*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Thu, 19 May 2011 11:31:02 +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/May/19/t1305804625a3m230t2fxez79b.htm/, Retrieved Thu, 19 May 2011 13:30:28 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP1W52

Dataseries X:

» Textbox « » Textfile « » CSV «

1686 1591 2304 1712 1471 1377 1966 2453 1984 2596 4087 5179 1530 1523 1633 1976 1170 1480 1781 2472 1981 2273 3857 4551 1510 1329 1518 1790 1537 1449 1954 1897 1706 2514 3593 4524 1609 1638 2030 1375 1320 1245 1600 2298 2191 2511 3440 4923 1609 1435 2061 1789 1567 1404 1597 3159 1759 2504 4273 5274 1771 1682 1846 1589 1896 1379 1645 2512 1771 3727 4388 5434 1606 1523 1577 1605 1765 1403 2584 3318 1562 2349 3987 5891 1389 1442 1548 1935 1518 1250 1847 1930 2638 3114 4405 7242 1853 1779 2108 2336 1728 1661 2230 1645 2421 3740 4988 6757 1757 1394 1982 1650 1654 1406 1971 1968 2608 3845 4514 6694 1720 1321 1859 1628 1615 1457 1899 1605 2424 3116 4286 6047 1902 2049 1874 1279 1432 1540 2214 1857 2408 3252 3627 6153 1577 1667 1993 1997 1783 1625 2076 1773 2377 3088 4096 6119 1494 1564 1898 2121 1831 1515 2048 2795 1749 3339 4227 6410 1197 1968 1720 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 'George Udny Yule' @ 216.218.223.82 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 2402.41711229947 94.7079355175903 25.3665872787741 Geometric Mean 2160.40799614105 Harmonic Mean 1996.68225648574 Quadratic Mean 2727.62759349037 Winsorized Mean ( 1 / 62 ) 2400.35828877005 93.9893230158137 25.5386272796772 Winsorized Mean ( 2 / 62 ) 2399.97326203209 93.802331702157 25.5854328829749 Winsorized Mean ( 3 / 62 ) 2396.1871657754 92.6586582375657 25.8603697846764 Winsorized Mean ( 4 / 62 ) 2390.79679144385 91.4028711610289 26.1566924657308 Winsorized Mean ( 5 / 62 ) 2390.66310160428 91.150886416504 26.2275354150743 Winsorized Mean ( 6 / 62 ) 2389.66844919786 90.5611657924842 26.387341950452 Winsorized Mean ( 7 / 62 ) 2387.90909090909 90.1702864929911 26.4822169672789 Winsorized Mean ( 8 / 62 ) 2383.63101604278 89.1624989887819 26.7335599952473 Winsorized Mean ( 9 / 62 ) 2363.85026737968 84.5448477821845 27.9597199520631 Winsorized Mean ( 10 / 62 ) 2355.40106951872 82.8927791648254 28.4150331699604 Winsorized Mean ( 11 / 62 ) 2349.93048128342 81.8370046368733 28.7147665253698 Winsorized Mean ( 12 / 62 ) 2338.31550802139 79.5590444248587 29.3909451141016 Winsorized Mean ( 13 / 62 ) 2334.14438502674 78.7327412767441 29.6464259617516 Winsorized Mean ( 14 / 62 ) 2306.89304812834 73.9543864513796 31.1934580059694 Winsorized Mean ( 15 / 62 ) 2304.80748663102 73.5968862760367 31.3166439947809 Winsorized Mean ( 16 / 62 ) 2304.03743315508 73.4526945485151 31.3676366444702 Winsorized Mean ( 17 / 62 ) 2294.31016042781 71.8565850271337 31.9290174945199 Winsorized Mean ( 18 / 62 ) 2295.17647058824 71.434033140322 32.1300138000005 Winsorized Mean ( 19 / 62 ) 2285.11764705882 69.7991284448383 32.7384839606519 Winsorized Mean ( 20 / 62 ) 2284.47593582888 69.536207751232 32.8530417419607 Winsorized Mean ( 21 / 62 ) 2280.09625668449 68.6943414602452 33.1919079245272 Winsorized Mean ( 22 / 62 ) 2265.6256684492 66.3146598819203 34.1647785343899 Winsorized Mean ( 23 / 62 ) 2266.24064171123 66.0382985351309 34.3170658842102 Winsorized Mean ( 24 / 62 ) 2254.5614973262 64.0791174140413 35.184028562044 Winsorized Mean ( 25 / 62 ) 2239.05347593583 61.4636869692988 36.4288832372559 Winsorized Mean ( 26 / 62 ) 2237.52406417112 61.2187903049726 36.5496288480136 Winsorized Mean ( 27 / 62 ) 2224.52941176471 58.9595650292822 37.7297459820115 Winsorized Mean ( 28 / 62 ) 2223.63101604278 58.6834898837638 37.8919355417886 Winsorized Mean ( 29 / 62 ) 2223.78609625668 58.6105554696585 37.9417338470354 Winsorized Mean ( 30 / 62 ) 2207.74331550802 56.4192317658206 39.1310417814923 Winsorized Mean ( 31 / 62 ) 2202.93582887701 55.6048112893492 39.6177197223753 Winsorized Mean ( 32 / 62 ) 2176.75401069519 52.1383598162526 41.7495682328052 Winsorized Mean ( 33 / 62 ) 2168.28342245989 50.7978135277254 42.6845817148497 Winsorized Mean ( 34 / 62 ) 2161.19251336898 49.6397779772525 43.5375136923327 Winsorized Mean ( 35 / 62 ) 2157.82352941176 49.1014538277327 43.9462248303739 Winsorized Mean ( 36 / 62 ) 2146.6577540107 47.3932467221279 45.2945915817247 Winsorized Mean ( 37 / 62 ) 2131.02673796791 44.9185914661619 47.4419759927971 Winsorized Mean ( 38 / 62 ) 2122.69518716578 43.8207655259361 48.4403949061415 Winsorized Mean ( 39 / 62 ) 2122.90374331551 43.7271093972475 48.548915594433 Winsorized Mean ( 40 / 62 ) 2119.48128342246 42.9049523282627 49.399455503562 Winsorized Mean ( 41 / 62 ) 2093.17112299465 39.750331497005 52.657953887815 Winsorized Mean ( 42 / 62 ) 2057.01069518717 35.0557873649846 58.6782055062842 Winsorized Mean ( 43 / 62 ) 2021.36898395722 31.0445227474984 65.1119361826911 Winsorized Mean ( 44 / 62 ) 2015.72192513369 30.1900443351227 66.7677696249233 Winsorized Mean ( 45 / 62 ) 2013.55614973262 29.832374429421 67.4956716736175 Winsorized Mean ( 46 / 62 ) 2011.83422459893 29.4316438953342 68.3561622229964 Winsorized Mean ( 47 / 62 ) 1994.24064171123 27.6213212269806 72.1993211448282 Winsorized Mean ( 48 / 62 ) 1993.98395721925 27.5499634732034 72.3770091077875 Winsorized Mean ( 49 / 62 ) 1994.50802139037 27.4640505208686 72.6225004529047 Winsorized Mean ( 50 / 62 ) 1992.63636363636 27.2751002360218 73.0569767441119 Winsorized Mean ( 51 / 62 ) 1985.54545454545 26.2769407240034 75.5622762710617 Winsorized Mean ( 52 / 62 ) 1983.04278074866 25.5424681162527 77.637085489278 Winsorized Mean ( 53 / 62 ) 1975.67379679144 24.6700852734745 80.0837846683776 Winsorized Mean ( 54 / 62 ) 1976.2513368984 24.4763179054456 80.741365777845 Winsorized Mean ( 55 / 62 ) 1973.89839572193 23.9927975588409 82.270455993348 Winsorized Mean ( 56 / 62 ) 1966.71122994652 22.9368411820689 85.7446417462236 Winsorized Mean ( 57 / 62 ) 1958.17647058824 22.1210081654537 88.5211223621495 Winsorized Mean ( 58 / 62 ) 1955.69518716578 21.6208844022211 90.4539865614778 Winsorized Mean ( 59 / 62 ) 1946.86096256684 20.5760106697893 94.617999271614 Winsorized Mean ( 60 / 62 ) 1947.18181818182 20.2250504497363 96.2757459132672 Winsorized Mean ( 61 / 62 ) 1940.98395721925 19.3188944096377 100.470757594231 Winsorized Mean ( 62 / 62 ) 1927.72192513369 17.9376259591539 107.468063473022 Trimmed Mean ( 1 / 62 ) 2402.41711229947 91.7765961135067 26.1767946735377 Trimmed Mean ( 2 / 62 ) 2383.45945945946 89.3779234935054 26.6672055726675 Trimmed Mean ( 3 / 62 ) 2348.74033149171 86.8785539871427 27.0347539605609 Trimmed Mean ( 4 / 62 ) 2348.74033149171 84.6199682567274 27.756336711991 Trimmed Mean ( 5 / 62 ) 2316.74576271186 82.5600650512858 28.0613364496832 Trimmed Mean ( 6 / 62 ) 2300.94857142857 80.384835170309 28.6241623380035 Trimmed Mean ( 7 / 62 ) 2300.94857142857 78.1473350472908 29.4437240890704 Trimmed Mean ( 8 / 62 ) 2284.96531791908 75.7833893894652 30.1512684550991 Trimmed Mean ( 9 / 62 ) 2253.01183431953 73.3863887849886 30.7006772185033 Trimmed Mean ( 10 / 62 ) 2239.22155688623 71.5664973159276 31.2886845223299 Trimmed Mean ( 11 / 62 ) 2226.05454545455 69.8439863429477 31.8718140531696 Trimmed Mean ( 12 / 62 ) 2213.13496932515 68.121765305809 32.4879274544641 Trimmed Mean ( 13 / 62 ) 2201.01863354037 66.5639347727147 33.0662338555532 Trimmed Mean ( 14 / 62 ) 2201.01863354037 64.9731518775725 33.8758174713104 Trimmed Mean ( 15 / 62 ) 2178.94267515924 63.8464687328792 34.1278494864844 Trimmed Mean ( 16 / 62 ) 2178.94267515924 62.6603437758582 34.773870423589 Trimmed Mean ( 17 / 62 ) 2158.49019607843 61.3810507815766 35.1654161763926 Trimmed Mean ( 18 / 62 ) 2148.59602649007 60.166161515475 35.7110371074186 Trimmed Mean ( 19 / 62 ) 2138.37583892617 58.8789124096841 36.3181952826707 Trimmed Mean ( 20 / 62 ) 2128.55102040816 57.6490270837639 36.9225835730997 Trimmed Mean ( 21 / 62 ) 2118.49655172414 56.3251882536932 37.6118858614774 Trimmed Mean ( 22 / 62 ) 2108.43356643357 54.9588938367947 38.3638282949207 Trimmed Mean ( 23 / 62 ) 2098.95744680851 53.7105547846286 39.0790498296847 Trimmed Mean ( 24 / 62 ) 2089.1726618705 52.3588449287682 39.9010456535611 Trimmed Mean ( 25 / 62 ) 2079.76642335766 51.0734420801446 40.7210937554216 Trimmed Mean ( 26 / 62 ) 2070.94074074074 49.9249350986678 41.481090293816 Trimmed Mean ( 27 / 62 ) 2061.93233082707 48.6778497820991 42.3587389347943 Trimmed Mean ( 28 / 62 ) 2061.93233082707 47.5280312390181 43.3834997384518 Trimmed Mean ( 29 / 62 ) 2044.51937984496 46.2771095657933 44.1799282415903 Trimmed Mean ( 30 / 62 ) 2035.41732283465 44.8881269059644 45.344224923856 Trimmed Mean ( 31 / 62 ) 2026.824 43.5764409033878 46.5119215333262 Trimmed Mean ( 32 / 62 ) 2026.824 42.1951356772619 48.0345415998322 Trimmed Mean ( 33 / 62 ) 2010.52892561983 41.0295080845625 49.0020236527355 Trimmed Mean ( 34 / 62 ) 2003.01680672269 39.8655981284908 50.2442431759525 Trimmed Mean ( 35 / 62 ) 1995.5811965812 38.6822246805405 51.5891010163409 Trimmed Mean ( 36 / 62 ) 1988.04347826087 37.4075017690942 53.145582683722 Trimmed Mean ( 37 / 62 ) 1980.75221238938 36.1595323250554 54.7781479744662 Trimmed Mean ( 38 / 62 ) 1973.90990990991 35.0254980236074 56.3563695391135 Trimmed Mean ( 39 / 62 ) 1967.19266055046 33.8664515478938 58.0867664204046 Trimmed Mean ( 40 / 62 ) 1960.21495327103 32.5558620276084 60.2108140035949 Trimmed Mean ( 41 / 62 ) 1953.12380952381 31.1518398119594 62.6969007709776 Trimmed Mean ( 42 / 62 ) 1946.92233009709 29.9466010237008 65.0131321600145 Trimmed Mean ( 43 / 62 ) 1942.06930693069 29.1327560783042 66.6627387299273 Trimmed Mean ( 44 / 62 ) 1938.58585858586 28.6493814295855 67.6658888203404 Trimmed Mean ( 45 / 62 ) 1935.20618556701 28.1849482834172 68.6609805385239 Trimmed Mean ( 46 / 62 ) 1931.77894736842 27.690588384767 69.7630155244776 Trimmed Mean ( 47 / 62 ) 1928.27956989247 27.1652965130209 70.9831961145062 Trimmed Mean ( 48 / 62 ) 1925.3956043956 26.761721059585 71.9458812125241 Trimmed Mean ( 49 / 62 ) 1922.39325842697 26.3032941558042 73.0856464988725 Trimmed Mean ( 50 / 62 ) 1919.22988505747 25.7813721564069 74.4425034251144 Trimmed Mean ( 51 / 62 ) 1919.22988505747 25.1996065061982 76.1611053166806 Trimmed Mean ( 52 / 62 ) 1912.92771084337 24.6441723062624 77.6219094344376 Trimmed Mean ( 53 / 62 ) 1909.81481481481 24.0824145096487 79.3032946945431 Trimmed Mean ( 54 / 62 ) 1906.87341772152 23.5369313281943 81.016228969378 Trimmed Mean ( 55 / 62 ) 1903.75324675325 22.9176745589976 83.0692155023131 Trimmed Mean ( 56 / 62 ) 1903.75324675325 22.2513825733689 85.5566273455615 Trimmed Mean ( 57 / 62 ) 1897.54794520548 21.6120231305633 87.8005697912663 Trimmed Mean ( 58 / 62 ) 1894.74647887324 20.9762893672272 90.3280101500478 Trimmed Mean ( 59 / 62 ) 1891.89855072464 20.2901916608301 93.2420246368059 Trimmed Mean ( 60 / 62 ) 1889.29850746269 19.6347357276395 96.222252933262 Trimmed Mean ( 61 / 62 ) 1886.52307692308 18.8944704427938 99.8452474566483 Trimmed Mean ( 62 / 62 ) 1883.87301587302 18.1528399313924 103.778418307714 Median 1874 Midrange 4156 Midmean - Weighted Average at Xnp 1924.84042553191 Midmean - Weighted Average at X(n+1)p 1931.77894736842 Midmean - Empirical Distribution Function 1931.77894736842 Midmean - Empirical Distribution Function - Averaging 1931.77894736842 Midmean - Empirical Distribution Function - Interpolation 1924.84042553191 Midmean - Closest Observation 1924.84042553191 Midmean - True Basic - Statistics Graphics Toolkit 1931.77894736842 Midmean - MS Excel (old versions) 1931.77894736842 Number of observations 187

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/19/t1305804625a3m230t2fxez79b/1c3wl1305804659.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305804625a3m230t2fxez79b/1c3wl1305804659.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305804625a3m230t2fxez79b/2bqm51305804659.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305804625a3m230t2fxez79b/2bqm51305804659.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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