workshop 6 - tutorial 8

*The author of this computation has been verified*

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

Title produced by software: Central Tendency

Date of computation: Tue, 16 Nov 2010 10:37:13 +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/Nov/16/t1289903787ac1i7kv0dpfinq0.htm/, Retrieved Tue, 16 Nov 2010 11:36:28 +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/Nov/16/t1289903787ac1i7kv0dpfinq0.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:

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5 3 0 7 4 1 6 3 12 0 5 6 6 6 2 1 5 7 3 3 3 7 8 6 3 5 5 10 2 6 4 6 8 4 5 10 6 7 4 10 4 3 3 3 3 7 15 0 0 4 5 5 2 3 0 9 2 7 7 0 0 10 2 1 8 6 11 3 8 6 9 9 8 8 7 6 5 4 6 3 2 12 8 5 9 6 5 2 4 7 5 6 7 8 6 0 1 5 5 5 7 7 1 3 4 8 6 6 2 2 3 3 0 2 8 8 0 5 9 6 6 3 9 7 8 0 7 0 5 0 14 5 2 8 4 2 6 3 5 9 3 3 0 10 4 2 3 10 7 0 6 8 0 4 10 5

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 5.01282051282051 0.249231603166887 20.1131014250384 Geometric Mean 0 Harmonic Mean 0 Quadratic Mean 5.89545674705618 Winsorized Mean ( 1 / 52 ) 5.00641025641026 0.247651799847872 20.2155213872284 Winsorized Mean ( 2 / 52 ) 4.98076923076923 0.242244664692653 20.5609037337874 Winsorized Mean ( 3 / 52 ) 4.98076923076923 0.242244664692653 20.5609037337874 Winsorized Mean ( 4 / 52 ) 4.9551282051282 0.23774216224448 20.8424461119885 Winsorized Mean ( 5 / 52 ) 4.92307692307692 0.232855198678424 21.1422246572891 Winsorized Mean ( 6 / 52 ) 4.92307692307692 0.232855198678424 21.1422246572891 Winsorized Mean ( 7 / 52 ) 4.92307692307692 0.232855198678424 21.1422246572891 Winsorized Mean ( 8 / 52 ) 4.92307692307692 0.232855198678424 21.1422246572891 Winsorized Mean ( 9 / 52 ) 4.92307692307692 0.232855198678424 21.1422246572891 Winsorized Mean ( 10 / 52 ) 4.92307692307692 0.232855198678424 21.1422246572891 Winsorized Mean ( 11 / 52 ) 4.92307692307692 0.232855198678424 21.1422246572891 Winsorized Mean ( 12 / 52 ) 4.84615384615385 0.222801518482337 21.7509911025944 Winsorized Mean ( 13 / 52 ) 4.84615384615385 0.222801518482337 21.7509911025944 Winsorized Mean ( 14 / 52 ) 4.84615384615385 0.222801518482337 21.7509911025944 Winsorized Mean ( 15 / 52 ) 4.84615384615385 0.222801518482337 21.7509911025944 Winsorized Mean ( 16 / 52 ) 4.94871794871795 0.209334459134691 23.6402452284926 Winsorized Mean ( 17 / 52 ) 4.94871794871795 0.209334459134691 23.6402452284926 Winsorized Mean ( 18 / 52 ) 4.94871794871795 0.209334459134691 23.6402452284926 Winsorized Mean ( 19 / 52 ) 4.82692307692308 0.195305419560532 24.7147421089717 Winsorized Mean ( 20 / 52 ) 4.82692307692308 0.195305419560532 24.7147421089717 Winsorized Mean ( 21 / 52 ) 4.96153846153846 0.179578731229673 27.6287644286387 Winsorized Mean ( 22 / 52 ) 4.96153846153846 0.179578731229673 27.6287644286387 Winsorized Mean ( 23 / 52 ) 4.96153846153846 0.179578731229673 27.6287644286387 Winsorized Mean ( 24 / 52 ) 4.96153846153846 0.179578731229673 27.6287644286387 Winsorized Mean ( 25 / 52 ) 4.96153846153846 0.179578731229673 27.6287644286387 Winsorized Mean ( 26 / 52 ) 4.96153846153846 0.179578731229673 27.6287644286387 Winsorized Mean ( 27 / 52 ) 4.96153846153846 0.179578731229673 27.6287644286387 Winsorized Mean ( 28 / 52 ) 4.96153846153846 0.179578731229673 27.6287644286387 Winsorized Mean ( 29 / 52 ) 4.96153846153846 0.179578731229673 27.6287644286387 Winsorized Mean ( 30 / 52 ) 4.96153846153846 0.179578731229673 27.6287644286387 Winsorized Mean ( 31 / 52 ) 4.96153846153846 0.179578731229673 27.6287644286387 Winsorized Mean ( 32 / 52 ) 4.96153846153846 0.179578731229673 27.6287644286387 Winsorized Mean ( 33 / 52 ) 4.75 0.158211938139089 30.0230188433955 Winsorized Mean ( 34 / 52 ) 4.96794871794872 0.135635584299539 36.6271782114159 Winsorized Mean ( 35 / 52 ) 4.96794871794872 0.135635584299539 36.6271782114159 Winsorized Mean ( 36 / 52 ) 4.96794871794872 0.135635584299539 36.6271782114159 Winsorized Mean ( 37 / 52 ) 4.96794871794872 0.135635584299539 36.6271782114159 Winsorized Mean ( 38 / 52 ) 4.96794871794872 0.135635584299539 36.6271782114159 Winsorized Mean ( 39 / 52 ) 4.96794871794872 0.135635584299539 36.6271782114159 Winsorized Mean ( 40 / 52 ) 4.96794871794872 0.135635584299539 36.6271782114159 Winsorized Mean ( 41 / 52 ) 4.96794871794872 0.135635584299539 36.6271782114159 Winsorized Mean ( 42 / 52 ) 4.96794871794872 0.135635584299539 36.6271782114159 Winsorized Mean ( 43 / 52 ) 4.96794871794872 0.135635584299539 36.6271782114159 Winsorized Mean ( 44 / 52 ) 4.96794871794872 0.135635584299539 36.6271782114159 Winsorized Mean ( 45 / 52 ) 4.96794871794872 0.135635584299539 36.6271782114159 Winsorized Mean ( 46 / 52 ) 4.96794871794872 0.135635584299539 36.6271782114159 Winsorized Mean ( 47 / 52 ) 4.96794871794872 0.135635584299539 36.6271782114159 Winsorized Mean ( 48 / 52 ) 4.66025641025641 0.108183281539316 43.0774177298622 Winsorized Mean ( 49 / 52 ) 4.66025641025641 0.108183281539316 43.0774177298622 Winsorized Mean ( 50 / 52 ) 4.66025641025641 0.108183281539316 43.0774177298622 Winsorized Mean ( 51 / 52 ) 4.66025641025641 0.108183281539316 43.0774177298622 Winsorized Mean ( 52 / 52 ) 4.66025641025641 0.108183281539316 43.0774177298622 Trimmed Mean ( 1 / 52 ) 4.98051948051948 0.241741759136859 20.60264431889 Trimmed Mean ( 2 / 52 ) 4.95394736842105 0.235292142777653 21.0544530299188 Trimmed Mean ( 3 / 52 ) 4.94 0.231371748288421 21.3509213486253 Trimmed Mean ( 4 / 52 ) 4.92567567567568 0.227113111748842 21.6882047793121 Trimmed Mean ( 5 / 52 ) 4.91780821917808 0.223872148148416 21.9670390437216 Trimmed Mean ( 6 / 52 ) 4.91666666666667 0.221577834172863 22.1893434648836 Trimmed Mean ( 7 / 52 ) 4.91549295774648 0.219081034380113 22.4368712319385 Trimmed Mean ( 8 / 52 ) 4.91428571428571 0.216361986440161 22.7132584385144 Trimmed Mean ( 9 / 52 ) 4.91304347826087 0.213398215449636 23.0228892397668 Trimmed Mean ( 10 / 52 ) 4.91176470588235 0.210164005942556 23.3711033621279 Trimmed Mean ( 11 / 52 ) 4.91044776119403 0.206629732422714 23.7644781494875 Trimmed Mean ( 12 / 52 ) 4.90909090909091 0.20276099816048 24.2112188913446 Trimmed Mean ( 13 / 52 ) 4.91538461538462 0.19989103035716 24.5903210694444 Trimmed Mean ( 14 / 52 ) 4.921875 0.196740595916788 25.0170788446819 Trimmed Mean ( 15 / 52 ) 4.92857142857143 0.193277026752521 25.5000374922062 Trimmed Mean ( 16 / 52 ) 4.93548387096774 0.189462064929078 26.0499845856492 Trimmed Mean ( 17 / 52 ) 4.9344262295082 0.186866467490214 26.4061620888007 Trimmed Mean ( 18 / 52 ) 4.93333333333333 0.184000568342402 26.8115113870357 Trimmed Mean ( 19 / 52 ) 4.93220338983051 0.180831699017211 27.2751039592957 Trimmed Mean ( 20 / 52 ) 4.93965517241379 0.178822063237082 27.6232981713492 Trimmed Mean ( 21 / 52 ) 4.94736842105263 0.176583748998498 28.0171218988828 Trimmed Mean ( 22 / 52 ) 4.94642857142857 0.175676338621516 28.1564871526913 Trimmed Mean ( 23 / 52 ) 4.94545454545455 0.17463717356676 28.3184527351732 Trimmed Mean ( 24 / 52 ) 4.94444444444444 0.173451380310124 28.5062271375643 Trimmed Mean ( 25 / 52 ) 4.94339622641509 0.17210195095836 28.7236501323052 Trimmed Mean ( 26 / 52 ) 4.94230769230769 0.170569337825258 28.9753583810644 Trimmed Mean ( 27 / 52 ) 4.94117647058824 0.168830946366858 29.2670068901432 Trimmed Mean ( 28 / 52 ) 4.94 0.166860493354252 29.6055699027102 Trimmed Mean ( 29 / 52 ) 4.93877551020408 0.164627183406095 29.9997570754846 Trimmed Mean ( 30 / 52 ) 4.9375 0.162094636100741 30.4606007871314 Trimmed Mean ( 31 / 52 ) 4.9375 0.159219463266976 31.010655975651 Trimmed Mean ( 32 / 52 ) 4.93478260869565 0.155949343572365 31.6434971488404 Trimmed Mean ( 33 / 52 ) 4.93333333333333 0.152220354107932 32.4091568584538 Trimmed Mean ( 34 / 52 ) 4.94318181818182 0.150198366350298 32.9110225250595 Trimmed Mean ( 35 / 52 ) 4.94186046511628 0.150106802982813 32.9222951053198 Trimmed Mean ( 36 / 52 ) 4.94047619047619 0.149919839430236 32.9541187427378 Trimmed Mean ( 37 / 52 ) 4.9390243902439 0.149623195276577 33.0097508017669 Trimmed Mean ( 38 / 52 ) 4.9375 0.149200135320806 33.093133524199 Trimmed Mean ( 39 / 52 ) 4.93589743589744 0.148630941282565 33.2090841469793 Trimmed Mean ( 40 / 52 ) 4.93421052631579 0.147892239474451 33.3635527046584 Trimmed Mean ( 41 / 52 ) 4.93243243243243 0.14695613476985 33.5639777145553 Trimmed Mean ( 42 / 52 ) 4.93055555555556 0.145789079655599 33.819786551936 Trimmed Mean ( 43 / 52 ) 4.92857142857143 0.144350373966816 34.1431150687869 Trimmed Mean ( 44 / 52 ) 4.92647058823529 0.14259013825925 34.5498689346822 Trimmed Mean ( 45 / 52 ) 4.92647058823529 0.140446517474371 35.0772000390418 Trimmed Mean ( 46 / 52 ) 4.921875 0.137841724531527 35.7067137452584 Trimmed Mean ( 47 / 52 ) 4.91935483870968 0.134676271460638 36.5272574400566 Trimmed Mean ( 48 / 52 ) 4.91666666666667 0.130820242966707 37.5833781926085 Trimmed Mean ( 49 / 52 ) 4.93103448275862 0.129828564343484 37.9811215482035 Trimmed Mean ( 50 / 52 ) 4.94642857142857 0.128476696702349 38.5005895885409 Trimmed Mean ( 51 / 52 ) 4.96296296296296 0.126675942954392 39.1784173633491 Trimmed Mean ( 52 / 52 ) 4.98076923076923 0.124309206194207 40.0675813421881 Median 5 Midrange 7.5 Midmean - Weighted Average at Xnp 4.96629213483146 Midmean - Weighted Average at X(n+1)p 4.96629213483146 Midmean - Empirical Distribution Function 4.96629213483146 Midmean - Empirical Distribution Function - Averaging 4.96629213483146 Midmean - Empirical Distribution Function - Interpolation 4.96629213483146 Midmean - Closest Observation 4.96629213483146 Midmean - True Basic - Statistics Graphics Toolkit 4.96629213483146 Midmean - MS Excel (old versions) 4.96629213483146 Number of observations 156

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/16/t1289903787ac1i7kv0dpfinq0/128xq1289903829.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/16/t1289903787ac1i7kv0dpfinq0/128xq1289903829.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/16/t1289903787ac1i7kv0dpfinq0/228xq1289903829.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/16/t1289903787ac1i7kv0dpfinq0/228xq1289903829.ps (open in new window)

Parameters (Session):

Parameters (R input):

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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