D3: Central Tendency

*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: Sun, 21 Dec 2008 09:45:32 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/21/t1229877981zvw5n3r0n7cn6jd.htm/, Retrieved Sun, 21 Dec 2008 17:46:23 +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/2008/Dec/21/t1229877981zvw5n3r0n7cn6jd.htm/}, year = {2008}, } @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 = {2008}, 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:

s0800650 Jan Werkhoven

Dataseries X:

» Textbox « » Textfile « » CSV «

29 29 30 32 25 27 27 23 24 26 24 25 28 27 26 23 22 25 22 21 24 24 27 31 35 41 43 48 42 38 45 50 44 43 45 43 47 52 51 54 61 62 57 59 55 56 58 66 68 65 69 71 74 78 78 77 73 78 80 82 68 68 66 64 60 53 52 53 54 47 44 41 39 37 40 40 43 39 42 40 44 53 54 60 68 61 60 64 59 55 59 60 57 55 55 44 44 49 58 55 69 73 66 61 59 53 54 44 49 50 39 32 28 32 30 28 30 27 32 41 35 34 25 27 27 32 30 41 30 33 24 24 19 21 17 14 12 9 5 5 8 6 9 3 4 0 5 16 17 23 26 24 25 33 41 61 78 54 46 41 41 40 40 42 50 45 45 45 47 56 58 63 68 61 62 62 67 60 54 75 60 57 51 50 52 55 56 46 49 46 48 57 56 57 56 55 50 50 62 61 60 56 55 53 55 51 42 42 49 43 46 42 43 34 35 29 30 57 37 32 33 25 30 30 32 27 26 22 25 21 23 18 16 24 29 27 35 39 39 59

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 43 1.12673235595352 38.1634553874245 Geometric Mean 0 Harmonic Mean 0 Quadratic Mean 46.3941447455028 Winsorized Mean ( 1 / 80 ) 43.0041666666667 1.12362573611628 38.2726786014238 Winsorized Mean ( 2 / 80 ) 42.9958333333333 1.12016040886353 38.3836395154826 Winsorized Mean ( 3 / 80 ) 43.0083333333333 1.11836126855801 38.4565654609866 Winsorized Mean ( 4 / 80 ) 43.0083333333333 1.11836126855801 38.4565654609866 Winsorized Mean ( 5 / 80 ) 43.0083333333333 1.11836126855801 38.4565654609866 Winsorized Mean ( 6 / 80 ) 43.0083333333333 1.11160656956696 38.6902475307319 Winsorized Mean ( 7 / 80 ) 43.0083333333333 1.09635174881797 39.228590075861 Winsorized Mean ( 8 / 80 ) 43.0083333333333 1.08792330395815 39.5325048896902 Winsorized Mean ( 9 / 80 ) 42.9708333333333 1.08351405932717 39.6587685812004 Winsorized Mean ( 10 / 80 ) 43.0958333333333 1.06769472854210 40.3634411421881 Winsorized Mean ( 11 / 80 ) 43.0958333333333 1.04628581842190 41.1893505336185 Winsorized Mean ( 12 / 80 ) 43.0958333333333 1.02405512997507 42.0835090532502 Winsorized Mean ( 13 / 80 ) 43.0958333333333 1.02405512997507 42.0835090532502 Winsorized Mean ( 14 / 80 ) 43.0958333333333 1.01158557062164 42.6022618203718 Winsorized Mean ( 15 / 80 ) 43.0958333333333 1.01158557062164 42.6022618203718 Winsorized Mean ( 16 / 80 ) 43.1625 1.00449358671925 42.969413215441 Winsorized Mean ( 17 / 80 ) 43.2333333333333 0.997179891462305 43.3556008334005 Winsorized Mean ( 18 / 80 ) 43.3833333333333 0.98240033273663 44.1605442177348 Winsorized Mean ( 19 / 80 ) 43.3041666666667 0.974221391301847 44.4500265066029 Winsorized Mean ( 20 / 80 ) 43.2208333333333 0.965868856017544 44.7481384911206 Winsorized Mean ( 21 / 80 ) 43.3083333333333 0.957583540289892 45.2266893812967 Winsorized Mean ( 22 / 80 ) 43.3083333333333 0.957583540289892 45.2266893812967 Winsorized Mean ( 23 / 80 ) 43.2125 0.948225260893997 45.5719772317168 Winsorized Mean ( 24 / 80 ) 43.2125 0.929507773976552 46.4896595916906 Winsorized Mean ( 25 / 80 ) 43.2125 0.929507773976552 46.4896595916906 Winsorized Mean ( 26 / 80 ) 43.1041666666667 0.91953458902521 46.8760688082011 Winsorized Mean ( 27 / 80 ) 42.9916666666667 0.909522528396423 47.2683911881382 Winsorized Mean ( 28 / 80 ) 43.1083333333333 0.898968719284479 47.9530960405884 Winsorized Mean ( 29 / 80 ) 43.1083333333333 0.898968719284479 47.9530960405884 Winsorized Mean ( 30 / 80 ) 43.1083333333333 0.898968719284479 47.9530960405884 Winsorized Mean ( 31 / 80 ) 42.9791666666667 0.887803770015609 48.4106602362266 Winsorized Mean ( 32 / 80 ) 42.9791666666667 0.887803770015609 48.4106602362266 Winsorized Mean ( 33 / 80 ) 42.9791666666667 0.887803770015609 48.4106602362266 Winsorized Mean ( 34 / 80 ) 42.9791666666667 0.887803770015609 48.4106602362266 Winsorized Mean ( 35 / 80 ) 42.9791666666667 0.887803770015609 48.4106602362266 Winsorized Mean ( 36 / 80 ) 43.1291666666667 0.874588923494892 49.3136438251708 Winsorized Mean ( 37 / 80 ) 42.975 0.861624204150004 49.8767325627709 Winsorized Mean ( 38 / 80 ) 42.975 0.861624204150004 49.8767325627709 Winsorized Mean ( 39 / 80 ) 42.975 0.861624204150004 49.8767325627709 Winsorized Mean ( 40 / 80 ) 42.975 0.861624204150004 49.8767325627709 Winsorized Mean ( 41 / 80 ) 42.975 0.861624204150004 49.8767325627709 Winsorized Mean ( 42 / 80 ) 42.975 0.861624204150004 49.8767325627709 Winsorized Mean ( 43 / 80 ) 43.1541666666667 0.846204255981107 50.9973406085423 Winsorized Mean ( 44 / 80 ) 42.9708333333333 0.831170091463683 51.6992054630624 Winsorized Mean ( 45 / 80 ) 42.9708333333333 0.831170091463683 51.6992054630624 Winsorized Mean ( 46 / 80 ) 42.9708333333333 0.831170091463683 51.6992054630624 Winsorized Mean ( 47 / 80 ) 43.1666666666667 0.814672514799125 52.9865263434232 Winsorized Mean ( 48 / 80 ) 43.1666666666667 0.814672514799125 52.9865263434232 Winsorized Mean ( 49 / 80 ) 42.9625 0.798323104132251 53.8159296375353 Winsorized Mean ( 50 / 80 ) 42.9625 0.798323104132251 53.8159296375353 Winsorized Mean ( 51 / 80 ) 42.9625 0.798323104132251 53.8159296375353 Winsorized Mean ( 52 / 80 ) 42.7458333333333 0.781514727347413 54.6961328271056 Winsorized Mean ( 53 / 80 ) 42.7458333333333 0.781514727347413 54.6961328271056 Winsorized Mean ( 54 / 80 ) 42.7458333333333 0.781514727347413 54.6961328271056 Winsorized Mean ( 55 / 80 ) 42.7458333333333 0.781514727347413 54.6961328271056 Winsorized Mean ( 56 / 80 ) 42.9791666666667 0.762081856371906 56.3970475183341 Winsorized Mean ( 57 / 80 ) 42.9791666666667 0.762081856371906 56.3970475183341 Winsorized Mean ( 58 / 80 ) 42.7375 0.743761390452258 57.4613048601685 Winsorized Mean ( 59 / 80 ) 42.9833333333333 0.72362905214889 59.399678890296 Winsorized Mean ( 60 / 80 ) 42.9833333333333 0.72362905214889 59.399678890296 Winsorized Mean ( 61 / 80 ) 42.9833333333333 0.72362905214889 59.399678890296 Winsorized Mean ( 62 / 80 ) 42.9833333333333 0.72362905214889 59.399678890296 Winsorized Mean ( 63 / 80 ) 43.2458333333333 0.702661009027557 61.5457991516893 Winsorized Mean ( 64 / 80 ) 42.9791666666667 0.682707504604518 62.9539976883128 Winsorized Mean ( 65 / 80 ) 42.9791666666667 0.682707504604518 62.9539976883128 Winsorized Mean ( 66 / 80 ) 42.9791666666667 0.682707504604518 62.9539976883128 Winsorized Mean ( 67 / 80 ) 42.9791666666667 0.682707504604518 62.9539976883128 Winsorized Mean ( 68 / 80 ) 42.9791666666667 0.682707504604518 62.9539976883128 Winsorized Mean ( 69 / 80 ) 42.9791666666667 0.682707504604518 62.9539976883128 Winsorized Mean ( 70 / 80 ) 42.9791666666667 0.682707504604518 62.9539976883128 Winsorized Mean ( 71 / 80 ) 43.275 0.65941642451527 65.626208858554 Winsorized Mean ( 72 / 80 ) 43.575 0.63631185941076 68.4805718383931 Winsorized Mean ( 73 / 80 ) 43.2708333333333 0.61375726046962 70.501542092104 Winsorized Mean ( 74 / 80 ) 43.2708333333333 0.61375726046962 70.501542092104 Winsorized Mean ( 75 / 80 ) 43.2708333333333 0.61375726046962 70.501542092104 Winsorized Mean ( 76 / 80 ) 43.2708333333333 0.61375726046962 70.501542092104 Winsorized Mean ( 77 / 80 ) 43.2708333333333 0.61375726046962 70.501542092104 Winsorized Mean ( 78 / 80 ) 43.2708333333333 0.61375726046962 70.501542092104 Winsorized Mean ( 79 / 80 ) 43.2708333333333 0.564670511444199 76.630233837896 Winsorized Mean ( 80 / 80 ) 43.2708333333333 0.564670511444199 76.630233837896 Trimmed Mean ( 1 / 80 ) 43.0168067226891 1.10961741272806 38.7672419603885 Trimmed Mean ( 2 / 80 ) 43.0296610169492 1.09485891982726 39.3015577054789 Trimmed Mean ( 3 / 80 ) 43.0470085470085 1.08118831674173 39.8145335835066 Trimmed Mean ( 4 / 80 ) 43.0603448275862 1.06744805472562 40.3395225059965 Trimmed Mean ( 5 / 80 ) 43.0739130434783 1.05295288021656 40.9077308707483 Trimmed Mean ( 6 / 80 ) 43.0877192982456 1.03764677419693 41.5244574258833 Trimmed Mean ( 7 / 80 ) 43.1017699115044 1.02285254136522 42.1387914371074 Trimmed Mean ( 8 / 80 ) 43.1160714285714 1.00994992041448 42.6912964267344 Trimmed Mean ( 9 / 80 ) 43.1306306306306 0.997665651255997 43.2315481407343 Trimmed Mean ( 10 / 80 ) 43.15 0.98532244239315 43.7927709179112 Trimmed Mean ( 11 / 80 ) 43.1559633027523 0.974397418234707 44.289899064939 Trimmed Mean ( 12 / 80 ) 43.162037037037 0.965482308229616 44.7051558263997 Trimmed Mean ( 13 / 80 ) 43.1682242990654 0.958577203026688 45.0336437824336 Trimmed Mean ( 14 / 80 ) 43.1745283018868 0.951268848338632 45.386252663787 Trimmed Mean ( 15 / 80 ) 43.1809523809524 0.944786075951831 45.7044758385642 Trimmed Mean ( 16 / 80 ) 43.1875 0.93791487166191 46.046289812502 Trimmed Mean ( 17 / 80 ) 43.1893203883495 0.931283548756791 46.3761229820979 Trimmed Mean ( 18 / 80 ) 43.1862745098039 0.924892688001812 46.6932813612203 Trimmed Mean ( 19 / 80 ) 43.1732673267327 0.919349272401356 46.9606803668462 Trimmed Mean ( 20 / 80 ) 43.165 0.914120867171839 47.220232630228 Trimmed Mean ( 21 / 80 ) 43.1616161616162 0.909210256113955 47.4715456313622 Trimmed Mean ( 22 / 80 ) 43.1530612244898 0.904599100880667 47.7040726466325 Trimmed Mean ( 23 / 80 ) 43.1443298969072 0.899672601022548 47.9555894531748 Trimmed Mean ( 24 / 80 ) 43.140625 0.895091617204879 48.1968819401037 Trimmed Mean ( 25 / 80 ) 43.1368421052632 0.891502664596123 48.3866664883222 Trimmed Mean ( 26 / 80 ) 43.1329787234043 0.887641529388052 48.5927903273527 Trimmed Mean ( 27 / 80 ) 43.1344086021505 0.884160066448483 48.7857462002485 Trimmed Mean ( 28 / 80 ) 43.1413043478261 0.881057859068443 48.9653476259097 Trimmed Mean ( 29 / 80 ) 43.1428571428571 0.878377212627547 49.1165487021243 Trimmed Mean ( 30 / 80 ) 43.1444444444444 0.875457572022233 49.2821649195225 Trimmed Mean ( 31 / 80 ) 43.1460674157303 0.872282846638521 49.4633908966575 Trimmed Mean ( 32 / 80 ) 43.1534090909091 0.869521027178769 49.6289425350918 Trimmed Mean ( 33 / 80 ) 43.1609195402299 0.86650465588994 49.8103723353934 Trimmed Mean ( 34 / 80 ) 43.1686046511628 0.863215960626074 50.0090436463355 Trimmed Mean ( 35 / 80 ) 43.1764705882353 0.859635622512799 50.2264790540279 Trimmed Mean ( 36 / 80 ) 43.1845238095238 0.855742601893354 50.464384633857 Trimmed Mean ( 37 / 80 ) 43.1867469879518 0.852314908099884 50.6699420337849 Trimmed Mean ( 38 / 80 ) 43.1951219512195 0.849337087740456 50.8574540953277 Trimmed Mean ( 39 / 80 ) 43.2037037037037 0.846067366809184 51.0641414603189 Trimmed Mean ( 40 / 80 ) 43.2125 0.842483763350626 51.2917896816674 Trimmed Mean ( 41 / 80 ) 43.2215189873418 0.838562222762828 51.5424113012616 Trimmed Mean ( 42 / 80 ) 43.2307692307692 0.834276364042622 51.8182835976408 Trimmed Mean ( 43 / 80 ) 43.2402597402597 0.829597185954415 52.1219942308672 Trimmed Mean ( 44 / 80 ) 43.2434210526316 0.8254299929594 52.3889626273353 Trimmed Mean ( 45 / 80 ) 43.2533333333333 0.821750555001812 52.6355997815395 Trimmed Mean ( 46 / 80 ) 43.2635135135135 0.817698232581368 52.9088993832556 Trimmed Mean ( 47 / 80 ) 43.2739726027397 0.813241880940101 53.2116872199393 Trimmed Mean ( 48 / 80 ) 43.2777777777778 0.809354571075921 53.4719631227215 Trimmed Mean ( 49 / 80 ) 43.2816901408451 0.805061291869307 53.7619813273440 Trimmed Mean ( 50 / 80 ) 43.2928571428571 0.801302666838165 54.0280956678767 Trimmed Mean ( 51 / 80 ) 43.3043478260870 0.797131261283386 54.3252409350585 Trimmed Mean ( 52 / 80 ) 43.3161764705882 0.79251002247103 54.6569446977204 Trimmed Mean ( 53 / 80 ) 43.3358208955224 0.788406627727235 54.9663325642606 Trimmed Mean ( 54 / 80 ) 43.3560606060606 0.783838944006245 55.3124604711082 Trimmed Mean ( 55 / 80 ) 43.3769230769231 0.778762943058198 55.699778043601 Trimmed Mean ( 56 / 80 ) 43.3984375 0.773129238458232 56.1334836935475 Trimmed Mean ( 57 / 80 ) 43.4126984126984 0.768198869859435 56.5123174688373 Trimmed Mean ( 58 / 80 ) 43.4274193548387 0.7627037041659 56.938781230034 Trimmed Mean ( 59 / 80 ) 43.4508196721311 0.75775380055037 57.3416057307427 Trimmed Mean ( 60 / 80 ) 43.4666666666667 0.75360323367153 57.6784503098514 Trimmed Mean ( 61 / 80 ) 43.4830508474576 0.748917133264487 58.0612312311744 Trimmed Mean ( 62 / 80 ) 43.5 0.743638689653534 58.4961495484681 Trimmed Mean ( 63 / 80 ) 43.5175438596491 0.737703361780991 58.9905727887527 Trimmed Mean ( 64 / 80 ) 43.5267857142857 0.732579738284778 59.4157651919188 Trimmed Mean ( 65 / 80 ) 43.5454545454545 0.728158343185065 59.8021775799213 Trimmed Mean ( 66 / 80 ) 43.5648148148148 0.72311309558362 60.246198113248 Trimmed Mean ( 67 / 80 ) 43.5849056603774 0.717371152253802 60.7564236775404 Trimmed Mean ( 68 / 80 ) 43.6057692307692 0.710848700856837 61.3432495244179 Trimmed Mean ( 69 / 80 ) 43.6274509803922 0.703448748277792 62.019373958945 Trimmed Mean ( 70 / 80 ) 43.65 0.695058314536313 62.8004860701792 Trimmed Mean ( 71 / 80 ) 43.6734693877551 0.68554482291807 63.706220115347 Trimmed Mean ( 72 / 80 ) 43.6875 0.676891842017226 64.5413303694214 Trimmed Mean ( 73 / 80 ) 43.6914893617021 0.669126084999179 65.2963474914533 Trimmed Mean ( 74 / 80 ) 43.7065217391304 0.662189184358288 66.0030740029157 Trimmed Mean ( 75 / 80 ) 43.7222222222222 0.65419567368648 66.8335545171702 Trimmed Mean ( 76 / 80 ) 43.7386363636364 0.644989999797292 67.8128907074259 Trimmed Mean ( 77 / 80 ) 43.7558139534884 0.63438543707002 68.9735473052149 Trimmed Mean ( 78 / 80 ) 43.7738095238095 0.6221553025141 70.3583323117582 Trimmed Mean ( 79 / 80 ) 43.7926829268293 0.608020716318005 72.0249849248967 Trimmed Mean ( 80 / 80 ) 43.8125 0.597222937722399 73.3603772271134 Median 43.5 Midrange 41 Midmean - Weighted Average at Xnp 43.5528455284553 Midmean - Weighted Average at X(n+1)p 43.5528455284553 Midmean - Empirical Distribution Function 43.5528455284553 Midmean - Empirical Distribution Function - Averaging 43.5528455284553 Midmean - Empirical Distribution Function - Interpolation 43.5528455284553 Midmean - Closest Observation 43.5528455284553 Midmean - True Basic - Statistics Graphics Toolkit 43.5528455284553 Midmean - MS Excel (old versions) 43.5528455284553 Number of observations 240

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229877981zvw5n3r0n7cn6jd/1vs701229877929.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229877981zvw5n3r0n7cn6jd/1vs701229877929.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229877981zvw5n3r0n7cn6jd/24g7x1229877929.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/21/t1229877981zvw5n3r0n7cn6jd/24g7x1229877929.ps (open in new window)

Parameters (Session):

par1 = Exchange rate of Japanese Yen (JPY refered to 1 EUR) ; par2 = http://www.nbb.be/belgostat/PresentationLinker?TableId=451000038&Lang=N ; par3 = Exchange rate of Japanese Yen (JPY refered to 1 EUR) ;

Parameters (R input):

par1 = Exchange rate of Japanese Yen (JPY refered to 1 EUR) ; par2 = http://www.nbb.be/belgostat/PresentationLinker?TableId=451000038&Lang=N ; par3 = Exchange rate of Japanese Yen (JPY refered to 1 EUR) ;

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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