Berekening van de mediaan voor Liked

*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 20:20:34 +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/t1290975543yhviuxtlef83tek.htm/, Retrieved Sun, 28 Nov 2010 21:19:05 +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/t1290975543yhviuxtlef83tek.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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13 13 16 12 11 12 18 11 14 9 14 12 11 12 13 11 12 16 9 11 13 15 10 11 13 16 15 14 14 14 8 13 15 13 11 15 15 9 13 16 13 11 12 12 12 14 14 8 13 16 13 11 14 13 13 13 12 16 15 15 12 14 12 15 12 13 12 12 13 5 13 13 14 17 13 13 12 13 14 11 12 12 16 12 12 12 10 15 15 12 16 15 16 13 12 11 13 10 15 13 16 15 18 13 10 16 13 15 14 15 14 13 13 15 16 14 14 16 14 12 13 12 12 14 14 14 16 13 14 4 16 13 16 15 14 13 14 12 15 14 13 14 16 6 13 13 14 15 14 15 13 16 12 15 12 14

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 13.1858974358974 0.174182359584324 75.7016810850696 Geometric Mean 12.9627913688514 Harmonic Mean 12.6561018903409 Quadratic Mean 13.3630278313155 Winsorized Mean ( 1 / 52 ) 13.1923076923077 0.172106922499235 76.651813307314 Winsorized Mean ( 2 / 52 ) 13.1923076923077 0.166239830093833 79.357081181215 Winsorized Mean ( 3 / 52 ) 13.2115384615385 0.154076027601979 85.7468787790109 Winsorized Mean ( 4 / 52 ) 13.2115384615385 0.154076027601979 85.7468787790109 Winsorized Mean ( 5 / 52 ) 13.2435897435897 0.147594944242360 89.7292912814342 Winsorized Mean ( 6 / 52 ) 13.2435897435897 0.147594944242360 89.7292912814342 Winsorized Mean ( 7 / 52 ) 13.2435897435897 0.147594944242360 89.7292912814342 Winsorized Mean ( 8 / 52 ) 13.2948717948718 0.138888994931138 95.723003838162 Winsorized Mean ( 9 / 52 ) 13.2948717948718 0.138888994931138 95.723003838162 Winsorized Mean ( 10 / 52 ) 13.2948717948718 0.138888994931138 95.723003838162 Winsorized Mean ( 11 / 52 ) 13.2948717948718 0.138888994931138 95.723003838162 Winsorized Mean ( 12 / 52 ) 13.3717948717949 0.128366317198332 104.169030970444 Winsorized Mean ( 13 / 52 ) 13.3717948717949 0.128366317198332 104.169030970444 Winsorized Mean ( 14 / 52 ) 13.3717948717949 0.128366317198332 104.169030970444 Winsorized Mean ( 15 / 52 ) 13.3717948717949 0.128366317198332 104.169030970444 Winsorized Mean ( 16 / 52 ) 13.3717948717949 0.128366317198332 104.169030970444 Winsorized Mean ( 17 / 52 ) 13.3717948717949 0.128366317198332 104.169030970444 Winsorized Mean ( 18 / 52 ) 13.3717948717949 0.128366317198332 104.169030970444 Winsorized Mean ( 19 / 52 ) 13.3717948717949 0.128366317198332 104.169030970444 Winsorized Mean ( 20 / 52 ) 13.3717948717949 0.128366317198332 104.169030970444 Winsorized Mean ( 21 / 52 ) 13.2371794871795 0.112536063326300 117.626111096477 Winsorized Mean ( 22 / 52 ) 13.2371794871795 0.112536063326300 117.626111096477 Winsorized Mean ( 23 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 24 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 25 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 26 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 27 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 28 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 29 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 30 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 31 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 32 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 33 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 34 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 35 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 36 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 37 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 38 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 39 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 40 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 41 / 52 ) 13.3846153846154 0.0960172727350939 139.397995832924 Winsorized Mean ( 42 / 52 ) 13.1153846153846 0.0698346969757784 187.806136252493 Winsorized Mean ( 43 / 52 ) 13.1153846153846 0.0698346969757784 187.806136252493 Winsorized Mean ( 44 / 52 ) 13.1153846153846 0.0698346969757784 187.806136252493 Winsorized Mean ( 45 / 52 ) 13.1153846153846 0.0698346969757784 187.806136252493 Winsorized Mean ( 46 / 52 ) 13.1153846153846 0.0698346969757784 187.806136252493 Winsorized Mean ( 47 / 52 ) 13.1153846153846 0.0698346969757784 187.806136252493 Winsorized Mean ( 48 / 52 ) 13.1153846153846 0.0698346969757784 187.806136252493 Winsorized Mean ( 49 / 52 ) 13.1153846153846 0.0698346969757784 187.806136252493 Winsorized Mean ( 50 / 52 ) 13.1153846153846 0.0698346969757784 187.806136252493 Winsorized Mean ( 51 / 52 ) 13.4423076923077 0.0398927269329795 336.961364283044 Winsorized Mean ( 52 / 52 ) 13.4423076923077 0.0398927269329795 336.961364283044 Trimmed Mean ( 1 / 52 ) 13.2142857142857 0.162988255782908 81.0750790037685 Trimmed Mean ( 2 / 52 ) 13.2368421052632 0.152742114516562 86.6613778862403 Trimmed Mean ( 3 / 52 ) 13.26 0.144838102937866 91.550494870044 Trimmed Mean ( 4 / 52 ) 13.2770270270270 0.141180946832999 94.0426263235948 Trimmed Mean ( 5 / 52 ) 13.2945205479452 0.137171579945519 96.9189139122363 Trimmed Mean ( 6 / 52 ) 13.3055555555556 0.134507424549405 98.9206030829054 Trimmed Mean ( 7 / 52 ) 13.3169014084507 0.131597576408753 101.194123568715 Trimmed Mean ( 8 / 52 ) 13.3285714285714 0.128411456948866 103.795811878989 Trimmed Mean ( 9 / 52 ) 13.3333333333333 0.126526784186638 105.379532239320 Trimmed Mean ( 10 / 52 ) 13.3382352941176 0.124468625788138 107.161424894504 Trimmed Mean ( 11 / 52 ) 13.3432835820896 0.122217419792812 109.176610050430 Trimmed Mean ( 12 / 52 ) 13.3484848484848 0.119750322646189 111.469301739787 Trimmed Mean ( 13 / 52 ) 13.3461538461538 0.118483529930631 112.641426652022 Trimmed Mean ( 14 / 52 ) 13.34375 0.117091085666329 113.960425971498 Trimmed Mean ( 15 / 52 ) 13.3412698412698 0.115559480135693 115.449375729315 Trimmed Mean ( 16 / 52 ) 13.3387096774194 0.113873170561811 117.136544206249 Trimmed Mean ( 17 / 52 ) 13.3360655737705 0.11201414724367 119.056975408293 Trimmed Mean ( 18 / 52 ) 13.3333333333333 0.109961371708313 121.254701775654 Trimmed Mean ( 19 / 52 ) 13.3305084745763 0.107690036486828 123.785903593846 Trimmed Mean ( 20 / 52 ) 13.3275862068966 0.105170570436324 126.723532558623 Trimmed Mean ( 21 / 52 ) 13.3245614035088 0.102367271264896 130.164272612375 Trimmed Mean ( 22 / 52 ) 13.3303571428571 0.100983255535315 132.005618874065 Trimmed Mean ( 23 / 52 ) 13.3363636363636 0.099437343466167 134.118261525171 Trimmed Mean ( 24 / 52 ) 13.3333333333333 0.0993227325042359 134.242514247830 Trimmed Mean ( 25 / 52 ) 13.3301886792453 0.0991624386482685 134.427802108899 Trimmed Mean ( 26 / 52 ) 13.3269230769231 0.0989510969321481 134.681913491687 Trimmed Mean ( 27 / 52 ) 13.3235294117647 0.098682610234937 135.013954130773 Trimmed Mean ( 28 / 52 ) 13.32 0.0983500242639892 135.434638676313 Trimmed Mean ( 29 / 52 ) 13.3163265306122 0.0979453756310791 135.956664057010 Trimmed Mean ( 30 / 52 ) 13.3125 0.0974595057608948 136.595193009296 Trimmed Mean ( 31 / 52 ) 13.3125 0.0968818309386321 137.409665682645 Trimmed Mean ( 32 / 52 ) 13.3043478260870 0.0962000553706516 138.298754349218 Trimmed Mean ( 33 / 52 ) 13.3 0.0953998092005724 139.413276729281 Trimmed Mean ( 34 / 52 ) 13.2954545454545 0.0944641861958235 140.745980893681 Trimmed Mean ( 35 / 52 ) 13.2906976744186 0.0933731449889888 142.339616770817 Trimmed Mean ( 36 / 52 ) 13.2857142857143 0.0921027211149805 144.248879130601 Trimmed Mean ( 37 / 52 ) 13.2804878048780 0.090623970770764 146.544978022111 Trimmed Mean ( 38 / 52 ) 13.275 0.0889015242426036 149.322524142261 Trimmed Mean ( 39 / 52 ) 13.2692307692308 0.0868915540517419 152.710248010172 Trimmed Mean ( 40 / 52 ) 13.2631578947368 0.0845388337063632 156.888347203902 Trimmed Mean ( 41 / 52 ) 13.2567567567568 0.0817723219002386 162.117895746312 Trimmed Mean ( 42 / 52 ) 13.25 0.0784982282028449 168.793618701317 Trimmed Mean ( 43 / 52 ) 13.2571428571429 0.0779810304631646 170.004714972381 Trimmed Mean ( 44 / 52 ) 13.2647058823529 0.0773168544833018 171.562927268565 Trimmed Mean ( 45 / 52 ) 13.2647058823529 0.0764767726684885 173.447511179019 Trimmed Mean ( 46 / 52 ) 13.28125 0.0754245589135368 176.086545169260 Trimmed Mean ( 47 / 52 ) 13.2903225806452 0.0741141828285769 179.322257541247 Trimmed Mean ( 48 / 52 ) 13.3 0.0724861178994681 183.483408760363 Trimmed Mean ( 49 / 52 ) 13.3103448275862 0.07046168958067 188.901868615391 Trimmed Mean ( 50 / 52 ) 13.3214285714286 0.0679339961235429 196.093698760229 Trimmed Mean ( 51 / 52 ) 13.3333333333333 0.0647523908238176 205.91260281974 Trimmed Mean ( 52 / 52 ) 13.3269230769231 0.0656855818330744 202.889625166 Median 13 Midrange 11 Midmean - Weighted Average at Xnp 13.3660714285714 Midmean - Weighted Average at X(n+1)p 13.3660714285714 Midmean - Empirical Distribution Function 13.3660714285714 Midmean - Empirical Distribution Function - Averaging 13.3660714285714 Midmean - Empirical Distribution Function - Interpolation 13.3660714285714 Midmean - Closest Observation 13.3660714285714 Midmean - True Basic - Statistics Graphics Toolkit 13.3660714285714 Midmean - MS Excel (old versions) 13.3660714285714 Number of observations 156

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290975543yhviuxtlef83tek/1escw1290975631.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290975543yhviuxtlef83tek/1escw1290975631.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290975543yhviuxtlef83tek/2p2bh1290975631.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t1290975543yhviuxtlef83tek/2p2bh1290975631.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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