workshop 2

*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, 06 Nov 2009 05:41:10 -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/Nov/06/t12575114595sc9k4cg3iohdgs.htm/, Retrieved Fri, 06 Nov 2009 13:44:21 +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/Nov/06/t12575114595sc9k4cg3iohdgs.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}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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6,9 6,8 6,7 6,6 6,5 6,5 7 7,5 7,6 7,6 7,6 7,8 8 8 8 7,9 7,9 8 8,5 9,2 9,4 9,5 9,5 9,6 9,7 9,7 9,6 9,5 9,4 9,3 9,6 10,2 10,2 10,1 9,9 9,8 9,8 9,7 9,5 9,3 9,1 9 9,5 10 10,2 10,1 10 9,9 10 9,9 9,7 9,5 9,2 9 9,3 9,8 9,8 9,6 9,4 9,3 9,2 9,2 9 8,8 8,7 8,7 9,1 9,7 9,8 9,6 9,4 9,4 9,5 9,4 9,3 9,2 9 8,9 9,2 9,8 9,9 9,6 9,2 9,1 9,1 9 8,9 8,7 8,5 8,3 8,5 8,7 8,4 8,1 7,8 7,7 7,5 7,2 6,8 6,7 6,4 6,3 6,8 7,3 7,1 7 6,8 6,6 6,3 6,1 6,1 6,3 6,3 6 6,2 6,4 6,8 7,5 7,5 7,6 7,6 7,4 7,3 7,1 6,9 6,8 7,5 7,6 7,8 8 8,1 8,2 8,3 8,2 8 7,9 7,6 7,6 8,3 8,4 8,4 8,4 8,4 8,6 8,9 8,8 8,3 7,5 7,2 7,4 8,8 9,3 9,3 8,7 8,2 8,3 8,5 8,6 8,5 8,2 8,1 7,9 8,6 8,7 8,7 8,5 8,4 8,5 8,7 8,7 8,6 8,5 8,3 8 8,2 8,1 8,1 8 7,9 7,9

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 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 8.39722222222222 0.0802480270445386 104.640855750406 Geometric Mean 8.32546910637334 Harmonic Mean 8.25067328937393 Quadratic Mean 8.4655806390085 Winsorized Mean ( 1 / 60 ) 8.39777777777778 0.0801571840195336 104.766377218682 Winsorized Mean ( 2 / 60 ) 8.39777777777778 0.0801571840195336 104.766377218682 Winsorized Mean ( 3 / 60 ) 8.39777777777778 0.0796911930781842 105.378994257732 Winsorized Mean ( 4 / 60 ) 8.4 0.0793557234886273 105.852478318137 Winsorized Mean ( 5 / 60 ) 8.39722222222222 0.0790321283762303 106.250741245984 Winsorized Mean ( 6 / 60 ) 8.39722222222222 0.0790321283762303 106.250741245984 Winsorized Mean ( 7 / 60 ) 8.39722222222222 0.0790321283762303 106.250741245984 Winsorized Mean ( 8 / 60 ) 8.39722222222222 0.0778929160190853 107.804697158401 Winsorized Mean ( 9 / 60 ) 8.39722222222222 0.0778929160190853 107.804697158401 Winsorized Mean ( 10 / 60 ) 8.40277777777778 0.0771120193965651 108.968457103486 Winsorized Mean ( 11 / 60 ) 8.40277777777778 0.0771120193965651 108.968457103486 Winsorized Mean ( 12 / 60 ) 8.40277777777778 0.0755013475346653 111.293083529665 Winsorized Mean ( 13 / 60 ) 8.40277777777778 0.0755013475346653 111.293083529665 Winsorized Mean ( 14 / 60 ) 8.41055555555556 0.0744835194891787 112.918342382807 Winsorized Mean ( 15 / 60 ) 8.41055555555556 0.0744835194891787 112.918342382807 Winsorized Mean ( 16 / 60 ) 8.41944444444444 0.0733650567164181 114.760961434114 Winsorized Mean ( 17 / 60 ) 8.41944444444444 0.0733650567164181 114.760961434114 Winsorized Mean ( 18 / 60 ) 8.40944444444444 0.0723409114027714 116.247421844374 Winsorized Mean ( 19 / 60 ) 8.40944444444444 0.0723409114027714 116.247421844374 Winsorized Mean ( 20 / 60 ) 8.40944444444444 0.0723409114027714 116.247421844374 Winsorized Mean ( 21 / 60 ) 8.40944444444444 0.0723409114027714 116.247421844374 Winsorized Mean ( 22 / 60 ) 8.42166666666667 0.0708478186525261 118.869808934709 Winsorized Mean ( 23 / 60 ) 8.40888888888889 0.0695926289864237 120.830165656327 Winsorized Mean ( 24 / 60 ) 8.42222222222222 0.0680058946743794 123.845473433573 Winsorized Mean ( 25 / 60 ) 8.42222222222222 0.0680058946743794 123.845473433573 Winsorized Mean ( 26 / 60 ) 8.43666666666667 0.0663488701005908 127.156146802137 Winsorized Mean ( 27 / 60 ) 8.43666666666667 0.0663488701005908 127.156146802137 Winsorized Mean ( 28 / 60 ) 8.45222222222222 0.0646311962634947 130.77619958887 Winsorized Mean ( 29 / 60 ) 8.43611111111111 0.0630724012696312 133.752813295425 Winsorized Mean ( 30 / 60 ) 8.45277777777778 0.0612837553424536 137.928521686435 Winsorized Mean ( 31 / 60 ) 8.45277777777778 0.0612837553424536 137.928521686435 Winsorized Mean ( 32 / 60 ) 8.47055555555556 0.059454885755817 142.470302446537 Winsorized Mean ( 33 / 60 ) 8.47055555555556 0.059454885755817 142.470302446537 Winsorized Mean ( 34 / 60 ) 8.48944444444444 0.0575977684570907 147.391898538030 Winsorized Mean ( 35 / 60 ) 8.48944444444444 0.0575977684570907 147.391898538030 Winsorized Mean ( 36 / 60 ) 8.46944444444444 0.0556832022086326 152.100527780556 Winsorized Mean ( 37 / 60 ) 8.46944444444444 0.0556832022086326 152.100527780556 Winsorized Mean ( 38 / 60 ) 8.46944444444444 0.0556832022086326 152.100527780556 Winsorized Mean ( 39 / 60 ) 8.46944444444444 0.0556832022086326 152.100527780556 Winsorized Mean ( 40 / 60 ) 8.49166666666667 0.0535683543795897 158.520207779653 Winsorized Mean ( 41 / 60 ) 8.49166666666667 0.0535683543795897 158.520207779653 Winsorized Mean ( 42 / 60 ) 8.46833333333333 0.0514077129279178 164.728848085643 Winsorized Mean ( 43 / 60 ) 8.46833333333333 0.0514077129279178 164.728848085643 Winsorized Mean ( 44 / 60 ) 8.46833333333333 0.0514077129279178 164.728848085643 Winsorized Mean ( 45 / 60 ) 8.46833333333333 0.0514077129279178 164.728848085643 Winsorized Mean ( 46 / 60 ) 8.46833333333333 0.0514077129279178 164.728848085643 Winsorized Mean ( 47 / 60 ) 8.46833333333333 0.0514077129279178 164.728848085643 Winsorized Mean ( 48 / 60 ) 8.495 0.0489382932755884 173.585947351325 Winsorized Mean ( 49 / 60 ) 8.495 0.0440308776594472 192.932788342395 Winsorized Mean ( 50 / 60 ) 8.495 0.0440308776594472 192.932788342395 Winsorized Mean ( 51 / 60 ) 8.495 0.0440308776594472 192.932788342395 Winsorized Mean ( 52 / 60 ) 8.52388888888889 0.0415434968246854 205.179860637633 Winsorized Mean ( 53 / 60 ) 8.52388888888889 0.0415434968246854 205.179860637633 Winsorized Mean ( 54 / 60 ) 8.52388888888889 0.0415434968246854 205.179860637633 Winsorized Mean ( 55 / 60 ) 8.52388888888889 0.0415434968246854 205.179860637633 Winsorized Mean ( 56 / 60 ) 8.49277777777778 0.0387661226190431 219.077307814269 Winsorized Mean ( 57 / 60 ) 8.49277777777778 0.0387661226190431 219.077307814269 Winsorized Mean ( 58 / 60 ) 8.525 0.0360776715725335 236.295737180839 Winsorized Mean ( 59 / 60 ) 8.525 0.0360776715725335 236.295737180839 Winsorized Mean ( 60 / 60 ) 8.49166666666667 0.0331641423105669 256.049639009088 Trimmed Mean ( 1 / 60 ) 8.40056179775281 0.079372967972881 105.836558872575 Trimmed Mean ( 2 / 60 ) 8.4034090909091 0.0785332162633787 107.004519752844 Trimmed Mean ( 3 / 60 ) 8.40632183908046 0.0776333901025866 108.282297449231 Trimmed Mean ( 4 / 60 ) 8.4093023255814 0.0768456030720996 109.431144911329 Trimmed Mean ( 5 / 60 ) 8.41176470588235 0.0760988582014844 110.537331369819 Trimmed Mean ( 6 / 60 ) 8.41488095238095 0.0753739838411492 111.641716724372 Trimmed Mean ( 7 / 60 ) 8.41807228915663 0.0745941393142025 112.851657872187 Trimmed Mean ( 8 / 60 ) 8.42134146341463 0.0737546375075483 114.180501023448 Trimmed Mean ( 9 / 60 ) 8.42469135802469 0.0730394477640646 115.344401086363 Trimmed Mean ( 10 / 60 ) 8.428125 0.0722676926336029 116.623690239158 Trimmed Mean ( 11 / 60 ) 8.43101265822785 0.0715460952565538 117.84029062656 Trimmed Mean ( 12 / 60 ) 8.43397435897436 0.07076620014249 119.180828446240 Trimmed Mean ( 13 / 60 ) 8.43397435897436 0.0701227372845098 120.274459976585 Trimmed Mean ( 14 / 60 ) 8.44013157894737 0.0694251395917788 121.571690436282 Trimmed Mean ( 15 / 60 ) 8.44266666666667 0.0687852194379325 122.739546891826 Trimmed Mean ( 16 / 60 ) 8.44527027027027 0.068090160502204 124.030700001022 Trimmed Mean ( 17 / 60 ) 8.4472602739726 0.0674538293115039 125.230255423497 Trimmed Mean ( 18 / 60 ) 8.44930555555556 0.0667612712182108 126.559986072446 Trimmed Mean ( 19 / 60 ) 8.45211267605634 0.0661053054399653 127.858310612183 Trimmed Mean ( 20 / 60 ) 8.455 0.0653893784335185 129.302345465728 Trimmed Mean ( 21 / 60 ) 8.45797101449275 0.0646075605608813 130.913022269624 Trimmed Mean ( 22 / 60 ) 8.4610294117647 0.0637531048764825 132.715566216852 Trimmed Mean ( 23 / 60 ) 8.4634328358209 0.06296227131739 134.420703998385 Trimmed Mean ( 24 / 60 ) 8.46666666666667 0.0622062067004162 136.106461328561 Trimmed Mean ( 25 / 60 ) 8.46923076923077 0.0615235521593115 137.658351509032 Trimmed Mean ( 26 / 60 ) 8.46923076923077 0.0607727621317806 139.358990313226 Trimmed Mean ( 27 / 60 ) 8.47380952380952 0.0600970492520073 141.002089608027 Trimmed Mean ( 28 / 60 ) 8.4758064516129 0.059351895330616 142.805994726857 Trimmed Mean ( 29 / 60 ) 8.47704918032787 0.0586829536282288 144.455053064167 Trimmed Mean ( 30 / 60 ) 8.47916666666667 0.0580723955758485 146.010278766475 Trimmed Mean ( 31 / 60 ) 8.48050847457627 0.0575497879111881 147.359508738130 Trimmed Mean ( 32 / 60 ) 8.48189655172414 0.0569673245717413 148.890554637905 Trimmed Mean ( 33 / 60 ) 8.48245614035088 0.056474128102236 150.200745463391 Trimmed Mean ( 34 / 60 ) 8.48303571428571 0.0559216736090982 151.694954152902 Trimmed Mean ( 35 / 60 ) 8.48272727272727 0.0554586470816643 152.955899920101 Trimmed Mean ( 36 / 60 ) 8.4824074074074 0.0549367726144833 154.403089291983 Trimmed Mean ( 37 / 60 ) 8.48301886792453 0.0545086967370329 155.626888473399 Trimmed Mean ( 38 / 60 ) 8.48365384615385 0.0540224861372085 157.039308124523 Trimmed Mean ( 39 / 60 ) 8.4843137254902 0.0534709645124574 158.671417335544 Trimmed Mean ( 40 / 60 ) 8.485 0.0528457828750485 160.561534684090 Trimmed Mean ( 41 / 60 ) 8.48469387755102 0.0523255126066467 162.152140607472 Trimmed Mean ( 42 / 60 ) 8.484375 0.0517315973932726 164.007597436056 Trimmed Mean ( 43 / 60 ) 8.48510638297872 0.0512474772815677 165.571201414642 Trimmed Mean ( 44 / 60 ) 8.4858695652174 0.0506899723424673 167.407263667178 Trimmed Mean ( 45 / 60 ) 8.48666666666667 0.0500486654553114 169.568290971603 Trimmed Mean ( 46 / 60 ) 8.4875 0.0493111508984105 172.121312225823 Trimmed Mean ( 47 / 60 ) 8.48837209302325 0.0484625062642191 175.153386552986 Trimmed Mean ( 48 / 60 ) 8.48928571428571 0.0474845705876976 178.779877531105 Trimmed Mean ( 49 / 60 ) 8.48928571428571 0.0466268412320528 182.068643081271 Trimmed Mean ( 50 / 60 ) 8.48875 0.0461489659359993 183.942366374415 Trimmed Mean ( 51 / 60 ) 8.48846153846154 0.0455835281587165 186.217738760934 Trimmed Mean ( 52 / 60 ) 8.48846153846154 0.0449157927629008 188.986123060768 Trimmed Mean ( 53 / 60 ) 8.48648648648649 0.0443941621187633 191.162217766008 Trimmed Mean ( 54 / 60 ) 8.48472222222222 0.0437690142996504 193.852257310030 Trimmed Mean ( 55 / 60 ) 8.48285714285714 0.0430210805167163 197.17908153332 Trimmed Mean ( 56 / 60 ) 8.48088235294118 0.0421261824234223 201.320933088534 Trimmed Mean ( 57 / 60 ) 8.48030303030303 0.0414323779659661 204.678163470825 Trimmed Mean ( 58 / 60 ) 8.4796875 0.0405920768607501 208.900065130673 Trimmed Mean ( 59 / 60 ) 8.4774193548387 0.0399245292011559 212.336113273272 Trimmed Mean ( 60 / 60 ) 8.475 0.0391018516474589 216.741653986372 Median 8.5 Midrange 8.1 Midmean - Weighted Average at Xnp 8.46632653061225 Midmean - Weighted Average at X(n+1)p 8.46632653061225 Midmean - Empirical Distribution Function 8.46632653061225 Midmean - Empirical Distribution Function - Averaging 8.46632653061225 Midmean - Empirical Distribution Function - Interpolation 8.46632653061225 Midmean - Closest Observation 8.46632653061225 Midmean - True Basic - Statistics Graphics Toolkit 8.46632653061225 Midmean - MS Excel (old versions) 8.46632653061225 Number of observations 180

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/06/t12575114595sc9k4cg3iohdgs/14xlp1257511267.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/06/t12575114595sc9k4cg3iohdgs/14xlp1257511267.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/06/t12575114595sc9k4cg3iohdgs/218ig1257511267.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/06/t12575114595sc9k4cg3iohdgs/218ig1257511267.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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