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paper G29 central tendency suiker

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 07:03:39 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Nov/30/t1196430762vik8dejc3siouug.htm/, Retrieved Fri, 30 Nov 2007 14:52:44 +0100

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G29 paper

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145,9 158,5 152,2 153,7 157,9 154,4 150,7 151,2 147,3 146,6 145,2 139,3 145,7 163,3 181,8 188,1 222,9 206,3 184,9 183,6 186,6 176,5 173,9 184,9 182,5 183,6 172,4 168,9 163,3 152,4 145,8 148,6 143,4 141,2 144,6 144,5 140,8 133,3 127,3 119,6 120,2 121,9 112,4 111 107,8 110,5 118,3 123 112,1 104,2 102,4 100,3 102,6 101,5 103,4 99,4 97,9 98 90,2 87,1 91,8 94,8 91,8 89,3 91,7 86,2 82,8 82,3 79,8 79,4 85,3 87,5 88,3 88,6 94,9 94,7 92,6 91,8 96,4 96,4 107,1 111,9 107,8 109,2 115,3 119,2 107,8 106,8 104,2 94,8 97,5 98,3 100,6 94,9 93,6 98 104,3 103,9 105,3 102,6 103,3 107,9 107,8 109,8 110,6 110,8 119,3 128,1 127,6 137,9 151,4 143,6 143,4 141,9 135,2 133,1 129,6 134,1 136,8 143,5 162,5 163,1 157,2 158,8 155,4 148,5 154,2 153,3 149,4 147,9 156 163 159,1 159,5 157,3 156,4 156,6 162,4 166,8 162,6 168,1

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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 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 129.653191489362 2.64292340540554 49.0567343813989 Geometric Mean 125.925004380601 Harmonic Mean 122.302162165636 Quadratic Mean 133.371122215923 Winsorized Mean ( 1 / 47 ) 129.538297872340 2.61534937245378 49.530016615258 Winsorized Mean ( 2 / 47 ) 129.315602836879 2.56234309005111 50.4677157945699 Winsorized Mean ( 3 / 47 ) 129.294326241135 2.55578406590613 50.5889084942294 Winsorized Mean ( 4 / 47 ) 129.317021276596 2.53915197721023 50.9292166980397 Winsorized Mean ( 5 / 47 ) 129.348936170213 2.53523610426686 51.0204694357719 Winsorized Mean ( 6 / 47 ) 129.331914893617 2.52203728280835 51.2807307708008 Winsorized Mean ( 7 / 47 ) 129.351773049645 2.51967166345698 51.3367574536182 Winsorized Mean ( 8 / 47 ) 129.334751773050 2.50479297100061 51.6349068647311 Winsorized Mean ( 9 / 47 ) 129.309219858156 2.49581390174165 51.8104413826369 Winsorized Mean ( 10 / 47 ) 128.982978723404 2.43558367529397 52.9577283801742 Winsorized Mean ( 11 / 47 ) 128.850354609929 2.39972254899847 53.6938550099074 Winsorized Mean ( 12 / 47 ) 128.850354609929 2.36828197172170 54.4066779836427 Winsorized Mean ( 13 / 47 ) 128.536879432624 2.32604334018369 55.2598815387823 Winsorized Mean ( 14 / 47 ) 128.457446808511 2.31626516839867 55.4588691144194 Winsorized Mean ( 15 / 47 ) 128.319148936170 2.29954577138721 55.8019546872343 Winsorized Mean ( 16 / 47 ) 128.012765957447 2.24321127172375 57.0667451483863 Winsorized Mean ( 17 / 47 ) 128.133333333333 2.22974427697342 57.4654836684937 Winsorized Mean ( 18 / 47 ) 128.248226950355 2.21149309862485 57.9916921423368 Winsorized Mean ( 19 / 47 ) 128.248226950355 2.20851846319345 58.06980067756 Winsorized Mean ( 20 / 47 ) 128.191489361702 2.20216378103497 58.2116055425518 Winsorized Mean ( 21 / 47 ) 128.191489361702 2.19889087114006 58.2982498332165 Winsorized Mean ( 22 / 47 ) 128.175886524823 2.1971534270679 58.33724898123 Winsorized Mean ( 23 / 47 ) 127.947517730496 2.11941872839269 60.3691550029514 Winsorized Mean ( 24 / 47 ) 127.879432624113 2.11220449279478 60.5431117395781 Winsorized Mean ( 25 / 47 ) 128.021276595745 2.08601966021587 61.3710786323541 Winsorized Mean ( 26 / 47 ) 128.039716312057 2.07251563398765 61.7798554627549 Winsorized Mean ( 27 / 47 ) 127.943971631206 2.05852402066673 62.1532565793262 Winsorized Mean ( 28 / 47 ) 127.824822695035 2.04620169928391 62.4693170471754 Winsorized Mean ( 29 / 47 ) 127.865957446808 2.03767673440822 62.750855073164 Winsorized Mean ( 30 / 47 ) 127.972340425532 2.00052047593957 63.9695229139948 Winsorized Mean ( 31 / 47 ) 128.126241134752 1.97598029366757 64.8418618066983 Winsorized Mean ( 32 / 47 ) 128.103546099291 1.9598896882268 65.3626307994873 Winsorized Mean ( 33 / 47 ) 128.173758865248 1.92467253899482 66.595099305666 Winsorized Mean ( 34 / 47 ) 128.149645390071 1.87909353864144 68.1975871636073 Winsorized Mean ( 35 / 47 ) 128.149645390071 1.86929263046520 68.5551546619957 Winsorized Mean ( 36 / 47 ) 128.021985815603 1.85663303131425 68.9538447589613 Winsorized Mean ( 37 / 47 ) 128.100709219858 1.82839987303051 70.0616484989882 Winsorized Mean ( 38 / 47 ) 127.885106382979 1.80206201158197 70.9659853884345 Winsorized Mean ( 39 / 47 ) 127.968085106383 1.78331235558937 71.7586488453901 Winsorized Mean ( 40 / 47 ) 127.826241134752 1.75320076239502 72.910213066603 Winsorized Mean ( 41 / 47 ) 127.768085106383 1.74762317072591 73.1096309814385 Winsorized Mean ( 42 / 47 ) 127.648936170213 1.73053587785411 73.7626638105297 Winsorized Mean ( 43 / 47 ) 127.557446808511 1.66381455072743 76.6656637019684 Winsorized Mean ( 44 / 47 ) 127.775886524823 1.59613511828254 80.053301917391 Winsorized Mean ( 45 / 47 ) 127.839716312057 1.58419111178391 80.6971553880897 Winsorized Mean ( 46 / 47 ) 127.872340425532 1.54473289034567 82.7795803563926 Winsorized Mean ( 47 / 47 ) 127.672340425532 1.52628609591670 83.6490227927099 Trimmed Mean ( 1 / 47 ) 129.343884892086 2.56955298832382 50.3371152413791 Trimmed Mean ( 2 / 47 ) 129.143795620438 2.51944772800247 51.2587715891326 Trimmed Mean ( 3 / 47 ) 129.054074074074 2.49485413197151 51.728104028307 Trimmed Mean ( 4 / 47 ) 128.969172932331 2.47044046215831 52.2049306218279 Trimmed Mean ( 5 / 47 ) 128.875572519084 2.44864099823356 52.6314688890917 Trimmed Mean ( 6 / 47 ) 128.772093023256 2.42558681282701 53.0890472945689 Trimmed Mean ( 7 / 47 ) 128.668503937008 2.40302290066108 53.5444351785455 Trimmed Mean ( 8 / 47 ) 128.5584 2.37853334695578 54.0494419237547 Trimmed Mean ( 9 / 47 ) 128.447154471545 2.35407188473411 54.5638199515104 Trimmed Mean ( 10 / 47 ) 128.335537190083 2.32844189958817 55.1164867857692 Trimmed Mean ( 11 / 47 ) 128.258823529412 2.30920496950272 55.5424162096065 Trimmed Mean ( 12 / 47 ) 128.194017094017 2.29278050757943 55.9120319935711 Trimmed Mean ( 13 / 47 ) 128.126956521739 2.2784662771832 56.2338612622078 Trimmed Mean ( 14 / 47 ) 128.087610619469 2.26759698823409 56.4860560690807 Trimmed Mean ( 15 / 47 ) 128.054054054054 2.25628970140828 56.7542607556682 Trimmed Mean ( 16 / 47 ) 128.031192660550 2.24523382276132 57.0235453263796 Trimmed Mean ( 17 / 47 ) 128.032710280374 2.23871364544078 57.1903023600706 Trimmed Mean ( 18 / 47 ) 128.024761904762 2.23227400151986 57.3517237658079 Trimmed Mean ( 19 / 47 ) 128.007766990291 2.22634216746680 57.4968973147297 Trimmed Mean ( 20 / 47 ) 127.990099009901 2.21932980025011 57.6706080346764 Trimmed Mean ( 21 / 47 ) 127.975757575758 2.21144075063907 57.8698559022099 Trimmed Mean ( 22 / 47 ) 127.960824742268 2.20222745167894 58.1051810269247 Trimmed Mean ( 23 / 47 ) 127.946315789474 2.19135776352546 58.3867764173904 Trimmed Mean ( 24 / 47 ) 127.946236559140 2.18615568277535 58.5256748031369 Trimmed Mean ( 25 / 47 ) 127.950549450549 2.18010113555233 58.6901898099938 Trimmed Mean ( 26 / 47 ) 127.946067415730 2.17489979350709 58.8284884653989 Trimmed Mean ( 27 / 47 ) 127.940229885057 2.16933900120262 58.9765960111035 Trimmed Mean ( 28 / 47 ) 127.94 2.16337362334241 59.1391143071869 Trimmed Mean ( 29 / 47 ) 127.946987951807 2.15673356466829 59.3244293351023 Trimmed Mean ( 30 / 47 ) 127.951851851852 2.14890950747829 59.5426896323807 Trimmed Mean ( 31 / 47 ) 127.950632911392 2.14257689389292 59.7181054626771 Trimmed Mean ( 32 / 47 ) 127.940259740260 2.13655347807223 59.8816088870838 Trimmed Mean ( 33 / 47 ) 127.930666666667 2.12995450895323 60.0626286284106 Trimmed Mean ( 34 / 47 ) 127.916438356164 2.12461655032517 60.2068351282431 Trimmed Mean ( 35 / 47 ) 127.902816901408 2.12170347625934 60.2830783531104 Trimmed Mean ( 36 / 47 ) 127.888405797101 2.11766464873473 60.3912455513259 Trimmed Mean ( 37 / 47 ) 127.880597014925 2.11258985748581 60.5326190324116 Trimmed Mean ( 38 / 47 ) 127.867692307692 2.10797376700159 60.6590529300434 Trimmed Mean ( 39 / 47 ) 127.866666666667 2.10360673376051 60.7844919939411 Trimmed Mean ( 40 / 47 ) 127.860655737705 2.09854005807541 60.9283845908414 Trimmed Mean ( 41 / 47 ) 127.862711864407 2.09395319114728 61.0628319701601 Trimmed Mean ( 42 / 47 ) 127.868421052632 2.08675281961413 61.2762660966603 Trimmed Mean ( 43 / 47 ) 127.881818181818 2.07785516667161 61.5451068164025 Trimmed Mean ( 44 / 47 ) 127.901886792453 2.07356800145247 61.6820315045668 Trimmed Mean ( 45 / 47 ) 127.909803921569 2.07470528008946 61.6520356645805 Trimmed Mean ( 46 / 47 ) 127.914285714286 2.07422020426528 61.6686142827322 Trimmed Mean ( 47 / 47 ) 127.917021276596 2.07571821247128 61.6254270488391 Median 128.1 Midrange 151.15 Midmean - Weighted Average at Xnp 127.527142857143 Midmean - Weighted Average at X(n+1)p 127.902816901408 Midmean - Empirical Distribution Function 127.902816901408 Midmean - Empirical Distribution Function - Averaging 127.902816901408 Midmean - Empirical Distribution Function - Interpolation 127.902816901408 Midmean - Closest Observation 127.548611111111 Midmean - True Basic - Statistics Graphics Toolkit 127.902816901408 Midmean - MS Excel (old versions) 127.902816901408 Number of observations 141

Charts produced by software:

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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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