cs.shw.paper.centraltendency

*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: Wed, 09 Dec 2009 10:53:46 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/09/t12603812795yk47qx6x4ayswm.htm/, Retrieved Wed, 09 Dec 2009 18:54:41 +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/2009/Dec/09/t12603812795yk47qx6x4ayswm.htm/}, year = {2009}, } @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 = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

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Dataseries X:

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2430.47 2516.3 2633.63 2799.84 3001.93 3229.29 3173.02 3322.08 3417.88 3486.95 3016.22 2709.61 2914.87 3203.08 3320.25 3446.25 3456.85 3566.53 3763.67 3607.75 3747.38 3623.91 3699.76 3629.61 3911.52 4281.47 4742.42 4522.42 4879.79 5059.11 5093.19 4941.81 4832.67 4876.18 5018.07 4780.34 4953.59 4622.32 4557.13 4560.03 4105.66 4004.89 4277.26 4245.98 4057.64 3931.42 3637.15 3339.91 3465.74 3571.25 3706.93 3584.17 3552.11 3695.24 3510 3357.7 3060.91 2736.98 2709.45 2314.96 2561.29 2663.49 2407.87 2237.74 2165.44 2098.89 2318.54 2315.49 2395.47 2474.07 2479.57 2386.92 2537.84 2567.13 2660.37 2696.28 2748.5 2663.32 2707.69 2669.36 2687.68 2650.24 2620.03 2668.47 2692.06 2737.67 2774.77 2819.19 2892.56 2866.08 2817.41 2934.75 3036.54 3139.5 3114.31 3261.3 3201.79 3264.53 3349.1 3446.17 3469.48 3507.13 3536.2 3359.05 3378.85 3449.15 3522.89 3551.04 3669.15 3602 3697.22 3760.9 3665.08 3708.8 3858.21 3933 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 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 3257.92468965517 63.5017659078115 51.3044738690394 Geometric Mean 3170.11736260120 Harmonic Mean 3083.62959368667 Quadratic Mean 3345.85558248348 Winsorized Mean ( 1 / 48 ) 3258.004 63.4046057590438 51.3843428406664 Winsorized Mean ( 2 / 48 ) 3259.74813793103 62.9487260892121 51.7841796085159 Winsorized Mean ( 3 / 48 ) 3259.31386206897 62.5690079655187 52.0915061313606 Winsorized Mean ( 4 / 48 ) 3260.05068965517 62.3647888349947 52.2738992716009 Winsorized Mean ( 5 / 48 ) 3259.04379310345 61.8207766618119 52.7176132213919 Winsorized Mean ( 6 / 48 ) 3258.91965517241 61.7903119813691 52.7415957400416 Winsorized Mean ( 7 / 48 ) 3258.07048275862 61.2499477325863 53.193032865647 Winsorized Mean ( 8 / 48 ) 3257.07186206897 60.4994311820562 53.836404713752 Winsorized Mean ( 9 / 48 ) 3255.11544827586 60.0411427868838 54.2147483739592 Winsorized Mean ( 10 / 48 ) 3251.81889655172 58.0320147670454 56.0349129632206 Winsorized Mean ( 11 / 48 ) 3251.16344827586 56.7802862130562 57.2586660813323 Winsorized Mean ( 12 / 48 ) 3252.87406896552 56.5149508255568 57.5577616444554 Winsorized Mean ( 13 / 48 ) 3249.80965517241 56.0145887807624 58.0172009811938 Winsorized Mean ( 14 / 48 ) 3226.84 52.5155703368678 61.4453957045696 Winsorized Mean ( 15 / 48 ) 3227.73793103448 52.295415706601 61.7212405221795 Winsorized Mean ( 16 / 48 ) 3230.40937931035 51.1012086857994 63.215909415623 Winsorized Mean ( 17 / 48 ) 3214.96048275862 48.8054807235068 65.8729395776683 Winsorized Mean ( 18 / 48 ) 3209.75662068966 47.9733704815132 66.9070483994147 Winsorized Mean ( 19 / 48 ) 3203.67006896552 47.0435038888149 68.1001584519989 Winsorized Mean ( 20 / 48 ) 3195.91144827586 46.0919851800186 69.3376828052381 Winsorized Mean ( 21 / 48 ) 3196.94262068966 45.5092851624409 70.2481396769578 Winsorized Mean ( 22 / 48 ) 3200.232 45.0674406668767 71.0098455258428 Winsorized Mean ( 23 / 48 ) 3200.27641379310 44.3477419712333 72.1632324790967 Winsorized Mean ( 24 / 48 ) 3196.81710344828 43.7621747674035 73.0497769007001 Winsorized Mean ( 25 / 48 ) 3198.51020689655 42.5458682560999 75.1779276813318 Winsorized Mean ( 26 / 48 ) 3195.58027586207 41.3959576115824 77.1954669063618 Winsorized Mean ( 27 / 48 ) 3195.098 40.4134663166619 79.0602314328752 Winsorized Mean ( 28 / 48 ) 3190.67786206897 39.7261631419485 80.3167889803028 Winsorized Mean ( 29 / 48 ) 3200.87986206897 38.5576434838123 83.0154431873617 Winsorized Mean ( 30 / 48 ) 3203.12055172414 38.2067299927798 83.8365532022618 Winsorized Mean ( 31 / 48 ) 3201.68172413793 37.9957411975449 84.2642260218576 Winsorized Mean ( 32 / 48 ) 3203.84889655172 37.5348253786803 85.3567017890406 Winsorized Mean ( 33 / 48 ) 3197.37406896552 36.4290714158255 87.7698482200816 Winsorized Mean ( 34 / 48 ) 3197.62731034483 36.3156014955722 88.0510628671455 Winsorized Mean ( 35 / 48 ) 3195.93765517241 36.1433604409669 88.4239211899611 Winsorized Mean ( 36 / 48 ) 3196.54344827586 35.956051478774 88.9014037084379 Winsorized Mean ( 37 / 48 ) 3196.26531034483 35.8840692541434 89.071985891783 Winsorized Mean ( 38 / 48 ) 3198.06572413793 35.1078742172884 91.0925481943047 Winsorized Mean ( 39 / 48 ) 3195.30613793103 34.6125851324571 92.3163099694253 Winsorized Mean ( 40 / 48 ) 3195.34751724138 34.3885308031632 92.9189890528111 Winsorized Mean ( 41 / 48 ) 3190.67634482759 33.3214733130605 95.7543598042822 Winsorized Mean ( 42 / 48 ) 3189.00213793103 33.0674901764961 96.4391951402992 Winsorized Mean ( 43 / 48 ) 3187.35924137931 32.9066397923605 96.8606719340356 Winsorized Mean ( 44 / 48 ) 3190.76089655172 31.6236247560259 100.898012835917 Winsorized Mean ( 45 / 48 ) 3189.19055172414 31.4392164831218 101.439886500869 Winsorized Mean ( 46 / 48 ) 3186.96986206897 30.5838547888437 104.204322315560 Winsorized Mean ( 47 / 48 ) 3191.29710344828 29.3634546691048 108.682617199197 Winsorized Mean ( 48 / 48 ) 3198.03365517241 28.4101606665262 112.566545916808 Trimmed Mean ( 1 / 48 ) 3255.49286713287 62.2220277490774 52.3205846048812 Trimmed Mean ( 2 / 48 ) 3252.9104964539 60.9325431760066 53.385437844892 Trimmed Mean ( 3 / 48 ) 3249.34410071942 59.7849886611076 54.3505012460301 Trimmed Mean ( 4 / 48 ) 3245.82678832117 58.6820405564237 55.3120981742319 Trimmed Mean ( 5 / 48 ) 3242.00740740741 57.5380391950959 56.3454621109808 Trimmed Mean ( 6 / 48 ) 3238.29270676692 56.4262575064377 57.3898190287998 Trimmed Mean ( 7 / 48 ) 3234.48748091603 55.2122546570422 58.5827820473454 Trimmed Mean ( 8 / 48 ) 3230.70062015504 53.9875051202176 59.8416358185291 Trimmed Mean ( 9 / 48 ) 3226.93700787402 52.7787265984721 61.1408651903214 Trimmed Mean ( 10 / 48 ) 3223.30512 51.5251028493273 62.5579560593169 Trimmed Mean ( 11 / 48 ) 3219.9437398374 50.4676190448538 63.8021725767492 Trimmed Mean ( 12 / 48 ) 3216.5426446281 49.4881149786776 64.9962651843574 Trimmed Mean ( 13 / 48 ) 3212.85352941176 48.4402070711567 66.3261724850228 Trimmed Mean ( 14 / 48 ) 3209.33042735043 47.3488290715546 67.7805658615215 Trimmed Mean ( 15 / 48 ) 3207.75347826087 46.6121079167208 68.8180307998939 Trimmed Mean ( 16 / 48 ) 3206.04389380531 45.8207825521571 69.9692086261495 Trimmed Mean ( 17 / 48 ) 3204.05459459459 45.0851955222445 71.0666673944833 Trimmed Mean ( 18 / 48 ) 3203.20119266055 44.5388071071446 71.9193305953342 Trimmed Mean ( 19 / 48 ) 3202.7076635514 44.0213984877884 72.7534284136802 Trimmed Mean ( 20 / 48 ) 3202.63771428571 43.542967508603 73.5512046498198 Trimmed Mean ( 21 / 48 ) 3203.11116504854 43.1056002140494 74.3084691813328 Trimmed Mean ( 22 / 48 ) 3203.53287128713 42.6720849310542 75.0732680735689 Trimmed Mean ( 23 / 48 ) 3203.75262626263 42.2264440209743 75.8707653590552 Trimmed Mean ( 24 / 48 ) 3203.97855670103 41.7944695181334 76.6603474967164 Trimmed Mean ( 25 / 48 ) 3204.434 41.361559111395 77.4737236420372 Trimmed Mean ( 26 / 48 ) 3204.80344086022 40.9909517143961 78.1831918221767 Trimmed Mean ( 27 / 48 ) 3205.36868131868 40.6788341928226 78.796965176653 Trimmed Mean ( 28 / 48 ) 3205.98842696629 40.4122752525178 79.332044705069 Trimmed Mean ( 29 / 48 ) 3206.89977011494 40.1639145205884 79.8452991545695 Trimmed Mean ( 30 / 48 ) 3207.25388235294 39.9844049203858 80.212620113742 Trimmed Mean ( 31 / 48 ) 3207.49457831325 39.7973130922843 80.5957570772562 Trimmed Mean ( 32 / 48 ) 3207.83024691358 39.5863918097063 81.033660817934 Trimmed Mean ( 33 / 48 ) 3208.05860759494 39.3732732263248 81.4780775058865 Trimmed Mean ( 34 / 48 ) 3208.66831168831 39.2207410892847 81.8104967569043 Trimmed Mean ( 35 / 48 ) 3209.29613333333 39.0339115438175 82.2181535593925 Trimmed Mean ( 36 / 48 ) 3210.05424657534 38.8126073857723 82.7064828360916 Trimmed Mean ( 37 / 48 ) 3210.82070422535 38.5529240291856 83.2834547593503 Trimmed Mean ( 38 / 48 ) 3211.64739130435 38.2345952512393 83.9984671002956 Trimmed Mean ( 39 / 48 ) 3212.42089552239 37.9293913527661 84.6947652190078 Trimmed Mean ( 40 / 48 ) 3213.39984615385 37.6031313526370 85.455645063679 Trimmed Mean ( 41 / 48 ) 3214.43857142857 37.2187364983883 86.366139043107 Trimmed Mean ( 42 / 48 ) 3215.8162295082 36.8690287066058 87.2226999821132 Trimmed Mean ( 43 / 48 ) 3217.38525423729 36.4535092391364 88.2599596415017 Trimmed Mean ( 44 / 48 ) 3219.16157894737 35.9444507280985 89.5593482092322 Trimmed Mean ( 45 / 48 ) 3220.86327272727 35.4880166183651 90.7591795665603 Trimmed Mean ( 46 / 48 ) 3222.78886792453 34.9241274217802 92.2797248161068 Trimmed Mean ( 47 / 48 ) 3225.00274509804 34.3321852700628 93.9352598656223 Trimmed Mean ( 48 / 48 ) 3227.12489795918 33.7738637692665 95.5509538383287 Median 3240.75 Midrange 3431.8 Midmean - Weighted Average at Xnp 3203.28805555556 Midmean - Weighted Average at X(n+1)p 3210.05424657534 Midmean - Empirical Distribution Function 3210.05424657534 Midmean - Empirical Distribution Function - Averaging 3210.05424657534 Midmean - Empirical Distribution Function - Interpolation 3210.05424657534 Midmean - Closest Observation 3202.66824324324 Midmean - True Basic - Statistics Graphics Toolkit 3210.05424657534 Midmean - MS Excel (old versions) 3210.05424657534 Number of observations 145

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/09/t12603812795yk47qx6x4ayswm/1udx11260381224.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t12603812795yk47qx6x4ayswm/1udx11260381224.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t12603812795yk47qx6x4ayswm/2i3371260381224.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t12603812795yk47qx6x4ayswm/2i3371260381224.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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