central tendency G29
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 3 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 | 0.000778810961422985 | 0.00430639538164326 | 0.180849850606565 |
| Geometric Mean | NaN | ||
| Harmonic Mean | 0.0113726372735930 | ||
| Quadratic Mean | 0.0489174494339759 | ||
| Winsorized Mean ( 1 / 43 ) | 0.00110935286707469 | 0.00416963408474007 | 0.266055208809491 |
| Winsorized Mean ( 2 / 43 ) | 0.00121985964641218 | 0.00404067743323740 | 0.301894834855656 |
| Winsorized Mean ( 3 / 43 ) | 0.00130011804933241 | 0.00399370101824324 | 0.325542158362247 |
| Winsorized Mean ( 4 / 43 ) | 0.000769516700481059 | 0.00382083237376294 | 0.201400277532511 |
| Winsorized Mean ( 5 / 43 ) | 0.000842321358116243 | 0.00379733770733308 | 0.221818922370172 |
| Winsorized Mean ( 6 / 43 ) | 0.00083815037879276 | 0.00376263629121217 | 0.222756151252329 |
| Winsorized Mean ( 7 / 43 ) | 0.00126922227432981 | 0.00369169450320084 | 0.343804795664795 |
| Winsorized Mean ( 8 / 43 ) | 0.000756283462880262 | 0.00353837173656169 | 0.213737707393955 |
| Winsorized Mean ( 9 / 43 ) | 0.000444331742627098 | 0.00346542337192768 | 0.128218602733072 |
| Winsorized Mean ( 10 / 43 ) | 0.00056851075160982 | 0.00344493543431546 | 0.165027984544154 |
| Winsorized Mean ( 11 / 43 ) | 0.000822849590781837 | 0.00339085334921964 | 0.242667407297696 |
| Winsorized Mean ( 12 / 43 ) | 0.00109597231134884 | 0.00333030815351667 | 0.329090360659731 |
| Winsorized Mean ( 13 / 43 ) | 0.00095872035596402 | 0.00330097657794096 | 0.290435370662956 |
| Winsorized Mean ( 14 / 43 ) | 0.00101363344299012 | 0.00317498783538860 | 0.319255850901883 |
| Winsorized Mean ( 15 / 43 ) | 0.000921480803981944 | 0.00312417525933414 | 0.294951700045900 |
| Winsorized Mean ( 16 / 43 ) | 0.000730340901425944 | 0.00307663265075334 | 0.237383199208757 |
| Winsorized Mean ( 17 / 43 ) | 0.00072445518655762 | 0.0030334993599642 | 0.238818308689595 |
| Winsorized Mean ( 18 / 43 ) | 0.000635470175182555 | 0.00297168597743086 | 0.213841630646299 |
| Winsorized Mean ( 19 / 43 ) | 0.000475377208326904 | 0.0029288041271794 | 0.162311027874957 |
| Winsorized Mean ( 20 / 43 ) | 0.000362999450937658 | 0.00289296853616908 | 0.125476460044169 |
| Winsorized Mean ( 21 / 43 ) | 0.000319757921433123 | 0.0028160133095539 | 0.113549861553665 |
| Winsorized Mean ( 22 / 43 ) | 0.000508705515848687 | 0.00277640385758664 | 0.183224610662684 |
| Winsorized Mean ( 23 / 43 ) | 0.000462641344565588 | 0.00271393567485295 | 0.170468795134820 |
| Winsorized Mean ( 24 / 43 ) | 0.000268992295925493 | 0.00268154065860833 | 0.100312592711197 |
| Winsorized Mean ( 25 / 43 ) | 0.000102586905403745 | 0.00263829690078377 | 0.0388837607220285 |
| Winsorized Mean ( 26 / 43 ) | 2.07859840444844e-05 | 0.00262618218326652 | 0.00791490559068153 |
| Winsorized Mean ( 27 / 43 ) | -0.000299884005075457 | 0.00257156664426588 | -0.116615295871932 |
| Winsorized Mean ( 28 / 43 ) | -0.000218576856587404 | 0.00254006028504741 | -0.0860518381686062 |
| Winsorized Mean ( 29 / 43 ) | 0.000143765784680018 | 0.00250020863318027 | 0.0575015151824138 |
| Winsorized Mean ( 30 / 43 ) | -0.000264440535165382 | 0.00241314634332811 | -0.109583298127156 |
| Winsorized Mean ( 31 / 43 ) | -0.000212904161399698 | 0.00239417924461572 | -0.0889257401585527 |
| Winsorized Mean ( 32 / 43 ) | -0.000558305733842112 | 0.00233478150465632 | -0.239125473937782 |
| Winsorized Mean ( 33 / 43 ) | -0.000534877710752014 | 0.00232795838122419 | -0.229762574393938 |
| Winsorized Mean ( 34 / 43 ) | -0.00124621462388242 | 0.00224036447429356 | -0.55625530496567 |
| Winsorized Mean ( 35 / 43 ) | -0.00131714595701072 | 0.00212350382622214 | -0.620270112417932 |
| Winsorized Mean ( 36 / 43 ) | -0.00142468925621159 | 0.00208482213359698 | -0.683362495654988 |
| Winsorized Mean ( 37 / 43 ) | -0.00218895796541004 | 0.00196176750833280 | -1.11580906305779 |
| Winsorized Mean ( 38 / 43 ) | -0.00231809166117765 | 0.00194433077344882 | -1.19223112282786 |
| Winsorized Mean ( 39 / 43 ) | -0.00287816272432587 | 0.00182233079627964 | -1.57938543880275 |
| Winsorized Mean ( 40 / 43 ) | -0.00362913009973892 | 0.00168191339175107 | -2.15773898795144 |
| Winsorized Mean ( 41 / 43 ) | -0.00420418553446138 | 0.00157316386165924 | -2.67243968471737 |
| Winsorized Mean ( 42 / 43 ) | -0.00425893642694506 | 0.00150379637755553 | -2.83212307896904 |
| Winsorized Mean ( 43 / 43 ) | -0.00390981937061430 | 0.00141288780356684 | -2.76725396081979 |
| Trimmed Mean ( 1 / 43 ) | 0.00101493500115878 | 0.00400463343924368 | 0.253440175376067 |
| Trimmed Mean ( 2 / 43 ) | 0.000917519742674112 | 0.00382003743608079 | 0.240186060484122 |
| Trimmed Mean ( 3 / 43 ) | 0.000759035115714641 | 0.00369149114822805 | 0.205617482268374 |
| Trimmed Mean ( 4 / 43 ) | 0.000566847188473358 | 0.00356814856208981 | 0.158863113070988 |
| Trimmed Mean ( 5 / 43 ) | 0.000511957528971272 | 0.00348791836425429 | 0.146780249852759 |
| Trimmed Mean ( 6 / 43 ) | 0.000439165498820686 | 0.00340537003322794 | 0.128962636816417 |
| Trimmed Mean ( 7 / 43 ) | 0.000364642460894867 | 0.00332194822766149 | 0.109767653167659 |
| Trimmed Mean ( 8 / 43 ) | 0.000217279834646067 | 0.00324411398437276 | 0.0669766338953336 |
| Trimmed Mean ( 9 / 43 ) | 0.000139076183228159 | 0.00318697957341887 | 0.0436388687232667 |
| Trimmed Mean ( 10 / 43 ) | 9.89921198727426e-05 | 0.00313607451520648 | 0.0315656147176162 |
| Trimmed Mean ( 11 / 43 ) | 4.2475988274761e-05 | 0.00308257461875054 | 0.0137793868853620 |
| Trimmed Mean ( 12 / 43 ) | -4.45296449378633e-05 | 0.00303104188288223 | -0.0146912008010658 |
| Trimmed Mean ( 13 / 43 ) | -0.000163331932051062 | 0.00298207988588297 | -0.0547711457443738 |
| Trimmed Mean ( 14 / 43 ) | -0.00027333705832705 | 0.00293124979721093 | -0.093249322724774 |
| Trimmed Mean ( 15 / 43 ) | -0.000392841462020787 | 0.00289108507970308 | -0.135880284111574 |
| Trimmed Mean ( 16 / 43 ) | -0.000509074043367967 | 0.00285227708717996 | -0.178479869875226 |
| Trimmed Mean ( 17 / 43 ) | -0.000613972443643494 | 0.00281433263654797 | -0.218159159891130 |
| Trimmed Mean ( 18 / 43 ) | -0.00072285579278376 | 0.00277649330811402 | -0.260348472899714 |
| Trimmed Mean ( 19 / 43 ) | -0.000829487662249714 | 0.00274086799496005 | -0.302636852185143 |
| Trimmed Mean ( 20 / 43 ) | -0.000928687915568404 | 0.00270524488134275 | -0.343291626563378 |
| Trimmed Mean ( 21 / 43 ) | -0.00102409664150351 | 0.00266860166568153 | -0.383757776469036 |
| Trimmed Mean ( 22 / 43 ) | -0.00112083035865842 | 0.00263547762232156 | -0.425285477351575 |
| Trimmed Mean ( 23 / 43 ) | -0.00123546221130232 | 0.00260159449287279 | -0.474886541575535 |
| Trimmed Mean ( 24 / 43 ) | -0.00135251070666967 | 0.0025694926819845 | -0.526372663425951 |
| Trimmed Mean ( 25 / 43 ) | -0.00146229997247038 | 0.00253581722746974 | -0.576658268833309 |
| Trimmed Mean ( 26 / 43 ) | -0.00156662576432866 | 0.00250150699892256 | -0.626272788764305 |
| Trimmed Mean ( 27 / 43 ) | -0.00167106074777426 | 0.00246257511932832 | -0.678582648975211 |
| Trimmed Mean ( 28 / 43 ) | -0.00176027645175366 | 0.00242356646819792 | -0.72631655655087 |
| Trimmed Mean ( 29 / 43 ) | -0.00185969160421974 | 0.00238124892227317 | -0.780973205625413 |
| Trimmed Mean ( 30 / 43 ) | -0.00198799183109509 | 0.00233580393033416 | -0.851095336075873 |
| Trimmed Mean ( 31 / 43 ) | -0.0020978259823063 | 0.00229334132993834 | -0.914746511965013 |
| Trimmed Mean ( 32 / 43 ) | -0.00221759129839519 | 0.00224481440088400 | -0.98787289386682 |
| Trimmed Mean ( 33 / 43 ) | -0.00232291704223889 | 0.00219492967761342 | -1.05831046248581 |
| Trimmed Mean ( 34 / 43 ) | -0.00243652657943014 | 0.00213543540108821 | -1.14099755871261 |
| Trimmed Mean ( 35 / 43 ) | -0.00251237979228367 | 0.00207705081613034 | -1.2095899497367 |
| Trimmed Mean ( 36 / 43 ) | -0.00258892186055238 | 0.0020239170823804 | -1.27916399495352 |
| Trimmed Mean ( 37 / 43 ) | -0.00266399638364975 | 0.00196465480138675 | -1.35596155709867 |
| Trimmed Mean ( 38 / 43 ) | -0.00269490478924092 | 0.00191216429058569 | -1.40934793234502 |
| Trimmed Mean ( 39 / 43 ) | -0.00271969512661351 | 0.00184944677213379 | -1.47054522876356 |
| Trimmed Mean ( 40 / 43 ) | -0.00270913062009935 | 0.00179290681340899 | -1.51102700923328 |
| Trimmed Mean ( 41 / 43 ) | -0.00264683898866542 | 0.00174677073576904 | -1.51527555074371 |
| Trimmed Mean ( 42 / 43 ) | -0.00253949272622985 | 0.00170689482806569 | -1.48778511978251 |
| Trimmed Mean ( 43 / 43 ) | -0.00241853618884188 | 0.00166765282021089 | -1.45026360374939 |
| Median | -0.00122524418381871 | ||
| Midrange | -0.014333127581668 | ||
| Midmean - Weighted Average at Xnp | -0.00277616457981561 | ||
| Midmean - Weighted Average at X(n+1)p | -0.00221759129839519 | ||
| Midmean - Empirical Distribution Function | -0.00221759129839519 | ||
| Midmean - Empirical Distribution Function - Averaging | -0.00221759129839519 | ||
| Midmean - Empirical Distribution Function - Interpolation | -0.00232291704223889 | ||
| Midmean - Closest Observation | -0.00221759129839519 | ||
| Midmean - True Basic - Statistics Graphics Toolkit | -0.00221759129839519 | ||
| Midmean - MS Excel (old versions) | -0.00221759129839519 | ||
| Number of observations | 130 | ||
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/12/t1197478568eaa3jqmgs8vsj1i/18qk61197479474.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/12/t1197478568eaa3jqmgs8vsj1i/18qk61197479474.ps (open in new window) |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/12/t1197478568eaa3jqmgs8vsj1i/2xet41197479474.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/12/t1197478568eaa3jqmgs8vsj1i/2xet41197479474.ps (open in new window) |
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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