jeroen Cornelissen - aantal liter rose wijn australie - opgave 5

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Wed, 28 Apr 2010 18:41:52 +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/Apr/28/t1272480177nihg5c09cjep891.htm/, Retrieved Wed, 28 Apr 2010 20:42:59 +0200

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/Apr/28/t1272480177nihg5c09cjep891.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 «

112 118 129 99 116 168 118 129 205 147 150 267 126 129 124 97 102 127 222 214 118 141 154 226 89 77 82 97 127 121 117 117 106 112 134 169 75 108 115 85 101 108 109 124 105 95 135 164 88 85 112 87 91 87 87 142 95 108 139 159 61 82 124 93 108 75 87 103 90 108 123 129 57 65 67 71 76 67 110 118 99 85 107 141 58 65 70 86 93 74 87 73 101 100 96 157 63 115 70 66 67 83 79 77 102 116 100 135 71 60 89 74 73 91 86 74 87 87 109 137 43 69 73 77 69 76 78 70 83 65 110 132 54 55 66 65 60 65 96 55 71 63 74 106 34 47 56 53 53 55 67 52 46 51 58 91 33 40 46 45 41 55 57 54 46 52 48 77 30 35 42 48 44 45

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 93.0057471264368 2.93792778132676 31.6569208125448 Geometric Mean 85.8105348987817 Harmonic Mean 79.0965171345831 Quadratic Mean 100.713974192766 Winsorized Mean ( 1 / 58 ) 92.7873563218391 2.86369606834930 32.4012584112405 Winsorized Mean ( 2 / 58 ) 92.7528735632184 2.85011002473940 32.5436115652059 Winsorized Mean ( 3 / 58 ) 92.632183908046 2.8127771850956 32.9326419450809 Winsorized Mean ( 4 / 58 ) 92.5402298850575 2.74934060740451 33.6590634262735 Winsorized Mean ( 5 / 58 ) 91.5344827586207 2.53102004950157 36.1650563679439 Winsorized Mean ( 6 / 58 ) 91.5344827586207 2.52099873576343 36.3088173984750 Winsorized Mean ( 7 / 58 ) 91.4137931034483 2.48877759470474 36.7303986093194 Winsorized Mean ( 8 / 58 ) 91.2298850574713 2.44583068131661 37.3001638070716 Winsorized Mean ( 9 / 58 ) 91.1781609195402 2.4236907172658 37.6195528043279 Winsorized Mean ( 10 / 58 ) 91.0057471264368 2.39706009345841 37.9655676446293 Winsorized Mean ( 11 / 58 ) 90.816091954023 2.35247128629264 38.6045485371871 Winsorized Mean ( 12 / 58 ) 90.6091954022989 2.32290797163096 39.0067951502531 Winsorized Mean ( 13 / 58 ) 90.235632183908 2.27208258677500 39.714943774112 Winsorized Mean ( 14 / 58 ) 90.235632183908 2.25255256533841 40.0592792250119 Winsorized Mean ( 15 / 58 ) 90.3218390804598 2.24306916496908 40.2670771329981 Winsorized Mean ( 16 / 58 ) 90.1379310344828 2.21935639535873 40.6144462525195 Winsorized Mean ( 17 / 58 ) 90.235632183908 2.16325382264878 41.7129193251209 Winsorized Mean ( 18 / 58 ) 90.132183908046 2.12692681559465 42.3767208383457 Winsorized Mean ( 19 / 58 ) 90.132183908046 2.12692681559465 42.3767208383457 Winsorized Mean ( 20 / 58 ) 90.132183908046 2.1011555914032 42.8964824293919 Winsorized Mean ( 21 / 58 ) 89.8908045977011 2.07241281289 43.3749511866545 Winsorized Mean ( 22 / 58 ) 89.6379310344828 2.01575244303617 44.4687200276777 Winsorized Mean ( 23 / 58 ) 89.6379310344828 2.01575244303617 44.4687200276777 Winsorized Mean ( 24 / 58 ) 89.7758620689655 2.00177864751839 44.848046601087 Winsorized Mean ( 25 / 58 ) 89.7758620689655 2.00177864751839 44.848046601087 Winsorized Mean ( 26 / 58 ) 89.4770114942529 1.96838516682372 45.4570645025928 Winsorized Mean ( 27 / 58 ) 89.4770114942529 1.96838516682372 45.4570645025928 Winsorized Mean ( 28 / 58 ) 89.4770114942529 1.93454281887938 46.2522776032862 Winsorized Mean ( 29 / 58 ) 89.3103448275862 1.88198451178223 47.4554090474473 Winsorized Mean ( 30 / 58 ) 89.3103448275862 1.88198451178223 47.4554090474473 Winsorized Mean ( 31 / 58 ) 89.4885057471264 1.86444726682553 47.9973380526298 Winsorized Mean ( 32 / 58 ) 89.3045977011494 1.84490002798964 48.4061988976516 Winsorized Mean ( 33 / 58 ) 89.3045977011494 1.76844434453061 50.4989585775474 Winsorized Mean ( 34 / 58 ) 88.7183908045977 1.70902781321874 51.9116132097977 Winsorized Mean ( 35 / 58 ) 88.919540229885 1.68965155658581 52.6259629586351 Winsorized Mean ( 36 / 58 ) 89.3333333333333 1.65080486111412 54.1150171274895 Winsorized Mean ( 37 / 58 ) 89.3333333333333 1.65080486111412 54.1150171274895 Winsorized Mean ( 38 / 58 ) 89.551724137931 1.58929122702669 56.3469568163843 Winsorized Mean ( 39 / 58 ) 89.551724137931 1.58929122702669 56.3469568163843 Winsorized Mean ( 40 / 58 ) 89.3218390804598 1.56650010011703 57.020002152433 Winsorized Mean ( 41 / 58 ) 89.3218390804598 1.56650010011703 57.020002152433 Winsorized Mean ( 42 / 58 ) 89.080459770115 1.54289834362416 57.7357932479669 Winsorized Mean ( 43 / 58 ) 89.3275862068966 1.52079387773092 58.7374709452254 Winsorized Mean ( 44 / 58 ) 88.5689655172414 1.44827170951501 61.154937250622 Winsorized Mean ( 45 / 58 ) 88.8275862068966 1.42517429536933 62.3275247775058 Winsorized Mean ( 46 / 58 ) 88.8275862068966 1.42517429536933 62.3275247775058 Winsorized Mean ( 47 / 58 ) 88.2873563218391 1.37512126033651 64.203324367361 Winsorized Mean ( 48 / 58 ) 88.2873563218391 1.37512126033651 64.203324367361 Winsorized Mean ( 49 / 58 ) 88.5689655172414 1.29974817038454 68.143173835773 Winsorized Mean ( 50 / 58 ) 88.5689655172414 1.29974817038454 68.143173835773 Winsorized Mean ( 51 / 58 ) 88.5689655172414 1.24787604302511 70.9757720025877 Winsorized Mean ( 52 / 58 ) 88.5689655172414 1.24787604302511 70.9757720025877 Winsorized Mean ( 53 / 58 ) 88.5689655172414 1.24787604302511 70.9757720025877 Winsorized Mean ( 54 / 58 ) 88.8793103448276 1.22139675364179 72.7685824281256 Winsorized Mean ( 55 / 58 ) 88.8793103448276 1.22139675364179 72.7685824281256 Winsorized Mean ( 56 / 58 ) 88.5574712643678 1.19244690261342 74.2653371569682 Winsorized Mean ( 57 / 58 ) 88.8850574712644 1.10812830812447 80.2118823421307 Winsorized Mean ( 58 / 58 ) 88.8850574712644 1.10812830812447 80.2118823421307 Trimmed Mean ( 1 / 58 ) 92.3604651162791 2.76897071164440 33.3555225874633 Trimmed Mean ( 2 / 58 ) 91.9235294117647 2.66529438305771 34.4890718248943 Trimmed Mean ( 3 / 58 ) 91.4940476190476 2.55943043145029 35.7478158010347 Trimmed Mean ( 4 / 58 ) 91.0963855421687 2.45856838807686 37.0526140269077 Trimmed Mean ( 5 / 58 ) 90.7134146341463 2.36842548269696 38.3011478709686 Trimmed Mean ( 6 / 58 ) 90.537037037037 2.32793978641520 38.8914857529264 Trimmed Mean ( 7 / 58 ) 90.35625 2.2863476902285 39.5199078364890 Trimmed Mean ( 8 / 58 ) 90.1898734177215 2.24761516223274 40.126919827382 Trimmed Mean ( 9 / 58 ) 90.0448717948718 2.21322707976242 40.6848771272659 Trimmed Mean ( 10 / 58 ) 89.9025974025974 2.17952774318321 41.2486593408967 Trimmed Mean ( 11 / 58 ) 89.7763157894737 2.14700658476992 41.8146438983066 Trimmed Mean ( 12 / 58 ) 89.6666666666667 2.11794646789408 42.3366067206713 Trimmed Mean ( 13 / 58 ) 89.5743243243243 2.09030566060426 42.8522612805012 Trimmed Mean ( 14 / 58 ) 89.513698630137 2.06658724333834 43.3147445957993 Trimmed Mean ( 15 / 58 ) 89.4513888888889 2.04306847031351 43.7828639561759 Trimmed Mean ( 16 / 58 ) 89.3802816901408 2.01851217704696 44.2802786658946 Trimmed Mean ( 17 / 58 ) 89.3214285714286 1.99437203672685 44.7867433590888 Trimmed Mean ( 18 / 58 ) 89.2536231884058 1.97386021552943 45.2178034119128 Trimmed Mean ( 19 / 58 ) 89.1911764705882 1.95509263792358 45.6199234453228 Trimmed Mean ( 20 / 58 ) 89.1268656716418 1.93452532781163 46.0716974806755 Trimmed Mean ( 21 / 58 ) 89.060606060606 1.91445424137107 46.5201017271762 Trimmed Mean ( 22 / 58 ) 89.0076923076923 1.89518097570951 46.9652732105808 Trimmed Mean ( 23 / 58 ) 88.96875 1.87922547170999 47.3433078357775 Trimmed Mean ( 24 / 58 ) 88.9285714285714 1.86159783123551 47.7700231147943 Trimmed Mean ( 25 / 58 ) 88.8790322580645 1.84334673591792 48.2161226242691 Trimmed Mean ( 26 / 58 ) 88.827868852459 1.82315610993288 48.7220311900385 Trimmed Mean ( 27 / 58 ) 88.7916666666667 1.80385891340161 49.2231770494891 Trimmed Mean ( 28 / 58 ) 88.7542372881356 1.78245901472325 49.7931433794655 Trimmed Mean ( 29 / 58 ) 88.7155172413793 1.76173750759005 50.3568306056769 Trimmed Mean ( 30 / 58 ) 88.6842105263158 1.74336704195821 50.8695004505204 Trimmed Mean ( 31 / 58 ) 88.6517857142857 1.72286415912086 51.4560508122258 Trimmed Mean ( 32 / 58 ) 88.609090909091 1.70149791508936 52.0771081311828 Trimmed Mean ( 33 / 58 ) 88.574074074074 1.67940117851513 52.7414623779104 Trimmed Mean ( 34 / 58 ) 88.5377358490566 1.66147481700131 53.288641478691 Trimmed Mean ( 35 / 58 ) 88.5288461538462 1.64655913039805 53.7659683879344 Trimmed Mean ( 36 / 58 ) 88.5098039215686 1.63131276677644 54.256795952421 Trimmed Mean ( 37 / 58 ) 88.47 1.61729634722505 54.7024051292744 Trimmed Mean ( 38 / 58 ) 88.4285714285714 1.60126974653810 55.2240318158457 Trimmed Mean ( 39 / 58 ) 88.375 1.5883519264021 55.6394326288791 Trimmed Mean ( 40 / 58 ) 88.3191489361702 1.57343050144791 56.1315856371773 Trimmed Mean ( 41 / 58 ) 88.2717391304348 1.55839535956699 56.6427117409807 Trimmed Mean ( 42 / 58 ) 88.2222222222222 1.54101461281142 57.2494390960188 Trimmed Mean ( 43 / 58 ) 88.1818181818182 1.52329057407927 57.8890329148911 Trimmed Mean ( 44 / 58 ) 88.1279069767442 1.50483002677183 58.5633629106913 Trimmed Mean ( 45 / 58 ) 88.1071428571429 1.49074577488273 59.1027285414066 Trimmed Mean ( 46 / 58 ) 88.0731707317073 1.47638038213825 59.6547961468781 Trimmed Mean ( 47 / 58 ) 88.0375 1.45941332824468 60.3238974841263 Trimmed Mean ( 48 / 58 ) 88.025641025641 1.44472749983380 60.9288886906131 Trimmed Mean ( 49 / 58 ) 88.0131578947368 1.42724042664754 61.6666654415554 Trimmed Mean ( 50 / 58 ) 87.9864864864865 1.41453988333358 62.2014886417575 Trimmed Mean ( 51 / 58 ) 87.9864864864865 1.39911629554183 62.8871858378378 Trimmed Mean ( 52 / 58 ) 87.9285714285714 1.38631606322358 63.4260640564983 Trimmed Mean ( 53 / 58 ) 87.8970588235294 1.37057047809227 64.1317321717564 Trimmed Mean ( 54 / 58 ) 87.8636363636364 1.35131574725581 65.0208040142106 Trimmed Mean ( 55 / 58 ) 87.8125 1.33107602820318 65.9710626135596 Trimmed Mean ( 56 / 58 ) 87.758064516129 1.30626879788477 67.1822404839149 Trimmed Mean ( 57 / 58 ) 87.7166666666667 1.28024796032826 68.515373103329 Trimmed Mean ( 58 / 58 ) 87.6551724137931 1.26029978576238 69.5510492059386 Median 87 Midrange 148.5 Midmean - Weighted Average at Xnp 87.8735632183908 Midmean - Weighted Average at X(n+1)p 88.4831460674157 Midmean - Empirical Distribution Function 88.4831460674157 Midmean - Empirical Distribution Function - Averaging 88.4831460674157 Midmean - Empirical Distribution Function - Interpolation 87.8735632183908 Midmean - Closest Observation 88.4831460674157 Midmean - True Basic - Statistics Graphics Toolkit 88.4831460674157 Midmean - MS Excel (old versions) 88.4831460674157 Number of observations 174

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Apr/28/t1272480177nihg5c09cjep891/17h6o1272480110.png (open in new window) http://www.freestatistics.org/blog/date/2010/Apr/28/t1272480177nihg5c09cjep891/17h6o1272480110.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Apr/28/t1272480177nihg5c09cjep891/27h6o1272480110.png (open in new window) http://www.freestatistics.org/blog/date/2010/Apr/28/t1272480177nihg5c09cjep891/27h6o1272480110.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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