mini tutorial 1

*The author of this computation has been verified*

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

Title produced by software: Central Tendency

Date of computation: Thu, 25 Nov 2010 22:00:41 +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/Nov/25/t1290722346wm0t5okvj37f7ex.htm/, Retrieved Thu, 25 Nov 2010 22:59:08 +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/Nov/25/t1290722346wm0t5okvj37f7ex.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:

Dataseries X:

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1687 1508 1507 1385 1632 1511 1559 1630 1579 1653 2152 2148 1752 1765 1717 1558 1575 1520 1805 1800 1719 2008 2242 2478 2030 1655 1693 1623 1805 1746 1795 1926 1619 1992 2233 2192 2080 1768 1835 1569 1976 1853 1965 1689 1778 1976 2397 2654 2097 1963 1677 1941 2003 1813 2012 1912 2084 2080 2118 2150 1608 1503 1548 1382 1731 1798 1779 1887 2004 2077 2092 2051 1577 1356 1652 1382 1519 1421 1442 1543 1656 1561 1905 2199 1473 1655 1407 1395 1530 1309 1526 1327 1627 1748 1958 2274 1648 1401 1411 1403 1394 1520 1528 1643 1515 1685 2000 2215 1956 1462 1563 1459 1446 1622 1657 1638 1643 1683 2050 2262 1813 1445 1762 1461 1556 1431 1427 1554 1645 1653 2016 2207 1665 1361 1506 1360 1453 1522 1460 1552 1548 1827 1737 1941 1474 1458 1542 1404 1522 1385 1641 1510 1681 1938 1868 1726 1456 1445 1456 1365 1487 1558 1488 1684 1594 1850 1998 2079 1494 1057 1218 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 3 seconds R Server 'George Udny Yule' @ 72.249.76.132 R Framework error message Warning: there are blank lines in the 'Data' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 1670.30729166667 20.9008705923746 79.9156802720006 Geometric Mean 1646.00694926890 Harmonic Mean 1622.25780784952 Quadratic Mean 1695.10002931243 Winsorized Mean ( 1 / 64 ) 1669.48958333333 20.6789704377981 80.7336897334962 Winsorized Mean ( 2 / 64 ) 1669 20.4620426592893 81.5656592936634 Winsorized Mean ( 3 / 64 ) 1667.53125 20.0670748961354 83.097873438502 Winsorized Mean ( 4 / 64 ) 1667.82291666667 19.9547262298519 83.5803457013423 Winsorized Mean ( 5 / 64 ) 1667.38020833333 19.8643903025996 83.9381517848613 Winsorized Mean ( 6 / 64 ) 1667.28645833333 19.7974839283951 84.217088614073 Winsorized Mean ( 7 / 64 ) 1667.03125 19.6485633018301 84.8423991307664 Winsorized Mean ( 8 / 64 ) 1668.07291666667 19.4283044675612 85.8578739823538 Winsorized Mean ( 9 / 64 ) 1667.88541666667 19.3514497412718 86.189171300664 Winsorized Mean ( 10 / 64 ) 1668.25 19.2123052890607 86.8323699264703 Winsorized Mean ( 11 / 64 ) 1668.59375 18.5938203945168 89.7391560527315 Winsorized Mean ( 12 / 64 ) 1668.59375 18.5632440917286 89.8869692040246 Winsorized Mean ( 13 / 64 ) 1669.33854166667 18.4506122546587 90.4760513432374 Winsorized Mean ( 14 / 64 ) 1668.02604166667 18.0676243808851 92.32127071622 Winsorized Mean ( 15 / 64 ) 1667.79166666667 17.7125582681979 94.1587116560748 Winsorized Mean ( 16 / 64 ) 1669.79166666667 17.4239807262422 95.8329610725408 Winsorized Mean ( 17 / 64 ) 1669.171875 17.3263018066927 96.3374581386574 Winsorized Mean ( 18 / 64 ) 1669.078125 17.2529620247963 96.7415405309053 Winsorized Mean ( 19 / 64 ) 1669.17708333333 17.2436914470150 96.7992896684705 Winsorized Mean ( 20 / 64 ) 1669.48958333333 17.1918868185342 97.1091539256466 Winsorized Mean ( 21 / 64 ) 1671.13020833333 16.9955367568987 98.3275922518304 Winsorized Mean ( 22 / 64 ) 1668.15104166667 16.6296736759734 100.311712314404 Winsorized Mean ( 23 / 64 ) 1668.390625 16.5829724251287 100.608659426571 Winsorized Mean ( 24 / 64 ) 1665.890625 16.2860141787223 102.289645994321 Winsorized Mean ( 25 / 64 ) 1665.23958333333 15.9694681857355 104.276458299393 Winsorized Mean ( 26 / 64 ) 1664.83333333333 15.8953141819711 104.737365633303 Winsorized Mean ( 27 / 64 ) 1665.11458333333 15.7565876891397 105.677359602487 Winsorized Mean ( 28 / 64 ) 1664.82291666667 15.6647321876384 106.278415534000 Winsorized Mean ( 29 / 64 ) 1664.82291666667 15.6344133766142 106.484514421046 Winsorized Mean ( 30 / 64 ) 1664.82291666667 15.5405755672271 107.127494053537 Winsorized Mean ( 31 / 64 ) 1665.14583333333 15.4482665597044 107.788522867461 Winsorized Mean ( 32 / 64 ) 1665.8125 15.1935692847749 109.639313105266 Winsorized Mean ( 33 / 64 ) 1664.09375 14.8005208909569 112.434809711107 Winsorized Mean ( 34 / 64 ) 1664.09375 14.8005208909569 112.434809711107 Winsorized Mean ( 35 / 64 ) 1662.81770833333 14.5195832542902 114.522412882754 Winsorized Mean ( 36 / 64 ) 1664.50520833333 14.3084934270233 116.329871961902 Winsorized Mean ( 37 / 64 ) 1663.92708333333 14.1721863725573 117.407931252960 Winsorized Mean ( 38 / 64 ) 1663.72916666667 14.1131559679284 117.88498408488 Winsorized Mean ( 39 / 64 ) 1660.68229166667 13.7857575073348 120.463622748557 Winsorized Mean ( 40 / 64 ) 1660.890625 13.7687131735077 120.627875972877 Winsorized Mean ( 41 / 64 ) 1661.74479166667 13.5792472508874 122.373851875926 Winsorized Mean ( 42 / 64 ) 1659.77604166667 13.2485113196437 125.280191986982 Winsorized Mean ( 43 / 64 ) 1656.640625 12.9213330426200 128.209730337861 Winsorized Mean ( 44 / 64 ) 1655.49479166667 12.7186955237776 130.162310165513 Winsorized Mean ( 45 / 64 ) 1651.51041666667 12.2708689585983 134.587894487247 Winsorized Mean ( 46 / 64 ) 1647.19791666667 11.7992872922710 139.601475569261 Winsorized Mean ( 47 / 64 ) 1643.77083333333 11.4228670337337 143.901774263763 Winsorized Mean ( 48 / 64 ) 1643.27083333333 11.3301391298353 145.035362276016 Winsorized Mean ( 49 / 64 ) 1642.25 10.7337583788451 152.998599561983 Winsorized Mean ( 50 / 64 ) 1640.42708333333 10.5175295416995 155.970760702827 Winsorized Mean ( 51 / 64 ) 1639.09895833333 9.9781353461252 164.269064457002 Winsorized Mean ( 52 / 64 ) 1640.18229166667 9.88984185728128 165.845148520661 Winsorized Mean ( 53 / 64 ) 1637.97395833333 9.68915444943557 169.052311724554 Winsorized Mean ( 54 / 64 ) 1638.25520833333 9.66623763696663 169.482198747954 Winsorized Mean ( 55 / 64 ) 1638.54166666667 9.39798907156718 174.350241757988 Winsorized Mean ( 56 / 64 ) 1640.58333333333 9.13624374018374 179.568691465355 Winsorized Mean ( 57 / 64 ) 1640.58333333333 8.98597697197025 182.571504295055 Winsorized Mean ( 58 / 64 ) 1636.05208333333 8.53263656541053 191.740509605851 Winsorized Mean ( 59 / 64 ) 1636.05208333333 8.48138469361463 192.899171825688 Winsorized Mean ( 60 / 64 ) 1633.55208333333 8.16017102274104 200.186010658465 Winsorized Mean ( 61 / 64 ) 1632.91666666667 8.05295225331572 202.77242622348 Winsorized Mean ( 62 / 64 ) 1632.91666666667 7.94643640335133 205.490434174745 Winsorized Mean ( 63 / 64 ) 1633.24479166667 7.86656813129194 207.618463910569 Winsorized Mean ( 64 / 64 ) 1631.24479166667 7.47792981405108 218.141227883892 Trimmed Mean ( 1 / 64 ) 1668.35789473684 20.2157064859530 82.5278055899808 Trimmed Mean ( 2 / 64 ) 1667.20212765957 19.7181559800024 84.551624875592 Trimmed Mean ( 3 / 64 ) 1666.27419354839 19.3061968330386 86.3077388031648 Trimmed Mean ( 4 / 64 ) 1665.83695652174 19.0176834253529 87.5941048793028 Trimmed Mean ( 5 / 64 ) 1665.31318681319 18.7425181060045 88.8521583596427 Trimmed Mean ( 6 / 64 ) 1664.87222222222 18.4703766223995 90.1374268786264 Trimmed Mean ( 7 / 64 ) 1664.43820224719 18.1931993793624 91.486833488741 Trimmed Mean ( 8 / 64 ) 1664.03409090909 17.9241201811415 92.8376999312841 Trimmed Mean ( 9 / 64 ) 1663.47701149425 17.6725520325738 94.127718986378 Trimmed Mean ( 10 / 64 ) 1662.93023255814 17.4145649787268 95.4907707766193 Trimmed Mean ( 11 / 64 ) 1662.32941176471 17.1574124825324 96.886952706714 Trimmed Mean ( 12 / 64 ) 1661.67857142857 16.9630196276903 97.9588898615707 Trimmed Mean ( 13 / 64 ) 1661.01204819277 16.7578091803535 99.1186873126652 Trimmed Mean ( 14 / 64 ) 1660.26219512195 16.5500656572981 100.317559428523 Trimmed Mean ( 15 / 64 ) 1659.60493827160 16.3696102774403 101.383289531259 Trimmed Mean ( 16 / 64 ) 1658.95 16.2128040672572 102.323447142025 Trimmed Mean ( 17 / 64 ) 1658.12658227848 16.0719947954135 103.168685865408 Trimmed Mean ( 18 / 64 ) 1657.32692307692 15.9292640359292 104.042906146746 Trimmed Mean ( 19 / 64 ) 1656.51298701299 15.7815794352336 104.964968418478 Trimmed Mean ( 20 / 64 ) 1655.67105263158 15.6222363225182 105.981692918386 Trimmed Mean ( 21 / 64 ) 1654.78666666667 15.4539355533308 107.078657145688 Trimmed Mean ( 22 / 64 ) 1653.77702702703 15.2880229584387 108.174682332890 Trimmed Mean ( 23 / 64 ) 1652.91780821918 15.1408674599479 109.169293806424 Trimmed Mean ( 24 / 64 ) 1652.02083333333 14.9843951098948 110.249417558566 Trimmed Mean ( 25 / 64 ) 1651.23943661972 14.8399207338052 111.270098152088 Trimmed Mean ( 26 / 64 ) 1650.47142857143 14.7085507038778 112.211696570233 Trimmed Mean ( 27 / 64 ) 1649.70289855072 14.5709011895179 113.219002523845 Trimmed Mean ( 28 / 64 ) 1648.89705882353 14.4311840065653 114.259305270682 Trimmed Mean ( 29 / 64 ) 1648.08208955224 14.2854024988713 115.368264190137 Trimmed Mean ( 30 / 64 ) 1647.24242424242 14.127710195746 116.596561043447 Trimmed Mean ( 31 / 64 ) 1646.37692307692 13.9619905931052 117.918495367698 Trimmed Mean ( 32 / 64 ) 1645.46875 13.7869713113669 119.349544786778 Trimmed Mean ( 33 / 64 ) 1644.5 13.6145615322083 120.789787912712 Trimmed Mean ( 34 / 64 ) 1643.58064516129 13.4571475405610 122.134400340592 Trimmed Mean ( 35 / 64 ) 1642.63114754098 13.2828962984975 123.66513376505 Trimmed Mean ( 36 / 64 ) 1641.70833333333 13.1137204210697 125.190127638806 Trimmed Mean ( 37 / 64 ) 1640.67796610169 12.9421364669263 126.770257004665 Trimmed Mean ( 38 / 64 ) 1639.63793103448 12.7629429649355 128.468640464756 Trimmed Mean ( 39 / 64 ) 1638.57017543860 12.5683658733474 130.372571259512 Trimmed Mean ( 40 / 64 ) 1637.59821428571 12.3815199652873 132.261484767368 Trimmed Mean ( 41 / 64 ) 1636.58181818182 12.1739366783402 134.433245500087 Trimmed Mean ( 42 / 64 ) 1635.49074074074 11.9574755310153 136.775587497428 Trimmed Mean ( 43 / 64 ) 1634.44339622642 11.7458951189475 139.150177970674 Trimmed Mean ( 44 / 64 ) 1633.49038461538 11.5402176517126 141.547623616351 Trimmed Mean ( 45 / 64 ) 1632.54901960784 11.3279729759362 144.116606128549 Trimmed Mean ( 46 / 64 ) 1631.74 11.1330486263724 146.567221141447 Trimmed Mean ( 47 / 64 ) 1631.08163265306 10.9591429268065 148.832955601243 Trimmed Mean ( 48 / 64 ) 1630.54166666667 10.7986650071049 150.994744775753 Trimmed Mean ( 49 / 64 ) 1630 10.6250439055978 153.411130766361 Trimmed Mean ( 50 / 64 ) 1629.47826086957 10.4839818010796 155.425514063919 Trimmed Mean ( 51 / 64 ) 1629.01111111111 10.3436036245201 157.489707673006 Trimmed Mean ( 52 / 64 ) 1628.57954545455 10.2337074120645 159.138763683493 Trimmed Mean ( 53 / 64 ) 1628.08139534884 10.113760791642 160.976854099049 Trimmed Mean ( 54 / 64 ) 1627.65476190476 9.99502879946467 162.846430416683 Trimmed Mean ( 55 / 64 ) 1627.19512195122 9.85849045713708 165.055200796305 Trimmed Mean ( 56 / 64 ) 1626.7 9.72658499660608 167.242665392592 Trimmed Mean ( 57 / 64 ) 1626.08974358974 9.59761850763653 169.426378251638 Trimmed Mean ( 58 / 64 ) 1625.44736842105 9.46141554515973 171.797482169843 Trimmed Mean ( 59 / 64 ) 1624.97297297297 9.35369765096348 173.725197628725 Trimmed Mean ( 60 / 64 ) 1624.47222222222 9.23006188172444 175.997977374202 Trimmed Mean ( 61 / 64 ) 1624.05714285714 9.12071983543374 178.062386758963 Trimmed Mean ( 62 / 64 ) 1623.64705882353 9.00123795763238 180.380417278803 Trimmed Mean ( 63 / 64 ) 1623.21212121212 8.86920013453002 183.016742952112 Trimmed Mean ( 64 / 64 ) 1622.734375 8.7184306403791 186.126889337671 Median 1631 Midrange 1855.5 Midmean - Weighted Average at Xnp 1628.79381443299 Midmean - Weighted Average at X(n+1)p 1630.54166666667 Midmean - Empirical Distribution Function 1628.79381443299 Midmean - Empirical Distribution Function - Averaging 1630.54166666667 Midmean - Empirical Distribution Function - Interpolation 1630.54166666667 Midmean - Closest Observation 1628.79381443299 Midmean - True Basic - Statistics Graphics Toolkit 1630.54166666667 Midmean - MS Excel (old versions) 1631.08163265306 Number of observations 192

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/25/t1290722346wm0t5okvj37f7ex/11kph1290722438.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t1290722346wm0t5okvj37f7ex/11kph1290722438.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t1290722346wm0t5okvj37f7ex/2prhd1290722438.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/25/t1290722346wm0t5okvj37f7ex/2prhd1290722438.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = 6 ; par3 = Pearson Chi-Squared ;

Parameters (R input):

par1 = 5 ; par2 = 6 ; par3 = Pearson Chi-Squared ;

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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