centrummaten - werkloosheid australie - Magali van de Wildebergh 2Mar 01A

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Wed, 22 Apr 2009 06:52:02 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Apr/22/t1240404874izmp7xi81mjt1vt.htm/, Retrieved Wed, 22 Apr 2009 14:54:34 +0200

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/2009/Apr/22/t1240404874izmp7xi81mjt1vt.htm/}, year = {2009}, } @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 = {2009}, 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 «

428800 424800 403400 398400 393500 380500 398300 387300 370400 372800 444600 449900 458100 424800 420600 400100 393000 387100 377500 400400 391400 363600 431000 441700 448500 415600 408000 416600 409300 387600 394500 407600 378500 359600 435700 433800 427700 413300 379500 379300 353700 378200 380600 394000 374000 375000 437600 443900 488800 463900 440000 453800 451600 453400 461400 509100 540600 555100 677400 694600 750100 733900 709300 720500 693200 687200 686800 720900 653100 624700 690000 717800 736500 699900 675600 635600 632500 594900 604000 620800 578400 571200 627400 657700 674100 672800 615300 609100 607600 566900 572700 589200 534800 543100 591100 624800 665300 642600 608700 594500 563800 596100 597600 633100 591000 584200 655800 670700 699700 712900 652000 635100 603100 610100 602000 597600 585400 567100 620600 646200 644800 645200 644800 593000 569100 518800 538700 554600 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 4 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 625260.663507109 12429.9652710595 50.3026878894742 Geometric Mean 599722.493050555 Harmonic Mean 575142.917731564 Quadratic Mean 650689.438229711 Winsorized Mean ( 1 / 70 ) 625127.01421801 12402.195949312 50.4045426126885 Winsorized Mean ( 2 / 70 ) 625138.388625592 12394.4525333237 50.436950478034 Winsorized Mean ( 3 / 70 ) 625104.265402844 12365.8765143065 50.5507445978165 Winsorized Mean ( 4 / 70 ) 624816.113744076 12314.5709896750 50.7379521599207 Winsorized Mean ( 5 / 70 ) 624839.81042654 12311.1549375726 50.7539555464113 Winsorized Mean ( 6 / 70 ) 624865.402843602 12308.0104641332 50.7690015916483 Winsorized Mean ( 7 / 70 ) 624281.516587678 12210.6042887236 51.1261770364794 Winsorized Mean ( 8 / 70 ) 623909.952606635 12156.6478466522 51.3225323688596 Winsorized Mean ( 9 / 70 ) 623918.483412322 12154.8734370870 51.3307264482591 Winsorized Mean ( 10 / 70 ) 623596.208530806 12105.7639031489 51.5123385454924 Winsorized Mean ( 11 / 70 ) 623095.734597156 12041.6398274934 51.7450898319106 Winsorized Mean ( 12 / 70 ) 622845.497630332 11998.9332099565 51.9084060834262 Winsorized Mean ( 13 / 70 ) 622839.336492891 11996.8571107198 51.9168754570188 Winsorized Mean ( 14 / 70 ) 623098.104265403 11935.0960868235 52.2072130574058 Winsorized Mean ( 15 / 70 ) 622977.251184834 11917.6043673653 52.2736979665786 Winsorized Mean ( 16 / 70 ) 622946.91943128 11909.1341262705 52.3083301293177 Winsorized Mean ( 17 / 70 ) 623212.796208531 11875.6992552799 52.4779874272626 Winsorized Mean ( 18 / 70 ) 622973.933649289 11818.5081877976 52.7117233199109 Winsorized Mean ( 19 / 70 ) 622883.886255924 11798.4156061318 52.7938586882974 Winsorized Mean ( 20 / 70 ) 622902.843601896 11790.6849407493 52.8300812660262 Winsorized Mean ( 21 / 70 ) 622624.170616114 11747.5746419879 53.0002310766975 Winsorized Mean ( 22 / 70 ) 622551.184834123 11656.4889937950 53.4081218766234 Winsorized Mean ( 23 / 70 ) 622136.966824645 11606.3907520317 53.6029658243017 Winsorized Mean ( 24 / 70 ) 622318.957345972 11587.3776493746 53.7066259663643 Winsorized Mean ( 25 / 70 ) 621513.27014218 11487.9746010467 54.1012051058636 Winsorized Mean ( 26 / 70 ) 621685.781990521 11431.8701684509 54.3818091729398 Winsorized Mean ( 27 / 70 ) 621967.298578199 11354.3019190354 54.778118726566 Winsorized Mean ( 28 / 70 ) 621290.521327014 11267.3829804941 55.1406233730213 Winsorized Mean ( 29 / 70 ) 621056.872037915 11205.2417626530 55.4255664619299 Winsorized Mean ( 30 / 70 ) 619350.710900474 10904.1877082992 56.7993442032444 Winsorized Mean ( 31 / 70 ) 618087.203791469 10702.2101917862 57.7532297268691 Winsorized Mean ( 32 / 70 ) 617844.549763033 10646.8580188894 58.0306930614522 Winsorized Mean ( 33 / 70 ) 617875.829383886 10528.2601146187 58.6873635963793 Winsorized Mean ( 34 / 70 ) 616828.436018957 10288.7749222916 59.9515919706377 Winsorized Mean ( 35 / 70 ) 615435.071090047 10146.6485428706 60.6540246752191 Winsorized Mean ( 36 / 70 ) 614872.037914692 9996.15031408924 61.5108835496452 Winsorized Mean ( 37 / 70 ) 613819.90521327 9855.46479536168 62.2821873913197 Winsorized Mean ( 38 / 70 ) 613891.943127962 9788.26361686998 62.7171444453054 Winsorized Mean ( 39 / 70 ) 613762.559241706 9678.99180125848 63.4118275791807 Winsorized Mean ( 40 / 70 ) 614122.748815166 9647.18528637112 63.6582309331983 Winsorized Mean ( 41 / 70 ) 613598.104265403 9527.65720651036 64.4017821974246 Winsorized Mean ( 42 / 70 ) 613757.345971564 9454.88370689665 64.9143199428114 Winsorized Mean ( 43 / 70 ) 613675.829383886 9383.23084765431 65.4013355684729 Winsorized Mean ( 44 / 70 ) 613675.829383886 9299.03774931514 65.993476521706 Winsorized Mean ( 45 / 70 ) 613803.791469194 9284.01610407323 66.1140377815476 Winsorized Mean ( 46 / 70 ) 613454.976303318 9166.82660734255 66.9211934053542 Winsorized Mean ( 47 / 70 ) 607128.909952607 8506.31665362391 71.3738900954215 Winsorized Mean ( 48 / 70 ) 606628.436018957 8404.69239146071 72.1773513847218 Winsorized Mean ( 49 / 70 ) 607023.222748815 8369.70900341329 72.5262040175187 Winsorized Mean ( 50 / 70 ) 604961.611374408 8112.10704613017 74.5751514291223 Winsorized Mean ( 51 / 70 ) 604985.781990521 8097.18234542968 74.7155931756922 Winsorized Mean ( 52 / 70 ) 605084.360189573 7949.85170576967 76.1126600324417 Winsorized Mean ( 53 / 70 ) 603476.777251185 7780.6386619032 77.5613421306946 Winsorized Mean ( 54 / 70 ) 602657.819905213 7564.39923186545 79.6702819923205 Winsorized Mean ( 55 / 70 ) 602631.753554502 7449.5442013115 80.8951175091234 Winsorized Mean ( 56 / 70 ) 601676.303317536 7119.4252744336 84.5119205728869 Winsorized Mean ( 57 / 70 ) 603054.028436019 6865.06702122814 87.8438661372507 Winsorized Mean ( 58 / 70 ) 604428.436018957 6699.53280266271 90.2194904962222 Winsorized Mean ( 59 / 70 ) 605714.691943128 6570.67479000482 92.1845489696932 Winsorized Mean ( 60 / 70 ) 605060.663507109 6497.33869708186 93.1243839541348 Winsorized Mean ( 61 / 70 ) 607257.819905213 6076.04148899333 99.943000883923 Winsorized Mean ( 62 / 70 ) 606464.454976303 5981.43076165692 101.391202062215 Winsorized Mean ( 63 / 70 ) 608166.3507109 5826.06326040119 104.387186257401 Winsorized Mean ( 64 / 70 ) 607104.739336493 5678.99302276968 106.903589580465 Winsorized Mean ( 65 / 70 ) 607813.27014218 5384.76901728606 112.876386747694 Winsorized Mean ( 66 / 70 ) 608501.421800948 5318.97925120355 114.401916808241 Winsorized Mean ( 67 / 70 ) 606977.251184834 5179.93435730266 117.178560444327 Winsorized Mean ( 68 / 70 ) 611489.099526066 4749.45882942436 128.749215750162 Winsorized Mean ( 69 / 70 ) 610638.862559242 4648.83359916245 131.353133970907 Winsorized Mean ( 70 / 70 ) 611003.791469194 4473.34742830544 136.587600507625 Trimmed Mean ( 1 / 70 ) 624557.416267943 12318.8475755614 50.6993379402602 Trimmed Mean ( 2 / 70 ) 623976.811594203 12230.3408379612 51.0187589913658 Trimmed Mean ( 3 / 70 ) 623379.024390244 12140.5207030544 51.3469759359999 Trimmed Mean ( 4 / 70 ) 622781.280788177 12055.6478586830 51.6588812221838 Trimmed Mean ( 5 / 70 ) 622247.263681592 11980.0122311948 51.9404531208506 Trimmed Mean ( 6 / 70 ) 621697.487437186 11900.2263958833 52.2424924329381 Trimmed Mean ( 7 / 70 ) 621131.979695431 11815.8299264995 52.5677826745298 Trimmed Mean ( 8 / 70 ) 620645.128205128 11743.0621390946 52.8520688091137 Trimmed Mean ( 9 / 70 ) 620198.96373057 11673.8798171718 53.1270643045581 Trimmed Mean ( 10 / 70 ) 619742.408376963 11600.1847488782 53.4252188041141 Trimmed Mean ( 11 / 70 ) 619312.169312169 11527.9328594899 53.7227425645828 Trimmed Mean ( 12 / 70 ) 618924.064171123 11458.5731116448 54.0140607509099 Trimmed Mean ( 13 / 70 ) 618551.351351351 11389.2337916995 54.3101812346807 Trimmed Mean ( 14 / 70 ) 618171.038251366 11315.1482098057 54.6321644921679 Trimmed Mean ( 15 / 70 ) 617760.773480663 11242.0806468855 54.9507509227668 Trimmed Mean ( 16 / 70 ) 617350.837988827 11165.4413039001 55.2912169958914 Trimmed Mean ( 17 / 70 ) 616933.898305085 11084.0050278169 55.6598356601971 Trimmed Mean ( 18 / 70 ) 616488.571428571 10999.6152450144 56.0463759591951 Trimmed Mean ( 19 / 70 ) 616049.132947977 10914.1645664194 56.4449188207615 Trimmed Mean ( 20 / 70 ) 615605.263157895 10824.0928841709 56.8736123890949 Trimmed Mean ( 21 / 70 ) 615149.704142012 10727.8888280435 57.3411706629512 Trimmed Mean ( 22 / 70 ) 614700 10628.0973536350 57.8372571822332 Trimmed Mean ( 23 / 70 ) 614243.636363636 10528.2265619018 58.3425549167398 Trimmed Mean ( 24 / 70 ) 613799.386503067 10424.8067897927 58.878730213404 Trimmed Mean ( 25 / 70 ) 613334.161490683 10314.7696488127 59.4617410153492 Trimmed Mean ( 26 / 70 ) 612900 10204.2003586658 60.0635011521994 Trimmed Mean ( 27 / 70 ) 612900 10089.1718495072 60.7482962072783 Trimmed Mean ( 28 / 70 ) 611965.806451613 9970.8294034886 61.3756169810205 Trimmed Mean ( 29 / 70 ) 611506.535947712 9849.78236578114 62.0832535419392 Trimmed Mean ( 30 / 70 ) 611046.357615894 9723.59447045702 62.8416126847353 Trimmed Mean ( 31 / 70 ) 611046.357615894 9610.54037921773 63.5808532616177 Trimmed Mean ( 32 / 70 ) 610310.204081633 9503.49796234817 64.2195333233737 Trimmed Mean ( 33 / 70 ) 609967.586206897 9391.42659907752 64.9494067564571 Trimmed Mean ( 34 / 70 ) 609613.986013986 9278.47987086642 65.7019247224018 Trimmed Mean ( 35 / 70 ) 609296.453900709 9173.7346102025 66.4174929611638 Trimmed Mean ( 36 / 70 ) 609030.215827338 9070.42900657608 67.1445876910331 Trimmed Mean ( 37 / 70 ) 608780.291970803 8968.9455670682 67.8764618893549 Trimmed Mean ( 38 / 70 ) 608567.407407407 8868.65062341434 68.6200678376836 Trimmed Mean ( 39 / 70 ) 608345.112781955 8763.8555121778 69.415237612787 Trimmed Mean ( 40 / 70 ) 608121.374045802 8657.24329275703 70.244228270052 Trimmed Mean ( 41 / 70 ) 607875.968992248 8542.58986137817 71.1582762202492 Trimmed Mean ( 42 / 70 ) 607644.094488189 8425.94460574699 72.1158425458601 Trimmed Mean ( 43 / 70 ) 607398.4 8303.24273879518 73.15195028107 Trimmed Mean ( 44 / 70 ) 607147.967479675 8173.74702385391 74.2802494018717 Trimmed Mean ( 45 / 70 ) 606889.256198347 8037.63734264389 75.5059267203412 Trimmed Mean ( 46 / 70 ) 606616.806722689 7888.38228135821 76.9000265309458 Trimmed Mean ( 47 / 70 ) 606348.717948718 7733.0884301621 78.4096449206138 Trimmed Mean ( 48 / 70 ) 606318.260869565 7617.19496697607 79.5986269877855 Trimmed Mean ( 49 / 70 ) 606306.194690265 7496.85029555479 80.8747901835216 Trimmed Mean ( 50 / 70 ) 606278.378378378 7365.8863961058 82.3089504474174 Trimmed Mean ( 51 / 70 ) 606329.357798165 7241.95609895783 83.7245282231716 Trimmed Mean ( 52 / 70 ) 606381.308411215 7104.82513301809 85.3478160346541 Trimmed Mean ( 53 / 70 ) 606431.428571429 6964.56787631652 87.0738054881537 Trimmed Mean ( 54 / 70 ) 606431.428571429 6822.53293139991 88.8865520575795 Trimmed Mean ( 55 / 70 ) 606696.03960396 6682.80369229018 90.7846567906661 Trimmed Mean ( 56 / 70 ) 606853.535353535 6536.2209035909 92.8447101627393 Trimmed Mean ( 57 / 70 ) 607054.639175258 6402.20058223287 94.8196844784793 Trimmed Mean ( 58 / 70 ) 607210.52631579 6275.49464885108 96.758990373286 Trimmed Mean ( 59 / 70 ) 607319.35483871 6148.2419860166 98.779351271466 Trimmed Mean ( 60 / 70 ) 607382.417582418 6016.58566268294 100.951345436603 Trimmed Mean ( 61 / 70 ) 607474.157303371 5873.8190798199 103.420644907231 Trimmed Mean ( 62 / 70 ) 607474.157303371 5755.30326359655 105.550329753389 Trimmed Mean ( 63 / 70 ) 607482.75862069 5629.23482594704 107.915689681411 Trimmed Mean ( 64 / 70 ) 607497.590361446 5501.19533290837 110.430107203678 Trimmed Mean ( 65 / 70 ) 607513.580246914 5369.95550050717 113.131958018933 Trimmed Mean ( 66 / 70 ) 607501.265822785 5251.98081476372 115.670884424226 Trimmed Mean ( 67 / 70 ) 607459.74025974 5122.33034338416 118.590504621459 Trimmed Mean ( 68 / 70 ) 607480 4988.13619528404 121.784966612246 Trimmed Mean ( 69 / 70 ) 607309.589041096 4883.97231329326 124.347467611172 Trimmed Mean ( 70 / 70 ) 607166.197183099 4774.08199848067 127.179675040422 Median 603100 Midrange 698750 Midmean - Weighted Average at Xnp 605025.471698113 Midmean - Weighted Average at X(n+1)p 606381.308411215 Midmean - Empirical Distribution Function 606381.308411215 Midmean - Empirical Distribution Function - Averaging 606381.308411215 Midmean - Empirical Distribution Function - Interpolation 606431.428571429 Midmean - Closest Observation 605025.471698113 Midmean - True Basic - Statistics Graphics Toolkit 606381.308411215 Midmean - MS Excel (old versions) 606381.308411215 Number of observations 211

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Apr/22/t1240404874izmp7xi81mjt1vt/1tr7k1240404716.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Apr/22/t1240404874izmp7xi81mjt1vt/1tr7k1240404716.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Apr/22/t1240404874izmp7xi81mjt1vt/25vw51240404716.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Apr/22/t1240404874izmp7xi81mjt1vt/25vw51240404716.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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