The total generation of electricity by the U.S. electric industry

*Unverified author*

R Software Module: /rwasp_centraltendency.wasp (opens new window with default values)

Title produced by software: Central Tendency

Date of computation: Wed, 17 Mar 2010 10:12:13 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Mar/17/t1268842644kfl0rgjbtdjwvxs.htm/, Retrieved Wed, 17 Mar 2010 17:17:26 +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/Mar/17/t1268842644kfl0rgjbtdjwvxs.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:

KDGP1W52

Dataseries X:

» Textbox « » Textfile « » CSV «

227.86 198.24 194.97 184.88 196.79 205.36 226.72 226.05 202.50 194.79 192.43 219.25 217.47 192.34 196.83 186.07 197.31 215.02 242.67 225.17 206.69 197.75 196.43 213.55 222.75 194.03 201.85 189.50 206.07 225.59 247.91 247.64 213.01 203.01 200.26 220.50 237.90 216.94 214.01 196.00 208.37 232.75 257.46 267.69 220.18 210.61 209.59 232.75 232.75 219.82 226.74 208.04 220.12 235.69 257.05 258.69 227.15 219.91 219.30 259.04 237.29 212.88 226.03 211.07 222.91 249.18 266.38 268.53 238.02 224.69 213.75 237.43 248.46 210.82 221.40 209.00 234.37 248.43 271.98 268.11 233.88 223.43 221.38 233.76 243.97 217.76 224.66 210.84 220.35 236.84 266.15 255.20 234.76 221.29 221.26 244.13 245.78 224.62 234.80 211.37 222.39 249.63 282.29 279.13 236.60 223.62 225.86 246.41 261.70 225.01 231.54 214.82 227.70 263.86 278.15 274.64 237.66 227.97 224.75 242.91 253.08 228.13 233.68 217.38 236.38 256.08 292.83 304.71 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 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 231.089366197183 2.04529311245124 112.985940641157 Geometric Mean 229.843714260542 Harmonic Mean 228.626783472958 Quadratic Mean 232.362066055433 Winsorized Mean ( 1 / 47 ) 231.014084507042 2.02421474445881 114.125284947870 Winsorized Mean ( 2 / 47 ) 231.024788732394 2.00887048624085 115.002331068493 Winsorized Mean ( 3 / 47 ) 231.059014084507 1.99499972351178 115.819070730384 Winsorized Mean ( 4 / 47 ) 230.874225352113 1.95791509483149 117.918405124704 Winsorized Mean ( 5 / 47 ) 230.819295774648 1.92994140462115 119.599121103865 Winsorized Mean ( 6 / 47 ) 230.81 1.91833290613162 120.318011155549 Winsorized Mean ( 7 / 47 ) 230.645845070423 1.88769927240338 122.183574705080 Winsorized Mean ( 8 / 47 ) 230.554014084507 1.85572586075672 124.23926343867 Winsorized Mean ( 9 / 47 ) 230.379084507042 1.82098812229339 126.513227454168 Winsorized Mean ( 10 / 47 ) 230.395281690141 1.81627995550581 126.850093231348 Winsorized Mean ( 11 / 47 ) 230.388309859155 1.81437037175099 126.979757521511 Winsorized Mean ( 12 / 47 ) 230.39338028169 1.80379401228372 127.727101161621 Winsorized Mean ( 13 / 47 ) 230.395211267606 1.79288580331515 128.505234879763 Winsorized Mean ( 14 / 47 ) 230.314366197183 1.76778604203349 130.284073253713 Winsorized Mean ( 15 / 47 ) 230.503450704225 1.73735516725362 132.674915900242 Winsorized Mean ( 16 / 47 ) 230.424577464789 1.67846642556129 137.282804085720 Winsorized Mean ( 17 / 47 ) 230.243802816901 1.63314350986915 140.981978268002 Winsorized Mean ( 18 / 47 ) 229.971267605634 1.58029687608162 145.524091761702 Winsorized Mean ( 19 / 47 ) 230.238873239437 1.53708423004812 149.789366606298 Winsorized Mean ( 20 / 47 ) 230.265633802817 1.51609717990737 151.880523791282 Winsorized Mean ( 21 / 47 ) 230.252323943662 1.49213052639384 154.311114122256 Winsorized Mean ( 22 / 47 ) 230.397957746479 1.46084659541461 157.715367561293 Winsorized Mean ( 23 / 47 ) 230.294295774648 1.43487145496639 160.498206984013 Winsorized Mean ( 24 / 47 ) 230.252042253521 1.40454076734834 163.934039941196 Winsorized Mean ( 25 / 47 ) 229.982676056338 1.34717312776611 170.715011542500 Winsorized Mean ( 26 / 47 ) 229.911267605634 1.29621305867081 177.371510082914 Winsorized Mean ( 27 / 47 ) 229.764859154930 1.27001130537165 180.915601446313 Winsorized Mean ( 28 / 47 ) 229.559788732394 1.24543366309612 184.321169030966 Winsorized Mean ( 29 / 47 ) 229.514859154930 1.22996967012637 186.602047781672 Winsorized Mean ( 30 / 47 ) 229.426126760563 1.20609658925189 190.222017709933 Winsorized Mean ( 31 / 47 ) 229.749225352113 1.17100991033736 196.197507231961 Winsorized Mean ( 32 / 47 ) 229.661338028169 1.15481949386862 198.87206550247 Winsorized Mean ( 33 / 47 ) 229.724084507042 1.13503265702073 202.394251025366 Winsorized Mean ( 34 / 47 ) 229.731267605634 1.12564890378461 204.087852645030 Winsorized Mean ( 35 / 47 ) 229.534084507042 1.09024155682667 210.53507185613 Winsorized Mean ( 36 / 47 ) 229.579718309859 1.05216642686783 218.197152510645 Winsorized Mean ( 37 / 47 ) 229.577112676056 1.04102141076657 220.530634914612 Winsorized Mean ( 38 / 47 ) 230.021338028169 0.983653998914351 233.843748189954 Winsorized Mean ( 39 / 47 ) 229.818098591549 0.936839108419591 245.312238276691 Winsorized Mean ( 40 / 47 ) 229.79838028169 0.929575029604394 247.207996087725 Winsorized Mean ( 41 / 47 ) 229.576056338028 0.88885753924905 258.282172565004 Winsorized Mean ( 42 / 47 ) 229.945774647887 0.840587126864194 273.553766527092 Winsorized Mean ( 43 / 47 ) 229.394647887324 0.779389398184448 294.326107619232 Winsorized Mean ( 44 / 47 ) 228.694366197183 0.677583746072507 337.514539749173 Winsorized Mean ( 45 / 47 ) 228.684859154930 0.67122960546863 340.695430135668 Winsorized Mean ( 46 / 47 ) 228.675140845070 0.657383167921678 347.856702154435 Winsorized Mean ( 47 / 47 ) 228.618873239437 0.64819051593376 352.703206263511 Trimmed Mean ( 1 / 47 ) 230.893571428571 1.97876495337712 116.685698841851 Trimmed Mean ( 2 / 47 ) 230.769565217391 1.92897864005156 119.633032956354 Trimmed Mean ( 3 / 47 ) 230.636323529412 1.88324263709207 122.467662417382 Trimmed Mean ( 4 / 47 ) 230.487014925373 1.83860510289293 125.359716756315 Trimmed Mean ( 5 / 47 ) 230.382878787879 1.80140210167685 127.890868215056 Trimmed Mean ( 6 / 47 ) 230.287538461538 1.76790442591550 130.260174184634 Trimmed Mean ( 7 / 47 ) 230.287538461538 1.73372487349554 132.828190898151 Trimmed Mean ( 8 / 47 ) 230.117698412698 1.70238687398978 135.173562442588 Trimmed Mean ( 9 / 47 ) 230.055241935484 1.67376772378291 137.447531498297 Trimmed Mean ( 10 / 47 ) 230.013360655738 1.64810329996066 139.562465933554 Trimmed Mean ( 11 / 47 ) 229.968166666667 1.62050070064008 141.911797122848 Trimmed Mean ( 12 / 47 ) 229.922203389831 1.59025623989552 144.581858961885 Trimmed Mean ( 13 / 47 ) 229.874137931034 1.55826537391637 147.519249146437 Trimmed Mean ( 14 / 47 ) 229.874137931034 1.52426382339241 150.809941430890 Trimmed Mean ( 15 / 47 ) 229.779821428571 1.48987560798026 154.227520873418 Trimmed Mean ( 16 / 47 ) 229.717545454545 1.45551319196451 157.825807916241 Trimmed Mean ( 17 / 47 ) 229.659444444444 1.42477117261396 161.190406472851 Trimmed Mean ( 18 / 47 ) 229.613396226415 1.39611543535527 164.465910491123 Trimmed Mean ( 19 / 47 ) 229.58625 1.37050463596353 167.519499004532 Trimmed Mean ( 20 / 47 ) 229.538431372549 1.34678209454763 170.434721624101 Trimmed Mean ( 21 / 47 ) 229.4868 1.32250121826909 173.52483069948 Trimmed Mean ( 22 / 47 ) 229.433979591837 1.29787759663002 176.776284749478 Trimmed Mean ( 23 / 47 ) 229.369166666667 1.27354905975480 180.102340706708 Trimmed Mean ( 24 / 47 ) 229.308404255319 1.24898473803532 183.595841704220 Trimmed Mean ( 25 / 47 ) 229.247717391304 1.22455227056111 187.209417599020 Trimmed Mean ( 26 / 47 ) 229.201333333333 1.20337921208981 190.464760426847 Trimmed Mean ( 27 / 47 ) 229.157272727273 1.18489293070863 193.399139102151 Trimmed Mean ( 28 / 47 ) 229.157272727273 1.16661091450094 196.429906388543 Trimmed Mean ( 29 / 47 ) 229.093571428571 1.14834149961487 199.499514304242 Trimmed Mean ( 30 / 47 ) 229.068414634146 1.12895752396703 202.902597990777 Trimmed Mean ( 31 / 47 ) 229.068414634146 1.10924090212888 206.509166939763 Trimmed Mean ( 32 / 47 ) 229.006025641026 1.09025148308166 210.048809100197 Trimmed Mean ( 33 / 47 ) 228.967763157895 1.06997928490661 213.992706576444 Trimmed Mean ( 34 / 47 ) 228.923783783784 1.04854179703867 218.325854467908 Trimmed Mean ( 35 / 47 ) 228.876944444444 1.02442878161199 223.419088327736 Trimmed Mean ( 36 / 47 ) 228.838857142857 1.00064936561882 228.690353489946 Trimmed Mean ( 37 / 47 ) 228.795882352941 0.977425019079908 234.080239288653 Trimmed Mean ( 38 / 47 ) 228.750454545455 0.951178447880209 240.491629152497 Trimmed Mean ( 39 / 47 ) 228.67625 0.927329582063379 246.596522340178 Trimmed Mean ( 40 / 47 ) 228.609193548387 0.90544160506303 252.483641429833 Trimmed Mean ( 41 / 47 ) 228.538833333333 0.8798181659746 259.756893153228 Trimmed Mean ( 42 / 47 ) 228.476896551724 0.855095230256145 267.194680156599 Trimmed Mean ( 43 / 47 ) 228.388214285714 0.831655917672018 274.618636665295 Trimmed Mean ( 44 / 47 ) 228.326666666667 0.813374015204177 280.715467175763 Trimmed Mean ( 45 / 47 ) 228.303846153846 0.80699315067556 282.906795383239 Trimmed Mean ( 46 / 47 ) 228.2798 0.799109529413835 285.66772338136 Trimmed Mean ( 47 / 47 ) 228.254375 0.790544122373525 288.730721714419 Median 226.73 Midrange 244.795 Midmean - Weighted Average at Xnp 228.63 Midmean - Weighted Average at X(n+1)p 228.876944444444 Midmean - Empirical Distribution Function 228.876944444444 Midmean - Empirical Distribution Function - Averaging 228.876944444444 Midmean - Empirical Distribution Function - Interpolation 228.838857142857 Midmean - Closest Observation 228.876944444444 Midmean - True Basic - Statistics Graphics Toolkit 228.876944444444 Midmean - MS Excel (old versions) 228.876944444444 Number of observations 142

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Mar/17/t1268842644kfl0rgjbtdjwvxs/18xt61268842330.png (open in new window) http://www.freestatistics.org/blog/date/2010/Mar/17/t1268842644kfl0rgjbtdjwvxs/18xt61268842330.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Mar/17/t1268842644kfl0rgjbtdjwvxs/2j6ab1268842330.png (open in new window) http://www.freestatistics.org/blog/date/2010/Mar/17/t1268842644kfl0rgjbtdjwvxs/2j6ab1268842330.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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