Berekening van de mediaan voor SumFriends

*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, 28 Nov 2010 20:18:50 +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/28/t1290975440ei2kcwdrxowzrjy.htm/, Retrieved Sun, 28 Nov 2010 21:17:22 +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/28/t1290975440ei2kcwdrxowzrjy.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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2 1 0 3 3 1 3 1 4 0 3 2 4 3 1 1 2 3 1 1 2 3 4 2 1 2 2 4 2 3 3 3 4 2 2 4 3 4 2 5 3 1 1 1 2 3 9 0 0 2 2 3 1 2 0 5 2 4 3 0 0 4 1 1 4 2 4 1 4 2 5 4 4 4 4 3 3 3 2 1 1 5 4 2 3 2 2 2 2 3 2 3 4 3 3 0 1 2 2 3 4 4 1 2 2 3 3 3 1 1 1 1 0 1 3 3 0 2 5 2 3 3 5 4 4 0 3 0 2 0 6 3 1 6 2 1 3 1 2 4 1 2 0 5 2 1 1 4 3 0 3 3 0 2 5 2

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 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 2.3974358974359 0.121228713284802 19.7761391049628 Geometric Mean 0 Harmonic Mean 0 Quadratic Mean 2.83295623433208 Winsorized Mean ( 1 / 52 ) 2.37820512820513 0.115878686714036 20.5232316282115 Winsorized Mean ( 2 / 52 ) 2.37820512820513 0.115878686714036 20.5232316282115 Winsorized Mean ( 3 / 52 ) 2.35897435897436 0.11247597591063 20.9731397294008 Winsorized Mean ( 4 / 52 ) 2.35897435897436 0.11247597591063 20.9731397294008 Winsorized Mean ( 5 / 52 ) 2.35897435897436 0.11247597591063 20.9731397294008 Winsorized Mean ( 6 / 52 ) 2.35897435897436 0.11247597591063 20.9731397294008 Winsorized Mean ( 7 / 52 ) 2.35897435897436 0.11247597591063 20.9731397294008 Winsorized Mean ( 8 / 52 ) 2.35897435897436 0.11247597591063 20.9731397294008 Winsorized Mean ( 9 / 52 ) 2.35897435897436 0.11247597591063 20.9731397294008 Winsorized Mean ( 10 / 52 ) 2.35897435897436 0.11247597591063 20.9731397294008 Winsorized Mean ( 11 / 52 ) 2.28846153846154 0.103299421601850 22.1536723340236 Winsorized Mean ( 12 / 52 ) 2.28846153846154 0.103299421601850 22.1536723340236 Winsorized Mean ( 13 / 52 ) 2.28846153846154 0.103299421601850 22.1536723340236 Winsorized Mean ( 14 / 52 ) 2.28846153846154 0.103299421601850 22.1536723340236 Winsorized Mean ( 15 / 52 ) 2.28846153846154 0.103299421601850 22.1536723340236 Winsorized Mean ( 16 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 17 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 18 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 19 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 20 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 21 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 22 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 23 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 24 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 25 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 26 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 27 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 28 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 29 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 30 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 31 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 32 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 33 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 34 / 52 ) 2.39102564102564 0.0907526282405396 26.3466269504420 Winsorized Mean ( 35 / 52 ) 2.16666666666667 0.0685627792672868 31.6012082622859 Winsorized Mean ( 36 / 52 ) 2.16666666666667 0.0685627792672868 31.6012082622859 Winsorized Mean ( 37 / 52 ) 2.16666666666667 0.0685627792672868 31.6012082622859 Winsorized Mean ( 38 / 52 ) 2.16666666666667 0.0685627792672868 31.6012082622859 Winsorized Mean ( 39 / 52 ) 2.16666666666667 0.0685627792672868 31.6012082622859 Winsorized Mean ( 40 / 52 ) 2.16666666666667 0.0685627792672868 31.6012082622859 Winsorized Mean ( 41 / 52 ) 2.16666666666667 0.0685627792672868 31.6012082622859 Winsorized Mean ( 42 / 52 ) 2.16666666666667 0.0685627792672868 31.6012082622859 Winsorized Mean ( 43 / 52 ) 2.16666666666667 0.0685627792672868 31.6012082622859 Winsorized Mean ( 44 / 52 ) 2.16666666666667 0.0685627792672868 31.6012082622859 Winsorized Mean ( 45 / 52 ) 2.16666666666667 0.0685627792672868 31.6012082622859 Winsorized Mean ( 46 / 52 ) 2.46153846153846 0.0400419707274329 61.4739588691636 Winsorized Mean ( 47 / 52 ) 2.46153846153846 0.0400419707274329 61.4739588691636 Winsorized Mean ( 48 / 52 ) 2.46153846153846 0.0400419707274329 61.4739588691636 Winsorized Mean ( 49 / 52 ) 2.46153846153846 0.0400419707274329 61.4739588691636 Winsorized Mean ( 50 / 52 ) 2.46153846153846 0.0400419707274329 61.4739588691636 Winsorized Mean ( 51 / 52 ) 2.46153846153846 0.0400419707274329 61.4739588691636 Winsorized Mean ( 52 / 52 ) 2.46153846153846 0.0400419707274329 61.4739588691636 Trimmed Mean ( 1 / 52 ) 2.37012987012987 0.113942437669439 20.8011160600750 Trimmed Mean ( 2 / 52 ) 2.36184210526316 0.111842105263158 21.1176470588235 Trimmed Mean ( 3 / 52 ) 2.35333333333333 0.109559486734296 21.4799594583775 Trimmed Mean ( 4 / 52 ) 2.35135135135135 0.108417923413892 21.6878471502810 Trimmed Mean ( 5 / 52 ) 2.34931506849315 0.107178556006267 21.9196372486655 Trimmed Mean ( 6 / 52 ) 2.34722222222222 0.105832000085413 22.1787570898014 Trimmed Mean ( 7 / 52 ) 2.34507042253521 0.104367598466278 22.4693339407722 Trimmed Mean ( 8 / 52 ) 2.34285714285714 0.102773175730267 22.7963875418823 Trimmed Mean ( 9 / 52 ) 2.34057971014493 0.101034727521814 23.1660911802784 Trimmed Mean ( 10 / 52 ) 2.33823529411765 0.0991360216478269 23.5861320159089 Trimmed Mean ( 11 / 52 ) 2.33582089552239 0.0970580776416917 24.0662184155915 Trimmed Mean ( 12 / 52 ) 2.34090909090909 0.0960891192374313 24.3618539693847 Trimmed Mean ( 13 / 52 ) 2.34615384615385 0.0950231672856386 24.6903351379705 Trimmed Mean ( 14 / 52 ) 2.3515625 0.093849909853331 25.0566303545207 Trimmed Mean ( 15 / 52 ) 2.35714285714286 0.0925575141414044 25.4667908814161 Trimmed Mean ( 16 / 52 ) 2.36290322580645 0.09113231045769 25.9282708178838 Trimmed Mean ( 17 / 52 ) 2.36065573770492 0.090957940504722 25.9532672420432 Trimmed Mean ( 18 / 52 ) 2.35833333333333 0.0907453059440929 25.9884884270077 Trimmed Mean ( 19 / 52 ) 2.35593220338983 0.0904904976501606 26.0351336832951 Trimmed Mean ( 20 / 52 ) 2.35344827586207 0.0901891318949692 26.0945884100848 Trimmed Mean ( 21 / 52 ) 2.35087719298246 0.089836277570017 26.1684617458712 Trimmed Mean ( 22 / 52 ) 2.34821428571429 0.0894263692620748 26.25863383576 Trimmed Mean ( 23 / 52 ) 2.34545454545455 0.088953102727212 26.3673157376784 Trimmed Mean ( 24 / 52 ) 2.34259259259259 0.0884093082596901 26.497126136442 Trimmed Mean ( 25 / 52 ) 2.33962264150943 0.0877867960166343 26.6511906991811 Trimmed Mean ( 26 / 52 ) 2.33653846153846 0.0870761653619223 26.8332723636474 Trimmed Mean ( 27 / 52 ) 2.33333333333333 0.0862665674676726 27.0479445494076 Trimmed Mean ( 28 / 52 ) 2.33 0.0853454063429215 27.3008249634194 Trimmed Mean ( 29 / 52 ) 2.3265306122449 0.0842979574793776 27.5988966021395 Trimmed Mean ( 30 / 52 ) 2.32291666666667 0.0831068743154231 27.9509569551405 Trimmed Mean ( 31 / 52 ) 2.32291666666667 0.081751538860303 28.4143479015859 Trimmed Mean ( 32 / 52 ) 2.31521739130435 0.0802071908285265 28.8654591613115 Trimmed Mean ( 33 / 52 ) 2.31111111111111 0.0784437335734342 29.4620233615932 Trimmed Mean ( 34 / 52 ) 2.30681818181818 0.0764240536981719 30.1844520172758 Trimmed Mean ( 35 / 52 ) 2.30232558139535 0.0741015819246625 31.0698573714131 Trimmed Mean ( 36 / 52 ) 2.30952380952381 0.0737803691980393 31.3026870782477 Trimmed Mean ( 37 / 52 ) 2.31707317073171 0.0733711998410888 31.5801455577958 Trimmed Mean ( 38 / 52 ) 2.325 0.0728597577802553 31.9106193986012 Trimmed Mean ( 39 / 52 ) 2.33333333333333 0.072228839233406 32.3047325430942 Trimmed Mean ( 40 / 52 ) 2.34210526315789 0.0714575854540923 32.7761601273608 Trimmed Mean ( 41 / 52 ) 2.35135135135135 0.0705204443678322 33.3428323152189 Trimmed Mean ( 42 / 52 ) 2.36111111111111 0.0693857341807906 34.0287688670848 Trimmed Mean ( 43 / 52 ) 2.37142857142857 0.0680136040813605 34.8669740922974 Trimmed Mean ( 44 / 52 ) 2.38235294117647 0.0663530470300963 35.9041980407604 Trimmed Mean ( 45 / 52 ) 2.38235294117647 0.0643373534456148 37.0290789656169 Trimmed Mean ( 46 / 52 ) 2.40625 0.0618768538282494 38.8877237792178 Trimmed Mean ( 47 / 52 ) 2.40322580645161 0.0628078980445251 38.2631146921673 Trimmed Mean ( 48 / 52 ) 2.4 0.063779280414328 37.6297754444536 Trimmed Mean ( 49 / 52 ) 2.39655172413793 0.064793660917625 36.9874412125713 Trimmed Mean ( 50 / 52 ) 2.39285714285714 0.0658538889806635 36.3358516846249 Trimmed Mean ( 51 / 52 ) 2.38888888888889 0.0669630031601025 35.6747573458925 Trimmed Mean ( 52 / 52 ) 2.38461538461538 0.0681242219646227 35.0039283509723 Median 2 Midrange 4.5 Midmean - Weighted Average at Xnp 2.06666666666667 Midmean - Weighted Average at X(n+1)p 2.06666666666667 Midmean - Empirical Distribution Function 2.06666666666667 Midmean - Empirical Distribution Function - Averaging 2.06666666666667 Midmean - Empirical Distribution Function - Interpolation 2.06666666666667 Midmean - Closest Observation 2.06666666666667 Midmean - True Basic - Statistics Graphics Toolkit 2.06666666666667 Midmean - MS Excel (old versions) 2.06666666666667 Number of observations 156

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290975440ei2kcwdrxowzrjy/18yxv1290975527.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290975440ei2kcwdrxowzrjy/18yxv1290975527.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290975440ei2kcwdrxowzrjy/28yxv1290975527.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290975440ei2kcwdrxowzrjy/28yxv1290975527.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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