Paper - Central tendency koers Apple

*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: Sat, 11 Dec 2010 10:56:18 +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/11/t1292065174ne0xo3cw72hnpw9.htm/, Retrieved Sat, 11 Dec 2010 11:59:34 +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/11/t1292065174ne0xo3cw72hnpw9.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:

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Dataseries X:

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25.94 28.66 33.95 31.01 21.00 26.19 25.41 30.47 12.88 9.78 8.25 7.44 10.81 9.12 11.03 12.74 9.98 11.62 9.40 9.27 7.76 8.78 10.65 10.95 12.36 10.85 11.84 12.14 11.65 8.86 7.63 7.38 7.25 8.03 7.75 7.16 7.18 7.51 7.07 7.11 8.98 9.53 10.54 11.31 10.36 11.44 10.45 10.69 11.28 11.96 13.52 12.89 14.03 16.27 16.17 17.25 19.38 26.20 33.53 32.20 38.45 44.86 41.67 36.06 39.76 36.81 42.65 46.89 53.61 57.59 67.82 71.89 75.51 68.49 62.72 70.39 59.77 57.27 67.96 67.85 76.98 81.08 91.66 84.84 85.73 84.61 92.91 99.80 121.19 122.04 131.76 138.48 153.47 189.95 182.22 198.08 135.36 125.02 143.50 173.95 188.75 167.44 158.95 169.53 113.66 107.59 92.67 85.35 90.13 89.31 105.12 125.83 135.81 142.43 163.39 168.21 185.35 188.50 199.91 210.73 192.06 204.62 235.00 261.09 256.88 251.53 257.25 243.10 283.75 300.98

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 75.6031538461538 6.7218625753749 11.2473519055747 Geometric Mean 40.1346925720332 Harmonic Mean 21.4236384347145 Quadratic Mean 107.445335765180 Winsorized Mean ( 1 / 43 ) 75.4709230769231 6.68861412284125 11.2834918700414 Winsorized Mean ( 2 / 43 ) 75.1230769230769 6.60842779419621 11.3677684409374 Winsorized Mean ( 3 / 43 ) 75.0349230769231 6.5892270169069 11.3875152403151 Winsorized Mean ( 4 / 43 ) 75.0256923076923 6.58661657999281 11.3906269473142 Winsorized Mean ( 5 / 43 ) 74.824923076923 6.54265504893843 11.4364768610357 Winsorized Mean ( 6 / 43 ) 74.4386153846154 6.46233619957034 11.5188397950519 Winsorized Mean ( 7 / 43 ) 74.0062307692308 6.37521075196471 11.608436747981 Winsorized Mean ( 8 / 43 ) 72.5200769230769 6.09685699011994 11.8946658976251 Winsorized Mean ( 9 / 43 ) 72.1053846153846 6.02292429044182 11.9718231772917 Winsorized Mean ( 10 / 43 ) 71.7438461538462 5.96177047195059 12.0339832758393 Winsorized Mean ( 11 / 43 ) 71.6118461538462 5.93417751270981 12.0676953125295 Winsorized Mean ( 12 / 43 ) 71.0764615384615 5.84197032712932 12.1665221763267 Winsorized Mean ( 13 / 43 ) 70.9184615384615 5.80385150784569 12.2192067530662 Winsorized Mean ( 14 / 43 ) 70.7978461538462 5.78264629208388 12.2431569523394 Winsorized Mean ( 15 / 43 ) 70.7828461538462 5.77693843047616 12.2526571826406 Winsorized Mean ( 16 / 43 ) 70.4123846153846 5.71466207829455 12.3213557775927 Winsorized Mean ( 17 / 43 ) 70.0226923076923 5.64961256267981 12.3942467790177 Winsorized Mean ( 18 / 43 ) 68.8956153846154 5.4747506283394 12.5842472217795 Winsorized Mean ( 19 / 43 ) 68.2686153846154 5.37794588158283 12.6941804339100 Winsorized Mean ( 20 / 43 ) 68.104 5.34512852806316 12.7413213063892 Winsorized Mean ( 21 / 43 ) 68.0119230769231 5.32436501861722 12.7737153330232 Winsorized Mean ( 22 / 43 ) 67.3908461538462 5.22045846488573 12.9089900067467 Winsorized Mean ( 23 / 43 ) 66.6212307692308 5.10807213208309 13.0423433825047 Winsorized Mean ( 24 / 43 ) 65.6261538461538 4.9663923890257 13.2140492948501 Winsorized Mean ( 25 / 43 ) 63.73 4.70702628102461 13.5393337948662 Winsorized Mean ( 26 / 43 ) 63.524 4.67827940023793 13.5784964012131 Winsorized Mean ( 27 / 43 ) 62.7285384615385 4.56973792206349 13.7269444181195 Winsorized Mean ( 28 / 43 ) 62.1620769230769 4.49550817451394 13.8275973505037 Winsorized Mean ( 29 / 43 ) 62.084 4.48079740645949 13.8555695266428 Winsorized Mean ( 30 / 43 ) 61.2716923076923 4.37460681887857 14.0062169800666 Winsorized Mean ( 31 / 43 ) 59.9172307692308 4.19479238018817 14.2837178431565 Winsorized Mean ( 32 / 43 ) 59.7252307692308 4.16988377681791 14.3229965068254 Winsorized Mean ( 33 / 43 ) 59.0017692307692 4.07563838368059 14.4766938762331 Winsorized Mean ( 34 / 43 ) 58.8265384615385 4.04476662648779 14.5438646759750 Winsorized Mean ( 35 / 43 ) 56.8073076923077 3.80536528584543 14.9282140938243 Winsorized Mean ( 36 / 43 ) 55.179 3.60845659478151 15.2915792529690 Winsorized Mean ( 37 / 43 ) 54.5101538461538 3.52658410251849 15.4569272308651 Winsorized Mean ( 38 / 43 ) 53.0076923076923 3.35091067915982 15.8188914546009 Winsorized Mean ( 39 / 43 ) 51.0066923076923 3.12517648568018 16.3212197907572 Winsorized Mean ( 40 / 43 ) 51.0497692307692 3.10631020959779 16.4342148034788 Winsorized Mean ( 41 / 43 ) 50.7753846153846 3.06909617891692 16.5440838785649 Winsorized Mean ( 42 / 43 ) 50.2843076923077 3.01796618864156 16.6616537592628 Winsorized Mean ( 43 / 43 ) 50.2214615384615 2.97026303044433 16.9080855882817 Trimmed Mean ( 1 / 43 ) 74.3778125 6.57165665454721 11.3179699442354 Trimmed Mean ( 2 / 43 ) 73.25 6.44153480774303 11.3715134958131 Trimmed Mean ( 3 / 43 ) 72.2681451612903 6.34354615889328 11.3923889495113 Trimmed Mean ( 4 / 43 ) 71.2854098360656 6.24206157077941 11.4201708886964 Trimmed Mean ( 5 / 43 ) 70.2724166666667 6.12948339541103 11.4646556868524 Trimmed Mean ( 6 / 43 ) 69.2693220338983 6.01545159796051 11.5152322158794 Trimmed Mean ( 7 / 43 ) 68.3037931034483 5.90676614499692 11.5636528392617 Trimmed Mean ( 8 / 43 ) 67.3748245614035 5.80333644543954 11.6096706084220 Trimmed Mean ( 9 / 43 ) 66.6283035714286 5.74043483678037 11.6068391099094 Trimmed Mean ( 10 / 43 ) 65.9090909090909 5.68202000775822 11.5995879667968 Trimmed Mean ( 11 / 43 ) 65.2067592592593 5.62554969711857 11.5911800215112 Trimmed Mean ( 12 / 43 ) 64.492641509434 5.56535936944643 11.5882258859142 Trimmed Mean ( 13 / 43 ) 63.8068269230769 5.51024583760984 11.5796697286294 Trimmed Mean ( 14 / 43 ) 63.1096078431373 5.45232933916374 11.5747974704728 Trimmed Mean ( 15 / 43 ) 62.3957 5.38870200494338 11.5789850585096 Trimmed Mean ( 16 / 43 ) 61.6539795918367 5.31629330802202 11.5971742000773 Trimmed Mean ( 17 / 43 ) 60.9127083333333 5.24147763893498 11.6212855475828 Trimmed Mean ( 18 / 43 ) 60.1715957446809 5.1639833711661 11.6521668293237 Trimmed Mean ( 19 / 43 ) 59.4867391304348 5.09763808771362 11.6694708621646 Trimmed Mean ( 20 / 43 ) 58.8191111111111 5.03308358297075 11.6864959902759 Trimmed Mean ( 21 / 43 ) 58.1332954545455 4.96192623719082 11.715872561511 Trimmed Mean ( 22 / 43 ) 57.4222093023256 4.8814396252902 11.7633759116527 Trimmed Mean ( 23 / 43 ) 56.7209523809524 4.80098266864159 11.8144463947839 Trimmed Mean ( 24 / 43 ) 56.0385365853659 4.72155570020199 11.8686594299774 Trimmed Mean ( 25 / 43 ) 55.389375 4.64693263927035 11.9195562534982 Trimmed Mean ( 26 / 43 ) 54.8333333333333 4.59271531756912 11.9391970853434 Trimmed Mean ( 27 / 43 ) 54.2615789473684 4.53133549135131 11.9747432188445 Trimmed Mean ( 28 / 43 ) 53.7106756756757 4.47202971497352 12.0103575107827 Trimmed Mean ( 29 / 43 ) 53.1656944444444 4.41038719971656 12.0546546225831 Trimmed Mean ( 30 / 43 ) 52.5945714285714 4.33759730977516 12.1252775839852 Trimmed Mean ( 31 / 43 ) 52.0416176470588 4.26461424544308 12.2031242808579 Trimmed Mean ( 32 / 43 ) 51.5412121212121 4.20170108472371 12.2667488909677 Trimmed Mean ( 33 / 43 ) 51.02171875 4.12785369396544 12.3603505677998 Trimmed Mean ( 34 / 43 ) 50.5146774193548 4.05135299176311 12.4685944478443 Trimmed Mean ( 35 / 43 ) 49.985 3.96136279908679 12.6181323284813 Trimmed Mean ( 36 / 43 ) 49.5481034482759 3.88958038081645 12.7386757946047 Trimmed Mean ( 37 / 43 ) 49.185 3.83159219727697 12.8367001151518 Trimmed Mean ( 38 / 43 ) 48.8385185185185 3.77090616323481 12.9514011763749 Trimmed Mean ( 39 / 43 ) 48.5642307692308 3.72251671855495 13.0460745890439 Trimmed Mean ( 40 / 43 ) 48.4014 3.69613650478994 13.095133239066 Trimmed Mean ( 41 / 43 ) 48.2220833333333 3.66103110546351 13.1717218303252 Trimmed Mean ( 42 / 43 ) 48.0460869565217 3.61826841547197 13.2787514467068 Trimmed Mean ( 43 / 43 ) 47.8886363636364 3.56842406026532 13.4201080238417 Median 42.16 Midrange 154.025 Midmean - Weighted Average at Xnp 50.4107692307692 Midmean - Weighted Average at X(n+1)p 51.5412121212121 Midmean - Empirical Distribution Function 51.5412121212121 Midmean - Empirical Distribution Function - Averaging 51.5412121212121 Midmean - Empirical Distribution Function - Interpolation 51.02171875 Midmean - Closest Observation 51.5412121212121 Midmean - True Basic - Statistics Graphics Toolkit 51.5412121212121 Midmean - MS Excel (old versions) 51.5412121212121 Number of observations 130

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292065174ne0xo3cw72hnpw9/1w80h1292064975.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292065174ne0xo3cw72hnpw9/1w80h1292064975.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292065174ne0xo3cw72hnpw9/2w80h1292064975.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292065174ne0xo3cw72hnpw9/2w80h1292064975.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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