Hapiness tutorial

*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 07:14:13 +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/t1289891577u3otl120hhare5r.htm/, Retrieved Tue, 16 Nov 2010 08:12:59 +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/Nov/16/t1289891577u3otl120hhare5r.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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14 18 11 12 16 18 14 14 15 15 17 19 10 16 18 14 14 17 14 16 18 11 14 12 17 9 16 14 15 11 16 13 17 15 14 16 9 15 17 13 15 16 16 12 12 11 15 15 17 13 16 14 11 12 12 15 16 15 12 12 8 13 11 14 15 10 11 12 15 15 14 16 15 15 13 12 17 13 15 13 15 16 15 16 15 14 15 14 13 7 17 13 15 14 13 16 12 14 17 15 17 12 16 11 15 9 16 15 10 10 15 11 13 14 18 16 14 14 14 14 12 14 15 15 15 13 17 17 19 15 13 9 15 15 15 16 11 14 11 15 13 15 16 14 15 16 16 11 12 9 16 13 16 12 9 13 13 14 19 13 12 13

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 1 seconds R Server 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 14.0370370370370 0.183660621112221 76.4292146679612 Geometric Mean 13.8257056950512 Harmonic Mean 13.5924203232325 Quadratic Mean 14.229164972073 Winsorized Mean ( 1 / 54 ) 14.0432098765432 0.182290205431898 77.0376545644392 Winsorized Mean ( 2 / 54 ) 14.0555555555556 0.179940690072783 78.1121576774565 Winsorized Mean ( 3 / 54 ) 14.0370370370370 0.177070905086577 79.2735375141037 Winsorized Mean ( 4 / 54 ) 14.0370370370370 0.177070905086577 79.2735375141037 Winsorized Mean ( 5 / 54 ) 14.0370370370370 0.177070905086577 79.2735375141037 Winsorized Mean ( 6 / 54 ) 14.0370370370370 0.177070905086577 79.2735375141037 Winsorized Mean ( 7 / 54 ) 14.0370370370370 0.177070905086577 79.2735375141037 Winsorized Mean ( 8 / 54 ) 14.0370370370370 0.162623818420556 86.315997086825 Winsorized Mean ( 9 / 54 ) 14.0370370370370 0.162623818420556 86.315997086825 Winsorized Mean ( 10 / 54 ) 14.0370370370370 0.162623818420556 86.315997086825 Winsorized Mean ( 11 / 54 ) 14.0370370370370 0.162623818420556 86.315997086825 Winsorized Mean ( 12 / 54 ) 14.1111111111111 0.152176651634411 92.72848994609 Winsorized Mean ( 13 / 54 ) 14.1111111111111 0.152176651634411 92.72848994609 Winsorized Mean ( 14 / 54 ) 14.1111111111111 0.152176651634411 92.72848994609 Winsorized Mean ( 15 / 54 ) 14.1111111111111 0.152176651634411 92.72848994609 Winsorized Mean ( 16 / 54 ) 14.1111111111111 0.152176651634411 92.72848994609 Winsorized Mean ( 17 / 54 ) 14.1111111111111 0.152176651634411 92.72848994609 Winsorized Mean ( 18 / 54 ) 14.1111111111111 0.152176651634411 92.72848994609 Winsorized Mean ( 19 / 54 ) 14.1111111111111 0.152176651634411 92.72848994609 Winsorized Mean ( 20 / 54 ) 13.9876543209877 0.139281750605594 100.427042740127 Winsorized Mean ( 21 / 54 ) 13.9876543209877 0.139281750605594 100.427042740127 Winsorized Mean ( 22 / 54 ) 13.9876543209877 0.139281750605594 100.427042740127 Winsorized Mean ( 23 / 54 ) 13.9876543209877 0.139281750605594 100.427042740127 Winsorized Mean ( 24 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 25 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 26 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 27 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 28 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 29 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 30 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 31 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 32 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 33 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 34 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 35 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 36 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 37 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 38 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 39 / 54 ) 14.1358024691358 0.121181393185612 116.649941856042 Winsorized Mean ( 40 / 54 ) 14.3827160493827 0.0963786442604564 149.231358873595 Winsorized Mean ( 41 / 54 ) 14.3827160493827 0.0963786442604564 149.231358873595 Winsorized Mean ( 42 / 54 ) 14.3827160493827 0.0963786442604564 149.231358873595 Winsorized Mean ( 43 / 54 ) 14.1172839506173 0.07188342388219 196.391367970371 Winsorized Mean ( 44 / 54 ) 14.1172839506173 0.07188342388219 196.391367970371 Winsorized Mean ( 45 / 54 ) 14.1172839506173 0.07188342388219 196.391367970371 Winsorized Mean ( 46 / 54 ) 14.1172839506173 0.07188342388219 196.391367970371 Winsorized Mean ( 47 / 54 ) 14.1172839506173 0.07188342388219 196.391367970371 Winsorized Mean ( 48 / 54 ) 14.1172839506173 0.07188342388219 196.391367970371 Winsorized Mean ( 49 / 54 ) 14.1172839506173 0.07188342388219 196.391367970371 Winsorized Mean ( 50 / 54 ) 14.1172839506173 0.07188342388219 196.391367970371 Winsorized Mean ( 51 / 54 ) 14.1172839506173 0.07188342388219 196.391367970371 Winsorized Mean ( 52 / 54 ) 14.1172839506173 0.07188342388219 196.391367970371 Winsorized Mean ( 53 / 54 ) 14.1172839506173 0.07188342388219 196.391367970371 Winsorized Mean ( 54 / 54 ) 14.1172839506173 0.07188342388219 196.391367970371 Trimmed Mean ( 1 / 54 ) 14.05 0.177951304200522 78.9541839163368 Trimmed Mean ( 2 / 54 ) 14.0569620253165 0.173240843382434 81.1411544232978 Trimmed Mean ( 3 / 54 ) 14.0576923076923 0.169473564457921 82.9491747144004 Trimmed Mean ( 4 / 54 ) 14.0649350649351 0.166520262836876 84.4638053370907 Trimmed Mean ( 5 / 54 ) 14.0723684210526 0.163319407290920 86.1647042104777 Trimmed Mean ( 6 / 54 ) 14.08 0.159843323736641 88.0862564094218 Trimmed Mean ( 7 / 54 ) 14.0878378378378 0.156059520131538 90.2722104102566 Trimmed Mean ( 8 / 54 ) 14.0958904109589 0.151929416748150 92.779204400721 Trimmed Mean ( 9 / 54 ) 14.1041666666667 0.150019019125763 94.015857115043 Trimmed Mean ( 10 / 54 ) 14.1126760563380 0.147938449335049 95.395592692579 Trimmed Mean ( 11 / 54 ) 14.1214285714286 0.145669894292090 96.9412975828281 Trimmed Mean ( 12 / 54 ) 14.1304347826087 0.143192834200872 98.681158603135 Trimmed Mean ( 13 / 54 ) 14.1323529411765 0.141886151977667 99.603469008032 Trimmed Mean ( 14 / 54 ) 14.1343283582090 0.140454336275617 100.632908409982 Trimmed Mean ( 15 / 54 ) 14.1363636363636 0.138884858239974 101.784772044321 Trimmed Mean ( 16 / 54 ) 14.1384615384615 0.137163464085760 103.077460406086 Trimmed Mean ( 17 / 54 ) 14.140625 0.135273843267071 104.533327792589 Trimmed Mean ( 18 / 54 ) 14.1428571428571 0.133197209425121 106.179830672863 Trimmed Mean ( 19 / 54 ) 14.1451612903226 0.130911763800170 108.051109233502 Trimmed Mean ( 20 / 54 ) 14.1475409836066 0.12839199727833 110.190208763069 Trimmed Mean ( 21 / 54 ) 14.1583333333333 0.126908861039215 111.563000545394 Trimmed Mean ( 22 / 54 ) 14.1694915254237 0.125258003905872 113.122443944355 Trimmed Mean ( 23 / 54 ) 14.1810344827586 0.123419020261765 114.901531811559 Trimmed Mean ( 24 / 54 ) 14.1929824561404 0.121368019201272 116.941699712535 Trimmed Mean ( 25 / 54 ) 14.1964285714286 0.120887487785421 117.435053300368 Trimmed Mean ( 26 / 54 ) 14.2 0.120327603968904 118.011158966231 Trimmed Mean ( 27 / 54 ) 14.2037037037037 0.119679434564495 118.681240059246 Trimmed Mean ( 28 / 54 ) 14.2075471698113 0.118932796513940 119.458615169669 Trimmed Mean ( 29 / 54 ) 14.2115384615385 0.118076029071743 120.359217474222 Trimmed Mean ( 30 / 54 ) 14.2156862745098 0.117095711888397 121.402278915719 Trimmed Mean ( 31 / 54 ) 14.22 0.115976312454333 122.611244478042 Trimmed Mean ( 32 / 54 ) 14.2244897959184 0.114699739965615 124.015013461954 Trimmed Mean ( 33 / 54 ) 14.2291666666667 0.113244773190513 125.649654865119 Trimmed Mean ( 34 / 54 ) 14.2340425531915 0.111586315533916 127.560825761517 Trimmed Mean ( 35 / 54 ) 14.2391304347826 0.109694408093935 129.80725893146 Trimmed Mean ( 36 / 54 ) 14.2444444444444 0.107532895540150 132.465924709765 Trimmed Mean ( 37 / 54 ) 14.25 0.105057579873226 135.639903538570 Trimmed Mean ( 38 / 54 ) 14.2558139534884 0.102213593736995 139.470822150818 Trimmed Mean ( 39 / 54 ) 14.2619047619048 0.098931537520614 144.159336035115 Trimmed Mean ( 40 / 54 ) 14.2682926829268 0.0951215651749823 150.000608764998 Trimmed Mean ( 41 / 54 ) 14.2625 0.0936998811179555 152.214707530370 Trimmed Mean ( 42 / 54 ) 14.2564102564103 0.0920334847559092 154.904601235312 Trimmed Mean ( 43 / 54 ) 14.25 0.0900779389624302 158.196337129154 Trimmed Mean ( 44 / 54 ) 14.2567567567568 0.0903750276168065 157.751063902364 Trimmed Mean ( 45 / 54 ) 14.2638888888889 0.0906269580341182 157.391235437021 Trimmed Mean ( 46 / 54 ) 14.2714285714286 0.0908242189392959 157.1324117961 Trimmed Mean ( 47 / 54 ) 14.2794117647059 0.0909552954749286 156.993737309577 Trimmed Mean ( 48 / 54 ) 14.2878787878788 0.091006164178508 156.999022174511 Trimmed Mean ( 49 / 54 ) 14.296875 0.090959631232151 157.178242769157 Trimmed Mean ( 50 / 54 ) 14.3064516129032 0.0907944537283319 157.569664505168 Trimmed Mean ( 51 / 54 ) 14.3166666666667 0.0904841549529628 158.222913990953 Trimmed Mean ( 52 / 54 ) 14.3275862068966 0.0899953989362408 159.203541250450 Trimmed Mean ( 53 / 54 ) 14.3392857142857 0.0892857142857143 160.6 Trimmed Mean ( 54 / 54 ) 14.3518518518519 0.08830022878509 162.534707433003 Median 14 Midrange 13 Midmean - Weighted Average at Xnp 14.6078431372549 Midmean - Weighted Average at X(n+1)p 14.6078431372549 Midmean - Empirical Distribution Function 14.6078431372549 Midmean - Empirical Distribution Function - Averaging 14.6078431372549 Midmean - Empirical Distribution Function - Interpolation 14.6078431372549 Midmean - Closest Observation 14.6078431372549 Midmean - True Basic - Statistics Graphics Toolkit 14.6078431372549 Midmean - MS Excel (old versions) 14.6078431372549 Number of observations 162

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/16/t1289891577u3otl120hhare5r/18aqu1289891651.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/16/t1289891577u3otl120hhare5r/18aqu1289891651.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/16/t1289891577u3otl120hhare5r/21jpf1289891651.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/16/t1289891577u3otl120hhare5r/21jpf1289891651.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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