Centrummaten - Verkoop witte wijn - Innes De Jonghe

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Sun, 14 Mar 2010 11:43:08 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Mar/14/t1268588640fefgo9w1lzlxubr.htm/, Retrieved Sun, 14 Mar 2010 18:44:03 +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/Mar/14/t1268588640fefgo9w1lzlxubr.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 «

1954 2302 3054 2414 2226 2725 2589 3470 2400 3180 4009 3924 2072 2434 2956 2828 2687 2629 3150 4119 3030 3055 3821 4001 2529 2472 3134 2789 2758 2993 3282 3437 2804 3076 3782 3889 2271 2452 3084 2522 2769 3438 2839 3746 2632 2851 3871 3618 2389 2344 2678 2492 2858 2246 2800 3869 3007 3023 3907 4209 2353 2570 2903 2910 3782 2759 2931 3641 2794 3070 3576 4106 2452 2206 2488 2416 2534 2521 3093 3903 2907 3025 3812 4209 2138 2419 2622 2912 2708 2798 3254 2895 3263 3736 4077 4097 2175 3138 2823 2498 2822 2738 4137 3515 3785 3632 4504 4451 2550 2867 3458 2961 3163 2880 3331 3062 3534 3622 4464 5411 2564 2820 3508 3088 3299 2939 3320 3418 3604 3495 4163 4882 2211 3260 2992 2425 2707 3244 3965 3315 3333 3583 4021 4904 2252 2952 3573 3048 3059 2731 3563 3092 3478 3478 4308 5029 2075 3264 3308 3688 3136 2824 3644 4694 2914 3686 4358 5587 2265 3685 3754 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 3200.77142857143 51.6296852204745 61.9947887519197 Geometric Mean 3131.98832681867 Harmonic Mean 3066.44898621515 Quadratic Mean 3272.42362521376 Winsorized Mean ( 1 / 58 ) 3200.44 51.2820697434854 62.4085575330463 Winsorized Mean ( 2 / 58 ) 3196.10857142857 50.2787823890641 63.5677400995244 Winsorized Mean ( 3 / 58 ) 3195.04571428571 49.7073192861669 64.2771680341826 Winsorized Mean ( 4 / 58 ) 3195.38857142857 49.5066383822689 64.544648472295 Winsorized Mean ( 5 / 58 ) 3190.90285714286 48.3966165234915 65.9323540850012 Winsorized Mean ( 6 / 58 ) 3184.56 47.2718904800374 67.3668847947771 Winsorized Mean ( 7 / 58 ) 3183.56 46.9474775951292 67.8110979136033 Winsorized Mean ( 8 / 58 ) 3183.88 46.7483155584601 68.1068389730206 Winsorized Mean ( 9 / 58 ) 3179.40571428571 45.9877167130777 69.1359767679349 Winsorized Mean ( 10 / 58 ) 3177.29142857143 45.4873201398173 69.8500465361596 Winsorized Mean ( 11 / 58 ) 3171.44571428571 44.5827594245531 71.1361466903527 Winsorized Mean ( 12 / 58 ) 3173.57142857143 44.3393070498386 71.5746735735912 Winsorized Mean ( 13 / 58 ) 3173.27428571429 43.5396913588264 72.8823330317659 Winsorized Mean ( 14 / 58 ) 3171.91428571429 43.1916284970378 73.4381729999326 Winsorized Mean ( 15 / 58 ) 3173.45714285714 42.6631112904077 74.384100148145 Winsorized Mean ( 16 / 58 ) 3173.27428571429 42.4064525693528 74.8299867932743 Winsorized Mean ( 17 / 58 ) 3173.76 42.1544699949874 75.288812796778 Winsorized Mean ( 18 / 58 ) 3171.90857142857 41.8759080629501 75.7454278164044 Winsorized Mean ( 19 / 58 ) 3166.15428571429 41.1008686855751 77.0337559027186 Winsorized Mean ( 20 / 58 ) 3165.46857142857 40.8660327141163 77.4596495229429 Winsorized Mean ( 21 / 58 ) 3165.58857142857 40.6402828531294 77.8928774405618 Winsorized Mean ( 22 / 58 ) 3163.32571428571 39.8784056755183 79.3242774052953 Winsorized Mean ( 23 / 58 ) 3157.93714285714 39.2649500080169 80.4263635179052 Winsorized Mean ( 24 / 58 ) 3158.34857142857 38.7232909115363 81.561987555341 Winsorized Mean ( 25 / 58 ) 3160.34857142857 38.4602777349952 82.1717563561158 Winsorized Mean ( 26 / 58 ) 3160.64571428571 38.3676100202099 82.37796705661 Winsorized Mean ( 27 / 58 ) 3159.41142857143 38.0358375547853 83.06406882774 Winsorized Mean ( 28 / 58 ) 3160.21142857143 37.3547425024815 84.6000056983795 Winsorized Mean ( 29 / 58 ) 3160.04571428571 37.3022262062695 84.7146681490713 Winsorized Mean ( 30 / 58 ) 3153.01714285714 36.2992556246293 86.8617575925661 Winsorized Mean ( 31 / 58 ) 3152.30857142857 36.0436851061453 87.4579988740139 Winsorized Mean ( 32 / 58 ) 3150.29714285714 35.2410188624296 89.3929075988116 Winsorized Mean ( 33 / 58 ) 3152.37142857143 34.9254034312029 90.260129271837 Winsorized Mean ( 34 / 58 ) 3153.53714285714 34.8128222742507 90.5855066278166 Winsorized Mean ( 35 / 58 ) 3151.73714285714 33.8716615347623 93.0493811064606 Winsorized Mean ( 36 / 58 ) 3156.88 33.0635435783478 95.4791791303135 Winsorized Mean ( 37 / 58 ) 3156.24571428571 32.7102533019343 96.4910202666962 Winsorized Mean ( 38 / 58 ) 3150.81714285714 32.0361813868519 98.3518324112835 Winsorized Mean ( 39 / 58 ) 3156.61142857143 30.6539976564226 102.975522603984 Winsorized Mean ( 40 / 58 ) 3158.21142857143 30.4245855866 103.804583289457 Winsorized Mean ( 41 / 58 ) 3162.66285714286 29.9880190134951 105.464214082284 Winsorized Mean ( 42 / 58 ) 3153.06285714286 28.9954564747031 108.743342595545 Winsorized Mean ( 43 / 58 ) 3156.50285714286 28.5580247902449 110.529452941055 Winsorized Mean ( 44 / 58 ) 3155.74857142857 28.2071384233539 111.877657494522 Winsorized Mean ( 45 / 58 ) 3154.97714285714 27.8027900860775 113.476997563530 Winsorized Mean ( 46 / 58 ) 3159.18285714286 27.2521764537029 115.924057020173 Winsorized Mean ( 47 / 58 ) 3155.69142857143 26.8672167250933 117.455092608238 Winsorized Mean ( 48 / 58 ) 3152.67428571429 26.0856316478223 120.858652313964 Winsorized Mean ( 49 / 58 ) 3156.31428571429 25.4306034132130 124.114801148381 Winsorized Mean ( 50 / 58 ) 3156.88571428571 25.2309982895879 125.119334481049 Winsorized Mean ( 51 / 58 ) 3155.13714285714 24.8591808763827 126.920398485642 Winsorized Mean ( 52 / 58 ) 3147.11428571429 24.0044698316319 131.105344454106 Winsorized Mean ( 53 / 58 ) 3143.17714285714 23.4292012571035 134.156393483716 Winsorized Mean ( 54 / 58 ) 3147.49714285714 22.9647918914967 137.057507759197 Winsorized Mean ( 55 / 58 ) 3145.92571428571 22.7113160413751 138.517984098963 Winsorized Mean ( 56 / 58 ) 3142.08571428571 22.3053210454092 140.867092111745 Winsorized Mean ( 57 / 58 ) 3136.87428571429 21.777469288548 144.042186176511 Winsorized Mean ( 58 / 58 ) 3138.2 21.6682010319406 144.829743612497 Trimmed Mean ( 1 / 58 ) 3194.18497109827 49.8391794913545 64.089838630918 Trimmed Mean ( 2 / 58 ) 3187.78362573099 48.269559361941 66.0412829093373 Trimmed Mean ( 3 / 58 ) 3183.47337278106 47.1429993567983 67.5280193499608 Trimmed Mean ( 4 / 58 ) 3179.43113772455 46.1504914942871 68.8926820664115 Trimmed Mean ( 5 / 58 ) 3175.2 45.1382128105224 70.3439459007515 Trimmed Mean ( 6 / 58 ) 3171.82822085890 44.3274128848914 71.5545531406365 Trimmed Mean ( 7 / 58 ) 3169.52173913043 43.6970336761307 72.5340251382275 Trimmed Mean ( 8 / 58 ) 3167.31446540881 43.0784643540667 73.5243122729793 Trimmed Mean ( 9 / 58 ) 3165.00636942675 42.4442785393938 74.5685043624716 Trimmed Mean ( 10 / 58 ) 3163.2 41.8795071732282 75.5309747776138 Trimmed Mean ( 11 / 58 ) 3161.58823529412 41.3417158863736 76.4745286331038 Trimmed Mean ( 12 / 58 ) 3160.54966887417 40.8828182618073 77.3075292567771 Trimmed Mean ( 13 / 58 ) 3159.27516778523 40.4158961726369 78.1691232155378 Trimmed Mean ( 14 / 58 ) 3157.99319727891 40.0053247511216 78.9393216259386 Trimmed Mean ( 15 / 58 ) 3156.79310344828 39.5990838555444 79.7188418541223 Trimmed Mean ( 16 / 58 ) 3155.43356643357 39.2144705815128 80.4660504054122 Trimmed Mean ( 17 / 58 ) 3154.04964539007 38.8222814450660 81.2432842169024 Trimmed Mean ( 18 / 58 ) 3152.58992805755 38.4199460502508 82.0560737887077 Trimmed Mean ( 19 / 58 ) 3151.21897810219 38.0087822828855 82.9076542007797 Trimmed Mean ( 20 / 58 ) 3150.2 37.6362397836515 83.7012416253227 Trimmed Mean ( 21 / 58 ) 3149.19548872180 37.2504646022497 84.541106328419 Trimmed Mean ( 22 / 58 ) 3148.15267175573 36.8484198441754 85.4352149988693 Trimmed Mean ( 23 / 58 ) 3147.21705426357 36.4778789709599 86.2774136832098 Trimmed Mean ( 24 / 58 ) 3146.57480314961 36.1265784949448 87.0986108908682 Trimmed Mean ( 25 / 58 ) 3145.888 35.7873003533466 87.9051498419554 Trimmed Mean ( 26 / 58 ) 3145.06504065041 35.4350519704486 88.7557620424337 Trimmed Mean ( 27 / 58 ) 3144.19834710744 35.0532134236948 89.6978633343232 Trimmed Mean ( 28 / 58 ) 3143.36974789916 34.6598665240392 90.6919172847651 Trimmed Mean ( 29 / 58 ) 3143.36974789916 34.2833875091064 91.6878399797632 Trimmed Mean ( 30 / 58 ) 3141.54782608696 33.8693731616684 92.7548263468424 Trimmed Mean ( 31 / 58 ) 3141.54782608696 33.4986163774981 93.7814204230004 Trimmed Mean ( 32 / 58 ) 3140.37837837838 33.1072885622881 94.8545928933464 Trimmed Mean ( 33 / 58 ) 3139.88073394495 32.7408862817977 95.9009083297349 Trimmed Mean ( 34 / 58 ) 3139.26168224299 32.3575274311566 97.0179717508404 Trimmed Mean ( 35 / 58 ) 3138.56190476190 31.9368078086306 98.2741269436998 Trimmed Mean ( 36 / 58 ) 3137.92233009709 31.5491502947006 99.4613896344515 Trimmed Mean ( 37 / 58 ) 3137.0099009901 31.1831335949722 100.599572247476 Trimmed Mean ( 38 / 58 ) 3136.09090909091 30.8007778821000 101.818561891369 Trimmed Mean ( 39 / 58 ) 3135.39175257732 30.4302888929653 103.035227946920 Trimmed Mean ( 40 / 58 ) 3134.38947368421 30.1326975444822 104.019544518283 Trimmed Mean ( 41 / 58 ) 3133.2688172043 29.8110542820358 105.104260572643 Trimmed Mean ( 42 / 58 ) 3131.89010989011 29.4794539512248 106.239759904372 Trimmed Mean ( 43 / 58 ) 3130.89887640449 29.1945765605095 107.242482860312 Trimmed Mean ( 44 / 58 ) 3129.70114942529 28.9029435005248 108.283128649802 Trimmed Mean ( 45 / 58 ) 3129.70114942529 28.5967844648102 109.442414872782 Trimmed Mean ( 46 / 58 ) 3127.24096385542 28.2789648013935 110.585411659105 Trimmed Mean ( 47 / 58 ) 3125.74074074074 27.9588309431777 111.797977071837 Trimmed Mean ( 48 / 58 ) 3124.32911392405 27.6236221200706 113.103527855386 Trimmed Mean ( 49 / 58 ) 3122.98701298701 27.3118587053695 114.345458750233 Trimmed Mean ( 50 / 58 ) 3121.4 27.0077399650178 115.574276264621 Trimmed Mean ( 51 / 58 ) 3119.69863013699 26.6642599151608 116.999258185418 Trimmed Mean ( 52 / 58 ) 3117.98591549296 26.2962191968234 118.571643024242 Trimmed Mean ( 53 / 58 ) 3116.56521739130 25.9614318034978 120.045968226275 Trimmed Mean ( 54 / 58 ) 3115.25373134328 25.6274878509785 121.559075530860 Trimmed Mean ( 55 / 58 ) 3113.64615384615 25.2729753694271 123.200616798478 Trimmed Mean ( 56 / 58 ) 3112.01587301587 24.8721718378623 125.120391307305 Trimmed Mean ( 57 / 58 ) 3110.47540983607 24.4390465877617 127.274826318049 Trimmed Mean ( 58 / 58 ) 3110.47540983607 23.9858190974647 129.679766081653 Median 3076 Midrange 3770.5 Midmean - Weighted Average at Xnp 3125.10227272727 Midmean - Weighted Average at X(n+1)p 3130.89887640449 Midmean - Empirical Distribution Function 3130.89887640449 Midmean - Empirical Distribution Function - Averaging 3130.89887640449 Midmean - Empirical Distribution Function - Interpolation 3129.70114942529 Midmean - Closest Observation 3125.10227272727 Midmean - True Basic - Statistics Graphics Toolkit 3130.89887640449 Midmean - MS Excel (old versions) 3130.89887640449 Number of observations 175

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Mar/14/t1268588640fefgo9w1lzlxubr/10xri1268588586.png (open in new window) http://www.freestatistics.org/blog/date/2010/Mar/14/t1268588640fefgo9w1lzlxubr/10xri1268588586.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Mar/14/t1268588640fefgo9w1lzlxubr/2iqo31268588586.png (open in new window) http://www.freestatistics.org/blog/date/2010/Mar/14/t1268588640fefgo9w1lzlxubr/2iqo31268588586.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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