WS6 - Mini Tutorial - Hyp 1 - Central tend. v2

*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: Thu, 11 Nov 2010 15:32:39 +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/11/t1289489467ulnhefwrrdakgp9.htm/, Retrieved Thu, 11 Nov 2010 16:31:08 +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/11/t1289489467ulnhefwrrdakgp9.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:

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Dataseries X:

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2.4 2.4 2.5 2.6 2.4 2.6 2.4 2.3 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.5 2.1 2.1 2 2 2 1.9 1.9 2 1.8 1.6 1.3 1.4 1.4 1.5 1.7 1.6 1.5 1.6 1.5 1.1 1.1 1.1 1.4 1.3 1.4 1.3 1.1 1 0.9 0.8 0.8 0.8 0.8 1 1.1 1 0.9 1.1 1.2 1.2 1.4 1.5 1.7 1.9 1.9 1.9 1.7 1.7 2.1 2 2 2.5 2.4 2.5 2.5 2 1.9 2.2 2.7 3.1 2.8 2.6 2.3 2.2 2.2 2 2 2.6 2.5 2.5 2.3 2 1.9 2 2.1 2.1 2.3 2.3 2.3 2.1 2.4 2.5 2.1 1.8 1.9 1.9 2.1 2.2 2 2.2 2 1.9 1.6 1.7 2 2.5 2.4 2.3 2.3 2.1 2.4 2.2 2.4 1.9 2.1 2.1 2.1 2 2.1 2.2 2.2 2.6 2.5 2.3 2.2 2.4 2.3 2.2 2.5 2.5 2.5 2.4 2.3 1.7 1.6 1.9 1.9 1.8 1.8 1.9 1.9 1.9 1.9 1.8 1.7 2.1 2.6 3.1 3.1 3.2 3.3 3.6 3.3 3.7 4 4 3.8 3.6 3.2 2.1 1.6 1.1 1.2 0.6 0.6 0 -0.1 -0.6 -0.2 -0.3 -0.1 0.5 0.9

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 1.96277777777778 0.056292612278318 34.8674132952571 Geometric Mean NaN Harmonic Mean 0 Quadratic Mean 2.10231354041737 Winsorized Mean ( 1 / 60 ) 1.96444444444444 0.0558919671681953 35.1471695124428 Winsorized Mean ( 2 / 60 ) 1.96333333333333 0.0552119104810691 35.5599600924246 Winsorized Mean ( 3 / 60 ) 1.96333333333333 0.0545502462015119 35.991282717234 Winsorized Mean ( 4 / 60 ) 1.96111111111111 0.0541647765829178 36.2063915856638 Winsorized Mean ( 5 / 60 ) 1.96388888888889 0.053585089512442 36.6499133762367 Winsorized Mean ( 6 / 60 ) 1.97055555555556 0.0488457536414719 40.3424127718336 Winsorized Mean ( 7 / 60 ) 1.97444444444444 0.048208901809416 40.9560137306178 Winsorized Mean ( 8 / 60 ) 1.97 0.047546246558092 41.4333442197809 Winsorized Mean ( 9 / 60 ) 1.98 0.0460237706104598 43.0212469282125 Winsorized Mean ( 10 / 60 ) 1.97444444444444 0.0452259807764925 43.6573051716043 Winsorized Mean ( 11 / 60 ) 1.97444444444444 0.0452259807764925 43.6573051716043 Winsorized Mean ( 12 / 60 ) 1.97444444444444 0.0452259807764925 43.6573051716043 Winsorized Mean ( 13 / 60 ) 1.96 0.0414405409684066 47.2966798742869 Winsorized Mean ( 14 / 60 ) 1.95222222222222 0.0405995993928519 48.0847656483511 Winsorized Mean ( 15 / 60 ) 1.94388888888889 0.039786549424887 48.8579411129572 Winsorized Mean ( 16 / 60 ) 1.95277777777778 0.0385203563003623 50.6946966572947 Winsorized Mean ( 17 / 60 ) 1.95277777777778 0.0385203563003623 50.6946966572947 Winsorized Mean ( 18 / 60 ) 1.95277777777778 0.0385203563003623 50.6946966572947 Winsorized Mean ( 19 / 60 ) 1.96333333333333 0.0371042117904215 52.9140288553487 Winsorized Mean ( 20 / 60 ) 1.96333333333333 0.0371042117904215 52.9140288553487 Winsorized Mean ( 21 / 60 ) 1.95166666666667 0.0360484104464669 54.1401588168487 Winsorized Mean ( 22 / 60 ) 1.95166666666667 0.0360484104464669 54.1401588168487 Winsorized Mean ( 23 / 60 ) 1.95166666666667 0.0360484104464669 54.1401588168487 Winsorized Mean ( 24 / 60 ) 1.95166666666667 0.0360484104464669 54.1401588168487 Winsorized Mean ( 25 / 60 ) 1.95166666666667 0.0360484104464669 54.1401588168487 Winsorized Mean ( 26 / 60 ) 1.96611111111111 0.0341897786106552 57.5058158024566 Winsorized Mean ( 27 / 60 ) 1.96611111111111 0.0341897786106552 57.5058158024566 Winsorized Mean ( 28 / 60 ) 1.96611111111111 0.0341897786106552 57.5058158024567 Winsorized Mean ( 29 / 60 ) 1.98222222222222 0.0322270378901075 61.5080488930287 Winsorized Mean ( 30 / 60 ) 1.98222222222222 0.0322270378901075 61.5080488930287 Winsorized Mean ( 31 / 60 ) 1.98222222222222 0.0322270378901075 61.5080488930287 Winsorized Mean ( 32 / 60 ) 2 0.0301866735100768 66.2544019410541 Winsorized Mean ( 33 / 60 ) 2 0.0301866735100768 66.2544019410541 Winsorized Mean ( 34 / 60 ) 1.98111111111111 0.0285354157084087 69.4263974057796 Winsorized Mean ( 35 / 60 ) 1.98111111111111 0.0285354157084087 69.4263974057796 Winsorized Mean ( 36 / 60 ) 1.98111111111111 0.0285354157084087 69.4263974057796 Winsorized Mean ( 37 / 60 ) 2.00166666666667 0.0262664922623217 76.2060897464404 Winsorized Mean ( 38 / 60 ) 2.00166666666667 0.0262664922623217 76.2060897464404 Winsorized Mean ( 39 / 60 ) 2.00166666666667 0.0262664922623217 76.2060897464404 Winsorized Mean ( 40 / 60 ) 2.00166666666667 0.0262664922623217 76.2060897464404 Winsorized Mean ( 41 / 60 ) 2.02444444444444 0.023918211328628 84.6402942356047 Winsorized Mean ( 42 / 60 ) 2.02444444444444 0.023918211328628 84.6402942356047 Winsorized Mean ( 43 / 60 ) 2.02444444444444 0.023918211328628 84.6402942356047 Winsorized Mean ( 44 / 60 ) 2.02444444444444 0.023918211328628 84.6402942356047 Winsorized Mean ( 45 / 60 ) 2.02444444444444 0.023918211328628 84.6402942356047 Winsorized Mean ( 46 / 60 ) 2.02444444444444 0.023918211328628 84.6402942356047 Winsorized Mean ( 47 / 60 ) 2.05055555555556 0.0214249838529422 95.7086161478699 Winsorized Mean ( 48 / 60 ) 2.05055555555556 0.0214249838529422 95.7086161478699 Winsorized Mean ( 49 / 60 ) 2.05055555555556 0.0214249838529422 95.7086161478699 Winsorized Mean ( 50 / 60 ) 2.05055555555556 0.0214249838529422 95.7086161478699 Winsorized Mean ( 51 / 60 ) 2.05055555555556 0.0214249838529422 95.7086161478699 Winsorized Mean ( 52 / 60 ) 2.05055555555556 0.0214249838529422 95.7086161478699 Winsorized Mean ( 53 / 60 ) 2.02111111111111 0.0188592896213255 107.167934301497 Winsorized Mean ( 54 / 60 ) 2.05111111111111 0.0161173707311543 127.260900386587 Winsorized Mean ( 55 / 60 ) 2.05111111111111 0.0161173707311543 127.260900386587 Winsorized Mean ( 56 / 60 ) 2.05111111111111 0.0161173707311543 127.260900386587 Winsorized Mean ( 57 / 60 ) 2.05111111111111 0.0161173707311543 127.260900386587 Winsorized Mean ( 58 / 60 ) 2.05111111111111 0.0161173707311543 127.260900386587 Winsorized Mean ( 59 / 60 ) 2.08388888888889 0.0134206611537271 155.274681703008 Winsorized Mean ( 60 / 60 ) 2.08388888888889 0.0134206611537271 155.274681703008 Trimmed Mean ( 1 / 60 ) 1.96573033707865 0.0538556425761855 36.4999885443365 Trimmed Mean ( 2 / 60 ) 1.96704545454545 0.0516320849471124 38.0973469609126 Trimmed Mean ( 3 / 60 ) 1.96896551724138 0.0495988873300657 39.6977759629668 Trimmed Mean ( 4 / 60 ) 1.97093023255814 0.0476478748421697 41.3644939902715 Trimmed Mean ( 5 / 60 ) 1.97352941176471 0.0456346675277349 43.2462756645542 Trimmed Mean ( 6 / 60 ) 1.97559523809524 0.0435822593861029 45.3302620360522 Trimmed Mean ( 7 / 60 ) 1.97650602409639 0.0424506558011449 46.5600822129838 Trimmed Mean ( 8 / 60 ) 1.97682926829268 0.0413522508400732 47.8046352528167 Trimmed Mean ( 9 / 60 ) 1.97777777777778 0.0402811985411096 49.099278308696 Trimmed Mean ( 10 / 60 ) 1.9775 0.039373533715336 50.2240925159776 Trimmed Mean ( 11 / 60 ) 1.97784810126582 0.0385124451522684 51.3560770666707 Trimmed Mean ( 12 / 60 ) 1.97820512820513 0.0375786450958708 52.6417363680442 Trimmed Mean ( 13 / 60 ) 1.97820512820513 0.036563206650478 54.1037099703963 Trimmed Mean ( 14 / 60 ) 1.98026315789474 0.0359485258245939 55.0860741148934 Trimmed Mean ( 15 / 60 ) 1.98266666666667 0.0353790173619954 56.0407499840985 Trimmed Mean ( 16 / 60 ) 1.98581081081081 0.0348480553265649 56.9848386718157 Trimmed Mean ( 17 / 60 ) 1.98835616438356 0.0344126817202142 57.7797505160893 Trimmed Mean ( 18 / 60 ) 1.99097222222222 0.0339384640477955 58.6641817207266 Trimmed Mean ( 19 / 60 ) 1.99366197183099 0.0334213420439392 59.6523613327649 Trimmed Mean ( 20 / 60 ) 1.99571428571429 0.0330042731135341 60.4683605316518 Trimmed Mean ( 21 / 60 ) 1.99782608695652 0.0325486947542701 61.3796068333715 Trimmed Mean ( 22 / 60 ) 2.00073529411765 0.0321437458302784 62.2433771310193 Trimmed Mean ( 23 / 60 ) 2.00373134328358 0.0316991014667384 63.2109823486979 Trimmed Mean ( 24 / 60 ) 2.00681818181818 0.0312103216862198 64.2998236927575 Trimmed Mean ( 25 / 60 ) 2.01 0.030672253256924 65.5315402870266 Trimmed Mean ( 26 / 60 ) 2.01 0.0300788639788043 66.8243322426135 Trimmed Mean ( 27 / 60 ) 2.01587301587302 0.029609365708622 68.0822762537602 Trimmed Mean ( 28 / 60 ) 2.01854838709677 0.0290907524779323 69.3879743615435 Trimmed Mean ( 29 / 60 ) 2.02131147540984 0.0285168182987927 70.8813814441357 Trimmed Mean ( 30 / 60 ) 2.02333333333333 0.0280738654879595 72.0717755879081 Trimmed Mean ( 31 / 60 ) 2.02542372881356 0.0275827660430265 73.4307692583874 Trimmed Mean ( 32 / 60 ) 2.02758620689655 0.0270372669373793 74.9922768300738 Trimmed Mean ( 33 / 60 ) 2.02894736842105 0.026628709329414 76.1939808393147 Trimmed Mean ( 34 / 60 ) 2.03035714285714 0.0261738349494776 77.5720159761178 Trimmed Mean ( 35 / 60 ) 2.03272727272727 0.0258018719995414 78.7821625021395 Trimmed Mean ( 36 / 60 ) 2.03518518518519 0.0253843700399284 80.174736736974 Trimmed Mean ( 37 / 60 ) 2.0377358490566 0.0249150517293277 81.7873416918485 Trimmed Mean ( 38 / 60 ) 2.03942307692308 0.0246111760661982 82.8657302453778 Trimmed Mean ( 39 / 60 ) 2.04117647058824 0.0242676839386019 84.1108890223099 Trimmed Mean ( 40 / 60 ) 2.043 0.0238791147493395 85.5559354459115 Trimmed Mean ( 41 / 60 ) 2.04489795918367 0.0234389524479426 87.2435730105834 Trimmed Mean ( 42 / 60 ) 2.04583333333333 0.0231753738527855 88.2761739391505 Trimmed Mean ( 43 / 60 ) 2.0468085106383 0.0228741360167319 89.4813473672231 Trimmed Mean ( 44 / 60 ) 2.04782608695652 0.022529815721383 90.8940451302907 Trimmed Mean ( 45 / 60 ) 2.04888888888889 0.0221359122885201 92.5594961790422 Trimmed Mean ( 46 / 60 ) 2.05 0.0216845437059828 94.5373823768496 Trimmed Mean ( 47 / 60 ) 2.05116279069767 0.0211660225129613 96.9082778515245 Trimmed Mean ( 48 / 60 ) 2.05119047619048 0.0208465452738298 98.3947435532871 Trimmed Mean ( 49 / 60 ) 2.05119047619048 0.020476402384676 100.173381908413 Trimmed Mean ( 50 / 60 ) 2.05125 0.0200469188579243 102.322457358037 Trimmed Mean ( 51 / 60 ) 2.05128205128205 0.0195472897339424 104.939461132566 Trimmed Mean ( 52 / 60 ) 2.05128205128205 0.0189638086570489 108.168252927377 Trimmed Mean ( 53 / 60 ) 2.05135135135135 0.0182786894937371 112.226390850078 Trimmed Mean ( 54 / 60 ) 2.05277777777778 0.0177986721892066 115.333197665308 Trimmed Mean ( 55 / 60 ) 2.05285714285714 0.0175899261320498 116.706410672 Trimmed Mean ( 56 / 60 ) 2.05294117647059 0.0173370487187437 118.413532186195 Trimmed Mean ( 57 / 60 ) 2.0530303030303 0.0170313979855221 120.543851114015 Trimmed Mean ( 58 / 60 ) 2.053125 0.0166620157201085 123.221885904368 Trimmed Mean ( 59 / 60 ) 2.05322580645161 0.0162147379173913 126.627134950445 Trimmed Mean ( 60 / 60 ) 2.05166666666667 0.0160316894279228 127.975699372845 Median 2 Midrange 1.7 Midmean - Weighted Average at Xnp 2.06857142857143 Midmean - Weighted Average at X(n+1)p 2.06857142857143 Midmean - Empirical Distribution Function 2.06857142857143 Midmean - Empirical Distribution Function - Averaging 2.06857142857143 Midmean - Empirical Distribution Function - Interpolation 2.06857142857143 Midmean - Closest Observation 2.06857142857143 Midmean - True Basic - Statistics Graphics Toolkit 2.06857142857143 Midmean - MS Excel (old versions) 2.06857142857143 Number of observations 180

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489467ulnhefwrrdakgp9/1wn9h1289489555.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489467ulnhefwrrdakgp9/1wn9h1289489555.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489467ulnhefwrrdakgp9/2wn9h1289489555.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/11/t1289489467ulnhefwrrdakgp9/2wn9h1289489555.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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