*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, 12 Dec 2010 13:26:53 +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/12/t1292160292dbpalh0ze5m6luw.htm/, Retrieved Sun, 12 Dec 2010 14:24:52 +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/12/t1292160292dbpalh0ze5m6luw.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 4 5 6 6 6 7 8 7 8 7 8 8 9 9 8 9 9 10 11 12 13 13 13 14 14 15 15 16 16 17 18 19 20 22 20 22 25 24 25 28 26 27 26 25 27 28 30 31 32 34 34 33 32 34 36 37 40 38 38 36 40 40 42 44 45 47 49 47 49 52 50 50 57 58 58 58 61 61 64 68 40 34 46 36 34 45 55 50 56 72 76 78 77 90 88 97 93 84 67 72 75 71 75 90 78 73 62 65 61 58 33 39 56 79 82 79 73 87 85 83 82 83 92 95 97 87 84 84 89 103 106 109 106 105 115 120 124 121 131 139 133 119 123 120 128 134 126 115 106 99 100 99 99 100 100 108 109 115 114 108 113 118 122 118 121 118 121 121 112 119 116 110 111 106 108

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 62.3295454545455 2.98128843389768 20.9069155288195 Geometric Mean 45.7336574606667 Harmonic Mean 28.1306203975214 Quadratic Mean 73.7589748006055 Winsorized Mean ( 1 / 58 ) 62.3068181818182 2.97661574490194 20.9320999153254 Winsorized Mean ( 2 / 58 ) 62.2954545454545 2.97506213164214 20.9392112799572 Winsorized Mean ( 3 / 58 ) 62.2784090909091 2.9686312270406 20.9788297460557 Winsorized Mean ( 4 / 58 ) 62.2102272727273 2.95979126584413 21.0184508585557 Winsorized Mean ( 5 / 58 ) 62.1534090909091 2.95267245201933 21.0498828098601 Winsorized Mean ( 6 / 58 ) 62.1193181818182 2.94068248063082 21.1241161162332 Winsorized Mean ( 7 / 58 ) 62.0795454545455 2.93593325924066 21.1447400104053 Winsorized Mean ( 8 / 58 ) 62.0340909090909 2.93058100847349 21.1678471708256 Winsorized Mean ( 9 / 58 ) 62.0340909090909 2.91919195507971 21.2504322647042 Winsorized Mean ( 10 / 58 ) 62.0340909090909 2.91919195507971 21.2504322647042 Winsorized Mean ( 11 / 58 ) 62.0340909090909 2.91919195507971 21.2504322647042 Winsorized Mean ( 12 / 58 ) 62.0340909090909 2.91919195507971 21.2504322647042 Winsorized Mean ( 13 / 58 ) 61.9602272727273 2.91072089963105 21.2869008775733 Winsorized Mean ( 14 / 58 ) 62.0397727272727 2.90235418099043 21.3756725948938 Winsorized Mean ( 15 / 58 ) 61.9545454545455 2.89268915220542 21.4176298228625 Winsorized Mean ( 16 / 58 ) 61.9545454545455 2.89268915220542 21.4176298228625 Winsorized Mean ( 17 / 58 ) 61.8579545454545 2.88187038417015 21.4645165463494 Winsorized Mean ( 18 / 58 ) 61.9602272727273 2.87122265457982 21.5797361357177 Winsorized Mean ( 19 / 58 ) 62.0681818181818 2.86013339654724 21.7011492866419 Winsorized Mean ( 20 / 58 ) 61.9545454545455 2.82346174855843 21.9427606859479 Winsorized Mean ( 21 / 58 ) 61.9545454545455 2.79847846161411 22.1386536663967 Winsorized Mean ( 22 / 58 ) 61.9545454545455 2.79847846161411 22.1386536663967 Winsorized Mean ( 23 / 58 ) 61.9545454545455 2.79847846161411 22.1386536663967 Winsorized Mean ( 24 / 58 ) 61.9545454545455 2.77021282624918 22.3645435713439 Winsorized Mean ( 25 / 58 ) 61.8125 2.75504738981562 22.4360931969801 Winsorized Mean ( 26 / 58 ) 61.8125 2.72485441348255 22.6846981967743 Winsorized Mean ( 27 / 58 ) 61.6590909090909 2.70879726901592 22.7625343595725 Winsorized Mean ( 28 / 58 ) 61.6590909090909 2.6767247471305 23.0352750969971 Winsorized Mean ( 29 / 58 ) 61.4943181818182 2.65981394182672 23.1197818820308 Winsorized Mean ( 30 / 58 ) 61.6647727272727 2.64325422704585 23.3291115535982 Winsorized Mean ( 31 / 58 ) 61.6647727272727 2.60837326520073 23.6410844835611 Winsorized Mean ( 32 / 58 ) 61.8465909090909 2.59108648145298 23.8689798089680 Winsorized Mean ( 33 / 58 ) 62.0340909090909 2.57347732974394 24.1051631549687 Winsorized Mean ( 34 / 58 ) 61.6477272727273 2.53443914676717 24.3240116265418 Winsorized Mean ( 35 / 58 ) 62.0454545454545 2.49754188480430 24.8426082152837 Winsorized Mean ( 36 / 58 ) 62.0454545454545 2.49754188480430 24.8426082152837 Winsorized Mean ( 37 / 58 ) 62.4659090909091 2.45948861920618 25.3979256513374 Winsorized Mean ( 38 / 58 ) 62.4659090909091 2.41851486208677 25.8282097290945 Winsorized Mean ( 39 / 58 ) 62.0227272727273 2.37438925223591 26.1215498740663 Winsorized Mean ( 40 / 58 ) 61.3409090909091 2.30812754727685 26.5760482618388 Winsorized Mean ( 41 / 58 ) 61.5738636363636 2.28729579865462 26.9199391143817 Winsorized Mean ( 42 / 58 ) 61.5738636363636 2.28729579865462 26.9199391143817 Winsorized Mean ( 43 / 58 ) 61.5738636363636 2.24229589967781 27.4601865191882 Winsorized Mean ( 44 / 58 ) 61.5738636363636 2.24229589967781 27.4601865191882 Winsorized Mean ( 45 / 58 ) 61.8295454545455 2.21989883912809 27.8524157789237 Winsorized Mean ( 46 / 58 ) 61.3068181818182 2.17032399144990 28.2477724170858 Winsorized Mean ( 47 / 58 ) 61.8409090909091 2.12402418736798 29.1149740472308 Winsorized Mean ( 48 / 58 ) 61.5681818181818 2.0494945672311 30.0406660269226 Winsorized Mean ( 49 / 58 ) 61.2897727272727 1.97431741937109 31.0435252841949 Winsorized Mean ( 50 / 58 ) 61.0056818181818 1.94836756777610 31.3111770218054 Winsorized Mean ( 51 / 58 ) 60.7159090909091 1.87152097543982 32.4420136817545 Winsorized Mean ( 52 / 58 ) 60.7159090909091 1.87152097543982 32.4420136817545 Winsorized Mean ( 53 / 58 ) 60.7159090909091 1.81930302280405 33.3731700161357 Winsorized Mean ( 54 / 58 ) 60.4090909090909 1.79217767714205 33.7070881305831 Winsorized Mean ( 55 / 58 ) 60.0965909090909 1.76481997494893 34.0525332680632 Winsorized Mean ( 56 / 58 ) 60.0965909090909 1.76481997494893 34.0525332680632 Winsorized Mean ( 57 / 58 ) 59.4488636363636 1.70892937332431 34.7871975075951 Winsorized Mean ( 58 / 58 ) 59.7784090909091 1.62521085499683 36.7819405753512 Trimmed Mean ( 1 / 58 ) 62.2241379310345 2.96408561874795 20.9926924976339 Trimmed Mean ( 2 / 58 ) 62.1395348837209 2.95048817935116 21.0607638826013 Trimmed Mean ( 3 / 58 ) 62.0588235294118 2.93659301756435 21.1329330139469 Trimmed Mean ( 4 / 58 ) 61.9821428571429 2.92391729690825 21.1983228536193 Trimmed Mean ( 5 / 58 ) 61.921686746988 2.91266312780639 21.2594742439792 Trimmed Mean ( 6 / 58 ) 61.8719512195122 2.90203862526641 21.3201680642112 Trimmed Mean ( 7 / 58 ) 61.8271604938272 2.89279584309196 21.3728046662786 Trimmed Mean ( 8 / 58 ) 61.7875 2.88343222691859 21.4284557906984 Trimmed Mean ( 9 / 58 ) 61.753164556962 2.87393610304931 21.4873129891233 Trimmed Mean ( 10 / 58 ) 61.7179487179487 2.86507729333036 21.5414602815856 Trimmed Mean ( 11 / 58 ) 61.6818181818182 2.85523009527977 21.6030989179436 Trimmed Mean ( 12 / 58 ) 61.6447368421053 2.84431654774934 21.6729522917847 Trimmed Mean ( 13 / 58 ) 61.6066666666667 2.83225108953172 21.7518379265069 Trimmed Mean ( 14 / 58 ) 61.5743243243243 2.81990031132053 21.8356386845071 Trimmed Mean ( 15 / 58 ) 61.5743243243243 2.80715672840562 21.9347654162854 Trimmed Mean ( 16 / 58 ) 61.5 2.79408577300614 22.0107774049587 Trimmed Mean ( 17 / 58 ) 61.4647887323944 2.77963436323386 22.1125445653527 Trimmed Mean ( 18 / 58 ) 61.4357142857143 2.76474298503638 22.2211303612028 Trimmed Mean ( 19 / 58 ) 61.3985507246377 2.74929262716050 22.3324902260589 Trimmed Mean ( 20 / 58 ) 61.3529411764706 2.73322830604381 22.4470605111197 Trimmed Mean ( 21 / 58 ) 61.3134328358209 2.71878831243431 22.5517494522856 Trimmed Mean ( 22 / 58 ) 61.2727272727273 2.70493784536586 22.6521756785283 Trimmed Mean ( 23 / 58 ) 61.2307692307692 2.68948655328092 22.7667132806719 Trimmed Mean ( 24 / 58 ) 61.1875 2.67228111696922 22.8971045042582 Trimmed Mean ( 25 / 58 ) 61.1428571428571 2.65556532792631 23.0244221446447 Trimmed Mean ( 26 / 58 ) 61.1048387096774 2.63822738066462 23.1613238333854 Trimmed Mean ( 27 / 58 ) 61.0655737704918 2.62141136573871 23.2949221814653 Trimmed Mean ( 28 / 58 ) 61.0333333333333 2.60394371594645 23.4388066683499 Trimmed Mean ( 29 / 58 ) 61 2.5870334914652 23.5791303828277 Trimmed Mean ( 30 / 58 ) 61 2.56943927189348 23.7405883327406 Trimmed Mean ( 31 / 58 ) 60.9385964912281 2.5509947369742 23.8881702137531 Trimmed Mean ( 32 / 58 ) 60.9017857142857 2.53311274974734 24.0422719913909 Trimmed Mean ( 33 / 58 ) 60.8545454545455 2.51430722956509 24.2033052838462 Trimmed Mean ( 34 / 58 ) 60.7962962962963 2.49446459734841 24.3724831215973 Trimmed Mean ( 35 / 58 ) 60.7547169811321 2.47535240108749 24.5438657358205 Trimmed Mean ( 36 / 58 ) 60.6923076923077 2.45664714266149 24.7053419428216 Trimmed Mean ( 37 / 58 ) 60.6274509803922 2.43529830675234 24.8952872887444 Trimmed Mean ( 38 / 58 ) 60.54 2.41411756887763 25.0774861922513 Trimmed Mean ( 39 / 58 ) 60.4489795918367 2.39345320204438 25.2559688822008 Trimmed Mean ( 40 / 58 ) 60.375 2.3737531353046 25.4344055841564 Trimmed Mean ( 41 / 58 ) 60.3297872340426 2.35702527147584 25.5957320289008 Trimmed Mean ( 42 / 58 ) 60.2717391304348 2.33929759738532 25.7648873738005 Trimmed Mean ( 43 / 58 ) 60.2111111111111 2.31859951941831 25.9687413056210 Trimmed Mean ( 44 / 58 ) 60.1477272727273 2.29866049385739 26.1664249389840 Trimmed Mean ( 45 / 58 ) 60.0813953488372 2.27531901921485 26.4057017242222 Trimmed Mean ( 46 / 58 ) 60 2.25013545840731 26.6650613303384 Trimmed Mean ( 47 / 58 ) 59.9390243902439 2.22583362112619 26.9287981910871 Trimmed Mean ( 48 / 58 ) 59.85 2.2017038856304 27.1834920175306 Trimmed Mean ( 49 / 58 ) 59.85 2.18095188877850 27.4421459308396 Trimmed Mean ( 50 / 58 ) 59.6973684210526 2.16398104029393 27.5868259977656 Trimmed Mean ( 51 / 58 ) 59.6351351351351 2.1461393771054 27.7871678658484 Trimmed Mean ( 52 / 58 ) 59.5833333333333 2.13260972909978 27.9391641706919 Trimmed Mean ( 53 / 58 ) 59.5285714285714 2.11557581925761 28.1382358820215 Trimmed Mean ( 54 / 58 ) 59.4705882352941 2.10026029870484 28.3158179355043 Trimmed Mean ( 55 / 58 ) 59.4242424242424 2.08413483204985 28.5126669879586 Trimmed Mean ( 56 / 58 ) 59.390625 2.06703352008735 28.7322989312192 Trimmed Mean ( 57 / 58 ) 59.3548387096774 2.04524403157691 29.0209079177285 Trimmed Mean ( 58 / 58 ) 59.35 2.02523975005374 29.3051723868372 Median 58 Midrange 71.5 Midmean - Weighted Average at Xnp 60.2111111111111 Midmean - Weighted Average at X(n+1)p 60.2111111111111 Midmean - Empirical Distribution Function 60.2111111111111 Midmean - Empirical Distribution Function - Averaging 60.2111111111111 Midmean - Empirical Distribution Function - Interpolation 60.2111111111111 Midmean - Closest Observation 60.2111111111111 Midmean - True Basic - Statistics Graphics Toolkit 60.2111111111111 Midmean - MS Excel (old versions) 60.2111111111111 Number of observations 176

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292160292dbpalh0ze5m6luw/1ncp31292160409.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292160292dbpalh0ze5m6luw/1ncp31292160409.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292160292dbpalh0ze5m6luw/2y3661292160409.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292160292dbpalh0ze5m6luw/2y3661292160409.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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