Centrummaten:Monthly Australian sales of red wine

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Fri, 12 Mar 2010 06:13:51 -0700

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/12/t1268400124szpthulk7mo7rz2.htm/, Retrieved Fri, 12 Mar 2010 14:22:06 +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/12/t1268400124szpthulk7mo7rz2.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 «

464 675 703 887 1139 1077 1318 1260 1120 963 996 960 530 883 894 1045 1199 1287 1565 1577 1076 918 1008 1063 544 635 804 980 1018 1064 1404 1286 1104 999 996 1015 615 722 832 977 1270 1437 1520 1708 1151 934 1159 1209 699 830 996 1124 1458 1270 1753 2258 1208 1241 1265 1828 809 997 1164 1205 1538 1513 1378 2083 1357 1536 1526 1376 779 1005 1193 1522 1539 1546 2116 2326 1596 1356 1553 1613 814 1150 1225 1691 1759 1754 2100 2062 2012 1897 1964 2186 966 1549 1538 1612 2078 2137 2907 2249 1883 1739 1828 1868 1138 1430 1809 1763 2200 2067 2503 2141 2103 1972 2181 2344 970 1199 1718 1683 2025 2051 2439 2353 2230 1852 2147 2286 1007 1665 1642 1518 1831 2207 2822 2393 2306 1785 2047 2171 1212 1335 2011 1860 1954 2152 2835 2224 2182 1992 2389 2724 891 1247 2017 2257 2255 2255 3057 3330 1896 2096 2374 2535 1041 1728 2201 2455 2204 2660 3670 2665 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 1664.06417112299 47.4580904334766 35.0638670018879 Geometric Mean 1536.17482508946 Harmonic Mean 1406.55618309747 Quadratic Mean 1785.50604879048 Winsorized Mean ( 1 / 62 ) 1663.06417112299 47.0830514315 35.3219283916325 Winsorized Mean ( 2 / 62 ) 1659.57754010695 46.2937353338631 35.8488579100032 Winsorized Mean ( 3 / 62 ) 1660.39572192513 46.0884941521660 36.0262523753361 Winsorized Mean ( 4 / 62 ) 1655.41176470588 45.062421740163 36.7359698120809 Winsorized Mean ( 5 / 62 ) 1652.47058823529 44.2928920874087 37.3078051659907 Winsorized Mean ( 6 / 62 ) 1650.93048128342 43.8586878565255 37.6420399690044 Winsorized Mean ( 7 / 62 ) 1650.5935828877 43.7709119747518 37.7098284778669 Winsorized Mean ( 8 / 62 ) 1647.21390374332 43.0933089596638 38.2243541633143 Winsorized Mean ( 9 / 62 ) 1648.03208556150 42.5288436447515 38.7509262966965 Winsorized Mean ( 10 / 62 ) 1648.35294117647 42.2513285690819 39.0130440154414 Winsorized Mean ( 11 / 62 ) 1648.35294117647 42.1818096237314 39.0773405854335 Winsorized Mean ( 12 / 62 ) 1647.32620320856 41.975056015619 39.2453604492054 Winsorized Mean ( 13 / 62 ) 1646.97860962567 41.6729091687739 39.5215655080728 Winsorized Mean ( 14 / 62 ) 1644.73262032086 41.3604727692285 39.7658080336187 Winsorized Mean ( 15 / 62 ) 1648.74331550802 40.9287404962195 40.2832653904977 Winsorized Mean ( 16 / 62 ) 1644.80748663102 40.3775847390886 40.7356580949409 Winsorized Mean ( 17 / 62 ) 1642.26203208556 40.0001754353391 41.0563707336809 Winsorized Mean ( 18 / 62 ) 1638.31550802139 39.4893080550281 41.4875719204401 Winsorized Mean ( 19 / 62 ) 1640.34759358289 39.1995393755146 41.8460935948525 Winsorized Mean ( 20 / 62 ) 1640.34759358289 38.8409191122191 42.2324607933092 Winsorized Mean ( 21 / 62 ) 1638.10160427807 37.9965456476583 43.1118559952312 Winsorized Mean ( 22 / 62 ) 1637.98395721925 37.9125299362232 43.2042905069824 Winsorized Mean ( 23 / 62 ) 1636.50802139037 37.6819102810343 43.4295397761202 Winsorized Mean ( 24 / 62 ) 1634.32620320856 37.3515989504485 43.755187170881 Winsorized Mean ( 25 / 62 ) 1634.05882352941 37.1382834088857 43.999309433307 Winsorized Mean ( 26 / 62 ) 1631.97326203209 36.8432601686966 44.2950285767238 Winsorized Mean ( 27 / 62 ) 1631.39572192513 36.3343920947146 44.8994913048909 Winsorized Mean ( 28 / 62 ) 1628.40106951872 36.0380243892459 45.1856364802463 Winsorized Mean ( 29 / 62 ) 1624.05882352941 35.6172383886131 45.5975504279587 Winsorized Mean ( 30 / 62 ) 1624.05882352941 35.5866886260044 45.6366941188976 Winsorized Mean ( 31 / 62 ) 1624.05882352941 35.5236197322196 45.717717838771 Winsorized Mean ( 32 / 62 ) 1625.08556149733 35.4267447219038 45.8717156841272 Winsorized Mean ( 33 / 62 ) 1624.37967914439 35.2925565255551 46.0261267264156 Winsorized Mean ( 34 / 62 ) 1621.10695187166 34.9492760498821 46.3845645774722 Winsorized Mean ( 35 / 62 ) 1621.66844919786 34.7559583179902 46.6587177473528 Winsorized Mean ( 36 / 62 ) 1621.86096256684 34.6658396866027 46.7855669220568 Winsorized Mean ( 37 / 62 ) 1623.04812834225 33.9301611884465 47.8349666341965 Winsorized Mean ( 38 / 62 ) 1623.25133689840 33.7989329427529 48.0267036727989 Winsorized Mean ( 39 / 62 ) 1626.37967914439 33.3982043893221 48.6966203387977 Winsorized Mean ( 40 / 62 ) 1626.37967914439 33.3590304215498 48.7538054491461 Winsorized Mean ( 41 / 62 ) 1625.94117647059 32.8387269748977 49.5129174073489 Winsorized Mean ( 42 / 62 ) 1625.26737967914 32.7362441679120 49.6473380190703 Winsorized Mean ( 43 / 62 ) 1631.24598930481 32.1619937825909 50.7196786471552 Winsorized Mean ( 44 / 62 ) 1632.65775401070 31.6161535522235 51.6399868603204 Winsorized Mean ( 45 / 62 ) 1629.04812834225 31.1163309654708 52.3534773476337 Winsorized Mean ( 46 / 62 ) 1631.26203208556 30.7062462684646 53.124762233177 Winsorized Mean ( 47 / 62 ) 1630.00534759358 30.5486360568135 53.3577127490127 Winsorized Mean ( 48 / 62 ) 1631.80213903743 30.2132786703800 54.0094359450367 Winsorized Mean ( 49 / 62 ) 1626.56149732620 29.6996281107756 54.7670661484161 Winsorized Mean ( 50 / 62 ) 1625.22459893048 29.2089379151783 55.6413452502134 Winsorized Mean ( 51 / 62 ) 1625.77005347594 29.0201803486607 56.0220520321807 Winsorized Mean ( 52 / 62 ) 1630.49732620321 28.4265194879138 57.3583173591284 Winsorized Mean ( 53 / 62 ) 1629.08021390374 27.9124965479329 58.3638303763404 Winsorized Mean ( 54 / 62 ) 1629.36898395722 27.6410957598669 58.9473368969314 Winsorized Mean ( 55 / 62 ) 1626.13368983957 27.3597918473277 59.4351630638739 Winsorized Mean ( 56 / 62 ) 1626.43315508021 27.0796141107358 60.0611644032035 Winsorized Mean ( 57 / 62 ) 1623.99465240642 26.7140264614383 60.7918336365599 Winsorized Mean ( 58 / 62 ) 1623.06417112299 26.5815572436362 61.059785032405 Winsorized Mean ( 59 / 62 ) 1617.06951871658 25.9107512760714 62.4092100413138 Winsorized Mean ( 60 / 62 ) 1618.67379679144 25.3441978057595 63.8676279753308 Winsorized Mean ( 61 / 62 ) 1622.26203208556 24.7724206303445 65.4866174078444 Winsorized Mean ( 62 / 62 ) 1623.91978609626 24.5800574611653 66.0665577638267 Trimmed Mean ( 1 / 62 ) 1664.06417112299 45.9235626441656 36.2355199664459 Trimmed Mean ( 2 / 62 ) 1658.34054054054 44.6720778450856 37.1225297889960 Trimmed Mean ( 3 / 62 ) 1650.38121546961 43.7716424399953 37.7043474603917 Trimmed Mean ( 4 / 62 ) 1650.38121546961 42.8832430827396 38.4854571816162 Trimmed Mean ( 5 / 62 ) 1644.64406779661 42.2393053541814 38.9363426790778 Trimmed Mean ( 6 / 62 ) 1642.97142857143 41.7380512412456 39.3638749225513 Trimmed Mean ( 7 / 62 ) 1642.97142857143 41.2914531621315 39.7896247952397 Trimmed Mean ( 8 / 62 ) 1641.53757225434 40.8285449988549 40.2056348640486 Trimmed Mean ( 9 / 62 ) 1639.14201183432 40.4438422282046 40.5288400292299 Trimmed Mean ( 10 / 62 ) 1638.03592814371 40.1143403051595 40.8341734073844 Trimmed Mean ( 11 / 62 ) 1636.86666666667 39.7974570244409 41.12993113257 Trimmed Mean ( 12 / 62 ) 1635.66871165644 39.464647278964 41.4464292584292 Trimmed Mean ( 13 / 62 ) 1634.54037267081 39.1308066690896 41.7711903179847 Trimmed Mean ( 14 / 62 ) 1634.54037267081 38.8050702458356 42.1218248624668 Trimmed Mean ( 15 / 62 ) 1632.45222929936 38.4875114651069 42.415115115441 Trimmed Mean ( 16 / 62 ) 1632.45222929936 38.188136392058 42.7476274971843 Trimmed Mean ( 17 / 62 ) 1630.09803921569 37.9195107912842 42.9883720860229 Trimmed Mean ( 18 / 62 ) 1629.2119205298 37.6642605368801 43.2561770045774 Trimmed Mean ( 19 / 62 ) 1628.57718120805 37.434304485262 43.5049402840977 Trimmed Mean ( 20 / 62 ) 1627.78911564626 37.2088332591719 43.7473839695044 Trimmed Mean ( 21 / 62 ) 1626.97931034483 36.9934361333294 43.9802159626636 Trimmed Mean ( 22 / 62 ) 1626.28671328671 36.829204319245 44.1575305073018 Trimmed Mean ( 23 / 62 ) 1625.58156028369 36.6540760680731 44.3492712042365 Trimmed Mean ( 24 / 62 ) 1624.94244604317 36.4790382901253 44.5445527680764 Trimmed Mean ( 25 / 62 ) 1624.40875912409 36.3115448658953 44.7353249530777 Trimmed Mean ( 26 / 62 ) 1623.87407407407 36.1419035898012 44.9305076042622 Trimmed Mean ( 27 / 62 ) 1623.43609022556 35.9760010786276 45.1255292848544 Trimmed Mean ( 28 / 62 ) 1623.43609022556 35.8297812152107 45.3096847137981 Trimmed Mean ( 29 / 62 ) 1622.73643410853 35.6877600985985 45.4703917989029 Trimmed Mean ( 30 / 62 ) 1622.66929133858 35.5589855269846 45.633171680536 Trimmed Mean ( 31 / 62 ) 1622.6 35.4143710577974 45.8175580007299 Trimmed Mean ( 32 / 62 ) 1622.6 35.2547767493566 46.024968801699 Trimmed Mean ( 33 / 62 ) 1622.40495867769 35.0811565551884 46.2471913126745 Trimmed Mean ( 34 / 62 ) 1622.31092436975 34.8948381438295 46.4914299840827 Trimmed Mean ( 35 / 62 ) 1622.36752136752 34.7102583301635 46.7402894537858 Trimmed Mean ( 36 / 62 ) 1622.4 34.5156147022782 47.0048125752462 Trimmed Mean ( 37 / 62 ) 1622.42477876106 34.3016426328522 47.2987488128978 Trimmed Mean ( 38 / 62 ) 1622.39639639640 34.1160765571398 47.5551868832016 Trimmed Mean ( 39 / 62 ) 1622.35779816514 33.9139682344813 47.8374511336494 Trimmed Mean ( 40 / 62 ) 1622.17757009346 33.7143126578386 48.1153979485416 Trimmed Mean ( 41 / 62 ) 1621.99047619048 33.4884144179261 48.4343766159988 Trimmed Mean ( 42 / 62 ) 1621.81553398058 33.2718061922902 48.7444391989874 Trimmed Mean ( 43 / 62 ) 1621.66336633663 33.0311082814749 49.0950334611123 Trimmed Mean ( 44 / 62 ) 1621.24242424242 32.8015656786179 49.4257633957775 Trimmed Mean ( 45 / 62 ) 1620.74226804124 32.5816687280252 49.7439919842765 Trimmed Mean ( 46 / 62 ) 1620.37894736842 32.3687709082596 50.0599467295481 Trimmed Mean ( 47 / 62 ) 1619.90322580645 32.1546893158903 50.3784443348958 Trimmed Mean ( 48 / 62 ) 1619.46153846154 31.9176034261952 50.738820106162 Trimmed Mean ( 49 / 62 ) 1618.92134831461 31.6694988350866 51.1192601040155 Trimmed Mean ( 50 / 62 ) 1618.58620689655 31.4258696799964 51.5048978239363 Trimmed Mean ( 51 / 62 ) 1618.58620689655 31.1839883119374 51.9044001269378 Trimmed Mean ( 52 / 62 ) 1617.96385542169 30.9151172442167 52.3356855689868 Trimmed Mean ( 53 / 62 ) 1617.40740740741 30.6533675761501 52.7644280319083 Trimmed Mean ( 54 / 62 ) 1616.88607594937 30.3925309166826 53.2001128955618 Trimmed Mean ( 55 / 62 ) 1616.32467532468 30.1074541853857 53.6851991992483 Trimmed Mean ( 56 / 62 ) 1616.32467532468 29.7960452058643 54.2462821544706 Trimmed Mean ( 57 / 62 ) 1615.39726027397 29.4529378473685 54.8467276386929 Trimmed Mean ( 58 / 62 ) 1615 29.0829591181227 55.5308004746199 Trimmed Mean ( 59 / 62 ) 1614.62318840580 28.6535297332835 56.3498878998588 Trimmed Mean ( 60 / 62 ) 1614.50746268657 28.2207680128302 57.2099052000482 Trimmed Mean ( 61 / 62 ) 1614.30769230769 27.76971686129 58.1319464066262 Trimmed Mean ( 62 / 62 ) 1613.92063492063 27.2971108155656 59.1242291473694 Median 1596 Midrange 2193.5 Midmean - Weighted Average at Xnp 1614.77659574468 Midmean - Weighted Average at X(n+1)p 1620.37894736842 Midmean - Empirical Distribution Function 1620.37894736842 Midmean - Empirical Distribution Function - Averaging 1620.37894736842 Midmean - Empirical Distribution Function - Interpolation 1619.90322580645 Midmean - Closest Observation 1614.77659574468 Midmean - True Basic - Statistics Graphics Toolkit 1620.37894736842 Midmean - MS Excel (old versions) 1620.37894736842 Number of observations 187

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Mar/12/t1268400124szpthulk7mo7rz2/1k7211268399629.png (open in new window) http://www.freestatistics.org/blog/date/2010/Mar/12/t1268400124szpthulk7mo7rz2/1k7211268399629.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Mar/12/t1268400124szpthulk7mo7rz2/2caog1268399629.png (open in new window) http://www.freestatistics.org/blog/date/2010/Mar/12/t1268400124szpthulk7mo7rz2/2caog1268399629.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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