*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: Sat, 18 Dec 2010 12:49:04 +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/18/t1292676417iko10h346g3eovy.htm/, Retrieved Sat, 18 Dec 2010 13:46:59 +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/18/t1292676417iko10h346g3eovy.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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11 7 17 10 12 12 11 11 12 13 14 16 11 10 11 15 9 11 17 17 11 18 14 10 11 15 15 13 16 13 9 18 18 12 17 9 9 12 18 12 18 14 15 16 10 11 14 9 12 17 5 12 12 6 24 12 12 11 7 13 12 13 12 8 11 9 11 13 10 11 12 9 15 18 15 12 13 14 10 13 13 11 13 16 8 16 11 9 16 12 14 8 9 15 11 21 14 18 12 13 15 12 16 15 11 11 10 13 15 12 12 16 9 18 8 13 17 10 15 8 7 12 14 6 8 17 10 11 14 11 13 12 11 9 12 20 12 13 12 12 9 15 24 7 17 11 17 11 12 14 11 16 21 14 20 9 11 15 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 12.7672955974843 0.269778481333856 47.325107378318 Geometric Mean 12.319117186036 Harmonic Mean 11.8607252184961 Quadratic Mean 13.2099638399993 Winsorized Mean ( 1 / 53 ) 12.7735849056604 0.268703588302282 47.5378277840136 Winsorized Mean ( 2 / 53 ) 12.7358490566038 0.25989728550317 49.0033939059684 Winsorized Mean ( 3 / 53 ) 12.7547169811321 0.257011692421888 49.6269911339093 Winsorized Mean ( 4 / 53 ) 12.7295597484277 0.252159771102574 50.4821197004082 Winsorized Mean ( 5 / 53 ) 12.7295597484277 0.252159771102574 50.4821197004082 Winsorized Mean ( 6 / 53 ) 12.6540880503145 0.239912713571502 52.7445497236774 Winsorized Mean ( 7 / 53 ) 12.6981132075472 0.233823801900607 54.3063328212618 Winsorized Mean ( 8 / 53 ) 12.6981132075472 0.233823801900607 54.3063328212618 Winsorized Mean ( 9 / 53 ) 12.6981132075472 0.233823801900607 54.3063328212618 Winsorized Mean ( 10 / 53 ) 12.6981132075472 0.233823801900607 54.3063328212618 Winsorized Mean ( 11 / 53 ) 12.6981132075472 0.233823801900607 54.3063328212618 Winsorized Mean ( 12 / 53 ) 12.6981132075472 0.233823801900607 54.3063328212618 Winsorized Mean ( 13 / 53 ) 12.7798742138365 0.224246351452613 56.9903328685242 Winsorized Mean ( 14 / 53 ) 12.6918238993711 0.212076612509838 59.845466924281 Winsorized Mean ( 15 / 53 ) 12.6918238993711 0.212076612509838 59.845466924281 Winsorized Mean ( 16 / 53 ) 12.6918238993711 0.212076612509838 59.845466924281 Winsorized Mean ( 17 / 53 ) 12.6918238993711 0.212076612509838 59.845466924281 Winsorized Mean ( 18 / 53 ) 12.6918238993711 0.212076612509838 59.845466924281 Winsorized Mean ( 19 / 53 ) 12.6918238993711 0.212076612509838 59.845466924281 Winsorized Mean ( 20 / 53 ) 12.6918238993711 0.212076612509838 59.845466924281 Winsorized Mean ( 21 / 53 ) 12.6918238993711 0.212076612509838 59.845466924281 Winsorized Mean ( 22 / 53 ) 12.6918238993711 0.212076612509838 59.845466924281 Winsorized Mean ( 23 / 53 ) 12.5471698113208 0.194604826222949 64.4751214800094 Winsorized Mean ( 24 / 53 ) 12.5471698113208 0.194604826222949 64.4751214800094 Winsorized Mean ( 25 / 53 ) 12.5471698113208 0.194604826222949 64.4751214800094 Winsorized Mean ( 26 / 53 ) 12.7106918238994 0.177184841203663 71.7369033239656 Winsorized Mean ( 27 / 53 ) 12.7106918238994 0.177184841203663 71.7369033239656 Winsorized Mean ( 28 / 53 ) 12.7106918238994 0.177184841203663 71.7369033239656 Winsorized Mean ( 29 / 53 ) 12.7106918238994 0.177184841203663 71.7369033239656 Winsorized Mean ( 30 / 53 ) 12.7106918238994 0.177184841203663 71.7369033239656 Winsorized Mean ( 31 / 53 ) 12.7106918238994 0.177184841203663 71.7369033239656 Winsorized Mean ( 32 / 53 ) 12.7106918238994 0.177184841203663 71.7369033239656 Winsorized Mean ( 33 / 53 ) 12.5031446540881 0.154252465942885 81.0563680629754 Winsorized Mean ( 34 / 53 ) 12.5031446540881 0.154252465942885 81.0563680629754 Winsorized Mean ( 35 / 53 ) 12.7232704402516 0.133811721311149 95.0833777159658 Winsorized Mean ( 36 / 53 ) 12.7232704402516 0.133811721311149 95.0833777159658 Winsorized Mean ( 37 / 53 ) 12.7232704402516 0.133811721311149 95.0833777159658 Winsorized Mean ( 38 / 53 ) 12.7232704402516 0.133811721311149 95.0833777159658 Winsorized Mean ( 39 / 53 ) 12.7232704402516 0.133811721311149 95.0833777159658 Winsorized Mean ( 40 / 53 ) 12.7232704402516 0.133811721311149 95.0833777159658 Winsorized Mean ( 41 / 53 ) 12.7232704402516 0.133811721311149 95.0833777159658 Winsorized Mean ( 42 / 53 ) 12.7232704402516 0.133811721311149 95.0833777159658 Winsorized Mean ( 43 / 53 ) 12.7232704402516 0.133811721311149 95.0833777159658 Winsorized Mean ( 44 / 53 ) 12.7232704402516 0.133811721311149 95.0833777159658 Winsorized Mean ( 45 / 53 ) 12.7232704402516 0.133811721311149 95.0833777159658 Winsorized Mean ( 46 / 53 ) 12.4339622641509 0.104255562661641 119.264257433486 Winsorized Mean ( 47 / 53 ) 12.4339622641509 0.104255562661641 119.264257433486 Winsorized Mean ( 48 / 53 ) 12.4339622641509 0.104255562661641 119.264257433486 Winsorized Mean ( 49 / 53 ) 12.4339622641509 0.104255562661641 119.264257433486 Winsorized Mean ( 50 / 53 ) 12.4339622641509 0.104255562661641 119.264257433486 Winsorized Mean ( 51 / 53 ) 12.4339622641509 0.104255562661641 119.264257433486 Winsorized Mean ( 52 / 53 ) 12.4339622641509 0.104255562661641 119.264257433486 Winsorized Mean ( 53 / 53 ) 12.4339622641509 0.104255562661641 119.264257433486 Trimmed Mean ( 1 / 53 ) 12.7452229299363 0.25891021161934 49.2264204266877 Trimmed Mean ( 2 / 53 ) 12.7161290322581 0.248118996329001 51.2501228055779 Trimmed Mean ( 3 / 53 ) 12.7058823529412 0.241448578321790 52.6235542211703 Trimmed Mean ( 4 / 53 ) 12.6887417218543 0.235330856373455 53.9187334690955 Trimmed Mean ( 5 / 53 ) 12.6778523489933 0.230195710150097 55.0742337497377 Trimmed Mean ( 6 / 53 ) 12.6666666666667 0.224601311385054 56.3962275578661 Trimmed Mean ( 7 / 53 ) 12.6666666666667 0.221250186140535 57.2504226442592 Trimmed Mean ( 8 / 53 ) 12.6643356643357 0.218774119199154 57.8877232402754 Trimmed Mean ( 9 / 53 ) 12.6595744680851 0.216078569154899 58.5878299620259 Trimmed Mean ( 10 / 53 ) 12.6546762589928 0.213141456779395 59.3721955841308 Trimmed Mean ( 11 / 53 ) 12.6496350364964 0.209937538216324 60.2542791726074 Trimmed Mean ( 12 / 53 ) 12.6444444444444 0.206437755498247 61.2506390312495 Trimmed Mean ( 13 / 53 ) 12.6390977443609 0.202608402379045 62.3819031982463 Trimmed Mean ( 14 / 53 ) 12.6390977443609 0.199628509320980 63.3130898354738 Trimmed Mean ( 15 / 53 ) 12.6201550387597 0.197854834828275 63.7849211504645 Trimmed Mean ( 16 / 53 ) 12.6141732283465 0.195898209533035 64.3914676832169 Trimmed Mean ( 17 / 53 ) 12.608 0.193739432594233 65.0770977863145 Trimmed Mean ( 18 / 53 ) 12.6016260162602 0.191356555642153 65.8541640968177 Trimmed Mean ( 19 / 53 ) 12.5950413223140 0.188724335965902 66.7377699746666 Trimmed Mean ( 20 / 53 ) 12.5882352941176 0.185813541176596 67.7465980918681 Trimmed Mean ( 21 / 53 ) 12.5811965811966 0.182590051923692 68.9040637682413 Trimmed Mean ( 22 / 53 ) 12.5739130434783 0.179013684524879 70.2399544305832 Trimmed Mean ( 23 / 53 ) 12.5663716814159 0.175036616268023 71.7928165508742 Trimmed Mean ( 24 / 53 ) 12.5675675675676 0.172480872479452 72.863543573881 Trimmed Mean ( 25 / 53 ) 12.5688073394495 0.169630613696753 74.0951592730702 Trimmed Mean ( 26 / 53 ) 12.5700934579439 0.166446651099582 75.5202545374342 Trimmed Mean ( 27 / 53 ) 12.5619047619048 0.164597304979634 76.3190184885415 Trimmed Mean ( 28 / 53 ) 12.5619047619048 0.16250944434463 77.2995367288624 Trimmed Mean ( 29 / 53 ) 12.5533980582524 0.160151384123531 78.3845742386436 Trimmed Mean ( 30 / 53 ) 12.5353535353535 0.157485610270939 79.5968184889251 Trimmed Mean ( 31 / 53 ) 12.5257731958763 0.154467258389399 81.090150278319 Trimmed Mean ( 32 / 53 ) 12.5157894736842 0.151042037780456 82.8629542980363 Trimmed Mean ( 33 / 53 ) 12.5053763440860 0.147143326267845 84.9877236112119 Trimmed Mean ( 34 / 53 ) 12.5054945054945 0.145232436641751 86.1067595825178 Trimmed Mean ( 35 / 53 ) 12.5056179775281 0.143040451846882 87.4271425744287 Trimmed Mean ( 36 / 53 ) 12.4942528735632 0.142413574121981 87.7321768700332 Trimmed Mean ( 37 / 53 ) 12.4823529411765 0.141630921217031 88.1329644255356 Trimmed Mean ( 38 / 53 ) 12.4698795180723 0.140668557534822 88.6472409798147 Trimmed Mean ( 39 / 53 ) 12.4567901234568 0.139497978564107 89.2972805174536 Trimmed Mean ( 40 / 53 ) 12.4430379746835 0.138084963476401 90.1114622578732 Trimmed Mean ( 41 / 53 ) 12.4285714285714 0.136388045857498 91.1265452219851 Trimmed Mean ( 42 / 53 ) 12.4133333333333 0.134356435095799 92.3910590846085 Trimmed Mean ( 43 / 53 ) 12.3972602739726 0.131927125234645 93.9705178288609 Trimmed Mean ( 44 / 53 ) 12.3802816901408 0.129020761954545 95.9557322603822 Trimmed Mean ( 45 / 53 ) 12.3623188405797 0.125535535658329 98.4766486696347 Trimmed Mean ( 46 / 53 ) 12.3432835820896 0.121337784284437 101.726627487731 Trimmed Mean ( 47 / 53 ) 12.3384615384615 0.120649131856601 102.267304775359 Trimmed Mean ( 48 / 53 ) 12.3333333333333 0.11973686801785 103.003640712355 Trimmed Mean ( 49 / 53 ) 12.3278688524590 0.118555309716489 103.984114097797 Trimmed Mean ( 50 / 53 ) 12.3220338983051 0.117046944095992 105.274289674745 Trimmed Mean ( 51 / 53 ) 12.3157894736842 0.115138291963260 106.965191715838 Trimmed Mean ( 52 / 53 ) 12.3090909090909 0.112733789319626 109.187236438863 Trimmed Mean ( 53 / 53 ) 12.3018867924528 0.109706382310134 112.134650085135 Median 12 Midrange 14.5 Midmean - Weighted Average at Xnp 12.5604395604396 Midmean - Weighted Average at X(n+1)p 12.5604395604396 Midmean - Empirical Distribution Function 12.5604395604396 Midmean - Empirical Distribution Function - Averaging 12.5604395604396 Midmean - Empirical Distribution Function - Interpolation 12.5604395604396 Midmean - Closest Observation 12.5604395604396 Midmean - True Basic - Statistics Graphics Toolkit 12.5604395604396 Midmean - MS Excel (old versions) 12.5604395604396 Number of observations 159

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676417iko10h346g3eovy/1b9aa1292676541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676417iko10h346g3eovy/1b9aa1292676541.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676417iko10h346g3eovy/2zfk61292676541.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676417iko10h346g3eovy/2zfk61292676541.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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