Central Tendency Personal Standards (Yt)

*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, 23 Nov 2010 16:15:28 +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/23/t1290528873phoua7znptyvyhs.htm/, Retrieved Tue, 23 Nov 2010 17:14:33 +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/23/t1290528873phoua7znptyvyhs.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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24 25 30 19 22 22 25 23 17 21 19 19 15 16 23 27 22 14 22 23 23 21 19 18 20 23 25 19 24 22 25 26 29 32 25 29 28 17 28 29 26 25 14 25 26 20 18 32 25 25 23 21 20 15 30 24 26 24 22 14 24 24 24 24 19 31 22 27 19 25 20 21 27 23 25 20 21 22 23 25 25 17 19 25 19 20 26 23 27 17 17 19 17 22 21 32 21 21 18 18 23 19 20 21 20 17 18 19 22 15 14 18 24 35 29 21 25 20 22 13 26 17 25 20 19 21 22 24 21 26 24 16 23 18 16 26 19 21 21 22 23 29 21 21 23 27 25 21 10 20 26 24 29 19 24 19 24 22 17

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 22.1446540880503 0.33442657416461 66.2167895699293 Geometric Mean 21.732007245107 Harmonic Mean 21.2968530783229 Quadratic Mean 22.5401109973661 Winsorized Mean ( 1 / 53 ) 22.1446540880503 0.326476300484548 67.8292851737899 Winsorized Mean ( 2 / 53 ) 22.1572327044025 0.324359901480804 68.310640751979 Winsorized Mean ( 3 / 53 ) 22.1572327044025 0.324359901480804 68.310640751979 Winsorized Mean ( 4 / 53 ) 22.1320754716981 0.319734535760195 69.2201592145099 Winsorized Mean ( 5 / 53 ) 22.1006289308176 0.314472573758037 70.2783987382708 Winsorized Mean ( 6 / 53 ) 22.1383647798742 0.308631499348409 71.7307365794265 Winsorized Mean ( 7 / 53 ) 22.0943396226415 0.30189176813032 73.1862937485063 Winsorized Mean ( 8 / 53 ) 22.0943396226415 0.30189176813032 73.1862937485063 Winsorized Mean ( 9 / 53 ) 22.1509433962264 0.293927702145393 75.3618772049915 Winsorized Mean ( 10 / 53 ) 22.1509433962264 0.293927702145393 75.3618772049915 Winsorized Mean ( 11 / 53 ) 22.1509433962264 0.293927702145393 75.3618772049915 Winsorized Mean ( 12 / 53 ) 22.2264150943396 0.284532744891556 78.1154910757663 Winsorized Mean ( 13 / 53 ) 22.1446540880503 0.27280713046853 81.1732964971194 Winsorized Mean ( 14 / 53 ) 22.1446540880503 0.27280713046853 81.1732964971194 Winsorized Mean ( 15 / 53 ) 22.0503144654088 0.260714777216752 84.5763891897725 Winsorized Mean ( 16 / 53 ) 22.0503144654088 0.260714777216752 84.5763891897725 Winsorized Mean ( 17 / 53 ) 22.0503144654088 0.260714777216752 84.5763891897725 Winsorized Mean ( 18 / 53 ) 22.0503144654088 0.260714777216752 84.5763891897725 Winsorized Mean ( 19 / 53 ) 22.0503144654088 0.260714777216752 84.5763891897725 Winsorized Mean ( 20 / 53 ) 21.9245283018868 0.246550466190792 88.925113955861 Winsorized Mean ( 21 / 53 ) 22.0566037735849 0.23082378149826 95.5560281978623 Winsorized Mean ( 22 / 53 ) 22.0566037735849 0.23082378149826 95.5560281978623 Winsorized Mean ( 23 / 53 ) 22.0566037735849 0.23082378149826 95.5560281978623 Winsorized Mean ( 24 / 53 ) 22.0566037735849 0.23082378149826 95.5560281978623 Winsorized Mean ( 25 / 53 ) 22.0566037735849 0.23082378149826 95.5560281978623 Winsorized Mean ( 26 / 53 ) 22.0566037735849 0.23082378149826 95.5560281978623 Winsorized Mean ( 27 / 53 ) 22.0566037735849 0.23082378149826 95.5560281978623 Winsorized Mean ( 28 / 53 ) 22.2327044025157 0.212497623312589 104.625661482392 Winsorized Mean ( 29 / 53 ) 22.0503144654088 0.193394365568331 114.017357230699 Winsorized Mean ( 30 / 53 ) 22.0503144654088 0.193394365568331 114.017357230699 Winsorized Mean ( 31 / 53 ) 22.0503144654088 0.193394365568331 114.017357230699 Winsorized Mean ( 32 / 53 ) 22.0503144654088 0.193394365568331 114.017357230699 Winsorized Mean ( 33 / 53 ) 22.0503144654088 0.193394365568331 114.017357230699 Winsorized Mean ( 34 / 53 ) 22.0503144654088 0.193394365568331 114.017357230699 Winsorized Mean ( 35 / 53 ) 22.0503144654088 0.193394365568331 114.017357230699 Winsorized Mean ( 36 / 53 ) 22.0503144654088 0.193394365568331 114.017357230699 Winsorized Mean ( 37 / 53 ) 22.0503144654088 0.193394365568331 114.017357230699 Winsorized Mean ( 38 / 53 ) 22.0503144654088 0.193394365568331 114.017357230699 Winsorized Mean ( 39 / 53 ) 22.0503144654088 0.193394365568331 114.017357230699 Winsorized Mean ( 40 / 53 ) 22.0503144654088 0.193394365568331 114.017357230699 Winsorized Mean ( 41 / 53 ) 22.0503144654088 0.193394365568331 114.017357230699 Winsorized Mean ( 42 / 53 ) 22.0503144654088 0.193394365568331 114.017357230699 Winsorized Mean ( 43 / 53 ) 22.0503144654088 0.193394365568331 114.017357230699 Winsorized Mean ( 44 / 53 ) 22.3270440251572 0.167281769652992 133.469678563733 Winsorized Mean ( 45 / 53 ) 22.3270440251572 0.167281769652992 133.469678563733 Winsorized Mean ( 46 / 53 ) 22.0377358490566 0.139627375085707 157.832486899717 Winsorized Mean ( 47 / 53 ) 22.0377358490566 0.139627375085707 157.832486899717 Winsorized Mean ( 48 / 53 ) 22.0377358490566 0.139627375085707 157.832486899717 Winsorized Mean ( 49 / 53 ) 22.0377358490566 0.139627375085707 157.832486899717 Winsorized Mean ( 50 / 53 ) 22.0377358490566 0.139627375085707 157.832486899717 Winsorized Mean ( 51 / 53 ) 22.0377358490566 0.139627375085707 157.832486899717 Winsorized Mean ( 52 / 53 ) 22.0377358490566 0.139627375085707 157.832486899717 Winsorized Mean ( 53 / 53 ) 22.0377358490566 0.139627375085707 157.832486899717 Trimmed Mean ( 1 / 53 ) 22.140127388535 0.319293402506816 69.3410111662498 Trimmed Mean ( 2 / 53 ) 22.1354838709677 0.311499665873872 71.061019628607 Trimmed Mean ( 3 / 53 ) 22.1241830065359 0.304241354817593 72.7191838197028 Trimmed Mean ( 4 / 53 ) 22.112582781457 0.296341644962877 74.6185463883317 Trimmed Mean ( 5 / 53 ) 22.1073825503356 0.289194252644307 76.4447507106113 Trimmed Mean ( 6 / 53 ) 22.108843537415 0.282784926192102 78.1825390593697 Trimmed Mean ( 7 / 53 ) 22.108843537415 0.277116401177958 79.7817936557899 Trimmed Mean ( 8 / 53 ) 22.1048951048951 0.272281163653487 81.184077547966 Trimmed Mean ( 9 / 53 ) 22.1063829787234 0.267015120803196 82.7907532435847 Trimmed Mean ( 10 / 53 ) 22.1007194244604 0.262582429482318 84.1667870467648 Trimmed Mean ( 11 / 53 ) 22.0948905109489 0.257744621261985 85.723963521592 Trimmed Mean ( 12 / 53 ) 22.0888888888889 0.252454324427689 87.4965756239834 Trimmed Mean ( 13 / 53 ) 22.0751879699248 0.247940723626152 89.0341354460596 Trimmed Mean ( 14 / 53 ) 22.0751879699248 0.244538905973872 90.2727027505533 Trimmed Mean ( 15 / 53 ) 22.062015503876 0.240807733641292 91.616723309807 Trimmed Mean ( 16 / 53 ) 22.0629921259843 0.238141586057815 92.6465322214991 Trimmed Mean ( 17 / 53 ) 22.064 0.235205880988578 93.8071782357836 Trimmed Mean ( 18 / 53 ) 22.0650406504065 0.231970804495152 95.1199039828638 Trimmed Mean ( 19 / 53 ) 22.0661157024793 0.228401903155436 96.610909968918 Trimmed Mean ( 20 / 53 ) 22.0672268907563 0.22445905551453 98.3129276748108 Trimmed Mean ( 21 / 53 ) 22.0769230769231 0.221572311328242 99.6375537384625 Trimmed Mean ( 22 / 53 ) 22.0782608695652 0.219960410447643 100.37379374149 Trimmed Mean ( 23 / 53 ) 22.0796460176991 0.218148452437648 101.213855844382 Trimmed Mean ( 24 / 53 ) 22.0810810810811 0.216113816865829 102.17338901006 Trimmed Mean ( 25 / 53 ) 22.0825688073394 0.213830508349203 103.271366550168 Trimmed Mean ( 26 / 53 ) 22.0841121495327 0.21126847141105 104.531035804984 Trimmed Mean ( 27 / 53 ) 22.0857142857143 0.208392717857646 105.98121907888 Trimmed Mean ( 28 / 53 ) 22.0857142857143 0.205162199030436 107.650017352553 Trimmed Mean ( 29 / 53 ) 22.0873786407767 0.203281577292925 108.654108920796 Trimmed Mean ( 30 / 53 ) 22.0808080808081 0.202884155507324 108.834561405713 Trimmed Mean ( 31 / 53 ) 22.0824742268041 0.202364726726326 109.122150801844 Trimmed Mean ( 32 / 53 ) 22.0842105263158 0.201707802532288 109.486149018854 Trimmed Mean ( 33 / 53 ) 22.0860215053763 0.200895560367648 109.937827719826 Trimmed Mean ( 34 / 53 ) 22.0879120879121 0.199907397150518 110.490719216764 Trimmed Mean ( 35 / 53 ) 22.0898876404494 0.198719373841325 111.161218020383 Trimmed Mean ( 36 / 53 ) 22.0919540229885 0.197303517122811 111.9693877998 Trimmed Mean ( 37 / 53 ) 22.0941176470588 0.195626931156658 112.940061557097 Trimmed Mean ( 38 / 53 ) 22.0963855421687 0.193650652787702 114.10436899685 Trimmed Mean ( 39 / 53 ) 22.0987654320988 0.191328153781684 115.501900767384 Trimmed Mean ( 40 / 53 ) 22.1012658227848 0.188603347161409 117.183847240369 Trimmed Mean ( 41 / 53 ) 22.1038961038961 0.185407879771083 119.217673656519 Trimmed Mean ( 42 / 53 ) 22.1066666666667 0.181657368139581 121.694302262931 Trimmed Mean ( 43 / 53 ) 22.1095890410959 0.177246017225721 124.739553458849 Trimmed Mean ( 44 / 53 ) 22.112676056338 0.172038664615239 128.533176572793 Trimmed Mean ( 45 / 53 ) 22.1014492753623 0.169177499184371 130.640595717022 Trimmed Mean ( 46 / 53 ) 22.089552238806 0.165734175574097 133.283024833524 Trimmed Mean ( 47 / 53 ) 22.0923076923077 0.165138457238681 133.780514010597 Trimmed Mean ( 48 / 53 ) 22.0952380952381 0.164290723225584 134.488653171851 Trimmed Mean ( 49 / 53 ) 22.0983606557377 0.163140152346177 135.456295326033 Trimmed Mean ( 50 / 53 ) 22.1016949152542 0.161623331419234 136.748170707636 Trimmed Mean ( 51 / 53 ) 22.1052631578947 0.159660138979316 138.451984942581 Trimmed Mean ( 52 / 53 ) 22.1090909090909 0.157147826077531 140.689766196213 Trimmed Mean ( 53 / 53 ) 22.1132075471698 0.153952232235279 143.636810107274 Median 22 Midrange 22.5 Midmean - Weighted Average at Xnp 22.0490196078431 Midmean - Weighted Average at X(n+1)p 22.0490196078431 Midmean - Empirical Distribution Function 22.0490196078431 Midmean - Empirical Distribution Function - Averaging 22.0490196078431 Midmean - Empirical Distribution Function - Interpolation 22.0490196078431 Midmean - Closest Observation 22.0490196078431 Midmean - True Basic - Statistics Graphics Toolkit 22.0490196078431 Midmean - MS Excel (old versions) 22.0490196078431 Number of observations 159

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528873phoua7znptyvyhs/19bm31290528923.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528873phoua7znptyvyhs/19bm31290528923.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528873phoua7znptyvyhs/21k361290528923.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290528873phoua7znptyvyhs/21k361290528923.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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