datareeks - maandelijks aantal doden in UK - De Wolf Davy

*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 Apr 2011 13:07:18 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/Apr/03/t13018359620v3ojmq9y7bdyeh.htm/, Retrieved Sun, 03 Apr 2011 15:06:04 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP1W52

Dataseries X:

» Textbox « » Textfile « » CSV «

1687 0 1508 0 1507 0 1385 0 1632 0 1511 0 1559 0 1630 0 1579 0 1653 0 2152 0 2148 0 1752 0 1765 0 1717 0 1558 0 1575 0 1520 0 1805 0 1800 0 1719 0 2008 0 2242 0 2478 0 2030 0 1655 0 1693 0 1623 0 1805 0 1746 0 1795 0 1926 0 1619 0 1992 0 2233 0 2192 0 2080 0 1768 0 1835 0 1569 0 1976 0 1853 0 1965 0 1689 0 1778 0 1976 0 2397 0 2654 0 2097 0 1963 0 1677 0 1941 0 2003 0 1813 0 2012 0 1912 0 2084 0 2080 0 2118 0 2150 0 1608 0 1503 0 1548 0 1382 0 1731 0 1798 0 1779 0 1887 0 2004 0 2077 0 2092 0 2051 0 1577 0 1356 0 1652 0 1382 0 1519 0 1421 0 1442 0 1543 0 1656 0 1561 0 1905 0 2199 0 1473 0 1655 0 1407 0 1395 0 1530 0 1309 0 1526 0 1327 0 1627 0 1748 0 1958 0 2274 0 1648 0 1401 0 1411 0 1403 0 1394 0 1520 0 1528 0 1643 0 1515 0 1685 0 2000 0 2215 0 1956 0 1462 0 1563 0 1459 0 1446 0 1622 0 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 'Herman Ole Andreas Wold' @ www.yougetit.org Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 16703.1927083333 208.998246041788 79.9202530388396 Geometric Mean 16460.2217308056 Harmonic Mean 16222.7695544056 Quadratic Mean 16951.0936998116 Winsorized Mean ( 1 / 64 ) 16695.015625 206.779182106221 80.738377311232 Winsorized Mean ( 2 / 64 ) 16690.1197916667 204.609898395726 81.5704417163001 Winsorized Mean ( 3 / 64 ) 16675.4322916667 200.66017766927 83.1028482350457 Winsorized Mean ( 4 / 64 ) 16678.3489583333 199.536764444175 83.5853433064935 Winsorized Mean ( 5 / 64 ) 16673.921875 198.633391999471 83.9431965952855 Winsorized Mean ( 6 / 64 ) 16672.984375 197.964345291755 84.2221580377403 Winsorized Mean ( 7 / 64 ) 16670.4322916667 196.475174500547 84.847525057779 Winsorized Mean ( 8 / 64 ) 16680.8489583333 194.272804513704 85.863016185349 Winsorized Mean ( 9 / 64 ) 16678.9739583333 193.504273388289 86.1943442709662 Winsorized Mean ( 10 / 64 ) 16682.6197916667 192.112941630625 86.8375636230812 Winsorized Mean ( 11 / 64 ) 16686.0572916667 185.928486855315 89.744490335423 Winsorized Mean ( 12 / 64 ) 16686.0572916667 185.622743078178 89.8923106886695 Winsorized Mean ( 13 / 64 ) 16693.5052083333 184.496589968906 90.4813753530445 Winsorized Mean ( 14 / 64 ) 16680.3072916667 180.674393738986 92.322475512297 Winsorized Mean ( 15 / 64 ) 16677.9635416667 177.123698772656 94.1599777852055 Winsorized Mean ( 16 / 64 ) 16697.9635416667 174.237863980731 95.8342989300722 Winsorized Mean ( 17 / 64 ) 16691.8541666667 173.252721181926 96.3439653518583 Winsorized Mean ( 18 / 64 ) 16690.8229166667 172.528161034021 96.7426002609245 Winsorized Mean ( 19 / 64 ) 16691.8125 172.435453219763 96.800351600125 Winsorized Mean ( 20 / 64 ) 16694.9375 171.917398566318 97.1102264181822 Winsorized Mean ( 21 / 64 ) 16711.34375 169.953859912451 98.3287096780772 Winsorized Mean ( 22 / 64 ) 16681.5520833333 166.295235014949 100.312868747164 Winsorized Mean ( 23 / 64 ) 16683.9479166667 165.828215125461 100.609826283447 Winsorized Mean ( 24 / 64 ) 16658.9479166667 162.858638631474 102.290845954838 Winsorized Mean ( 25 / 64 ) 16652.4375 159.693157799451 104.277714395959 Winsorized Mean ( 26 / 64 ) 16648.375 158.951616227282 104.738633020219 Winsorized Mean ( 27 / 64 ) 16651.1875 157.564333973043 105.678658869899 Winsorized Mean ( 28 / 64 ) 16648.2708333333 156.645773972359 106.279731723059 Winsorized Mean ( 29 / 64 ) 16648.2708333333 156.342582860337 106.485837247588 Winsorized Mean ( 30 / 64 ) 16648.2708333333 155.404195401623 107.128837740242 Winsorized Mean ( 31 / 64 ) 16651.5 154.481091443057 107.789890946867 Winsorized Mean ( 32 / 64 ) 16658.1666666667 151.934082733032 109.640749244771 Winsorized Mean ( 33 / 64 ) 16640.9791666667 148.003581369089 112.436327639719 Winsorized Mean ( 34 / 64 ) 16641.15625 147.988730692175 112.448807231238 Winsorized Mean ( 35 / 64 ) 16628.2135416667 145.194635482533 114.523608165035 Winsorized Mean ( 36 / 64 ) 16645.0885416667 143.083697037613 116.331132660705 Winsorized Mean ( 37 / 64 ) 16639.5 141.70496697644 117.423548059303 Winsorized Mean ( 38 / 64 ) 16637.3229166667 141.130740281481 117.885889944913 Winsorized Mean ( 39 / 64 ) 16606.8541666667 137.856772376753 120.464550854863 Winsorized Mean ( 40 / 64 ) 16608.9375 137.686325569192 120.628809225165 Winsorized Mean ( 41 / 64 ) 16617.4791666667 135.791644803126 122.374827926704 Winsorized Mean ( 42 / 64 ) 16597.7916666667 132.484289140949 125.281207109836 Winsorized Mean ( 43 / 64 ) 16566.4375 129.212525206389 128.210771158127 Winsorized Mean ( 44 / 64 ) 16554.9791666667 127.186151928942 130.163377974638 Winsorized Mean ( 45 / 64 ) 16515.1354166667 122.707910085786 134.58900412468 Winsorized Mean ( 46 / 64 ) 16472.0104166667 117.992122066902 139.60262878675 Winsorized Mean ( 47 / 64 ) 16437.7395833333 114.227943825536 143.902963082651 Winsorized Mean ( 48 / 64 ) 16432.7395833333 113.300666060893 145.036566462033 Winsorized Mean ( 49 / 64 ) 16422.53125 107.336833816102 152.99995971687 Winsorized Mean ( 50 / 64 ) 16404.3020833333 105.174558383748 155.972150826432 Winsorized Mean ( 51 / 64 ) 16391.2864583333 99.758844151219 164.309105601571 Winsorized Mean ( 52 / 64 ) 16401.8489583333 98.8980720742695 165.845992892723 Winsorized Mean ( 53 / 64 ) 16380.0416666667 96.8687047814789 169.095289377695 Winsorized Mean ( 54 / 64 ) 16382.5729166667 96.6624755993986 169.482240291067 Winsorized Mean ( 55 / 64 ) 16385.4375 93.9799894530416 174.350280260323 Winsorized Mean ( 56 / 64 ) 16405.8541666667 91.3625145930826 179.568767778955 Winsorized Mean ( 57 / 64 ) 16405.8541666667 89.8598482017666 182.571576682723 Winsorized Mean ( 58 / 64 ) 16360.5416666667 85.3265062300718 191.74043787229 Winsorized Mean ( 59 / 64 ) 16360.5416666667 84.8139883615932 192.899095806174 Winsorized Mean ( 60 / 64 ) 16335.5416666667 81.6018906366932 200.185823382396 Winsorized Mean ( 61 / 64 ) 16329.1875 80.5297139509689 202.772202940422 Winsorized Mean ( 62 / 64 ) 16329.8333333333 79.4667276644822 205.492711393374 Winsorized Mean ( 63 / 64 ) 16332.4583333333 78.6658394136148 207.618179060664 Winsorized Mean ( 64 / 64 ) 16312.4583333333 74.7794790441276 218.140839463555 Trimmed Mean ( 1 / 64 ) 16683.6947368421 202.146924869141 82.5325181060372 Trimmed Mean ( 2 / 64 ) 16672.1329787234 197.171830477238 84.5563635452887 Trimmed Mean ( 3 / 64 ) 16662.8494623656 193.052682621889 86.3124471313422 Trimmed Mean ( 4 / 64 ) 16658.472826087 190.168037404369 87.5986998312694 Trimmed Mean ( 5 / 64 ) 16653.2307692308 187.416883673016 88.8566197604984 Trimmed Mean ( 6 / 64 ) 16648.8166666667 184.696006125663 90.1417254000565 Trimmed Mean ( 7 / 64 ) 16644.4719101124 181.924805376103 91.4909425116733 Trimmed Mean ( 8 / 64 ) 16640.4261363636 179.234617612341 92.8415858389284 Trimmed Mean ( 9 / 64 ) 16634.8505747126 176.719540602222 94.1313593167153 Trimmed Mean ( 10 / 64 ) 16629.3779069767 174.140314861411 95.4941302375107 Trimmed Mean ( 11 / 64 ) 16623.3647058824 171.569462063771 96.889997240322 Trimmed Mean ( 12 / 64 ) 16616.8511904762 169.626180906493 97.9615947354041 Trimmed Mean ( 13 / 64 ) 16610.1807228916 167.574765708222 99.1210141496656 Trimmed Mean ( 14 / 64 ) 16602.6768292683 165.498043777223 100.319474782537 Trimmed Mean ( 15 / 64 ) 16596.1049382716 163.693409196591 101.385297182858 Trimmed Mean ( 16 / 64 ) 16589.55625 162.125266700603 102.325544855608 Trimmed Mean ( 17 / 64 ) 16581.3227848101 160.717096746322 103.170870557613 Trimmed Mean ( 18 / 64 ) 16573.3205128205 159.290487433693 104.044634301966 Trimmed Mean ( 19 / 64 ) 16565.1818181818 157.813581792879 104.966769209526 Trimmed Mean ( 20 / 64 ) 16556.7631578947 156.22008747817 105.983573720686 Trimmed Mean ( 21 / 64 ) 16547.92 154.537013519904 107.080625043066 Trimmed Mean ( 22 / 64 ) 16537.8243243243 152.877821799811 108.176739631862 Trimmed Mean ( 23 / 64 ) 16529.2328767123 151.406197559833 109.171441744848 Trimmed Mean ( 24 / 64 ) 16520.2638888889 149.841401192967 110.251664475655 Trimmed Mean ( 25 / 64 ) 16512.4507042254 148.396580345311 111.272447557765 Trimmed Mean ( 26 / 64 ) 16504.7714285714 147.082801502162 112.214149173171 Trimmed Mean ( 27 / 64 ) 16497.0869565217 145.706223087214 113.221567390758 Trimmed Mean ( 28 / 64 ) 16489.0294117647 144.308964848481 114.261989399464 Trimmed Mean ( 29 / 64 ) 16480.8805970149 142.851058355076 115.371078008043 Trimmed Mean ( 30 / 64 ) 16472.4848484848 141.274037762234 116.599518987404 Trimmed Mean ( 31 / 64 ) 16463.8307692308 139.616738423435 117.921611370828 Trimmed Mean ( 32 / 64 ) 16454.75 137.866436384988 119.352834754142 Trimmed Mean ( 33 / 64 ) 16445.0634920635 136.142226632709 120.793260833244 Trimmed Mean ( 34 / 64 ) 16435.8709677419 134.567970967655 122.138060413295 Trimmed Mean ( 35 / 64 ) 16426.368852459 132.826432959027 123.667921260267 Trimmed Mean ( 36 / 64 ) 16417.1416666667 131.134589539321 125.193068620114 Trimmed Mean ( 37 / 64 ) 16406.8389830508 129.418664737037 126.773360058903 Trimmed Mean ( 38 / 64 ) 16396.4310344828 127.627787756103 128.470698448651 Trimmed Mean ( 39 / 64 ) 16385.7543859649 125.681971710142 130.374740012475 Trimmed Mean ( 40 / 64 ) 16376.0357142857 123.813463069076 132.263772520032 Trimmed Mean ( 41 / 64 ) 16365.8727272727 121.737577256485 134.435669709378 Trimmed Mean ( 42 / 64 ) 16354.962962963 119.572912337282 136.778160231058 Trimmed Mean ( 43 / 64 ) 16344.4905660377 117.457051274204 139.152910691427 Trimmed Mean ( 44 / 64 ) 16334.9615384615 115.400213533691 141.550531305495 Trimmed Mean ( 45 / 64 ) 16325.5490196078 113.277698805548 144.11970927863 Trimmed Mean ( 46 / 64 ) 16317.46 111.328379875289 146.570533212457 Trimmed Mean ( 47 / 64 ) 16310.8775510204 109.589238677992 148.836489310296 Trimmed Mean ( 48 / 64 ) 16305.4791666667 107.984365725068 150.99851776859 Trimmed Mean ( 49 / 64 ) 16300.0638297872 106.248051978332 153.415178220034 Trimmed Mean ( 50 / 64 ) 16294.847826087 104.837327558412 155.42982834056 Trimmed Mean ( 51 / 64 ) 16290.1777777778 103.433431737245 157.494317883218 Trimmed Mean ( 52 / 64 ) 16285.8522727273 102.336210523288 159.14066183857 Trimmed Mean ( 53 / 64 ) 16280.8720930233 101.136726401571 160.978832045432 Trimmed Mean ( 54 / 64 ) 16276.5952380952 99.9514011555459 162.845093214505 Trimmed Mean ( 55 / 64 ) 16272 98.5861172951666 165.053665226329 Trimmed Mean ( 56 / 64 ) 16267.05 97.2671739741183 167.240902920943 Trimmed Mean ( 57 / 64 ) 16260.9487179487 95.9776393674767 169.424345348704 Trimmed Mean ( 58 / 64 ) 16254.5263157895 94.6157575127619 171.79512951209 Trimmed Mean ( 59 / 64 ) 16249.7837837838 93.5387239624035 173.722530043441 Trimmed Mean ( 60 / 64 ) 16244.7777777778 92.3025325786418 175.994930192595 Trimmed Mean ( 61 / 64 ) 16240.6285714286 91.2092858123389 178.058938043253 Trimmed Mean ( 62 / 64 ) 16236.5294117647 90.0146619375697 180.376497142495 Trimmed Mean ( 63 / 64 ) 16232.1515151515 88.6937273384959 183.013523078156 Trimmed Mean ( 64 / 64 ) 16227.375 87.1862017739155 186.123201490983 Median 16310 Midrange 18555.5 Midmean - Weighted Average at Xnp 16288 Midmean - Weighted Average at X(n+1)p 16305.4791666667 Midmean - Empirical Distribution Function 16288 Midmean - Empirical Distribution Function - Averaging 16305.4791666667 Midmean - Empirical Distribution Function - Interpolation 16305.4791666667 Midmean - Closest Observation 16288 Midmean - True Basic - Statistics Graphics Toolkit 16305.4791666667 Midmean - MS Excel (old versions) 16310.8775510204 Number of observations 192

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Apr/03/t13018359620v3ojmq9y7bdyeh/1a8gu1301836036.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/03/t13018359620v3ojmq9y7bdyeh/1a8gu1301836036.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/03/t13018359620v3ojmq9y7bdyeh/2fn2n1301836036.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/03/t13018359620v3ojmq9y7bdyeh/2fn2n1301836036.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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