*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: Sun, 19 Dec 2010 16:41:46 +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/19/t1292776778zcep65peaf36cjc.htm/, Retrieved Sun, 19 Dec 2010 17:39:38 +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/19/t1292776778zcep65peaf36cjc.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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12 8 8 8 9 7 4 11 7 7 12 10 10 8 8 4 9 8 7 11 9 11 13 8 8 9 6 9 9 6 6 16 5 7 9 6 6 5 12 7 10 9 8 5 8 8 10 6 8 7 4 8 8 4 20 8 8 6 4 8 9 6 7 9 5 5 8 8 6 8 7 7 9 11 6 8 6 9 8 6 10 8 8 10 5 7 5 8 14 7 8 6 5 6 10 12 9 12 7 8 10 6 10 10 10 5 7 10 11 6 7 12 11 11 11 5 8 6 9 4 4 7 11 6 7 8 4 8 9 8 11 8 5 4 8 10 6 9 9 13 9 10 20 5 11 6 9 7 9 10 9 8 7 6 13 6 8 10 16

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 8.22641509433962 0.214683599073892 38.318786948919 Geometric Mean 7.82435031212604 Harmonic Mean 7.4415614472703 Quadratic Mean 8.65771187108738 Winsorized Mean ( 1 / 53 ) 8.22641509433962 0.214683599073892 38.318786948919 Winsorized Mean ( 2 / 53 ) 8.17610062893082 0.199620408662868 40.9582401103043 Winsorized Mean ( 3 / 53 ) 8.17610062893082 0.199620408662868 40.9582401103043 Winsorized Mean ( 4 / 53 ) 8.12578616352201 0.188377807665575 43.1355809063646 Winsorized Mean ( 5 / 53 ) 8.09433962264151 0.182594341756378 44.3296300683906 Winsorized Mean ( 6 / 53 ) 8.09433962264151 0.182594341756378 44.3296300683906 Winsorized Mean ( 7 / 53 ) 8.09433962264151 0.182594341756378 44.3296300683906 Winsorized Mean ( 8 / 53 ) 8.0440251572327 0.174696140476735 46.0458092278458 Winsorized Mean ( 9 / 53 ) 8.10062893081761 0.167209918363407 48.4458637986537 Winsorized Mean ( 10 / 53 ) 8.10062893081761 0.167209918363407 48.4458637986537 Winsorized Mean ( 11 / 53 ) 8.10062893081761 0.167209918363407 48.4458637986537 Winsorized Mean ( 12 / 53 ) 8.10062893081761 0.167209918363407 48.4458637986537 Winsorized Mean ( 13 / 53 ) 8.10062893081761 0.167209918363407 48.4458637986537 Winsorized Mean ( 14 / 53 ) 8.0125786163522 0.155310276077400 51.5907821344614 Winsorized Mean ( 15 / 53 ) 8.0125786163522 0.155310276077400 51.5907821344614 Winsorized Mean ( 16 / 53 ) 8.0125786163522 0.155310276077400 51.5907821344614 Winsorized Mean ( 17 / 53 ) 8.0125786163522 0.155310276077400 51.5907821344614 Winsorized Mean ( 18 / 53 ) 8.0125786163522 0.155310276077400 51.5907821344614 Winsorized Mean ( 19 / 53 ) 8.0125786163522 0.155310276077400 51.5907821344614 Winsorized Mean ( 20 / 53 ) 8.0125786163522 0.155310276077400 51.5907821344614 Winsorized Mean ( 21 / 53 ) 8.14465408805031 0.140748869434422 57.866568454712 Winsorized Mean ( 22 / 53 ) 8.14465408805031 0.140748869434422 57.866568454712 Winsorized Mean ( 23 / 53 ) 8.14465408805031 0.140748869434422 57.866568454712 Winsorized Mean ( 24 / 53 ) 8.14465408805031 0.140748869434422 57.866568454712 Winsorized Mean ( 25 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 26 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 27 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 28 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 29 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 30 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 31 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 32 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 33 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 34 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 35 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 36 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 37 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 38 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 39 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 40 / 53 ) 7.9874213836478 0.122335439782335 65.29114864719 Winsorized Mean ( 41 / 53 ) 7.72955974842767 0.098020181301083 78.8568195429594 Winsorized Mean ( 42 / 53 ) 7.72955974842767 0.098020181301083 78.8568195429594 Winsorized Mean ( 43 / 53 ) 8 0.0702560513109048 113.869194905326 Winsorized Mean ( 44 / 53 ) 8 0.0702560513109048 113.869194905326 Winsorized Mean ( 45 / 53 ) 8 0.0702560513109048 113.869194905326 Winsorized Mean ( 46 / 53 ) 8 0.0702560513109048 113.869194905326 Winsorized Mean ( 47 / 53 ) 8 0.0702560513109048 113.869194905326 Winsorized Mean ( 48 / 53 ) 8 0.0702560513109048 113.869194905326 Winsorized Mean ( 49 / 53 ) 8 0.0702560513109048 113.869194905326 Winsorized Mean ( 50 / 53 ) 8 0.0702560513109048 113.869194905326 Winsorized Mean ( 51 / 53 ) 8 0.0702560513109048 113.869194905326 Winsorized Mean ( 52 / 53 ) 8 0.0702560513109048 113.869194905326 Winsorized Mean ( 53 / 53 ) 8 0.0702560513109048 113.869194905326 Trimmed Mean ( 1 / 53 ) 8.17834394904459 0.202165151260641 40.4537770137281 Trimmed Mean ( 2 / 53 ) 8.12903225806452 0.187973042978014 43.2457342248554 Trimmed Mean ( 3 / 53 ) 8.1045751633987 0.181289242265687 44.7052183687828 Trimmed Mean ( 4 / 53 ) 8.0794701986755 0.173910529586114 46.4576251817741 Trimmed Mean ( 5 / 53 ) 8.06711409395973 0.169469148420824 47.6022578099439 Trimmed Mean ( 6 / 53 ) 8.06122448979592 0.166146188461704 48.5188650093769 Trimmed Mean ( 7 / 53 ) 8.06122448979592 0.162528898209937 49.5987149275037 Trimmed Mean ( 8 / 53 ) 8.04895104895105 0.158581528510544 50.7559179467479 Trimmed Mean ( 9 / 53 ) 8.04964539007092 0.155717199155853 51.6940031910944 Trimmed Mean ( 10 / 53 ) 8.0431654676259 0.153800260849658 52.2961757229282 Trimmed Mean ( 11 / 53 ) 8.03649635036496 0.151707314902838 52.9736905271312 Trimmed Mean ( 12 / 53 ) 8.02962962962963 0.149419640401175 53.7387829877717 Trimmed Mean ( 13 / 53 ) 8.02255639097744 0.146915644456896 54.6065493612641 Trimmed Mean ( 14 / 53 ) 8.02255639097744 0.144170224966235 55.646416538896 Trimmed Mean ( 15 / 53 ) 8.015503875969 0.142642768191552 56.1928513978722 Trimmed Mean ( 16 / 53 ) 8.01574803149606 0.140962930154006 56.8642268058603 Trimmed Mean ( 17 / 53 ) 8.016 0.139114248707846 57.6217035598878 Trimmed Mean ( 18 / 53 ) 8.01626016260163 0.137077773004886 58.4796498139484 Trimmed Mean ( 19 / 53 ) 8.01652892561983 0.134831529956091 59.4558923141381 Trimmed Mean ( 20 / 53 ) 8.01680672268908 0.132349832551896 60.5728512693478 Trimmed Mean ( 21 / 53 ) 8.01709401709402 0.129602367451821 61.8591633372312 Trimmed Mean ( 22 / 53 ) 8.00869565217391 0.128058703747456 62.5392528411643 Trimmed Mean ( 23 / 53 ) 8 0.126336095801643 63.323153602598 Trimmed Mean ( 24 / 53 ) 7.99099099099099 0.124412128189095 64.2299999791453 Trimmed Mean ( 25 / 53 ) 7.98165137614679 0.122260431921994 65.2840109483611 Trimmed Mean ( 26 / 53 ) 7.98130841121495 0.121691850336135 65.586219530471 Trimmed Mean ( 27 / 53 ) 7.98095238095238 0.121031095496560 65.9413380355563 Trimmed Mean ( 28 / 53 ) 7.98095238095238 0.120267313252692 66.3601120296395 Trimmed Mean ( 29 / 53 ) 7.98058252427184 0.119388041940458 66.8457443020295 Trimmed Mean ( 30 / 53 ) 7.97979797979798 0.118378899290007 67.4089557147252 Trimmed Mean ( 31 / 53 ) 7.97938144329897 0.117223189187049 68.0699910882523 Trimmed Mean ( 32 / 53 ) 7.97894736842105 0.11590140166944 68.842544209928 Trimmed Mean ( 33 / 53 ) 7.97849462365591 0.114390568270800 69.7478362444028 Trimmed Mean ( 34 / 53 ) 7.97802197802198 0.112663417576774 70.8128880662205 Trimmed Mean ( 35 / 53 ) 7.97752808988764 0.110687248724070 72.0726929420269 Trimmed Mean ( 36 / 53 ) 7.97701149425287 0.108422396504075 73.5734659208813 Trimmed Mean ( 37 / 53 ) 7.9764705882353 0.105820087512127 75.3776600999423 Trimmed Mean ( 38 / 53 ) 7.97590361445783 0.102819356375095 77.5720048797134 Trimmed Mean ( 39 / 53 ) 7.97530864197531 0.0993424502434017 80.280973767355 Trimmed Mean ( 40 / 53 ) 7.9746835443038 0.0952876775581115 83.690606683539 Trimmed Mean ( 41 / 53 ) 7.97402597402597 0.090517661358326 88.0935925030117 Trimmed Mean ( 42 / 53 ) 7.98666666666667 0.0880076436756794 90.7496932436763 Trimmed Mean ( 43 / 53 ) 8 0.0850284221440003 94.0861866923928 Trimmed Mean ( 44 / 53 ) 8 0.0851085234543963 93.9976359040777 Trimmed Mean ( 45 / 53 ) 8 0.0851256530758749 93.9787209957658 Trimmed Mean ( 46 / 53 ) 8 0.0850678820118227 94.04254356407 Trimmed Mean ( 47 / 53 ) 8 0.0849207775608447 94.2054492408303 Trimmed Mean ( 48 / 53 ) 8 0.0846667513334603 94.4880944881417 Trimmed Mean ( 49 / 53 ) 8 0.0842841926852955 94.9169677625174 Trimmed Mean ( 50 / 53 ) 8 0.0837462980156539 95.5266106031892 Trimmed Mean ( 51 / 53 ) 8 0.0830194590228165 96.3629502548473 Trimmed Mean ( 52 / 53 ) 8 0.0820609939862218 97.4884608556315 Trimmed Mean ( 53 / 53 ) 8 0.0808158687660548 98.9904596974431 Median 8 Midrange 12 Midmean - Weighted Average at Xnp 7.91150442477876 Midmean - Weighted Average at X(n+1)p 7.91150442477876 Midmean - Empirical Distribution Function 7.91150442477876 Midmean - Empirical Distribution Function - Averaging 7.91150442477876 Midmean - Empirical Distribution Function - Interpolation 7.91150442477876 Midmean - Closest Observation 7.91150442477876 Midmean - True Basic - Statistics Graphics Toolkit 7.91150442477876 Midmean - MS Excel (old versions) 7.91150442477876 Number of observations 159

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292776778zcep65peaf36cjc/1xunn1292776902.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292776778zcep65peaf36cjc/1xunn1292776902.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292776778zcep65peaf36cjc/2xunn1292776902.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292776778zcep65peaf36cjc/2xunn1292776902.ps (open in new window)

Parameters (Session):

par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

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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