Kristof Nagels

*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: Fri, 24 Dec 2010 14:57:15 +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/24/t129320254972yyg26os8t6tpk.htm/, Retrieved Fri, 24 Dec 2010 15:55:51 +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/24/t129320254972yyg26os8t6tpk.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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15 14,4 13 13,7 13,6 15,2 12,9 14 14,1 13,2 11,3 13,3 14,4 13,3 11,6 13,2 13,1 14,6 14 14,3 13,8 13,7 11 14,4 15,6 13,7 12,6 13,2 13,3 14,3 14 13,4 13,9 13,7 10,5 14,5 15 13,5 13,5 13,2 13,8 16,2 14,7 13,9 16 14,4 12,3 15,9 15,9 15,5 15,1 14,5 15,1 17,4 16,2 15,6 17,2 14,9 13,8 17,5 16,2 17,5 16,6 16,2 16,6 19,6 15,9 18 18,3 16,3 14,9 18,2 18,4 18,5 16 17,4 17,2 19,6 17,2 18,3 19,3 18,1 16,2 18,4 20,5 19 16,5 18,7 19 19,2 20,5 19,3 20,6 20,1 16,1 20,4 19,7 15,6 14,4 13,7 14,1 15 14,2 13,6 15,4 14,8 12,5 16,2 16,1 16 15,8 15,2 15,7 18,9 17,4 17 19,8 17,7 16 19,6 19,7

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 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 15.7173553719008 0.211793072012634 74.2109041742557 Geometric Mean 15.5481040815154 Harmonic Mean 15.3809402533699 Quadratic Mean 15.8876686939565 Winsorized Mean ( 1 / 40 ) 15.7206611570248 0.210825863135024 74.5670427871387 Winsorized Mean ( 2 / 40 ) 15.7256198347107 0.209927604357078 74.9097284412495 Winsorized Mean ( 3 / 40 ) 15.7305785123967 0.208192546287051 75.5578371701539 Winsorized Mean ( 4 / 40 ) 15.7438016528926 0.202824044581956 77.6229548392173 Winsorized Mean ( 5 / 40 ) 15.7396694214876 0.199518978497724 78.8880814246306 Winsorized Mean ( 6 / 40 ) 15.7396694214876 0.198022174158505 79.4843783953655 Winsorized Mean ( 7 / 40 ) 15.7570247933884 0.195820047423237 80.4668623092092 Winsorized Mean ( 8 / 40 ) 15.7570247933884 0.193925756313646 81.2528727122962 Winsorized Mean ( 9 / 40 ) 15.7644628099174 0.193057391316106 81.6568726141397 Winsorized Mean ( 10 / 40 ) 15.7727272727273 0.192120973138175 82.0978939211568 Winsorized Mean ( 11 / 40 ) 15.7454545454545 0.18770397536852 83.8845022570322 Winsorized Mean ( 12 / 40 ) 15.7454545454545 0.18770397536852 83.8845022570322 Winsorized Mean ( 13 / 40 ) 15.7347107438017 0.186022269556837 84.5850917811432 Winsorized Mean ( 14 / 40 ) 15.7231404958678 0.181179789595367 86.7819778960036 Winsorized Mean ( 15 / 40 ) 15.7231404958678 0.181179789595367 86.7819778960036 Winsorized Mean ( 16 / 40 ) 15.7099173553719 0.179202414751376 87.6657682161691 Winsorized Mean ( 17 / 40 ) 15.6958677685950 0.173528431845327 90.4512741899578 Winsorized Mean ( 18 / 40 ) 15.6809917355372 0.167670451283435 93.5226905844586 Winsorized Mean ( 19 / 40 ) 15.6652892561983 0.165489135344536 94.660529971254 Winsorized Mean ( 20 / 40 ) 15.6818181818182 0.163712103683759 95.7889968362422 Winsorized Mean ( 21 / 40 ) 15.6644628099174 0.161329955956380 97.0958103661338 Winsorized Mean ( 22 / 40 ) 15.6826446280992 0.159418181498279 98.3742536811492 Winsorized Mean ( 23 / 40 ) 15.6636363636364 0.15683684561751 99.872171631381 Winsorized Mean ( 24 / 40 ) 15.6438016528926 0.154183589549857 101.462170510916 Winsorized Mean ( 25 / 40 ) 15.6231404958678 0.151461022136694 103.149577861477 Winsorized Mean ( 26 / 40 ) 15.5586776859504 0.143224714216435 108.631235684917 Winsorized Mean ( 27 / 40 ) 15.5363636363636 0.135344677834711 114.791094004725 Winsorized Mean ( 28 / 40 ) 15.5363636363636 0.135344677834711 114.791094004725 Winsorized Mean ( 29 / 40 ) 15.5123966942149 0.132472619177945 117.098890249748 Winsorized Mean ( 30 / 40 ) 15.5371900826446 0.129834246053251 119.669428944595 Winsorized Mean ( 31 / 40 ) 15.5371900826446 0.129834246053251 119.669428944595 Winsorized Mean ( 32 / 40 ) 15.5107438016529 0.120820209145112 128.378720012176 Winsorized Mean ( 33 / 40 ) 15.5107438016529 0.120820209145112 128.378720012176 Winsorized Mean ( 34 / 40 ) 15.5107438016529 0.120820209145112 128.378720012176 Winsorized Mean ( 35 / 40 ) 15.4818181818182 0.111148524463252 139.289462065120 Winsorized Mean ( 36 / 40 ) 15.3628099173554 0.0980887060681991 156.621598277318 Winsorized Mean ( 37 / 40 ) 15.3933884297521 0.0948446281807894 162.301109983896 Winsorized Mean ( 38 / 40 ) 15.3933884297521 0.0882703050036697 174.389206303435 Winsorized Mean ( 39 / 40 ) 15.3289256198347 0.0817039439563894 187.615491707680 Winsorized Mean ( 40 / 40 ) 15.3289256198347 0.075024679286925 204.318442484914 Trimmed Mean ( 1 / 40 ) 15.7201680672269 0.206753170290910 76.0335043235755 Trimmed Mean ( 2 / 40 ) 15.7196581196581 0.202242946541134 77.7266074713805 Trimmed Mean ( 3 / 40 ) 15.7165217391304 0.197763739012017 79.4712004215062 Trimmed Mean ( 4 / 40 ) 15.7115044247788 0.193497126944787 81.1976109043826 Trimmed Mean ( 5 / 40 ) 15.7027027027027 0.190466729042553 82.4432843554239 Trimmed Mean ( 6 / 40 ) 15.6944954128440 0.187960940332556 83.4987066199818 Trimmed Mean ( 7 / 40 ) 15.6859813084112 0.18549712833418 84.5618552118626 Trimmed Mean ( 8 / 40 ) 15.6742857142857 0.183170233334747 85.5722320648062 Trimmed Mean ( 9 / 40 ) 15.6621359223301 0.180898809867526 86.5795409809473 Trimmed Mean ( 10 / 40 ) 15.6485148514851 0.178473249299765 87.6798899156129 Trimmed Mean ( 11 / 40 ) 15.6333333333333 0.175868431548756 88.8922087702778 Trimmed Mean ( 12 / 40 ) 15.6206185567010 0.173619605725037 89.9703607289577 Trimmed Mean ( 13 / 40 ) 15.6073684210526 0.171053269014231 91.242736902936 Trimmed Mean ( 14 / 40 ) 15.5946236559140 0.168376217752181 92.6177334548899 Trimmed Mean ( 15 / 40 ) 15.5824175824176 0.166003026044424 93.8682742942744 Trimmed Mean ( 16 / 40 ) 15.5696629213483 0.163273807753165 95.3592198014164 Trimmed Mean ( 17 / 40 ) 15.5574712643678 0.160414394066946 96.9830129949262 Trimmed Mean ( 18 / 40 ) 15.5458823529412 0.157888314080502 98.4612600588971 Trimmed Mean ( 19 / 40 ) 15.5349397590361 0.155735047563141 99.752367897391 Trimmed Mean ( 20 / 40 ) 15.5246913580247 0.153513690870099 101.129034615951 Trimmed Mean ( 21 / 40 ) 15.5126582278481 0.151122244481231 102.649734200941 Trimmed Mean ( 22 / 40 ) 15.5012987012987 0.14863808140235 104.288877756287 Trimmed Mean ( 23 / 40 ) 15.488 0.145935441730764 106.129119947256 Trimmed Mean ( 24 / 40 ) 15.4753424657534 0.143103522122369 108.140891546473 Trimmed Mean ( 25 / 40 ) 15.4633802816901 0.140124110504032 110.354886293785 Trimmed Mean ( 26 / 40 ) 15.4521739130435 0.136975126951801 112.81007184962 Trimmed Mean ( 27 / 40 ) 15.4447761194030 0.134424810841378 114.895278801083 Trimmed Mean ( 28 / 40 ) 15.4384615384615 0.132445422498584 116.564704519151 Trimmed Mean ( 29 / 40 ) 15.4317460317460 0.130024720892378 118.683169829828 Trimmed Mean ( 30 / 40 ) 15.4262295081967 0.127500169063303 120.989874927442 Trimmed Mean ( 31 / 40 ) 15.4262295081967 0.124766867957738 123.640432437737 Trimmed Mean ( 32 / 40 ) 15.4105263157895 0.121387245930022 126.953422476307 Trimmed Mean ( 33 / 40 ) 15.4036363636364 0.118689920442175 129.780492785323 Trimmed Mean ( 34 / 40 ) 15.3962264150943 0.115301153738135 133.530549486619 Trimmed Mean ( 35 / 40 ) 15.3882352941176 0.111025418704979 138.601011134287 Trimmed Mean ( 36 / 40 ) 15.3816326530612 0.107486491375089 143.102937460158 Trimmed Mean ( 37 / 40 ) 15.3829787234043 0.105462250588218 145.862416529189 Trimmed Mean ( 38 / 40 ) 15.3822222222222 0.103391424965775 148.776576271331 Trimmed Mean ( 39 / 40 ) 15.3813953488372 0.101879122671189 150.97691210475 Trimmed Mean ( 40 / 40 ) 15.3853658536585 0.101004767794358 152.323164436976 Median 15.5 Midrange 15.55 Midmean - Weighted Average at Xnp 15.4580645161290 Midmean - Weighted Average at X(n+1)p 15.4580645161290 Midmean - Empirical Distribution Function 15.4580645161290 Midmean - Empirical Distribution Function - Averaging 15.4580645161290 Midmean - Empirical Distribution Function - Interpolation 15.4580645161290 Midmean - Closest Observation 15.3815384615385 Midmean - True Basic - Statistics Graphics Toolkit 15.4580645161290 Midmean - MS Excel (old versions) 15.4580645161290 Number of observations 121

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t129320254972yyg26os8t6tpk/1uccc1293202633.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t129320254972yyg26os8t6tpk/1uccc1293202633.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t129320254972yyg26os8t6tpk/2n4bx1293202633.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t129320254972yyg26os8t6tpk/2n4bx1293202633.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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