Univariate analysis of Y

*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 11:12:58 +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/t129206587015ite1pdsw6l6xp.htm/, Retrieved Sat, 11 Dec 2010 12:11:10 +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/t129206587015ite1pdsw6l6xp.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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69 53 43 60 49 62 45 50 75 82 60 59 21 62 54 47 59 37 43 48 79 62 16 38 58 60 67 55 47 59 49 47 57 39 49 26 53 75 65 49 48 45 31 61 49 69 54 80 57 34 69 44 70 51 66 18 74 59 48 55 44 56 65 77 46 70 39 55 44 45 45 49 65 45 71 48 41 40 64 56 52 41 42 54 40 40 51 48 80 38 57 28 51 46 58 67 72 26 54 53 64 47 43 66 54 62 52 64 55 57 74 32 38 66 37 26 64 28 66 65 48 44 64 39 50 66 48 70 66 61 31 61 54 34 62 47 52 37 46 38 63 34 46 40 30 35 51 56 68 39 44 58

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 52.0723684210526 1.08909891947577 47.8123405412221 Geometric Mean 50.1266913823368 Harmonic Mean 47.8358517275049 Quadratic Mean 53.7646552848127 Winsorized Mean ( 1 / 50 ) 52.0723684210526 1.08396620994775 48.0387376868165 Winsorized Mean ( 2 / 50 ) 52.1118421052632 1.07607741618384 48.4275957487062 Winsorized Mean ( 3 / 50 ) 52.1907894736842 1.05515108351729 49.4628591952054 Winsorized Mean ( 4 / 50 ) 52.1381578947368 1.04658190441609 49.8175610286572 Winsorized Mean ( 5 / 50 ) 52.0723684210526 1.03658680151712 50.2344505494774 Winsorized Mean ( 6 / 50 ) 52.1513157894737 1.02384263218748 50.9368472751037 Winsorized Mean ( 7 / 50 ) 52.1052631578947 1.01715667124586 51.2263888453624 Winsorized Mean ( 8 / 50 ) 52.2105263157895 1.00115964387948 52.1500508285317 Winsorized Mean ( 9 / 50 ) 52.1513157894737 0.976044536533584 53.4312870339798 Winsorized Mean ( 10 / 50 ) 52.0855263157895 0.967354197907584 53.8432834926979 Winsorized Mean ( 11 / 50 ) 52.0855263157895 0.94783531323573 54.9520845957715 Winsorized Mean ( 12 / 50 ) 52.2434210526316 0.926451679477277 56.3908752176999 Winsorized Mean ( 13 / 50 ) 52.2434210526316 0.926451679477277 56.3908752176999 Winsorized Mean ( 14 / 50 ) 52.1513157894737 0.91498884662091 56.9966683004613 Winsorized Mean ( 15 / 50 ) 52.25 0.902257441511752 57.9103009806758 Winsorized Mean ( 16 / 50 ) 52.4605263157895 0.876620223722248 59.844074886882 Winsorized Mean ( 17 / 50 ) 52.3486842105263 0.862913706703164 60.6650280368462 Winsorized Mean ( 18 / 50 ) 52.2302631578947 0.848977323127337 61.521387833419 Winsorized Mean ( 19 / 50 ) 52.3552631578947 0.834428646112323 62.7438468247975 Winsorized Mean ( 20 / 50 ) 52.2236842105263 0.819454404967893 63.7298230309379 Winsorized Mean ( 21 / 50 ) 52.2236842105263 0.819454404967893 63.7298230309379 Winsorized Mean ( 22 / 50 ) 52.2236842105263 0.819454404967893 63.7298230309379 Winsorized Mean ( 23 / 50 ) 52.375 0.802402093655298 65.272760893991 Winsorized Mean ( 24 / 50 ) 52.375 0.802402093655298 65.272760893991 Winsorized Mean ( 25 / 50 ) 52.375 0.802402093655298 65.272760893991 Winsorized Mean ( 26 / 50 ) 52.2039473684211 0.783530076608827 66.6266030199675 Winsorized Mean ( 27 / 50 ) 52.3815789473684 0.764082053626869 68.5549133090206 Winsorized Mean ( 28 / 50 ) 52.3815789473684 0.764082053626869 68.5549133090206 Winsorized Mean ( 29 / 50 ) 52.3815789473684 0.764082053626869 68.5549133090206 Winsorized Mean ( 30 / 50 ) 52.1842105263158 0.742888971117568 70.24496601129 Winsorized Mean ( 31 / 50 ) 52.3881578947368 0.721142237017957 72.6460817374538 Winsorized Mean ( 32 / 50 ) 52.3881578947368 0.721142237017957 72.6460817374538 Winsorized Mean ( 33 / 50 ) 52.6052631578947 0.698873632867485 75.2714949941019 Winsorized Mean ( 34 / 50 ) 52.8289473684211 0.676870806111249 78.0487899484591 Winsorized Mean ( 35 / 50 ) 52.5986842105263 0.652118024690789 80.6582278345499 Winsorized Mean ( 36 / 50 ) 52.3618421052632 0.627555725613984 83.4377569482514 Winsorized Mean ( 37 / 50 ) 52.6052631578947 0.604038192432496 87.089299678304 Winsorized Mean ( 38 / 50 ) 52.6052631578947 0.604038192432496 87.089299678304 Winsorized Mean ( 39 / 50 ) 52.6052631578947 0.604038192432496 87.089299678304 Winsorized Mean ( 40 / 50 ) 52.6052631578947 0.604038192432496 87.089299678304 Winsorized Mean ( 41 / 50 ) 52.3355263157895 0.576716834618572 90.7473532490222 Winsorized Mean ( 42 / 50 ) 52.6118421052632 0.550835841172252 95.5127429495113 Winsorized Mean ( 43 / 50 ) 52.6118421052632 0.550835841172252 95.5127429495113 Winsorized Mean ( 44 / 50 ) 52.3223684210526 0.52213149141372 100.209179644355 Winsorized Mean ( 45 / 50 ) 52.3223684210526 0.52213149141372 100.209179644355 Winsorized Mean ( 46 / 50 ) 52.3223684210526 0.52213149141372 100.209179644355 Winsorized Mean ( 47 / 50 ) 52.3223684210526 0.463987330741423 112.766804079423 Winsorized Mean ( 48 / 50 ) 52.3223684210526 0.463987330741423 112.766804079423 Winsorized Mean ( 49 / 50 ) 52.3223684210526 0.463987330741423 112.766804079423 Winsorized Mean ( 50 / 50 ) 52.3223684210526 0.463987330741423 112.766804079423 Trimmed Mean ( 1 / 50 ) 52.1133333333333 1.05819637472775 49.2473179628318 Trimmed Mean ( 2 / 50 ) 52.1554054054054 1.03010007411845 50.631396614586 Trimmed Mean ( 3 / 50 ) 52.1780821917808 1.00399226386252 51.9706018361571 Trimmed Mean ( 4 / 50 ) 52.1736111111111 0.983909488133079 53.0268401111855 Trimmed Mean ( 5 / 50 ) 52.1830985915493 0.964695946780268 54.0927934503235 Trimmed Mean ( 6 / 50 ) 52.2071428571429 0.946323900046773 55.1683655612655 Trimmed Mean ( 7 / 50 ) 52.2173913043478 0.929137350482733 56.1998624608281 Trimmed Mean ( 8 / 50 ) 52.2352941176471 0.91169090993788 57.2949598907443 Trimmed Mean ( 9 / 50 ) 52.2388059701493 0.895516776999591 58.3336988338445 Trimmed Mean ( 10 / 50 ) 52.25 0.881963550215837 59.2428110971403 Trimmed Mean ( 11 / 50 ) 52.2692307692308 0.868434630469527 60.187870146278 Trimmed Mean ( 12 / 50 ) 52.2890625 0.856393303985368 61.0572995569491 Trimmed Mean ( 13 / 50 ) 52.2936507936508 0.846060334845596 61.8084179577976 Trimmed Mean ( 14 / 50 ) 52.2983870967742 0.834681299410334 62.6567135668677 Trimmed Mean ( 15 / 50 ) 52.3114754098361 0.823552662289691 63.5192839573811 Trimmed Mean ( 16 / 50 ) 52.3166666666667 0.812798821010489 64.366071055105 Trimmed Mean ( 17 / 50 ) 52.3050847457627 0.803846726854506 65.0684800949993 Trimmed Mean ( 18 / 50 ) 52.301724137931 0.795434859576833 65.75236615322 Trimmed Mean ( 19 / 50 ) 52.3070175438597 0.78756907692027 66.415783804517 Trimmed Mean ( 20 / 50 ) 52.3035714285714 0.780311879299498 67.0290595543995 Trimmed Mean ( 21 / 50 ) 52.3090909090909 0.773707370394913 67.6083657861388 Trimmed Mean ( 22 / 50 ) 52.3148148148148 0.766267916094912 68.2722240041366 Trimmed Mean ( 23 / 50 ) 52.3207547169811 0.757893137027824 69.0344748629916 Trimmed Mean ( 24 / 50 ) 52.3173076923077 0.750188478069514 69.7388845892937 Trimmed Mean ( 25 / 50 ) 52.3137254901961 0.741479837539203 70.553132859031 Trimmed Mean ( 26 / 50 ) 52.31 0.731636466554203 71.4972563442129 Trimmed Mean ( 27 / 50 ) 52.3163265306122 0.722427375310082 72.4174198245974 Trimmed Mean ( 28 / 50 ) 52.3125 0.713945133402261 73.2724372679845 Trimmed Mean ( 29 / 50 ) 52.3085106382979 0.704274395460192 74.2729126253668 Trimmed Mean ( 30 / 50 ) 52.304347826087 0.693244160065263 75.4486670629334 Trimmed Mean ( 31 / 50 ) 52.3111111111111 0.682882274524706 76.6034103718979 Trimmed Mean ( 32 / 50 ) 52.3068181818182 0.673299932417244 77.6872470401551 Trimmed Mean ( 33 / 50 ) 52.3023255813954 0.662266094255969 78.9747897937536 Trimmed Mean ( 34 / 50 ) 52.2857142857143 0.65192495506548 80.2020445443182 Trimmed Mean ( 35 / 50 ) 52.2560975609756 0.64223782944665 81.3656486196076 Trimmed Mean ( 36 / 50 ) 52.2375 0.633766056646269 82.4239472155195 Trimmed Mean ( 37 / 50 ) 52.2307692307692 0.626563466216441 83.360700147695 Trimmed Mean ( 38 / 50 ) 52.2105263157895 0.620502283658855 84.1423596508377 Trimmed Mean ( 39 / 50 ) 52.1891891891892 0.613218479213605 85.1070066513926 Trimmed Mean ( 40 / 50 ) 52.1666666666667 0.604495418084482 86.2978694395596 Trimmed Mean ( 41 / 50 ) 52.1428571428571 0.594065096810731 87.7729686911229 Trimmed Mean ( 42 / 50 ) 52.1323529411765 0.585118498674492 89.0970855634805 Trimmed Mean ( 43 / 50 ) 52.1060606060606 0.57740226385291 90.242217372626 Trimmed Mean ( 44 / 50 ) 52.078125 0.56795402066827 91.6942623959655 Trimmed Mean ( 45 / 50 ) 52.0645161290323 0.560402782427117 92.9055275270755 Trimmed Mean ( 46 / 50 ) 52.05 0.551026263895674 94.4601072769458 Trimmed Mean ( 47 / 50 ) 52.0344827586207 0.539411925332947 96.4652064866806 Trimmed Mean ( 48 / 50 ) 52.0178571428571 0.533605155491029 97.4837979123155 Trimmed Mean ( 49 / 50 ) 52 0.526050909844691 98.8497482408161 Trimmed Mean ( 50 / 50 ) 51.9807692307692 0.516317732083734 100.675932668412 Median 52 Midrange 49 Midmean - Weighted Average at Xnp 52.3544303797468 Midmean - Weighted Average at X(n+1)p 52.3544303797468 Midmean - Empirical Distribution Function 52.3544303797468 Midmean - Empirical Distribution Function - Averaging 52.3544303797468 Midmean - Empirical Distribution Function - Interpolation 52.3544303797468 Midmean - Closest Observation 52.3544303797468 Midmean - True Basic - Statistics Graphics Toolkit 52.3544303797468 Midmean - MS Excel (old versions) 52.3544303797468 Number of observations 152

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t129206587015ite1pdsw6l6xp/1k56a1292065974.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t129206587015ite1pdsw6l6xp/1k56a1292065974.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t129206587015ite1pdsw6l6xp/2k56a1292065974.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t129206587015ite1pdsw6l6xp/2k56a1292065974.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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