmed Mean ( 22 / 39 ) 0.000552383648344206 0.000131829319418460 4.19014260849513 Trimmed Mean ( 23 / 39 ) 0.000554997164692627 0.000129191687209745 4.29592009114008 Trimmed Mean ( 24 / 39 ) 0.000556855514273707 0.000126570566393502 4.39956563473431 Trimmed Mean ( 25 / 39 ) 0.000558689663205094 0.000124239243415697 4.49688558820141 Trimmed Mean ( 26 / 39 ) 0.000561816557580807 0.000121928187095182 4.60776602166841 Trimmed Mean ( 27 / 39 ) 0.000565855922027663 0.000120025573753508 4.71446129630457 Trimmed Mean ( 28 / 39 ) 0.000568842394206706 0.000118242125523221 4.81082686639454 Trimmed Mean ( 29 / 39 ) 0.000572339075063811 0.000116264621002771 4.92272774062687 Trimmed Mean ( 30 / 39 ) 0.000572339075063811 0.000114091141940726 5.01650755113978 Trimmed Mean ( 31 / 39 ) 0.000577540274503396 0.000111791432640266 5.16623019191338 Trimmed Mean ( 32 / 39 ) 0.000581618817573382 0.000109349648638543 5.31889059374973 Trimmed Mean ( 33 / 39 ) 0.000585833032237761 0.000106436702134307 5.50405095695778 Trimmed Mean ( 34 / 39 ) 0.00058817558926585 0.000103882399713850 5.66193687175124 Trimmed Mean ( 35 / 39 ) 0.000591012060639968 0.000100700753713977 5.86899341705662 Trimmed Mean ( 36 / 39 ) 0.000592001096660995 9.76106510261321e-05 6.06492314555412 Trimmed Mean ( 37 / 39 ) 0.000596366730461866 9.48235739540676e-05 6.28922435206613 Trimmed Mean ( 38 / 39 ) 0.000601097614259415 9.14785978370255e-05 6.57090979171222 Trimmed Mean ( 39 / 39 ) 0.000606431673609367 8.7418661582587e-05 6.93709629764182 Median 0.000638022815746765 Midrange 0.00020012942379962 Midmean - Weighted Average at Xnp 0.000547225150345612 Midmean - Weighted Average at X(n+1)p 0.000572339075063811 Midmean - Empirical Distribution Function 0.000572339075063811 Midmean - Empirical Distribution Function - Averaging 0.000572339075063811 Midmean - Empirical Distribution Function - Interpolation 0.000572339075063811 Midmean - Closest Observation 0.000544494280185163 Midmean - True Basic - Statistics Graphics Toolkit 0.000572339075063811 Midmean - MS Excel (old versions) 0.000572339075063811 Number of observations 117

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/30/t1196418935xljq63we7x6ts8c/1tn231196419482.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/30/t1196418935xljq63we7x6ts8c/1tn231196419482.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/30/t1196418935xljq63we7x6ts8c/2wl6y1196419482.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/30/t1196418935xljq63we7x6ts8c/2wl6y1196419482.ps (open in new window)

Parameters:

R code (references can be found in the software module):

geomean <- function(x) { return(exp(mean(log(x)))) } harmean <- function(x) { return(1/mean(1/x)) } quamean <- function(x) { return(sqrt(mean(x*x))) } winmean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) win <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { win[j,1] <- (j*x[j+1]+sum(x[(j+1):(n-j)])+j*x[n-j])/n win[j,2] <- sd(c(rep(x[j+1],j),x[(j+1):(n-j)],rep(x[n-j],j)))/sqrtn } return(win) } trimean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) tri <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { tri[j,1] <- mean(x,trim=j/n) tri[j,2] <- sd(x[(j+1):(n-j)]) / sqrt(n-j*2) } return(tri) } midrange <- function(x) { return((max(x)+min(x))/2) } q1 <- function(data,n,p,i,f) { np <- n*p; i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q2 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q3 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- data[i+1] } } q4 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- (data[i]+data[i+1])/2 } else { qvalue <- data[i+1] } } q5 <- function(data,n,p,i,f) { np <- (n-1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i+1] } else { qvalue <- data[i+1] + f*(data[i+2]-data[i+1]) } } q6 <- function(data,n,p,i,f) { np <- n*p+0.5 i <<- floor(np) f <<- np - i qvalue <- data[i] } q7 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- f*data[i] + (1-f)*data[i+1] } } q8 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { if (f == 0.5) { qvalue <- (data[i]+data[i+1])/2 } else { if (f < 0.5) { qvalue <- data[i] } else { qvalue <- data[i+1] } } } } midmean <- function(x,def) { x <-sort(x[!is.na(x)]) n<-length(x) if (def==1) { qvalue1 <- q1(x,n,0.25,i,f) qvalue3 <- q1(x,n,0.75,i,f) } if (def==2) { qvalue1 <- q2(x,n,0.25,i,f) qvalue3 <- q2(x,n,0.75,i,f) } if (def==3) { qvalue1 <- q3(x,n,0.25,i,f) qvalue3 <- q3(x,n,0.75,i,f) } if (def==4) { qvalue1 <- q4(x,n,0.25,i,f) qvalue3 <- q4(x,n,0.75,i,f) } if (def==5) { qvalue1 <- q5(x,n,0.25,i,f) qvalue3 <- q5(x,n,0.75,i,f) } if (def==6) { qvalue1 <- q6(x,n,0.25,i,f) qvalue3 <- q6(x,n,0.75,i,f) } if (def==7) { qvalue1 <- q7(x,n,0.25,i,f) qvalue3 <- q7(x,n,0.75,i,f) } if (def==8) { qvalue1 <- q8(x,n,0.25,i,f) qvalue3 <- q8(x,n,0.75,i,f) } midm <- 0 myn <- 0 roundno4 <- round(n/4) round3no4 <- round(3*n/4) for (i in 1:n) { if ((x[i]>=qvalue1) & (x[i]<=qvalue3)){ midm = midm + x[i] myn = myn + 1 } } midm = midm / myn return(midm) } (arm <- mean(x)) sqrtn <- sqrt(length(x)) (armse <- sd(x) / sqrtn) (armose <- arm / armse) (geo <- geomean(x)) (har <- harmean(x)) (qua <- quamean(x)) (win <- winmean(x)) (tri <- trimean(x)) (midr <- midrange(x)) midm <- array(NA,dim=8) for (j in 1:8) midm[j] <- midmean(x,j) midm bitmap(file='test1.png') lb <- win[,1] - 2*win[,2] ub <- win[,1] + 2*win[,2] if ((ylimmin == '') | (ylimmax == '')) plot(win[,1],type='b',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(win[,1],type='l',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() bitmap(file='test2.png') lb <- tri[,1] - 2*tri[,2] ub <- tri[,1] + 2*tri[,2] if ((ylimmin == '') | (ylimmax == '')) plot(tri[,1],type='b',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(tri[,1],type='l',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Central Tendency - Ungrouped Data',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Measure',header=TRUE) a<-table.element(a,'Value',header=TRUE) a<-table.element(a,'S.E.',header=TRUE) a<-table.element(a,'Value/S.E.',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', 'Arithmetic Mean', 'click to view the definition of the Arithmetic Mean'),header=TRUE) a<-table.element(a,arm) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean_standard_error.htm', armse, 'click to view the definition of the Standard Error of the Arithmetic Mean')) a<-table.element(a,armose) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/geometric_mean.htm', 'Geometric Mean', 'click to view the definition of the Geometric Mean'),header=TRUE) a<-table.element(a,geo) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/harmonic_mean.htm', 'Harmonic Mean', 'click to view the definition of the Harmonic Mean'),header=TRUE) a<-table.element(a,har) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/quadratic_mean.htm', 'Quadratic Mean', 'click to view the definition of the Quadratic Mean'),header=TRUE) a<-table.element(a,qua) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) for (j in 1:length(win[,1])) { a<-table.row.start(a) mylabel <- paste('Winsorized Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(win[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/winsorized_mean.htm', mylabel, 'click to view the definition of the Winsorized Mean'),header=TRUE) a<-table.element(a,win[j,1]) a<-table.element(a,win[j,2]) a<-table.element(a,win[j,1]/win[j,2]) a<-table.row.end(a) } for (j in 1:length(tri[,1])) { a<-table.row.start(a) mylabel <- paste('Trimmed Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(tri[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', mylabel, 'click to view the definition of the Trimmed Mean'),header=TRUE) a<-table.element(a,tri[j,1]) a<-table.element(a,tri[j,2]) a<-table.element(a,tri[j,1]/tri[j,2]) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/median_1.htm', 'Median', 'click to view the definition of the Median'),header=TRUE) a<-table.element(a,median(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/midrange.htm', 'Midrange', 'click to view the definition of the Midrange'),header=TRUE) a<-table.element(a,midr) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_1.htm','Weighted Average at Xnp',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[1]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_2.htm','Weighted Average at X(n+1)p',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[2]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_3.htm','Empirical Distribution Function',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[3]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_4.htm','Empirical Distribution Function - Averaging',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[4]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_5.htm','Empirical Distribution Function - Interpolation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[5]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_6.htm','Closest Observation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[6]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_7.htm','True Basic - Statistics Graphics Toolkit',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[7]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_8.htm','MS Excel (old versions)',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[8]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of observations',header=TRUE) a<-table.element(a,length(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')

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