depression tutorial

*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: Tue, 16 Nov 2010 07:16:13 +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/16/t1289891677l3ev3bm5cuod4am.htm/, Retrieved Tue, 16 Nov 2010 08:14:39 +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/16/t1289891677l3ev3bm5cuod4am.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?

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User-defined keywords:

Dataseries X:

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12 11 14 12 21 12 22 11 10 13 10 8 15 14 10 14 14 11 10 13 7 14 12 14 11 9 11 15 14 13 9 15 10 11 13 8 20 12 10 10 9 14 8 14 11 13 9 11 15 11 10 14 18 14 11 12 13 9 10 15 20 12 12 14 13 11 17 12 13 14 13 15 13 10 11 19 13 17 13 9 11 10 9 12 12 13 13 12 15 22 13 15 13 15 10 11 16 11 11 10 10 16 12 11 16 19 11 16 15 24 14 15 11 15 12 10 14 13 9 15 15 14 11 8 11 11 8 10 11 13 11 20 10 15 12 14 23 14 16 11 12 10 14 12 12 11 12 13 11 19 12 17 9 12 19 18 15 14 11 9 18 16

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 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 12.9197530864198 0.248757112140656 51.9372209109522 Geometric Mean 12.5675697475656 Harmonic Mean 12.2416261297238 Quadratic Mean 13.299726164451 Winsorized Mean ( 1 / 54 ) 12.9197530864198 0.246278706068825 52.4598869819026 Winsorized Mean ( 2 / 54 ) 12.9074074074074 0.243275572201619 53.0567343469659 Winsorized Mean ( 3 / 54 ) 12.9074074074074 0.243275572201619 53.0567343469659 Winsorized Mean ( 4 / 54 ) 12.8827160493827 0.237789097346891 54.1770678013433 Winsorized Mean ( 5 / 54 ) 12.8518518518519 0.231553933910992 55.5026279829564 Winsorized Mean ( 6 / 54 ) 12.8888888888889 0.227170578852824 56.7366115540832 Winsorized Mean ( 7 / 54 ) 12.8888888888889 0.227170578852824 56.7366115540832 Winsorized Mean ( 8 / 54 ) 12.8395061728395 0.218026924047137 58.8895441650301 Winsorized Mean ( 9 / 54 ) 12.8395061728395 0.218026924047137 58.8895441650301 Winsorized Mean ( 10 / 54 ) 12.8395061728395 0.218026924047137 58.8895441650301 Winsorized Mean ( 11 / 54 ) 12.8395061728395 0.218026924047137 58.8895441650301 Winsorized Mean ( 12 / 54 ) 12.7654320987654 0.205652656430051 62.0727799988684 Winsorized Mean ( 13 / 54 ) 12.7654320987654 0.205652656430051 62.0727799988684 Winsorized Mean ( 14 / 54 ) 12.7654320987654 0.205652656430051 62.0727799988684 Winsorized Mean ( 15 / 54 ) 12.6728395061728 0.191817560333338 66.0671498696478 Winsorized Mean ( 16 / 54 ) 12.7716049382716 0.181219846386462 70.4757519274981 Winsorized Mean ( 17 / 54 ) 12.7716049382716 0.181219846386462 70.4757519274981 Winsorized Mean ( 18 / 54 ) 12.6604938271605 0.166186005825053 76.1826711238757 Winsorized Mean ( 19 / 54 ) 12.6604938271605 0.166186005825053 76.1826711238757 Winsorized Mean ( 20 / 54 ) 12.6604938271605 0.166186005825053 76.1826711238757 Winsorized Mean ( 21 / 54 ) 12.6604938271605 0.166186005825053 76.1826711238757 Winsorized Mean ( 22 / 54 ) 12.6604938271605 0.166186005825053 76.1826711238757 Winsorized Mean ( 23 / 54 ) 12.6604938271605 0.166186005825053 76.1826711238757 Winsorized Mean ( 24 / 54 ) 12.5123456790123 0.149183719274392 83.872058826999 Winsorized Mean ( 25 / 54 ) 12.5123456790123 0.149183719274392 83.872058826999 Winsorized Mean ( 26 / 54 ) 12.5123456790123 0.149183719274392 83.872058826999 Winsorized Mean ( 27 / 54 ) 12.5123456790123 0.149183719274392 83.872058826999 Winsorized Mean ( 28 / 54 ) 12.5123456790123 0.149183719274392 83.872058826999 Winsorized Mean ( 29 / 54 ) 12.5123456790123 0.149183719274392 83.872058826999 Winsorized Mean ( 30 / 54 ) 12.5123456790123 0.149183719274392 83.872058826999 Winsorized Mean ( 31 / 54 ) 12.5123456790123 0.149183719274392 83.872058826999 Winsorized Mean ( 32 / 54 ) 12.5123456790123 0.149183719274392 83.872058826999 Winsorized Mean ( 33 / 54 ) 12.5123456790123 0.149183719274392 83.872058826999 Winsorized Mean ( 34 / 54 ) 12.7222222222222 0.129366455386078 98.3425122397794 Winsorized Mean ( 35 / 54 ) 12.7222222222222 0.129366455386078 98.3425122397794 Winsorized Mean ( 36 / 54 ) 12.7222222222222 0.129366455386078 98.3425122397794 Winsorized Mean ( 37 / 54 ) 12.7222222222222 0.129366455386078 98.3425122397794 Winsorized Mean ( 38 / 54 ) 12.7222222222222 0.129366455386078 98.3425122397794 Winsorized Mean ( 39 / 54 ) 12.7222222222222 0.129366455386078 98.3425122397794 Winsorized Mean ( 40 / 54 ) 12.4753086419753 0.104422790174048 119.469213772031 Winsorized Mean ( 41 / 54 ) 12.4753086419753 0.104422790174048 119.469213772031 Winsorized Mean ( 42 / 54 ) 12.4753086419753 0.104422790174048 119.469213772031 Winsorized Mean ( 43 / 54 ) 12.4753086419753 0.104422790174048 119.469213772031 Winsorized Mean ( 44 / 54 ) 12.4753086419753 0.104422790174048 119.469213772031 Winsorized Mean ( 45 / 54 ) 12.4753086419753 0.104422790174048 119.469213772031 Winsorized Mean ( 46 / 54 ) 12.4753086419753 0.104422790174048 119.469213772031 Winsorized Mean ( 47 / 54 ) 12.4753086419753 0.104422790174048 119.469213772031 Winsorized Mean ( 48 / 54 ) 12.4753086419753 0.104422790174048 119.469213772031 Winsorized Mean ( 49 / 54 ) 12.4753086419753 0.104422790174048 119.469213772031 Winsorized Mean ( 50 / 54 ) 12.4753086419753 0.104422790174048 119.469213772031 Winsorized Mean ( 51 / 54 ) 12.4753086419753 0.104422790174048 119.469213772031 Winsorized Mean ( 52 / 54 ) 12.4753086419753 0.104422790174048 119.469213772031 Winsorized Mean ( 53 / 54 ) 12.4753086419753 0.104422790174048 119.469213772031 Winsorized Mean ( 54 / 54 ) 12.4753086419753 0.104422790174048 119.469213772031 Trimmed Mean ( 1 / 54 ) 12.8875 0.239231523211539 53.8704089954078 Trimmed Mean ( 2 / 54 ) 12.8544303797468 0.231521117145276 55.5216324897089 Trimmed Mean ( 3 / 54 ) 12.8269230769231 0.224835839670935 57.0501708966697 Trimmed Mean ( 4 / 54 ) 12.7987012987013 0.217512671287061 58.8411756564302 Trimmed Mean ( 5 / 54 ) 12.7763157894737 0.211262980057698 60.4758854863467 Trimmed Mean ( 6 / 54 ) 12.76 0.206099387418806 61.9118773704597 Trimmed Mean ( 7 / 54 ) 12.7364864864865 0.201429321376171 63.2305485590202 Trimmed Mean ( 8 / 54 ) 12.7123287671233 0.196319200643476 64.7533645484297 Trimmed Mean ( 9 / 54 ) 12.6944444444444 0.192436070419004 65.9670737237773 Trimmed Mean ( 10 / 54 ) 12.6760563380282 0.188191317568733 67.357285669667 Trimmed Mean ( 11 / 54 ) 12.6571428571429 0.183539323596923 68.961477078011 Trimmed Mean ( 12 / 54 ) 12.6376811594203 0.178425364816355 70.8289495298365 Trimmed Mean ( 13 / 54 ) 12.625 0.174626117879748 72.2973181405424 Trimmed Mean ( 14 / 54 ) 12.6119402985075 0.170455714734952 73.9895421993814 Trimmed Mean ( 15 / 54 ) 12.5984848484848 0.165864312664567 75.956573455094 Trimmed Mean ( 16 / 54 ) 12.5923076923077 0.162602622555097 77.4422176865005 Trimmed Mean ( 17 / 54 ) 12.578125 0.160204630079832 78.5128681595044 Trimmed Mean ( 18 / 54 ) 12.5634920634921 0.157559172757828 79.7382459147742 Trimmed Mean ( 19 / 54 ) 12.5564516129032 0.156300672603603 80.3352372305388 Trimmed Mean ( 20 / 54 ) 12.5491803278689 0.154901168178447 81.0141103223453 Trimmed Mean ( 21 / 54 ) 12.5416666666667 0.153345435522890 81.7870230300694 Trimmed Mean ( 22 / 54 ) 12.5338983050847 0.151616048781069 82.6686779259329 Trimmed Mean ( 23 / 54 ) 12.5258620689655 0.149692942538918 83.6770381857447 Trimmed Mean ( 24 / 54 ) 12.5175438596491 0.147552856249605 84.834303976292 Trimmed Mean ( 25 / 54 ) 12.5178571428571 0.146814164120979 85.263279723761 Trimmed Mean ( 26 / 54 ) 12.5181818181818 0.145967091120035 85.760302011414 Trimmed Mean ( 27 / 54 ) 12.5185185185185 0.144999421992785 86.3349546258287 Trimmed Mean ( 28 / 54 ) 12.5188679245283 0.143897195555279 86.9986928947418 Trimmed Mean ( 29 / 54 ) 12.5192307692308 0.142644374952259 87.7653309036598 Trimmed Mean ( 30 / 54 ) 12.5196078431373 0.141222435839915 88.6516916995333 Trimmed Mean ( 31 / 54 ) 12.52 0.139609845966927 89.6784887433078 Trimmed Mean ( 32 / 54 ) 12.5204081632653 0.137781398710339 90.8715420256927 Trimmed Mean ( 33 / 54 ) 12.5208333333333 0.135707346588638 92.2634893988978 Trimmed Mean ( 34 / 54 ) 12.5212765957447 0.133352255066888 93.8962493694928 Trimmed Mean ( 35 / 54 ) 12.5108695652174 0.132503698455889 94.4190215896678 Trimmed Mean ( 36 / 54 ) 12.5 0.131494815076527 95.0607823793303 Trimmed Mean ( 37 / 54 ) 12.4886363636364 0.130302194962208 95.8436376859078 Trimmed Mean ( 38 / 54 ) 12.4767441860465 0.128898038354616 96.7954543398195 Trimmed Mean ( 39 / 54 ) 12.4642857142857 0.127249048332845 97.9518973036478 Trimmed Mean ( 40 / 54 ) 12.4512195121951 0.125314946834556 99.3594126376124 Trimmed Mean ( 41 / 54 ) 12.45 0.125662799748002 99.0746666870917 Trimmed Mean ( 42 / 54 ) 12.4487179487179 0.125962740603951 98.8285733466132 Trimmed Mean ( 43 / 54 ) 12.4473684210526 0.126206092718756 98.6273178489964 Trimmed Mean ( 44 / 54 ) 12.4459459459459 0.126382604477476 98.4783150925179 Trimmed Mean ( 45 / 54 ) 12.4444444444444 0.126480108614565 98.3905262318172 Trimmed Mean ( 46 / 54 ) 12.4428571428571 0.126484091399303 98.3748786523335 Trimmed Mean ( 47 / 54 ) 12.4411764705882 0.126377142520023 98.4448312606618 Trimmed Mean ( 48 / 54 ) 12.4393939393939 0.126138244874216 98.617147811105 Trimmed Mean ( 49 / 54 ) 12.4375 0.125741846275462 98.912974227794 Trimmed Mean ( 50 / 54 ) 12.4354838709677 0.125156628980894 99.3593705121771 Trimmed Mean ( 51 / 54 ) 12.4333333333333 0.124343852268565 99.9915404460774 Trimmed Mean ( 52 / 54 ) 12.4310344827586 0.123255078013895 100.856164979728 Trimmed Mean ( 53 / 54 ) 12.4285714285714 0.121828980774635 102.016542776160 Trimmed Mean ( 54 / 54 ) 12.4259259259259 0.119986756350054 103.560812075576 Median 12 Midrange 15.5 Midmean - Weighted Average at Xnp 12.3522727272727 Midmean - Weighted Average at X(n+1)p 12.3522727272727 Midmean - Empirical Distribution Function 12.3522727272727 Midmean - Empirical Distribution Function - Averaging 12.3522727272727 Midmean - Empirical Distribution Function - Interpolation 12.3522727272727 Midmean - Closest Observation 12.3522727272727 Midmean - True Basic - Statistics Graphics Toolkit 12.3522727272727 Midmean - MS Excel (old versions) 12.3522727272727 Number of observations 162

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/16/t1289891677l3ev3bm5cuod4am/1rrek1289891771.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/16/t1289891677l3ev3bm5cuod4am/1rrek1289891771.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/16/t1289891677l3ev3bm5cuod4am/2rrek1289891771.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/16/t1289891677l3ev3bm5cuod4am/2rrek1289891771.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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