*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Sun, 03 Jan 2010 10:27:45 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/03/t1262540587kmxxqz1qn00p7g0.htm/, Retrieved Sun, 03 Jan 2010 18:43:09 +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/Jan/03/t1262540587kmxxqz1qn00p7g0.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 «

8357,00 7454,00 8076,00 7248,00 7339,00 7292,00 7359,00 7537,00 7441,00 8057,00 8037,00 8257,00 8692,00 8119,00 8236,00 7432,00 7669,00 7453,00 7566,00 7731,00 7657,00 8130,00 8401,00 8737,00 9009,00 7919,00 8228,00 7903,00 7912,00 7857,00 7965,00 8091,00 8024,00 8772,00 8656,00 8953,00 9014,00 8103,00 8876,00 8231,00 8173,00 8087,00 8296,00 8007,00 8382,00 9168,00 9137,00 9321,00 9234,00 8451,00 9101,00 8279,00 8284,00 8225,00 8597,00 8305,00 8620,00 9102,00 9258,00 9652,00 9522,00 8874,00 9415,00 8525,00 8862,00 8421,00 8626,00 8750,00 8852,00 9412,00 9570,00 9513,00 9986,00 8907,00 9663,00 8799,00 8931,00 8732,00 8936,00 9127,00 9070,00 9773,00 9670,00 9929,00 10095,00 9025,00 9659,00 8954,00 9022,00 8855,00 9034,00 9196,00 9038,00 9650,00 9715,00 10052,00 10436,00 9314,00 9717,00 8997,00 9062,00 8885,00 9058,00 9095,00 9149,00 9857,00 9848,00 10269,00 10341,00 9690,00 10125,00 9349,00 9224,00 9 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 9212.85142857143 71.6403941923656 128.598558570651 Geometric Mean 9163.98618728869 Harmonic Mean 9114.71155621063 Quadratic Mean 9261.1910499059 Winsorized Mean ( 1 / 58 ) 9211.14285714286 71.2503930052414 129.278484912571 Winsorized Mean ( 2 / 58 ) 9211.14285714286 71.0800897594805 129.588227706399 Winsorized Mean ( 3 / 58 ) 9209.54857142857 70.7219865890254 130.221859079644 Winsorized Mean ( 4 / 58 ) 9210.62285714286 70.3842480111678 130.861991388775 Winsorized Mean ( 5 / 58 ) 9207.42285714286 69.8340869122148 131.847114557638 Winsorized Mean ( 6 / 58 ) 9207.25142857143 69.690769222679 132.115795696730 Winsorized Mean ( 7 / 58 ) 9206.97142857143 69.6393153913939 132.209390296637 Winsorized Mean ( 8 / 58 ) 9209.76 68.9573957424888 133.557247933093 Winsorized Mean ( 9 / 58 ) 9206.05714285714 68.029845046349 135.323799967279 Winsorized Mean ( 10 / 58 ) 9208.97142857143 67.0155943073397 137.4153512142 Winsorized Mean ( 11 / 58 ) 9208.40571428571 66.7394881901606 137.975372062312 Winsorized Mean ( 12 / 58 ) 9211.69714285714 66.0565163179587 139.451755198794 Winsorized Mean ( 13 / 58 ) 9216.74857142857 64.317870540326 143.299964597706 Winsorized Mean ( 14 / 58 ) 9219.70857142857 63.7820318797604 144.550248709687 Winsorized Mean ( 15 / 58 ) 9211.82285714286 62.5864544929314 147.185568055836 Winsorized Mean ( 16 / 58 ) 9212.18857142857 62.4760722084266 147.451468150811 Winsorized Mean ( 17 / 58 ) 9214.32571428571 61.661077570609 149.435041963616 Winsorized Mean ( 18 / 58 ) 9215.35428571429 60.7580563652018 151.672960542435 Winsorized Mean ( 19 / 58 ) 9209.38285714286 59.6067868840959 154.502252823161 Winsorized Mean ( 20 / 58 ) 9207.89714285714 59.0878503645921 155.834018094097 Winsorized Mean ( 21 / 58 ) 9202.01714285714 57.8621381062406 159.033479301462 Winsorized Mean ( 22 / 58 ) 9203.90285714286 57.5348539610064 159.970908475421 Winsorized Mean ( 23 / 58 ) 9196.41142857143 56.3771216734415 163.123110148132 Winsorized Mean ( 24 / 58 ) 9191.61142857143 55.7361923602826 164.912797938478 Winsorized Mean ( 25 / 58 ) 9186.61142857143 54.8314328538477 167.542793438541 Winsorized Mean ( 26 / 58 ) 9188.84 54.5469278746135 168.457516454864 Winsorized Mean ( 27 / 58 ) 9188.06857142857 54.0985038376844 169.839605897350 Winsorized Mean ( 28 / 58 ) 9193.98857142857 53.2326640149896 172.713290637486 Winsorized Mean ( 29 / 58 ) 9200.61714285714 52.0861750734207 176.642211294800 Winsorized Mean ( 30 / 58 ) 9197.18857142857 51.621016575491 178.167521322997 Winsorized Mean ( 31 / 58 ) 9192.40571428571 51.0189076623265 180.176450956703 Winsorized Mean ( 32 / 58 ) 9185.45714285714 50.1292632642957 183.235430659110 Winsorized Mean ( 33 / 58 ) 9176.97142857143 48.4824572939451 189.284370900019 Winsorized Mean ( 34 / 58 ) 9170.94857142857 47.0432161784451 194.947312629332 Winsorized Mean ( 35 / 58 ) 9171.14857142857 46.8599128581701 195.714161893273 Winsorized Mean ( 36 / 58 ) 9162.30285714286 45.5567561166036 201.118421023914 Winsorized Mean ( 37 / 58 ) 9160.61142857143 45.0277767135825 203.443565220669 Winsorized Mean ( 38 / 58 ) 9169.94857142857 43.6323882568498 210.163801198505 Winsorized Mean ( 39 / 58 ) 9164.6 42.0752738547325 217.814387415311 Winsorized Mean ( 40 / 58 ) 9165.51428571429 41.3161823019671 221.838363930297 Winsorized Mean ( 41 / 58 ) 9167.62285714286 40.5981043781508 225.814061951048 Winsorized Mean ( 42 / 58 ) 9173.14285714286 39.6971083098412 231.078364336900 Winsorized Mean ( 43 / 58 ) 9189.85142857143 37.6970370916481 243.781796596621 Winsorized Mean ( 44 / 58 ) 9206.44571428571 35.7602999891382 257.448783066195 Winsorized Mean ( 45 / 58 ) 9209.01714285714 34.8901597809837 263.943106040929 Winsorized Mean ( 46 / 58 ) 9204.28571428571 34.1886833053073 269.220245544141 Winsorized Mean ( 47 / 58 ) 9211.80571428571 33.3648819804002 276.092860741935 Winsorized Mean ( 48 / 58 ) 9220.85714285714 32.3567579369370 284.974692483978 Winsorized Mean ( 49 / 58 ) 9231.21714285714 31.2427161947232 295.467816732793 Winsorized Mean ( 50 / 58 ) 9230.93142857143 30.9611893168324 298.145246751 Winsorized Mean ( 51 / 58 ) 9230.93142857143 30.2835788075084 304.81639859166 Winsorized Mean ( 52 / 58 ) 9231.52571428571 29.1737446542647 316.432663125274 Winsorized Mean ( 53 / 58 ) 9237.58285714286 28.2555326396058 326.930055609517 Winsorized Mean ( 54 / 58 ) 9252.70285714286 26.7131183363211 346.372996991602 Winsorized Mean ( 55 / 58 ) 9252.70285714286 26.549527128702 348.50725635486 Winsorized Mean ( 56 / 58 ) 9253.66285714286 26.2453493314423 352.58295632807 Winsorized Mean ( 57 / 58 ) 9256.92 25.8545145730784 358.038824277095 Winsorized Mean ( 58 / 58 ) 9256.92 25.7402171893009 359.628667152339 Trimmed Mean ( 1 / 58 ) 9210.36416184971 70.2102277022036 131.182656192420 Trimmed Mean ( 2 / 58 ) 9209.56725146199 69.09402715227 133.290352741575 Trimmed Mean ( 3 / 58 ) 9208.75147928994 67.990206654905 135.442322245473 Trimmed Mean ( 4 / 58 ) 9208.47305389222 66.9422014679543 137.558563237577 Trimmed Mean ( 5 / 58 ) 9207.90303030303 65.9163231481546 139.690786599356 Trimmed Mean ( 6 / 58 ) 9208.00613496933 64.948862420779 141.773170333826 Trimmed Mean ( 7 / 58 ) 9208.14285714286 63.9373716140147 144.018163785833 Trimmed Mean ( 8 / 58 ) 9208.32704402516 62.8561512383296 146.498423187100 Trimmed Mean ( 9 / 58 ) 9208.12738853503 61.8069833838524 148.981990131243 Trimmed Mean ( 10 / 58 ) 9208.38709677419 60.8208680997342 151.401770222587 Trimmed Mean ( 11 / 58 ) 9208.32026143791 59.9040129564206 153.717919835134 Trimmed Mean ( 12 / 58 ) 9208.31125827815 58.9498759548584 156.205778368889 Trimmed Mean ( 13 / 58 ) 9207.97986577181 58.0056950907422 158.742686409794 Trimmed Mean ( 14 / 58 ) 9207.1768707483 57.197558375013 160.971501797015 Trimmed Mean ( 15 / 58 ) 9206.09655172414 56.3833284272424 163.276926150321 Trimmed Mean ( 16 / 58 ) 9205.62937062937 55.6350873530102 165.464454332906 Trimmed Mean ( 17 / 58 ) 9205.12056737589 54.8331663986557 167.875050301701 Trimmed Mean ( 18 / 58 ) 9204.43884892086 54.0469025903123 170.304650364380 Trimmed Mean ( 19 / 58 ) 9203.66423357664 53.2835054528707 172.730081389208 Trimmed Mean ( 20 / 58 ) 9203.27407407407 52.5672734155262 175.076116300067 Trimmed Mean ( 21 / 58 ) 9202.96992481203 51.8357642000127 177.540932729410 Trimmed Mean ( 22 / 58 ) 9203.03053435114 51.1544194163973 179.906851438160 Trimmed Mean ( 23 / 58 ) 9202.97674418605 50.4387165870541 182.458582749668 Trimmed Mean ( 24 / 58 ) 9203.37007874016 49.7624387751906 184.946122120698 Trimmed Mean ( 25 / 58 ) 9204.056 49.0781881832942 187.538626438801 Trimmed Mean ( 26 / 58 ) 9205.0487804878 48.4076618570231 190.156855905907 Trimmed Mean ( 27 / 58 ) 9205.95041322314 47.6944704755683 193.019239367359 Trimmed Mean ( 28 / 58 ) 9206.9243697479 46.9479208530094 196.109310113522 Trimmed Mean ( 29 / 58 ) 9206.9243697479 46.2032262366004 199.270161841091 Trimmed Mean ( 30 / 58 ) 9207.98260869565 45.4847557175621 202.441069836072 Trimmed Mean ( 31 / 58 ) 9207.98260869565 44.7297264167329 205.858236710588 Trimmed Mean ( 32 / 58 ) 9209.36036036036 43.9454669000515 209.563375018996 Trimmed Mean ( 33 / 58 ) 9210.55963302752 43.153761135047 213.435848713248 Trimmed Mean ( 34 / 58 ) 9212.22429906542 42.4221388047241 217.156054801262 Trimmed Mean ( 35 / 58 ) 9214.24761904762 41.73507669273 220.779458173433 Trimmed Mean ( 36 / 58 ) 9216.33980582524 40.9806906966547 224.894691845142 Trimmed Mean ( 37 / 58 ) 9218.9405940594 40.251350931839 229.034315138158 Trimmed Mean ( 38 / 58 ) 9221.72727272727 39.4778244409381 233.592590354711 Trimmed Mean ( 39 / 58 ) 9224.18556701031 38.7464617880742 238.065235929476 Trimmed Mean ( 40 / 58 ) 9227 38.0657120286859 242.396621743123 Trimmed Mean ( 41 / 58 ) 9229.89247311828 37.3663317014929 247.010933448132 Trimmed Mean ( 42 / 58 ) 9232.81318681319 36.6420227275965 251.973349163926 Trimmed Mean ( 43 / 58 ) 9235.60674157303 35.9107202788278 257.182442175023 Trimmed Mean ( 44 / 58 ) 9237.74712643678 35.2931747586857 261.743161095570 Trimmed Mean ( 45 / 58 ) 9237.74712643678 34.7910614028137 265.520704283247 Trimmed Mean ( 46 / 58 ) 9240.6265060241 34.2995983662779 269.409175213817 Trimmed Mean ( 47 / 58 ) 9242.33333333333 33.7978513087211 273.459198601435 Trimmed Mean ( 48 / 58 ) 9243.77215189873 33.3042048128636 277.555708170770 Trimmed Mean ( 49 / 58 ) 9244.85714285714 32.8393358683662 281.517786471517 Trimmed Mean ( 50 / 58 ) 9245.50666666667 32.4174426040208 285.201605185226 Trimmed Mean ( 51 / 58 ) 9246.20547945206 31.9475881122312 289.417950643733 Trimmed Mean ( 52 / 58 ) 9246.94366197183 31.467513108234 293.856830381433 Trimmed Mean ( 53 / 58 ) 9247.69565217391 31.0265149671906 298.057827698438 Trimmed Mean ( 54 / 58 ) 9248.19402985075 30.6085809514815 302.143834910554 Trimmed Mean ( 55 / 58 ) 9247.96923076923 30.2917382842917 305.296749363668 Trimmed Mean ( 56 / 58 ) 9247.73015873016 29.9181953992786 309.100533481814 Trimmed Mean ( 57 / 58 ) 9247.4262295082 29.4949607534071 313.525632626583 Trimmed Mean ( 58 / 58 ) 9247.4262295082 29.0231559178391 318.622352982098 Median 9227 Midrange 9428 Midmean - Weighted Average at Xnp 9229.64772727273 Midmean - Weighted Average at X(n+1)p 9235.60674157303 Midmean - Empirical Distribution Function 9235.60674157303 Midmean - Empirical Distribution Function - Averaging 9235.60674157303 Midmean - Empirical Distribution Function - Interpolation 9237.74712643678 Midmean - Closest Observation 9229.64772727273 Midmean - True Basic - Statistics Graphics Toolkit 9235.60674157303 Midmean - MS Excel (old versions) 9235.60674157303 Number of observations 175

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/03/t1262540587kmxxqz1qn00p7g0/119y81262539662.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/03/t1262540587kmxxqz1qn00p7g0/119y81262539662.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/03/t1262540587kmxxqz1qn00p7g0/2i9mc1262539662.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/03/t1262540587kmxxqz1qn00p7g0/2i9mc1262539662.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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