Measures of central tendency - Tijd

*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: Fri, 12 Nov 2010 16:05:56 +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/12/t128957788485gn3o5ntadebgd.htm/, Retrieved Fri, 12 Nov 2010 17:04:47 +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/12/t128957788485gn3o5ntadebgd.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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294,371 155,359 167,804 82,893 103,131 130,093 166,796 72,174 96,396 81,056 138,981 197,604 264,405 119,403 189,62 155,779 134,168 126,862 159,104 163,426 113,344 175,645 147,312 164,642 125,814 138,558 163,136 148,763 178,322 107,805 180,363 179,006 111,603 251,469 123,646 108,398 134,505 129,605 211,562 300,239 329,183 115,475 115,108 101,922 391,433 103,906 112,704 619,22 110,688 95,539 150,985 138,107 177,705 757,365 103,671 81,101 130,586 166,608 201,734 175,329 126,372 107,822 146,123 98,815 219,641 156 123,928 156,405 104,828 188,594 575,698 564,424 149,465 168,474 280,909 172,095 73,594 128,317 202,431 149,787 243,719 188,553 125,634 166,548 284,966 122,213 172,936 152,882 242,736 107,814 155,723 192,582 126,668 127,17 125,77 144,493 123,241 138,431 150,875 375,445 154,611 83,401 115,175 198,463 520,108 235,576 105,44 152,272 310,469 217,089 229,36 555,232 466,984 127,703 101,383 172,705 etc...

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 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 180.715282051282 9.595725350383 18.8328943829211 Geometric Mean 158.790796099286 Harmonic Mean 145.178243159328 Quadratic Mean 216.633549206654 Winsorized Mean ( 1 / 52 ) 180.093224358974 9.32553552876336 19.3118372455395 Winsorized Mean ( 2 / 52 ) 178.386942307692 8.67445237322153 20.5646344728783 Winsorized Mean ( 3 / 52 ) 177.558923076923 8.40859375895801 21.1163635878785 Winsorized Mean ( 4 / 52 ) 177.276051282051 8.32057797641511 21.3057376283889 Winsorized Mean ( 5 / 52 ) 177.035923076923 8.2285559354607 21.5148228274164 Winsorized Mean ( 6 / 52 ) 175.686730769231 7.83638548488318 22.4193578925054 Winsorized Mean ( 7 / 52 ) 173.358423076923 7.17843311573114 24.1498973776070 Winsorized Mean ( 8 / 52 ) 169.512525641026 6.21373756742759 27.2802840161146 Winsorized Mean ( 9 / 52 ) 168.619448717949 6.00221891436587 28.0928521807777 Winsorized Mean ( 10 / 52 ) 165.798551282051 5.36557235776481 30.9004408527108 Winsorized Mean ( 11 / 52 ) 164.854737179487 5.07793006673221 32.4649483181197 Winsorized Mean ( 12 / 52 ) 164.418044871795 4.90244863112699 33.5379434325654 Winsorized Mean ( 13 / 52 ) 164.000461538462 4.80950373845426 34.0992481671654 Winsorized Mean ( 14 / 52 ) 163.615910256410 4.68597166914901 34.9161117071208 Winsorized Mean ( 15 / 52 ) 163.603121794872 4.6200404702783 35.4116209256965 Winsorized Mean ( 16 / 52 ) 163.236352564103 4.54573847786748 35.9097544565918 Winsorized Mean ( 17 / 52 ) 161.444160256410 4.25426023081046 37.9488210634577 Winsorized Mean ( 18 / 52 ) 160.698775641026 4.10576211604651 39.1398164576969 Winsorized Mean ( 19 / 52 ) 160.123051282051 4.00417096662032 39.9890645571514 Winsorized Mean ( 20 / 52 ) 160.038051282051 3.98452113704348 40.1649397199081 Winsorized Mean ( 21 / 52 ) 159.625993589744 3.90919151072634 40.833505637099 Winsorized Mean ( 22 / 52 ) 159.221391025641 3.8367793191429 41.4987096680895 Winsorized Mean ( 23 / 52 ) 159.166692307692 3.80798998622684 41.7980858361981 Winsorized Mean ( 24 / 52 ) 158.429 3.62128056711162 43.7494408577032 Winsorized Mean ( 25 / 52 ) 157.434288461538 3.48635623494508 45.1572581377412 Winsorized Mean ( 26 / 52 ) 155.838788461538 3.27934882493972 47.5212600978497 Winsorized Mean ( 27 / 52 ) 155.914596153846 3.26695748695477 47.7247092367826 Winsorized Mean ( 28 / 52 ) 156.231032051282 3.21698205394993 48.5644711195874 Winsorized Mean ( 29 / 52 ) 156.024685897436 3.15423856626343 49.4650872531387 Winsorized Mean ( 30 / 52 ) 155.540262820513 3.06487891849551 50.74923576324 Winsorized Mean ( 31 / 52 ) 155.161307692308 3.00213373911977 51.6836760702744 Winsorized Mean ( 32 / 52 ) 153.382435897436 2.77278430997185 55.3171176516766 Winsorized Mean ( 33 / 52 ) 153.273282051282 2.75241339721419 55.6868681886285 Winsorized Mean ( 34 / 52 ) 152.901461538462 2.64042773349887 57.907838036472 Winsorized Mean ( 35 / 52 ) 152.890243589744 2.63007125772459 58.131597438929 Winsorized Mean ( 36 / 52 ) 152.764935897436 2.61301960595369 58.4629887771853 Winsorized Mean ( 37 / 52 ) 151.644974358974 2.47686398370849 61.22458696013 Winsorized Mean ( 38 / 52 ) 151.597474358974 2.37429886373429 63.8493648270305 Winsorized Mean ( 39 / 52 ) 151.996474358974 2.32170742451531 65.4675402912603 Winsorized Mean ( 40 / 52 ) 152.122628205128 2.28849712865797 66.4727197164266 Winsorized Mean ( 41 / 52 ) 152.381769230769 2.21970264157750 68.6496318815362 Winsorized Mean ( 42 / 52 ) 152.589884615385 2.19486281469898 69.5213767318353 Winsorized Mean ( 43 / 52 ) 152.613314102564 2.19066003385681 69.6654486519653 Winsorized Mean ( 44 / 52 ) 150.417544871795 1.94311959445141 77.4103381497016 Winsorized Mean ( 45 / 52 ) 150.107448717949 1.89718677844738 79.1210704308164 Winsorized Mean ( 46 / 52 ) 150.408807692308 1.83303581002121 82.0544840804648 Winsorized Mean ( 47 / 52 ) 150.362410256410 1.82088951319445 82.5763502765328 Winsorized Mean ( 48 / 52 ) 150.275333333333 1.80985838135846 83.0315426229858 Winsorized Mean ( 49 / 52 ) 149.803551282051 1.73177654137932 86.5028181769552 Winsorized Mean ( 50 / 52 ) 149.797141025641 1.71376261747444 87.4083373614465 Winsorized Mean ( 51 / 52 ) 149.078237179487 1.63375183420183 91.249009830382 Winsorized Mean ( 52 / 52 ) 149.103903846154 1.61750585318674 92.1813689591265 Trimmed Mean ( 1 / 52 ) 177.036259740260 8.63486702219718 20.5024882589578 Trimmed Mean ( 2 / 52 ) 173.898848684211 7.83184935071179 22.2040594624571 Trimmed Mean ( 3 / 52 ) 171.56504 7.32629171601685 23.4177189020363 Trimmed Mean ( 4 / 52 ) 169.459081081081 6.87094802256993 24.6631295309521 Trimmed Mean ( 5 / 52 ) 167.37098630137 6.38234875872775 26.2240426884371 Trimmed Mean ( 6 / 52 ) 165.276916666667 5.84634260283223 28.2701387678853 Trimmed Mean ( 7 / 52 ) 163.370894366197 5.33711349568831 30.6103466786268 Trimmed Mean ( 8 / 52 ) 161.781042857143 4.92085874659009 32.8765874389808 Trimmed Mean ( 9 / 52 ) 160.688550724638 4.66871928718258 34.4181221530687 Trimmed Mean ( 10 / 52 ) 159.67775 4.42721512484186 36.0673121809737 Trimmed Mean ( 11 / 52 ) 158.965179104478 4.27416621903025 37.1920910320949 Trimmed Mean ( 12 / 52 ) 158.332416666667 4.15099103146006 38.1432808374378 Trimmed Mean ( 13 / 52 ) 157.723853846154 4.04082540028356 39.0325832526903 Trimmed Mean ( 14 / 52 ) 157.135421875 3.93215108186491 39.9616949103784 Trimmed Mean ( 15 / 52 ) 156.562317460317 3.82859806052229 40.8928581651529 Trimmed Mean ( 16 / 52 ) 155.971798387097 3.72200245076577 41.9053454290464 Trimmed Mean ( 17 / 52 ) 155.391229508197 3.61311950935970 43.0074978438048 Trimmed Mean ( 18 / 52 ) 154.928358333333 3.53002146113559 43.8887865241175 Trimmed Mean ( 19 / 52 ) 154.504542372881 3.45575768966985 44.7093101564196 Trimmed Mean ( 20 / 52 ) 154.106862068966 3.38519527706343 45.52377321129 Trimmed Mean ( 21 / 52 ) 153.701043859649 3.30870571777578 46.4535250245742 Trimmed Mean ( 22 / 52 ) 153.3080625 3.23206495409423 47.4334719993163 Trimmed Mean ( 23 / 52 ) 152.926872727273 3.15471589296447 48.4756402528432 Trimmed Mean ( 24 / 52 ) 152.535 3.07106887939023 49.6683747550092 Trimmed Mean ( 25 / 52 ) 152.173575471698 2.99864557002287 50.7474364402917 Trimmed Mean ( 26 / 52 ) 151.857932692308 2.9329018356582 51.7773663086912 Trimmed Mean ( 27 / 52 ) 151.623764705882 2.88230373615385 52.6050612931618 Trimmed Mean ( 28 / 52 ) 151.37585 2.82636770986878 53.5584416250736 Trimmed Mean ( 29 / 52 ) 151.099826530612 2.7679997207812 54.5880931259516 Trimmed Mean ( 30 / 52 ) 150.823864583333 2.70864849143183 55.6823320044772 Trimmed Mean ( 31 / 52 ) 150.823864583333 2.6512487456317 56.8878589124603 Trimmed Mean ( 32 / 52 ) 150.311434782609 2.59295791330263 57.9690993098914 Trimmed Mean ( 33 / 52 ) 150.145088888889 2.55255903235245 58.8213972667714 Trimmed Mean ( 34 / 52 ) 149.977045454545 2.50838186308059 59.7903563496333 Trimmed Mean ( 35 / 52 ) 149.821023255814 2.46975262816268 60.6623600871598 Trimmed Mean ( 36 / 52 ) 149.658166666667 2.42617753146396 61.6847550213537 Trimmed Mean ( 37 / 52 ) 149.493987804878 2.37767401088664 62.8740471235296 Trimmed Mean ( 38 / 52 ) 149.380625 2.33758997771554 63.9036898789177 Trimmed Mean ( 39 / 52 ) 149.263948717949 2.30228907661632 64.8328440741778 Trimmed Mean ( 40 / 52 ) 149.120131578947 2.26637724305539 65.7966947187984 Trimmed Mean ( 41 / 52 ) 148.961891891892 2.22764475934776 66.8696798566335 Trimmed Mean ( 42 / 52 ) 148.781166666667 2.18957242853406 67.9498721886432 Trimmed Mean ( 43 / 52 ) 148.579071428571 2.14685565182691 69.2077603364415 Trimmed Mean ( 44 / 52 ) 148.363838235294 2.0961488389551 70.7792478654576 Trimmed Mean ( 45 / 52 ) 148.363838235294 2.07002210817135 71.6725863214854 Trimmed Mean ( 46 / 52 ) 148.15309375 2.0439444436553 72.4839142325461 Trimmed Mean ( 47 / 52 ) 148.029709677419 2.01962253116014 73.2957309564109 Trimmed Mean ( 48 / 52 ) 147.900666666667 1.99077495779408 74.2930114162933 Trimmed Mean ( 49 / 52 ) 147.767603448276 1.9563080369601 75.5339142182799 Trimmed Mean ( 50 / 52 ) 147.651857142857 1.92529785727802 76.6903970649024 Trimmed Mean ( 51 / 52 ) 147.527907407407 1.88873607131275 78.1093291159888 Trimmed Mean ( 52 / 52 ) 147.436711538462 1.85611761841107 79.4328495543698 Median 146.938 Midrange 464 Midmean - Weighted Average at Xnp 148.861645569620 Midmean - Weighted Average at X(n+1)p 149.263948717949 Midmean - Empirical Distribution Function 148.861645569620 Midmean - Empirical Distribution Function - Averaging 149.263948717949 Midmean - Empirical Distribution Function - Interpolation 149.263948717949 Midmean - Closest Observation 148.861645569620 Midmean - True Basic - Statistics Graphics Toolkit 149.263948717949 Midmean - MS Excel (old versions) 149.380625 Number of observations 156

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/12/t128957788485gn3o5ntadebgd/120oz1289577954.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/12/t128957788485gn3o5ntadebgd/120oz1289577954.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/12/t128957788485gn3o5ntadebgd/2v95k1289577954.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/12/t128957788485gn3o5ntadebgd/2v95k1289577954.ps (open in new window)

Parameters (Session):

par4 = 12 ;

Parameters (R input):

par4 = 12 ;

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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