*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: Mon, 22 Nov 2010 11:38:18 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/22/t1290425799xydtgz4ttouowf7.htm/, Retrieved Mon, 22 Nov 2010 12:36:42 +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/22/t1290425799xydtgz4ttouowf7.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:

» Textbox « » Textfile « » CSV «

41.82 41.32 41.82 41.82 41.82 41.82 41.82 41.82 41.82 41.82 41.82 41.32 41.32 41.32 41.82 41.32 41.32 41.32 41.82 41.82 41.82 41.82 41.32 41.82 41.32 41.32 41.82 41.32 41.32 41.82 41.82 41.82 41.82 41.82 41.82 41.82 42.17 42.17 41.67 42.17 42.17 42.16 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 41.67 41.67 42.17 42.17 42.17 42.17 42.17 42.17 41.67 42.17 42.17 42.17 42.17 42.17 42.17 41.67 41.67 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 42.17 41.67 42.17 42.17 42.17 41.67 41.67 42.17 42.17 42.17 42.17 41.96 42.46 41.96 42.46 42.46 42.46 41.96 41.96 42.46 41.96 41.96 41.96 41.96 41.96 42.46 42.46 42.46 42.46 42.46 42.46 42.46 42.46 41.96 41.96 41.96 42.46 42.46 42.46 42.46 41.96 42.46 42.46 42.46 42.46 42.46 42.46 42.46 42.46 42.46 42.46 42.46 42.46 41.96 42.46 5 5 42.46 42.46 42. 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 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 41.5971641791045 0.319469822132366 130.206865554486 Geometric Mean 40.8302079750825 Harmonic Mean 37.9413956949818 Quadratic Mean 41.8418004057429 Winsorized Mean ( 1 / 67 ) 41.5971641791045 0.319469822132366 130.206865554486 Winsorized Mean ( 2 / 67 ) 41.5971641791045 0.319469822132366 130.206865554486 Winsorized Mean ( 3 / 67 ) 42.1392537313433 0.0254110427966362 1658.30477987788 Winsorized Mean ( 4 / 67 ) 42.1392537313433 0.0254110427966362 1658.30477987789 Winsorized Mean ( 5 / 67 ) 42.1392537313433 0.0254110427966362 1658.30477987789 Winsorized Mean ( 6 / 67 ) 42.1392537313433 0.0254110427966362 1658.30477987789 Winsorized Mean ( 7 / 67 ) 42.1392537313433 0.0254110427966362 1658.30477987789 Winsorized Mean ( 8 / 67 ) 42.1392537313433 0.0254110427966362 1658.30477987789 Winsorized Mean ( 9 / 67 ) 42.1392537313433 0.0254110427966362 1658.30477987789 Winsorized Mean ( 10 / 67 ) 42.1392537313433 0.0254110427966362 1658.30477987789 Winsorized Mean ( 11 / 67 ) 42.1392537313433 0.0254110427966362 1658.30477987789 Winsorized Mean ( 12 / 67 ) 42.1392537313433 0.0254110427966362 1658.30477987789 Winsorized Mean ( 13 / 67 ) 42.1392537313433 0.0254110427966362 1658.30477987789 Winsorized Mean ( 14 / 67 ) 42.1392537313433 0.0254110427966362 1658.30477987789 Winsorized Mean ( 15 / 67 ) 42.1653731343284 0.0217723391397217 1936.64873873847 Winsorized Mean ( 16 / 67 ) 42.1653731343284 0.0217723391397217 1936.64873873847 Winsorized Mean ( 17 / 67 ) 42.1653731343284 0.0217723391397217 1936.64873873847 Winsorized Mean ( 18 / 67 ) 42.1483582089552 0.0197731904336214 2131.59117394066 Winsorized Mean ( 19 / 67 ) 42.1483582089552 0.0197731904336214 2131.59117394066 Winsorized Mean ( 20 / 67 ) 42.1394029850746 0.0189377931372869 2225.14855239948 Winsorized Mean ( 21 / 67 ) 42.1394029850746 0.0189377931372869 2225.14855239948 Winsorized Mean ( 22 / 67 ) 42.1394029850746 0.0189377931372869 2225.14855239948 Winsorized Mean ( 23 / 67 ) 42.1394029850746 0.0189377931372869 2225.14855239948 Winsorized Mean ( 24 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 25 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 26 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 27 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 28 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 29 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 30 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 31 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 32 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 33 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 34 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 35 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 36 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 37 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 38 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 39 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 40 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 41 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 42 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 43 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 44 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 45 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 46 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 47 / 67 ) 42.1573134328358 0.0169232605488794 2491.08694574979 Winsorized Mean ( 48 / 67 ) 42.1907462686567 0.0138360946051595 3049.32478944773 Winsorized Mean ( 49 / 67 ) 42.1907462686567 0.0138360946051595 3049.32478944773 Winsorized Mean ( 50 / 67 ) 42.1907462686567 0.0138360946051595 3049.32478944773 Winsorized Mean ( 51 / 67 ) 42.1907462686567 0.0138360946051595 3049.32478944773 Winsorized Mean ( 52 / 67 ) 42.1907462686567 0.0138360946051595 3049.32478944773 Winsorized Mean ( 53 / 67 ) 42.1907462686567 0.0138360946051595 3049.32478944773 Winsorized Mean ( 54 / 67 ) 42.1907462686567 0.0138360946051595 3049.32478944773 Winsorized Mean ( 55 / 67 ) 42.1907462686567 0.0138360946051595 3049.32478944773 Winsorized Mean ( 56 / 67 ) 42.1350248756219 0.00903341623342545 4664.3510923048 Winsorized Mean ( 57 / 67 ) 42.1350248756219 0.00903341623342545 4664.3510923048 Winsorized Mean ( 58 / 67 ) 42.1350248756219 0.00903341623342545 4664.3510923048 Winsorized Mean ( 59 / 67 ) 42.1350248756219 0.00903341623342545 4664.3510923048 Winsorized Mean ( 60 / 67 ) 42.1350248756219 0.00903341623342545 4664.3510923048 Winsorized Mean ( 61 / 67 ) 42.1350248756219 0.00903341623342545 4664.3510923048 Winsorized Mean ( 62 / 67 ) 42.1350248756219 0.00903341623342545 4664.3510923048 Winsorized Mean ( 63 / 67 ) 42.1350248756219 0.00903341623342545 4664.3510923048 Winsorized Mean ( 64 / 67 ) 42.1636815920398 0.00634316261712779 6647.10715096434 Winsorized Mean ( 65 / 67 ) 42.1636815920398 0.00634316261712779 6647.10715096434 Winsorized Mean ( 66 / 67 ) 42.1636815920398 0.00634316261712779 6647.10715096434 Winsorized Mean ( 67 / 67 ) 42.1636815920398 0.00634316261712779 6647.10715096434 Trimmed Mean ( 1 / 67 ) 41.7753266331658 0.264466509672893 157.960744008139 Trimmed Mean ( 2 / 67 ) 41.9571065989848 0.190144272510709 220.659323812247 Trimmed Mean ( 3 / 67 ) 42.1426153846154 0.0245819211477231 1714.3743620103 Trimmed Mean ( 4 / 67 ) 42.1437823834197 0.0242691859074399 1736.51405301157 Trimmed Mean ( 5 / 67 ) 42.1449738219895 0.0239358095315541 1760.74988257367 Trimmed Mean ( 6 / 67 ) 42.1461904761905 0.0235800491845237 1787.36652101016 Trimmed Mean ( 7 / 67 ) 42.1474331550802 0.023199940661388 1816.70435154288 Trimmed Mean ( 8 / 67 ) 42.1487027027027 0.0227932570949553 1849.17418897676 Trimmed Mean ( 9 / 67 ) 42.15 0.0223574569623447 1885.27702730193 Trimmed Mean ( 10 / 67 ) 42.1513259668508 0.0218896177278454 1925.63097679092 Trimmed Mean ( 11 / 67 ) 42.1526815642458 0.0213863498607263 1971.00869661048 Trimmed Mean ( 12 / 67 ) 42.1540677966102 0.0208436834996815 2022.39051448149 Trimmed Mean ( 13 / 67 ) 42.1554857142857 0.0202569161205614 2081.04162861674 Trimmed Mean ( 14 / 67 ) 42.156936416185 0.0196204031509812 2148.62743093415 Trimmed Mean ( 15 / 67 ) 42.156936416185 0.0189272625809094 2227.31291627485 Trimmed Mean ( 16 / 67 ) 42.1578698224852 0.0186139476787145 2264.85378331077 Trimmed Mean ( 17 / 67 ) 42.1573053892216 0.0182767913441577 2306.60319940115 Trimmed Mean ( 18 / 67 ) 42.1567272727273 0.0179133505351085 2353.36919188312 Trimmed Mean ( 19 / 67 ) 42.1573006134969 0.0177201822114163 2379.05570668099 Trimmed Mean ( 20 / 67 ) 42.1578881987578 0.0175120654808610 2407.36241220844 Trimmed Mean ( 21 / 67 ) 42.1590566037736 0.0173592563771218 2428.62111647455 Trimmed Mean ( 22 / 67 ) 42.1602547770701 0.0171936803649708 2452.07854759034 Trimmed Mean ( 23 / 67 ) 42.1614838709677 0.0170141993358398 2478.01750988988 Trimmed Mean ( 24 / 67 ) 42.1627450980392 0.0168195350814263 2506.77232717326 Trimmed Mean ( 25 / 67 ) 42.1630463576159 0.0167743084795392 2513.54900317024 Trimmed Mean ( 26 / 67 ) 42.163355704698 0.0167233902812268 2521.22057762590 Trimmed Mean ( 27 / 67 ) 42.1636734693878 0.0166663167326938 2529.87352548488 Trimmed Mean ( 28 / 67 ) 42.164 0.0166025779284342 2539.60560713817 Trimmed Mean ( 29 / 67 ) 42.1643356643357 0.0165316119374030 2550.52779027181 Trimmed Mean ( 30 / 67 ) 42.1643356643357 0.0164527979751358 2562.74560278783 Trimmed Mean ( 31 / 67 ) 42.165035971223 0.0163654484267154 2576.46688754289 Trimmed Mean ( 32 / 67 ) 42.165401459854 0.0162687994761386 2591.79551150643 Trimmed Mean ( 33 / 67 ) 42.1657777777778 0.0161620000331315 2608.94553219525 Trimmed Mean ( 34 / 67 ) 42.1661654135338 0.0160440985632451 2628.14175862338 Trimmed Mean ( 35 / 67 ) 42.1665648854962 0.0159140273131702 2649.64763825684 Trimmed Mean ( 36 / 67 ) 42.1669767441860 0.0157705832690752 2673.77407827850 Trimmed Mean ( 37 / 67 ) 42.1674015748032 0.0156124049743042 2700.89083931685 Trimmed Mean ( 38 / 67 ) 42.16784 0.0154379440382789 2731.44143387509 Trimmed Mean ( 39 / 67 ) 42.1682926829268 0.0152454297515567 2765.9628734717 Trimmed Mean ( 40 / 67 ) 42.1687603305785 0.0150328246210499 2805.11223895550 Trimmed Mean ( 41 / 67 ) 42.169243697479 0.0147977677556207 2849.70303588267 Trimmed Mean ( 42 / 67 ) 42.1697435897436 0.0145375017031354 2900.75588301728 Trimmed Mean ( 43 / 67 ) 42.1702608695652 0.0142487762904473 2959.57070347417 Trimmed Mean ( 44 / 67 ) 42.170796460177 0.0139277197663742 3027.83206207166 Trimmed Mean ( 45 / 67 ) 42.1713513513514 0.013569662220718 3107.76721376046 Trimmed Mean ( 46 / 67 ) 42.1719266055046 0.0131688871946712 3202.39105871978 Trimmed Mean ( 47 / 67 ) 42.172523364486 0.0127182713167142 3315.90059012684 Trimmed Mean ( 48 / 67 ) 42.1731428571429 0.0122087417531960 3454.33982548634 Trimmed Mean ( 49 / 67 ) 42.172427184466 0.0119484472453399 3529.53202357855 Trimmed Mean ( 50 / 67 ) 42.1716831683168 0.011655168663425 3618.28167280457 Trimmed Mean ( 51 / 67 ) 42.1709090909091 0.0113235533023733 3724.17632211529 Trimmed Mean ( 52 / 67 ) 42.1701030927835 0.0109468950521836 3852.24329746102 Trimmed Mean ( 53 / 67 ) 42.1692631578947 0.0105166024613238 4009.78008943268 Trimmed Mean ( 54 / 67 ) 42.1683870967742 0.0100213543881223 4207.85309685823 Trimmed Mean ( 55 / 67 ) 42.1683870967742 0.00944567838801694 4464.30477140427 Trimmed Mean ( 56 / 67 ) 42.1665168539326 0.00876736730195804 4809.48446684963 Trimmed Mean ( 57 / 67 ) 42.167816091954 0.00857756297970726 4916.06021333966 Trimmed Mean ( 58 / 67 ) 42.1691764705882 0.00835690520214193 5046.02785966508 Trimmed Mean ( 59 / 67 ) 42.1706024096386 0.0080993489462203 5206.66570728727 Trimmed Mean ( 60 / 67 ) 42.1706024096386 0.00779703182165245 5408.54563303568 Trimmed Mean ( 61 / 67 ) 42.1736708860759 0.00743941714550135 5668.94826049358 Trimmed Mean ( 62 / 67 ) 42.1753246753247 0.0070118160587962 6014.89319195937 Trimmed Mean ( 63 / 67 ) 42.1770666666667 0.00649261072250551 6496.16440432309 Trimmed Mean ( 64 / 67 ) 42.1789041095890 0.00584737918838345 7213.30065157784 Trimmed Mean ( 65 / 67 ) 42.1795774647887 0.00561127285802922 7516.93573489901 Trimmed Mean ( 66 / 67 ) 42.1802898550725 0.00532799317512703 7916.73120978928 Trimmed Mean ( 67 / 67 ) 42.1810447761194 0.00498362006419903 8463.93670318821 Median 42.17 Midrange 23.87 Midmean - Weighted Average at Xnp 42.2335338345865 Midmean - Weighted Average at X(n+1)p 42.2335338345865 Midmean - Empirical Distribution Function 42.2335338345865 Midmean - Empirical Distribution Function - Averaging 42.2335338345865 Midmean - Empirical Distribution Function - Interpolation 42.2335338345865 Midmean - Closest Observation 42.2335338345865 Midmean - True Basic - Statistics Graphics Toolkit 42.2335338345865 Midmean - MS Excel (old versions) 42.2335338345865 Number of observations 201

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290425799xydtgz4ttouowf7/1ta6n1290425895.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290425799xydtgz4ttouowf7/1ta6n1290425895.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290425799xydtgz4ttouowf7/2m25q1290425895.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290425799xydtgz4ttouowf7/2m25q1290425895.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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