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Paper G29 central tendency oliezaden

R Software Module: rwasp_centraltendency.wasp (opens new window with default values)

Title produced by software: Central Tendency

Date of computation: Fri, 30 Nov 2007 07:00:47 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Nov/30/t1196430607dadn7rqtn1cn8y2.htm/, Retrieved Fri, 30 Nov 2007 14:50:09 +0100

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Dataseries X:

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145,3 143,6 142,8 155,9 156,2 149,8 152,7 155,5 159,3 143 141,4 142,8 146,4 152,3 164,3 168 171,3 162,7 150,2 142,5 138,2 138 145,1 138,4 131,8 130,8 126,3 123 124 120,8 122,1 106,5 104,3 108,7 113,8 112,5 106,1 98,4 96 99,3 97,5 95,3 88 94,7 99,4 98,9 96,4 95,3 99,5 101,6 103,9 106,6 108,3 102 93,8 91,6 97,7 94,8 98 103,8 97,8 91,2 89,3 87,5 90,4 94,2 102,2 101,3 96 90,8 93,2 90,9 91,1 90,2 94,3 96 99 103,3 113,1 112,8 112,1 107,4 111 110,5 110,8 112,4 111,5 116,2 122,5 121,3 113,9 110,7 120,8 141,1 147,4 148 158,1 165 187 190,3 182,4 168,8 151,2 120,1 112,5 106,2 107,1 108,5 106,5 108,3 125,6 124 127,2 136,9 135,8 124,3 115,4 113,6 114,4 118,4 117 116,5 115,4 113,6 117,4 116,9 116,4 111,1 110,2 118,9 131,8 130,6 138,3 148,4 148,7 144,3 152,5 162,9 167,2 166,5 185,6

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G29 paper

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Summary of compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 122.533333333333 2.10133856761623 58.3120374896729 Geometric Mean 120.158120840437 Harmonic Mean 117.941573741679 Quadratic Mean 125.030416724823 Winsorized Mean ( 1 / 47 ) 122.513475177305 2.09565097498959 58.4608203557911 Winsorized Mean ( 2 / 47 ) 122.512056737589 2.08919531325274 58.6407866992796 Winsorized Mean ( 3 / 47 ) 122.463120567376 2.07266779406315 59.08477997205 Winsorized Mean ( 4 / 47 ) 122.15390070922 2.01196510011067 60.7137274411474 Winsorized Mean ( 5 / 47 ) 122.079432624113 1.99522213437045 61.1858852812062 Winsorized Mean ( 6 / 47 ) 122.049645390071 1.98909020969982 61.3595325113433 Winsorized Mean ( 7 / 47 ) 122.019858156028 1.98147045064167 61.5804581473894 Winsorized Mean ( 8 / 47 ) 121.985815602837 1.97440604013138 61.7835506594782 Winsorized Mean ( 9 / 47 ) 121.915602836879 1.95633030210910 62.3185168197023 Winsorized Mean ( 10 / 47 ) 121.979432624113 1.93624484270775 62.9979380363543 Winsorized Mean ( 11 / 47 ) 121.917021276596 1.91442742833610 63.6832817332536 Winsorized Mean ( 12 / 47 ) 121.934042553191 1.90827474232241 63.8975299776779 Winsorized Mean ( 13 / 47 ) 121.629787234043 1.86071467806105 65.3672423118554 Winsorized Mean ( 14 / 47 ) 121.550354609929 1.83948988130348 66.078294773656 Winsorized Mean ( 15 / 47 ) 121.358865248227 1.81013612113103 67.0440547710844 Winsorized Mean ( 16 / 47 ) 121.381560283688 1.79955375613307 67.4509221355609 Winsorized Mean ( 17 / 47 ) 121.333333333333 1.79296770697183 67.6717895484324 Winsorized Mean ( 18 / 47 ) 121.065248226950 1.73612023224375 69.7332166162747 Winsorized Mean ( 19 / 47 ) 121.038297872340 1.73261860592902 69.8585929171876 Winsorized Mean ( 20 / 47 ) 121.009929078014 1.72894522048714 69.9906090974466 Winsorized Mean ( 21 / 47 ) 120.905673758865 1.70179736114761 71.0458698075148 Winsorized Mean ( 22 / 47 ) 120.921276595745 1.66462168619984 72.641896713358 Winsorized Mean ( 23 / 47 ) 120.888652482269 1.65317388156036 73.1251889657052 Winsorized Mean ( 24 / 47 ) 120.718439716312 1.62827834774881 74.1386998624727 Winsorized Mean ( 25 / 47 ) 120.700709219858 1.61820271812095 74.589362549097 Winsorized Mean ( 26 / 47 ) 120.700709219858 1.60184116977474 75.3512342530388 Winsorized Mean ( 27 / 47 ) 120.681560283688 1.57842232122802 76.4570791103596 Winsorized Mean ( 28 / 47 ) 120.502836879433 1.55262530334464 77.6123103364653 Winsorized Mean ( 29 / 47 ) 120.338297872340 1.51977230589900 79.1817941445878 Winsorized Mean ( 30 / 47 ) 120.317021276596 1.51268361188735 79.538788105517 Winsorized Mean ( 31 / 47 ) 120.163120567376 1.49004104012740 80.6441684029734 Winsorized Mean ( 32 / 47 ) 120.412765957447 1.43194196896951 84.0905347889909 Winsorized Mean ( 33 / 47 ) 120.342553191489 1.40907366225336 85.4054379236928 Winsorized Mean ( 34 / 47 ) 120.390780141844 1.39441057503964 86.338114682199 Winsorized Mean ( 35 / 47 ) 120.440425531915 1.38974496312682 86.6636891857717 Winsorized Mean ( 36 / 47 ) 120.644680851064 1.35492214080039 89.0417812345995 Winsorized Mean ( 37 / 47 ) 120.487234042553 1.30986277039555 91.984623706931 Winsorized Mean ( 38 / 47 ) 120.433333333333 1.29820375735460 92.7692071841984 Winsorized Mean ( 39 / 47 ) 119.797163120567 1.20438490620408 99.4675061962858 Winsorized Mean ( 40 / 47 ) 120.279432624113 1.15536961358802 104.104722168159 Winsorized Mean ( 41 / 47 ) 120.279432624113 1.14958453255860 104.628610787248 Winsorized Mean ( 42 / 47 ) 120.309219858156 1.13516058998493 105.984317038132 Winsorized Mean ( 43 / 47 ) 119.973758865248 1.09801730008077 109.263996893694 Winsorized Mean ( 44 / 47 ) 119.661702127660 1.05766592231020 113.137522542363 Winsorized Mean ( 45 / 47 ) 118.544680851064 0.907026732208118 130.695906351593 Winsorized Mean ( 46 / 47 ) 118.642553191489 0.898241123840255 132.083190184230 Winsorized Mean ( 47 / 47 ) 118.609219858156 0.836840381689823 141.734579799614 Trimmed Mean ( 1 / 47 ) 122.297841726619 2.05919399673717 59.3911219245985 Trimmed Mean ( 2 / 47 ) 122.075912408759 2.01922371501499 60.456853542775 Trimmed Mean ( 3 / 47 ) 121.848148148148 1.97903630328504 61.5694355610809 Trimmed Mean ( 4 / 47 ) 121.630827067669 1.94150919153612 62.6475669535383 Trimmed Mean ( 5 / 47 ) 121.490076335878 1.91926930427069 63.3001716150736 Trimmed Mean ( 6 / 47 ) 121.361240310078 1.89906019892585 63.9059469408722 Trimmed Mean ( 7 / 47 ) 121.233858267717 1.87811723809925 64.5507403948922 Trimmed Mean ( 8 / 47 ) 121.1072 1.85648477261941 65.234685350596 Trimmed Mean ( 9 / 47 ) 120.981300813008 1.83383440364990 65.9717696277361 Trimmed Mean ( 10 / 47 ) 120.860330578512 1.81176385424595 66.708655377615 Trimmed Mean ( 11 / 47 ) 120.727731092437 1.79030962462224 67.4339954564624 Trimmed Mean ( 12 / 47 ) 120.597435897436 1.76963010780431 68.148386131986 Trimmed Mean ( 13 / 47 ) 120.460869565217 1.74740417508639 68.9370388846999 Trimmed Mean ( 14 / 47 ) 120.348672566372 1.72903877544294 69.6043803503141 Trimmed Mean ( 15 / 47 ) 120.239639639640 1.71116020685636 70.2679031208518 Trimmed Mean ( 16 / 47 ) 120.143119266055 1.69472777179006 70.8922820915086 Trimmed Mean ( 17 / 47 ) 120.041121495327 1.67742326665088 71.5628093885923 Trimmed Mean ( 18 / 47 ) 119.939047619048 1.65867101973783 72.3103292888094 Trimmed Mean ( 19 / 47 ) 119.853398058252 1.64402697702558 72.9023305171639 Trimmed Mean ( 20 / 47 ) 119.766336633663 1.62773990017728 73.5783011896551 Trimmed Mean ( 21 / 47 ) 119.677777777778 1.60960677285321 74.3521832761896 Trimmed Mean ( 22 / 47 ) 119.592783505155 1.59197086527155 75.1224699610033 Trimmed Mean ( 23 / 47 ) 119.503157894737 1.57579710370746 75.8366401445819 Trimmed Mean ( 24 / 47 ) 119.411827956989 1.55844368720400 76.6224849427996 Trimmed Mean ( 25 / 47 ) 119.327472527473 1.54124576250820 77.4227416744235 Trimmed Mean ( 26 / 47 ) 119.240449438202 1.52247775234548 78.3199946629791 Trimmed Mean ( 27 / 47 ) 119.149425287356 1.50255673893228 79.2977876975373 Trimmed Mean ( 28 / 47 ) 119.055294117647 1.48206127360446 80.3308852596206 Trimmed Mean ( 29 / 47 ) 118.967469879518 1.4612469852112 81.41503187589 Trimmed Mean ( 30 / 47 ) 118.885185185185 1.44079143964876 82.5138058941876 Trimmed Mean ( 31 / 47 ) 118.8 1.41766570344989 83.7997277573268 Trimmed Mean ( 32 / 47 ) 118.719480519481 1.39333689140969 85.2051512103204 Trimmed Mean ( 33 / 47 ) 118.62 1.37137926230532 86.496859957323 Trimmed Mean ( 34 / 47 ) 118.519178082192 1.34816050550676 87.9117713344091 Trimmed Mean ( 35 / 47 ) 118.409859154930 1.32228105288369 89.5496905871078 Trimmed Mean ( 36 / 47 ) 118.291304347826 1.29190828661184 91.5632367821223 Trimmed Mean ( 37 / 47 ) 118.153731343284 1.25987789239508 93.7818911312658 Trimmed Mean ( 38 / 47 ) 118.016923076923 1.22781969288037 96.1190993769323 Trimmed Mean ( 39 / 47 ) 117.874603174603 1.19071972248787 98.9944156869406 Trimmed Mean ( 40 / 47 ) 117.760655737705 1.16079226451508 101.448518686415 Trimmed Mean ( 41 / 47 ) 117.610169491525 1.13037519028331 104.045250198784 Trimmed Mean ( 42 / 47 ) 117.449122807018 1.09350475498133 107.406138173604 Trimmed Mean ( 43 / 47 ) 117.274545454545 1.04976257755639 111.715303976195 Trimmed Mean ( 44 / 47 ) 117.107547169811 1.00257596035854 116.806657849577 Trimmed Mean ( 45 / 47 ) 116.947058823529 0.951590424916908 122.896422411713 Trimmed Mean ( 46 / 47 ) 116.844897959184 0.920062022941164 126.996762224426 Trimmed Mean ( 47 / 47 ) 116.727659574468 0.880851398999789 132.516857788968 Median 115.4 Midrange 138.9 Midmean - Weighted Average at Xnp 118.061428571429 Midmean - Weighted Average at X(n+1)p 118.748611111111 Midmean - Empirical Distribution Function 118.748611111111 Midmean - Empirical Distribution Function - Averaging 118.748611111111 Midmean - Empirical Distribution Function - Interpolation 118.748611111111 Midmean - Closest Observation 118.519178082192 Midmean - True Basic - Statistics Graphics Toolkit 118.748611111111 Midmean - MS Excel (old versions) 118.748611111111 Number of observations 141

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/30/t1196430607dadn7rqtn1cn8y2/1o5ya1196431244.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/30/t1196430607dadn7rqtn1cn8y2/1o5ya1196431244.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/30/t1196430607dadn7rqtn1cn8y2/2doos1196431244.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/30/t1196430607dadn7rqtn1cn8y2/2doos1196431244.ps (open in new window)

Parameters:

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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