robustness van getrimde gem.

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Tue, 20 Oct 2009 09:36:58 -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/Oct/20/t12560532008b2kghc11ckmf95.htm/, Retrieved Tue, 20 Oct 2009 17:40:03 +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/Oct/20/t12560532008b2kghc11ckmf95.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 «

9.913463687 9.913463687 9.723463687 9.553463687 10.22346369 13.35346369 13.32346369 12.93346369 12.18346369 11.57346369 11.04346369 10.45346369 9.703463687 9.453463687 9.033463687 8.653463687 8.293463687 7.953463687 7.633463687 7.303463687 6.573463687 5.463463687 4.583463687 3.823463687 3.113463687 1.493463687 -7.896536313 -8.596536313 -7.306536313 -3.416536313 -1.566536313 0.423463687 4.203463687 5.703463687 6.973463687 7.733463687 7.883463687 6.893463687 6.263463687 9.073463687 7.933463687 6.733463687 5.993463687 5.113463687 4.213463687 3.103463687 1.973463687 4.763463687 4.323463687 3.333463687 2.513463687 1.793463687 1.013463687 0.053463687 -0.896536313 -1.906536313 -2.886536313 0.903463687 3.333463687 2.543463687 1.383463687 0.413463687 3.773463687 3.713463687 2.663463687 1.543463687 0.643463687 -0.466536313 -1.396536313 4.563463687 2.903463687 1.303463687 0.053463687 -1.206536313 -2.666536313 -0.976536313 -1.326536313 -2.716536 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 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 0.17163035515 0.993560981023738 0.172742648340675 Geometric Mean NaN Harmonic Mean 18.9314271227017 Quadratic Mean 18.8260756887999 Winsorized Mean ( 1 / 120 ) 0.175019244038889 0.99319454144739 0.176218491680222 Winsorized Mean ( 2 / 120 ) 0.096241466261111 0.973042432546687 0.0989077793958316 Winsorized Mean ( 3 / 120 ) 0.0138247995944444 0.955044856652982 0.0144755500206497 Winsorized Mean ( 4 / 120 ) -0.147841867072222 0.924609648060273 -0.159896522151135 Winsorized Mean ( 5 / 120 ) -0.239091867072222 0.909725896868309 -0.262817479303695 Winsorized Mean ( 6 / 120 ) -0.258091867072222 0.905989502539036 -0.284872911163893 Winsorized Mean ( 7 / 120 ) -0.324008533738889 0.896283439208592 -0.361502310056052 Winsorized Mean ( 8 / 120 ) -0.38911964485 0.885616688028245 -0.43937704665022 Winsorized Mean ( 9 / 120 ) -0.38611964485 0.884119772358129 -0.436727756715744 Winsorized Mean ( 10 / 120 ) -0.444175200405555 0.876301225030422 -0.506875019363502 Winsorized Mean ( 11 / 120 ) -0.503452978183333 0.868738956461268 -0.579521586362496 Winsorized Mean ( 12 / 120 ) -0.532452978183333 0.863359864766 -0.616721948648431 Winsorized Mean ( 13 / 120 ) -0.541480755961111 0.862181055810338 -0.628036016695113 Winsorized Mean ( 14 / 120 ) -0.560536311516666 0.858033680552283 -0.653280080049854 Winsorized Mean ( 15 / 120 ) -0.587202978183333 0.85380461103798 -0.687748661218244 Winsorized Mean ( 16 / 120 ) -0.619647422627778 0.84955361280569 -0.729380010028282 Winsorized Mean ( 17 / 120 ) -0.608786311516666 0.847797381366363 -0.718079962143205 Winsorized Mean ( 18 / 120 ) -0.688786311516667 0.838736421202812 -0.821219031515163 Winsorized Mean ( 19 / 120 ) -0.695647422627778 0.836272071377527 -0.831843423255653 Winsorized Mean ( 20 / 120 ) -0.724536311516667 0.832342300030284 -0.870478781975042 Winsorized Mean ( 21 / 120 ) -0.722786311516667 0.83172994140215 -0.869015620981705 Winsorized Mean ( 22 / 120 ) -0.774730755961111 0.824516820727613 -0.93961789072712 Winsorized Mean ( 23 / 120 ) -0.785591867072222 0.816656487525029 -0.961961215116344 Winsorized Mean ( 24 / 120 ) -0.796925200405555 0.815551331047781 -0.97716130189096 Winsorized Mean ( 25 / 120 ) -0.817064089294445 0.81307753656176 -1.00490304128871 Winsorized Mean ( 26 / 120 ) -0.837286311516667 0.811131884593634 -1.03224435806285 Winsorized Mean ( 27 / 120 ) -0.913786311516667 0.797668598640249 -1.14557137271589 Winsorized Mean ( 28 / 120 ) -0.944897422627778 0.793949437798395 -1.19012291922261 Winsorized Mean ( 29 / 120 ) -0.940064089294444 0.788824579425174 -1.19172768422034 Winsorized Mean ( 30 / 120 ) -0.87423075596111 0.782650864250984 -1.11701244564238 Winsorized Mean ( 31 / 120 ) -0.862175200405555 0.776507878313447 -1.11032382862383 Winsorized Mean ( 32 / 120 ) -0.941286311516667 0.757250506804861 -1.24303160322510 Winsorized Mean ( 33 / 120 ) -0.966952978183333 0.754857678504742 -1.28097389179205 Winsorized Mean ( 34 / 120 ) -0.950897422627777 0.751678434769493 -1.26503219813586 Winsorized Mean ( 35 / 120 ) -0.994647422627777 0.746746624263225 -1.33197444797175 Winsorized Mean ( 36 / 120 ) -0.999647422627778 0.746148739295183 -1.33974282871811 Winsorized Mean ( 37 / 120 ) -1.09728631151667 0.73723916536041 -1.48837224481996 Winsorized Mean ( 38 / 120 ) -1.09623075596111 0.732865862784536 -1.49581364288952 Winsorized Mean ( 39 / 120 ) -1.14931408929444 0.725254082100779 -1.58470544000984 Winsorized Mean ( 40 / 120 ) -1.20264742262778 0.719305581522038 -1.67195619431035 Winsorized Mean ( 41 / 120 ) -1.20720297818333 0.710294828731239 -1.69958013116848 Winsorized Mean ( 42 / 120 ) -1.24686964485 0.699205935069621 -1.78326524749228 Winsorized Mean ( 43 / 120 ) -1.22656408929444 0.694585948743356 -1.76589245940483 Winsorized Mean ( 44 / 120 ) -1.27667520040556 0.68781729962519 -1.85612545817218 Winsorized Mean ( 45 / 120 ) -1.27667520040556 0.681634744252203 -1.87296086528886 Winsorized Mean ( 46 / 120 ) -1.27028631151667 0.680489556944174 -1.86672418195667 Winsorized Mean ( 47 / 120 ) -1.25723075596111 0.678982503612636 -1.85163940053214 Winsorized Mean ( 48 / 120 ) -1.27989742262778 0.675599264184335 -1.89446242836428 Winsorized Mean ( 49 / 120 ) -1.33978631151667 0.670529501175751 -1.9981019614609 Winsorized Mean ( 50 / 120 ) -1.37867520040556 0.666386411912033 -2.06888252185363 Winsorized Mean ( 51 / 120 ) -1.35884186707222 0.661630003643723 -2.05377909041129 Winsorized Mean ( 52 / 120 ) -1.28517520040556 0.654403588211945 -1.96388776522008 Winsorized Mean ( 53 / 120 ) -1.23806408929444 0.64963361729916 -1.9057882109637 Winsorized Mean ( 54 / 120 ) -1.34456408929444 0.62691939195065 -2.14471606167877 Winsorized Mean ( 55 / 120 ) -1.36136964485 0.625748110534431 -2.17558730411075 Winsorized Mean ( 56 / 120 ) -1.30536964485 0.619123056373707 -2.10841710934776 Winsorized Mean ( 57 / 120 ) -1.27845297818333 0.616256688079517 -2.07454621250029 Winsorized Mean ( 58 / 120 ) -1.25750853373889 0.614586949273108 -2.04610354194176 Winsorized Mean ( 59 / 120 ) -1.25914742262778 0.613493788204903 -2.05242081800384 Winsorized Mean ( 60 / 120 ) -1.37414742262778 0.604343645797991 -2.27378484440474 Winsorized Mean ( 61 / 120 ) -1.35550853373889 0.602104334407925 -2.25128512830226 Winsorized Mean ( 62 / 120 ) -1.36584186707222 0.596838805128671 -2.28846022633827 Winsorized Mean ( 63 / 120 ) -1.34659186707222 0.595053027745895 -2.26297792681303 Winsorized Mean ( 64 / 120 ) -1.35548075596111 0.592108264966793 -2.28924478201150 Winsorized Mean ( 65 / 120 ) -1.14964742262778 0.575772991880748 -1.99670258737299 Winsorized Mean ( 66 / 120 ) -1.18081408929444 0.569949114637005 -2.07178861931647 Winsorized Mean ( 67 / 120 ) -1.14731408929444 0.566844583879041 -2.02403643242584 Winsorized Mean ( 68 / 120 ) -1.17186964485 0.557609135223107 -2.10159692663772 Winsorized Mean ( 69 / 120 ) -1.08561964485 0.5510766283344 -1.96999761744792 Winsorized Mean ( 70 / 120 ) -1.02145297818333 0.542686868665238 -1.88221428813201 Winsorized Mean ( 71 / 120 ) -1.03525853373889 0.541761563082897 -1.91091174473092 Winsorized Mean ( 72 / 120 ) -1.04725853373889 0.540394145643021 -1.93795314435308 Winsorized Mean ( 73 / 120 ) -1.01886964485 0.52887622724858 -1.92648032253322 Winsorized Mean ( 74 / 120 ) -1.12164742262778 0.521265827074275 -2.15177624231246 Winsorized Mean ( 75 / 120 ) -1.08623075596111 0.518646047186739 -2.09435849719301 Winsorized Mean ( 76 / 120 ) -0.961675200405555 0.507780550864815 -1.89387954849334 Winsorized Mean ( 77 / 120 ) -0.946702978183334 0.501102533946595 -1.88924005378154 Winsorized Mean ( 78 / 120 ) -0.704036311516667 0.480084930950349 -1.46648283694928 Winsorized Mean ( 79 / 120 ) -0.73036964485 0.478352125561351 -1.52684519587512 Winsorized Mean ( 80 / 120 ) -0.723702978183334 0.475489264947366 -1.52201749131696 Winsorized Mean ( 81 / 120 ) -0.635952978183333 0.4691436961315 -1.35556117118768 Winsorized Mean ( 82 / 120 ) -0.667841867072222 0.464305286080112 -1.43836800289410 Winsorized Mean ( 83 / 120 ) -0.561786311516667 0.453740460250503 -1.23812258489471 Winsorized Mean ( 84 / 120 ) -0.561786311516667 0.450648760111228 -1.24661679170716 Winsorized Mean ( 85 / 120 ) -0.559425201113888 0.449553717881668 -1.24440123362775 Winsorized Mean ( 86 / 120 ) -0.511647423336111 0.444164428490531 -1.15193246130699 Winsorized Mean ( 87 / 120 ) -0.504397423336111 0.443683873388121 -1.136839659022 Winsorized Mean ( 88 / 120 ) -0.484841867780556 0.436294722239165 -1.11127144809874 Winsorized Mean ( 89 / 120 ) -0.474952978891667 0.434997783604533 -1.09185149164681 Winsorized Mean ( 90 / 120 ) -0.419952978891667 0.428468424804933 -0.980125849606905 Winsorized Mean ( 91 / 120 ) -0.369397423336112 0.424866592453698 -0.869443326204491 Winsorized Mean ( 92 / 120 ) -0.359175201113889 0.422549979166141 -0.850018267241863 Winsorized Mean ( 93 / 120 ) -0.354008535222222 0.418872585956862 -0.845146106693839 Winsorized Mean ( 94 / 120 ) -0.359230757444444 0.416170368180146 -0.863181968037055 Winsorized Mean ( 95 / 120 ) -0.425202979666667 0.41053730485223 -1.03572312343142 Winsorized Mean ( 96 / 120 ) -0.398536313 0.407458435504251 -0.978102987380274 Winsorized Mean ( 97 / 120 ) -0.441647424111111 0.404686420202919 -1.09133245412500 Winsorized Mean ( 98 / 120 ) -0.460702979666666 0.398245173381078 -1.15683255055002 Winsorized Mean ( 99 / 120 ) -0.501952979666667 0.388280228948572 -1.29275956446691 Winsorized Mean ( 100 / 120 ) -0.410286313 0.380017586594581 -1.07965085688971 Winsorized Mean ( 101 / 120 ) -0.483230757444444 0.375070736817405 -1.28837232556400 Winsorized Mean ( 102 / 120 ) -0.474730757444444 0.373822804363409 -1.26993525248646 Winsorized Mean ( 103 / 120 ) -0.483314090777778 0.372566849611932 -1.29725468404180 Winsorized Mean ( 104 / 120 ) -0.463091868555556 0.370570331020731 -1.24967335425903 Winsorized Mean ( 105 / 120 ) -0.463091868555555 0.369108178566174 -1.25462369962775 Winsorized Mean ( 106 / 120 ) -0.439536313000000 0.364318527496121 -1.20646159837336 Winsorized Mean ( 107 / 120 ) -0.466286313 0.362278640057108 -1.28709303128248 Winsorized Mean ( 108 / 120 ) -0.538286313 0.354455834187868 -1.51862731850169 Winsorized Mean ( 109 / 120 ) -0.423230757444444 0.346549433859355 -1.22127095326957 Winsorized Mean ( 110 / 120 ) -0.438508535222222 0.341830081849195 -1.28282605454156 Winsorized Mean ( 111 / 120 ) -0.423091868555555 0.338600419308836 -1.24953143714112 Winsorized Mean ( 112 / 120 ) -0.413758535222222 0.334200698607936 -1.23805407034058 Winsorized Mean ( 113 / 120 ) -0.391786313 0.329775595665591 -1.18803913373048 Winsorized Mean ( 114 / 120 ) -0.432952979666666 0.326085655181052 -1.32772776964469 Winsorized Mean ( 115 / 120 ) -0.448925201888889 0.324327754448249 -1.38417139986248 Winsorized Mean ( 116 / 120 ) -0.436036313 0.320000617914716 -1.36261084694595 Winsorized Mean ( 117 / 120 ) -0.523786313 0.313486202775661 -1.67084327272558 Winsorized Mean ( 118 / 120 ) -0.510675201888889 0.312286658009560 -1.6352770404724 Winsorized Mean ( 119 / 120 ) -0.543730757444444 0.308287796411395 -1.76371158305229 Winsorized Mean ( 120 / 120 ) -0.580397424111111 0.305682842846175 -1.89869152847148 Trimmed Mean ( 1 / 120 ) 1.32402237439650e-09 0.959119157088188 1.38045660396997e-09 Trimmed Mean ( 2 / 120 ) -0.176985749735955 0.922845871912865 -0.191782566431273 Trimmed Mean ( 3 / 120 ) -0.315914842615819 0.89564947342646 -0.352721518840661 Trimmed Mean ( 4 / 120 ) -0.428326084278409 0.873850527825909 -0.490159438759007 Trimmed Mean ( 5 / 120 ) -0.500450597274286 0.859808183050386 -0.582049121117702 Trimmed Mean ( 6 / 120 ) -0.554524817316092 0.848626317554076 -0.653438157461758 Trimmed Mean ( 7 / 120 ) -0.60592937516185 0.837725363490303 -0.723303127217376 Trimmed Mean ( 8 / 120 ) -0.648077009261628 0.828048959717623 -0.782655423518232 Trimmed Mean ( 9 / 120 ) -0.68215034668421 0.819608716911479 -0.832287813207684 Trimmed Mean ( 10 / 120 ) -0.716977488076471 0.811047446012102 -0.884014235667505 Trimmed Mean ( 11 / 120 ) -0.746033353035503 0.803134488553717 -0.928902149849098 Trimmed Mean ( 12 / 120 ) -0.769661311625 0.79579045299458 -0.967165801912732 Trimmed Mean ( 13 / 120 ) -0.769661311625 0.788742066806101 -0.975808624917946 Trimmed Mean ( 14 / 120 ) -0.8117772755 0.781544424038353 -1.03868347151072 Trimmed Mean ( 15 / 120 ) -0.831354493472727 0.774464146000292 -1.07345769041245 Trimmed Mean ( 16 / 120 ) -0.849219238493902 0.767492480968707 -1.10648541784024 Trimmed Mean ( 17 / 120 ) -0.86506391903681 0.760616892106542 -1.13731883687332 Trimmed Mean ( 18 / 120 ) -0.881814089462963 0.753617633457293 -1.17010808971860 Trimmed Mean ( 19 / 120 ) -0.893803392440994 0.747060309477669 -1.19642735814184 Trimmed Mean ( 20 / 120 ) -0.90553631170625 0.74043344052601 -1.22298138109882 Trimmed Mean ( 21 / 120 ) -0.91578159473585 0.733834035935036 -1.24794101921013 Trimmed Mean ( 22 / 120 ) -0.926251501601266 0.727017028706775 -1.27404375004653 Trimmed Mean ( 23 / 120 ) -0.934147776707006 0.720425536484441 -1.29666111124474 Trimmed Mean ( 24 / 120 ) -0.941600414314103 0.71409380422044 -1.31859485231359 Trimmed Mean ( 25 / 120 ) -0.948600827890323 0.707580161178835 -1.34062665961401 Trimmed Mean ( 26 / 120 ) -0.948600827890323 0.700960352368778 -1.35328742158481 Trimmed Mean ( 27 / 120 ) -0.96006572354902 0.694191700527394 -1.38299798574318 Trimmed Mean ( 28 / 120 ) -0.962095522322368 0.687963469119694 -1.39846890933533 Trimmed Mean ( 29 / 120 ) -0.962827702536424 0.681699797592894 -1.41239253104695 Trimmed Mean ( 30 / 120 ) -0.963769645153333 0.67547489056831 -1.42680306642112 Trimmed Mean ( 31 / 120 ) -0.967375238006711 0.669342414238268 -1.44526212208980 Trimmed Mean ( 32 / 120 ) -0.97150252806081 0.663297254485501 -1.46465633845322 Trimmed Mean ( 33 / 120 ) -0.972658760836735 0.658051260735271 -1.47808965482406 Trimmed Mean ( 34 / 120 ) -0.97287192830822 0.652712987432606 -1.49050493408279 Trimmed Mean ( 35 / 120 ) -0.97367424291724 0.64731664348578 -1.50416994946096 Trimmed Mean ( 36 / 120 ) -0.972925200784722 0.641947387605509 -1.51558401758402 Trimmed Mean ( 37 / 120 ) -0.971990857363636 0.636378472046298 -1.52737859632204 Trimmed Mean ( 38 / 120 ) -0.96769828375352 0.631021841633158 -1.53354166196372 Trimmed Mean ( 39 / 120 ) -0.963380283567376 0.625652380451387 -1.53980119578915 Trimmed Mean ( 40 / 120 ) -0.957250597664286 0.620425052670916 -1.54289481629303 Trimmed Mean ( 41 / 120 ) -0.94930609613669 0.61525760115231 -1.54294086632777 Trimmed Mean ( 42 / 120 ) -0.941101529369565 0.610298424235554 -1.54203499795754 Trimmed Mean ( 43 / 120 ) -0.9315363119927 0.605646172741307 -1.53808668149645 Trimmed Mean ( 44 / 120 ) -0.922455429654412 0.601004163073768 -1.53485697160003 Trimmed Mean ( 45 / 120 ) -0.911721497207407 0.596465745579504 -1.52853957492833 Trimmed Mean ( 46 / 120 ) -0.900827356813433 0.592006914081181 -1.52165006081315 Trimmed Mean ( 47 / 120 ) -0.88995736468421 0.587396063263795 -1.51508908612576 Trimmed Mean ( 48 / 120 ) -0.879301463583333 0.582641658806019 -1.50916339450434 Trimmed Mean ( 49 / 120 ) -0.879301463583333 0.577819035504201 -1.52175925255917 Trimmed Mean ( 50 / 120 ) -0.854497850561538 0.57299665884934 -1.49127894092348 Trimmed Mean ( 51 / 120 ) -0.839869645449612 0.568130496380639 -1.47830410583507 Trimmed Mean ( 52 / 120 ) -0.839869645449612 0.563249841373672 -1.49111386059393 Trimmed Mean ( 53 / 120 ) -0.813032375141732 0.558469004035016 -1.45582363437802 Trimmed Mean ( 54 / 120 ) -0.80157599470635 0.55367559722517 -1.44773581989810 Trimmed Mean ( 55 / 120 ) -0.787096312184 0.549651039654542 -1.43199276522554 Trimmed Mean ( 56 / 120 ) -0.771939538008064 0.545476102003856 -1.41516655848400 Trimmed Mean ( 57 / 120 ) -0.757999726853658 0.541390026060246 -1.40009917133070 Trimmed Mean ( 58 / 120 ) -0.744528115516393 0.537226586241459 -1.38587354867386 Trimmed Mean ( 59 / 120 ) -0.731371023 0.53292723446809 -1.37236563586392 Trimmed Mean ( 60 / 120 ) -0.717952978941667 0.528455753323551 -1.35858673962074 Trimmed Mean ( 61 / 120 ) -0.701410261873950 0.524144706539825 -1.33819964815510 Trimmed Mean ( 62 / 120 ) -0.685053261466102 0.519709813552254 -1.31814571055280 Trimmed Mean ( 63 / 120 ) -0.685053261466102 0.515273513059699 -1.32949442209488 Trimmed Mean ( 64 / 120 ) -0.651450105456897 0.51068332144142 -1.27564398151511 Trimmed Mean ( 65 / 120 ) -0.634231964547826 0.50597735416854 -1.25347895379635 Trimmed Mean ( 66 / 120 ) -0.621711750991228 0.501774394077191 -1.23902645955980 Trimmed Mean ( 67 / 120 ) -0.608217728345133 0.49759681981628 -1.22231032057177 Trimmed Mean ( 68 / 120 ) -0.5952863124375 0.493331456401993 -1.20666603500027 Trimmed Mean ( 69 / 120 ) -0.581536312459459 0.48923737926295 -1.18865879245686 Trimmed Mean ( 70 / 120 ) -0.569581767027273 0.485214443759132 -1.17387636405569 Trimmed Mean ( 71 / 120 ) -0.558921633605505 0.481343192265185 -1.16117074591881 Trimmed Mean ( 72 / 120 ) -0.547740016231481 0.477290445073536 -1.14760314581008 Trimmed Mean ( 73 / 120 ) -0.536069022831776 0.473064351788218 -1.13318414462091 Trimmed Mean ( 74 / 120 ) -0.524838199367925 0.469115664865086 -1.11878208014832 Trimmed Mean ( 75 / 120 ) -0.511012503076191 0.465263001246036 -1.09833041034346 Trimmed Mean ( 76 / 120 ) -0.497738235701923 0.461304009157681 -1.07898094493211 Trimmed Mean ( 77 / 120 ) -0.48707029323301 0.457614443546175 -1.06436826918874 Trimmed Mean ( 78 / 120 ) -0.476536312676471 0.454003356382226 -1.04963169539935 Trimmed Mean ( 79 / 120 ) -0.47133829290099 0.451125953497328 -1.0448042043402 Trimmed Mean ( 80 / 120 ) -0.46543631273 0.448140915716058 -1.03859365750237 Trimmed Mean ( 81 / 120 ) -0.459566615787879 0.44509331009558 -1.03251746401039 Trimmed Mean ( 82 / 120 ) -0.455566925030612 0.442138324679628 -1.03037194380450 Trimmed Mean ( 83 / 120 ) -0.450763116938144 0.439205762932719 -1.02631421302001 Trimmed Mean ( 84 / 120 ) -0.44825506284375 0.436556647263033 -1.02679701627282 Trimmed Mean ( 85 / 120 ) -0.445694207610526 0.433866347361899 -1.02726152954832 Trimmed Mean ( 86 / 120 ) -0.443132057569149 0.4310419028294 -1.02804867615048 Trimmed Mean ( 87 / 120 ) -0.441590076344086 0.428270857086829 -1.03109998973046 Trimmed Mean ( 88 / 120 ) -0.440177617266304 0.425329434325098 -1.03490984103832 Trimmed Mean ( 89 / 120 ) -0.439173675571429 0.422526498902898 -1.03939913049656 Trimmed Mean ( 90 / 120 ) -0.438369646283333 0.419585215907983 -1.04476904729520 Trimmed Mean ( 91 / 120 ) -0.438783503977528 0.416742555179145 -1.05288864437880 Trimmed Mean ( 92 / 120 ) -0.440343131164773 0.413863964936758 -1.06398036183716 Trimmed Mean ( 93 / 120 ) -0.442168496908046 0.410885318649921 -1.07613603319027 Trimmed Mean ( 94 / 120 ) -0.444152592069767 0.407865535362539 -1.08896818574037 Trimmed Mean ( 95 / 120 ) -0.446065724764706 0.404753643702255 -1.10206722460747 Trimmed Mean ( 96 / 120 ) -0.446536313 0.401689607680064 -1.11164517194992 Trimmed Mean ( 97 / 120 ) -0.447620650349398 0.398546208277207 -1.12313363181731 Trimmed Mean ( 98 / 120 ) -0.447620650349398 0.395302449383711 -1.13234980215087 Trimmed Mean ( 99 / 120 ) -0.447462238925926 0.392139503391394 -1.14107922067549 Trimmed Mean ( 100 / 120 ) -0.446223813 0.389242455098642 -1.14639039795111 Trimmed Mean ( 101 / 120 ) -0.447042642113924 0.386538061466049 -1.15652942537766 Trimmed Mean ( 102 / 120 ) -0.446215800179487 0.383865298738306 -1.16242807475986 Trimmed Mean ( 103 / 120 ) -0.445562287025974 0.381031560695428 -1.16935795610413 Trimmed Mean ( 104 / 120 ) -0.445562287025974 0.378023957465234 -1.17866150604212 Trimmed Mean ( 105 / 120 ) -0.444269646333333 0.374869166100343 -1.18513253825313 Trimmed Mean ( 106 / 120 ) -0.443833610297297 0.371524707816399 -1.19462743919748 Trimmed Mean ( 107 / 120 ) -0.443933573273973 0.368164901835762 -1.20580090894164 Trimmed Mean ( 108 / 120 ) -0.443411313 0.364628440016867 -1.21606343427158 Trimmed Mean ( 109 / 120 ) -0.443411313 0.361240021082387 -1.22747006732920 Trimmed Mean ( 110 / 120 ) -0.441607741571429 0.358018563546746 -1.23347721748447 Trimmed Mean ( 111 / 120 ) -0.441681240536232 0.354783264945283 -1.24493256637782 Trimmed Mean ( 112 / 120 ) -0.442124548294118 0.35144178239492 -1.25803069083373 Trimmed Mean ( 113 / 120 ) -0.442804969716418 0.348057592773596 -1.27221752638062 Trimmed Mean ( 114 / 120 ) -0.444036313 0.344628192356518 -1.28845034401783 Trimmed Mean ( 115 / 120 ) -0.444036313 0.341103356602257 -1.30176471267554 Trimmed Mean ( 116 / 120 ) -0.444192563 0.337345774115623 -1.31672781188525 Trimmed Mean ( 117 / 120 ) -0.444393455857143 0.333507356614663 -1.33248471748286 Trimmed Mean ( 118 / 120 ) -0.442423409774194 0.32973001958112 -1.34177473539181 Trimmed Mean ( 119 / 120 ) -0.440716640868852 0.32564714347501 -1.35335638496910 Trimmed Mean ( 120 / 120 ) -0.438119646333333 0.321428272551474 -1.36304016711278 Median -0.591536313 Midrange 30.89346369 Midmean - Weighted Average at Xnp -0.495210346082874 Midmean - Weighted Average at X(n+1)p -0.438369646283334 Midmean - Empirical Distribution Function -0.495210346082874 Midmean - Empirical Distribution Function - Averaging -0.438369646283334 Midmean - Empirical Distribution Function - Interpolation -0.438369646283334 Midmean - Closest Observation -0.495210346082874 Midmean - True Basic - Statistics Graphics Toolkit -0.438369646283334 Midmean - MS Excel (old versions) -0.439173675571429 Number of observations 360

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Oct/20/t12560532008b2kghc11ckmf95/15zf31256053015.png (open in new window) http://www.freestatistics.org/blog/date/2009/Oct/20/t12560532008b2kghc11ckmf95/15zf31256053015.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Oct/20/t12560532008b2kghc11ckmf95/2yz741256053015.png (open in new window) http://www.freestatistics.org/blog/date/2009/Oct/20/t12560532008b2kghc11ckmf95/2yz741256053015.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')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by