WS4- Deel 1: Vraag 1

*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: Wed, 28 Oct 2009 10:42:26 -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/28/t1256748219gcyob033lvp5glh.htm/, Retrieved Wed, 28 Oct 2009 17:43:42 +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/2009/Oct/28/t1256748219gcyob033lvp5glh.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:

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-71.15 -45.95 -25.90 13.72 47.48 61.03 56.78 50.00 44.88 39.80 29.18 24.96 20.74 17.60 15.77 4.47 -0.26 -8.24 -13.05 -27.34 -34.40 -36.76 -36.04 -36.14 -39.13 -40.25 -29.97 -32.19 -36.46 -38.92 -20.32 2.62 6.46 8.60 10.56 11.17 -1.01 6.41 0.57 -9.90 -5.80 -5.89 2.37 0.89 -8.46 -12.31 -24.04 -38.97 -41.46 -55.24 -59.23 -60.20 -61.37 -58.50 -60.15 -59.90 -58.99 -65.57 -73.24 -62.78 -62.45 -49.27 -54.35 -53.94 -51.51 -51.24 -52.49 -49.15 -48.83 -62.10 -59.95 -56.50 -52.50 -48.58 -45.70 -52.91 -52.66 -50.91 -49.70 -45.44 -52.43 -52.38 -49.30 -48.82 -43.81 -35.68 -36.15 -35.09 -33.17 -28.47 -23.15 -26.59 -26.28 -22.77 -22.47 -22.89 -20.24 -15.04 -17.27 -12.20 -7.86 -10.46 -6.44 -8.55 -13.10 -8.56 -15.69 -23.45 -13.44 52.76 46.15 36.75 31.01 29.73 25.37 14.72 -1.56 -1.09 0.57 3.27 3.09 -2.19 -4.98 -11.71 -18.84 -17.77 -18.36 -25.92 -20.11 -16.45 -31.49 -32.77 -22.89 -26.12 -29.49 -2 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 0.1305 1.82371284217610 0.0715573181160933 Geometric Mean NaN Harmonic Mean 25.8489923384337 Quadratic Mean 34.5546722279025 Winsorized Mean ( 1 / 120 ) 0.139083333333333 1.80121854408578 0.0772162455189057 Winsorized Mean ( 2 / 120 ) 0.146138888888889 1.79936405151583 0.0812169659418158 Winsorized Mean ( 3 / 120 ) 0.132055555555556 1.79725451465805 0.0734762686522897 Winsorized Mean ( 4 / 120 ) 0.163944444444445 1.78769298715778 0.0917072705560574 Winsorized Mean ( 5 / 120 ) 0.190472222222223 1.78240098724253 0.106862722577872 Winsorized Mean ( 6 / 120 ) 0.186472222222222 1.78162734682035 0.104663987424204 Winsorized Mean ( 7 / 120 ) 0.125416666666667 1.77385675393596 0.0707028154265463 Winsorized Mean ( 8 / 120 ) 0.0236388888888892 1.76153796328024 0.0134194603702268 Winsorized Mean ( 9 / 120 ) -0.0093611111111108 1.75459571544999 -0.00533519546906567 Winsorized Mean ( 10 / 120 ) -0.00519444444444414 1.74866159277180 -0.00297052583868469 Winsorized Mean ( 11 / 120 ) -0.0113055555555554 1.74776837434542 -0.00646856627085358 Winsorized Mean ( 12 / 120 ) -0.0109722222222223 1.74651291336303 -0.00628235963116619 Winsorized Mean ( 13 / 120 ) -0.0575555555555553 1.74167278334745 -0.033046135936588 Winsorized Mean ( 14 / 120 ) -0.144666666666666 1.72858735249952 -0.0836906890805835 Winsorized Mean ( 15 / 120 ) -0.176750000000000 1.72380362546422 -0.102534881229526 Winsorized Mean ( 16 / 120 ) -0.198083333333334 1.71786829102713 -0.115307637010342 Winsorized Mean ( 17 / 120 ) -0.240111111111111 1.70933189783350 -0.140470736792218 Winsorized Mean ( 18 / 120 ) -0.182611111111111 1.70136227516246 -0.107332291174537 Winsorized Mean ( 19 / 120 ) -0.145666666666666 1.69693646451274 -0.0858409667733155 Winsorized Mean ( 20 / 120 ) -0.124555555555555 1.69432520932547 -0.0735133697297353 Winsorized Mean ( 21 / 120 ) -0.079638888888889 1.68903975926108 -0.047150393264709 Winsorized Mean ( 22 / 120 ) -0.0661944444444442 1.68579246791095 -0.0392660696405134 Winsorized Mean ( 23 / 120 ) -0.129444444444444 1.66889355474416 -0.0775630321517346 Winsorized Mean ( 24 / 120 ) -0.149444444444444 1.66433750037110 -0.0897921511779448 Winsorized Mean ( 25 / 120 ) -0.297361111111111 1.65024164309425 -0.180192465967318 Winsorized Mean ( 26 / 120 ) -0.300250000000000 1.64988549558491 -0.181982325927144 Winsorized Mean ( 27 / 120 ) -0.3055 1.64870036487669 -0.185297466118320 Winsorized Mean ( 28 / 120 ) -0.305500000000001 1.64804378919052 -0.185371288071208 Winsorized Mean ( 29 / 120 ) -0.31275 1.64555775022562 -0.190057140174581 Winsorized Mean ( 30 / 120 ) -0.279416666666666 1.63802351907312 -0.170581596303803 Winsorized Mean ( 31 / 120 ) -0.269944444444445 1.63489628145674 -0.165114109993519 Winsorized Mean ( 32 / 120 ) -0.294833333333333 1.62802559744788 -0.181098708641632 Winsorized Mean ( 33 / 120 ) -0.188500000000000 1.61813110784886 -0.116492414666320 Winsorized Mean ( 34 / 120 ) -0.152611111111111 1.61476919332338 -0.0945095508027492 Winsorized Mean ( 35 / 120 ) -0.18275 1.61189208123074 -0.113376076554991 Winsorized Mean ( 36 / 120 ) -0.210749999999999 1.60770874112744 -0.131087176805549 Winsorized Mean ( 37 / 120 ) -0.225138888888889 1.60120342005456 -0.140606050467477 Winsorized Mean ( 38 / 120 ) -0.301138888888889 1.59510015808980 -0.188789956142639 Winsorized Mean ( 39 / 120 ) -0.298972222222222 1.59105205194561 -0.187908511136783 Winsorized Mean ( 40 / 120 ) -0.223416666666666 1.58128853863340 -0.141287729094496 Winsorized Mean ( 41 / 120 ) -0.190388888888889 1.57688275883451 -0.120737504308569 Winsorized Mean ( 42 / 120 ) -0.0538888888888891 1.56168996769899 -0.0345067779159071 Winsorized Mean ( 43 / 120 ) -0.0300000000000005 1.55878585478278 -0.0192457481622330 Winsorized Mean ( 44 / 120 ) -0.103333333333333 1.54808340336357 -0.0667492029879122 Winsorized Mean ( 45 / 120 ) 0.0591666666666665 1.53061743637549 0.0386554244454274 Winsorized Mean ( 46 / 120 ) 0.0246666666666663 1.52576844311297 0.0161667170257761 Winsorized Mean ( 47 / 120 ) 0.118666666666667 1.50994931558104 0.0785898344018276 Winsorized Mean ( 48 / 120 ) 0.134666666666667 1.50248694167505 0.0896291760889004 Winsorized Mean ( 49 / 120 ) 0.0502777777777775 1.47935723496072 0.0339862317157711 Winsorized Mean ( 50 / 120 ) 0.196111111111111 1.46466043293646 0.133895274768864 Winsorized Mean ( 51 / 120 ) 0.351944444444445 1.45255116460153 0.242294008652694 Winsorized Mean ( 52 / 120 ) 0.262388888888888 1.44561818401830 0.181506356097113 Winsorized Mean ( 53 / 120 ) 0.234416666666666 1.43999338459413 0.162790099714755 Winsorized Mean ( 54 / 120 ) 0.135416666666665 1.43154976087800 0.0945944530657541 Winsorized Mean ( 55 / 120 ) 0.283611111111111 1.39347425491628 0.203528059532145 Winsorized Mean ( 56 / 120 ) 0.322500000000001 1.38945875023929 0.232104767373958 Winsorized Mean ( 57 / 120 ) 0.349416666666668 1.38423059739962 0.252426631316395 Winsorized Mean ( 58 / 120 ) 0.307527777777779 1.38094871404337 0.222693120063343 Winsorized Mean ( 59 / 120 ) 0.312444444444444 1.37891210794256 0.226587643001145 Winsorized Mean ( 60 / 120 ) 0.352444444444444 1.37306551927333 0.256684360285275 Winsorized Mean ( 61 / 120 ) 0.420222222222221 1.36345066154035 0.308204934784715 Winsorized Mean ( 62 / 120 ) 0.327222222222223 1.35573723413533 0.241361094158427 Winsorized Mean ( 63 / 120 ) 0.304472222222221 1.33771749996634 0.227605770448455 Winsorized Mean ( 64 / 120 ) 0.332916666666666 1.33387962241231 0.249585240731535 Winsorized Mean ( 65 / 120 ) 0.394305555555556 1.32688409525734 0.297166539990128 Winsorized Mean ( 66 / 120 ) 0.447472222222221 1.31602473273924 0.340018094713791 Winsorized Mean ( 67 / 120 ) 0.447472222222223 1.30544391002688 0.342773993417312 Winsorized Mean ( 68 / 120 ) 0.458805555555556 1.29074692622688 0.355457407050923 Winsorized Mean ( 69 / 120 ) 0.495222222222223 1.27443979713434 0.388580318455027 Winsorized Mean ( 70 / 120 ) 0.76161111111111 1.25194560644528 0.608342013574851 Winsorized Mean ( 71 / 120 ) 0.848388888888889 1.24493795413167 0.681470820351552 Winsorized Mean ( 72 / 120 ) 1.02238888888889 1.22906905643167 0.83184006914727 Winsorized Mean ( 73 / 120 ) 1.01833333333333 1.21540134595732 0.837857664647584 Winsorized Mean ( 74 / 120 ) 1.13961111111111 1.20569204211291 0.94519252952355 Winsorized Mean ( 75 / 120 ) 1.27502777777778 1.19399643372542 1.06786564998317 Winsorized Mean ( 76 / 120 ) 1.30247222222222 1.18705722234262 1.09722783173994 Winsorized Mean ( 77 / 120 ) 1.23188888888889 1.17743747625454 1.04624569349326 Winsorized Mean ( 78 / 120 ) 1.22105555555556 1.17082514609987 1.04290171732561 Winsorized Mean ( 79 / 120 ) 1.14863888888889 1.16515626407938 0.985823897017328 Winsorized Mean ( 80 / 120 ) 1.11752777777778 1.16238404472802 0.961410114708914 Winsorized Mean ( 81 / 120 ) 1.41677777777778 1.13993273047488 1.24286086354198 Winsorized Mean ( 82 / 120 ) 1.435 1.12908113354539 1.27094498115827 Winsorized Mean ( 83 / 120 ) 1.44883333333333 1.12545720242937 1.28732867869691 Winsorized Mean ( 84 / 120 ) 1.51183333333333 1.11904301116291 1.35100556301427 Winsorized Mean ( 85 / 120 ) 1.50238888888889 1.11556985015383 1.34674569116557 Winsorized Mean ( 86 / 120 ) 1.42116666666667 1.10989950328019 1.28044625884286 Winsorized Mean ( 87 / 120 ) 1.41875 1.10494657023292 1.28399873642844 Winsorized Mean ( 88 / 120 ) 1.43830555555556 1.10147358412878 1.3058012250863 Winsorized Mean ( 89 / 120 ) 1.43336111111111 1.09267105423062 1.31179562738613 Winsorized Mean ( 90 / 120 ) 1.34586111111111 1.08660807076474 1.23858928285331 Winsorized Mean ( 91 / 120 ) 1.16638888888889 1.07027975118066 1.08979814632782 Winsorized Mean ( 92 / 120 ) 1.04116666666667 1.05172854915957 0.989957596471688 Winsorized Mean ( 93 / 120 ) 0.79575 1.03045902770566 0.772228665677036 Winsorized Mean ( 94 / 120 ) 0.79313888888889 1.02250958241178 0.775678685590525 Winsorized Mean ( 95 / 120 ) 0.708694444444445 1.01380108995947 0.699046836172545 Winsorized Mean ( 96 / 120 ) 1.09002777777778 0.983448533215997 1.10837297627894 Winsorized Mean ( 97 / 120 ) 1.07386111111111 0.980299153003755 1.09544225129714 Winsorized Mean ( 98 / 120 ) 1.08202777777778 0.978064628733654 1.10629476415964 Winsorized Mean ( 99 / 120 ) 1.08477777777778 0.973686800580847 1.11409313254597 Winsorized Mean ( 100 / 120 ) 0.965333333333333 0.951239205294147 1.01481659708803 Winsorized Mean ( 101 / 120 ) 0.808222222222222 0.93836179851894 0.861311941191421 Winsorized Mean ( 102 / 120 ) 0.802555555555556 0.90976574614084 0.882156268204138 Winsorized Mean ( 103 / 120 ) 0.914138888888889 0.899851490262083 1.01587750732362 Winsorized Mean ( 104 / 120 ) 1.00658333333333 0.885693633168135 1.13649155378116 Winsorized Mean ( 105 / 120 ) 0.957 0.881354616878634 1.08582854355409 Winsorized Mean ( 106 / 120 ) 0.98938888888889 0.873210434490325 1.13304748753532 Winsorized Mean ( 107 / 120 ) 0.98938888888889 0.872101840812557 1.13448778868194 Winsorized Mean ( 108 / 120 ) 1.02538888888889 0.86737466057474 1.18217528767724 Winsorized Mean ( 109 / 120 ) 0.828583333333333 0.854552096963363 0.969611257496986 Winsorized Mean ( 110 / 120 ) 0.849972222222223 0.845350860423366 1.00546679729709 Winsorized Mean ( 111 / 120 ) 0.927055555555556 0.840068098110001 1.10354810239939 Winsorized Mean ( 112 / 120 ) 0.755944444444445 0.818650473047067 0.92340317306698 Winsorized Mean ( 113 / 120 ) 0.887777777777777 0.802496310423761 1.10627022983941 Winsorized Mean ( 114 / 120 ) 0.539444444444445 0.775537864538002 0.69557460584571 Winsorized Mean ( 115 / 120 ) 0.360555555555556 0.760701428099043 0.473977755578253 Winsorized Mean ( 116 / 120 ) 0.296111111111111 0.754290263681377 0.392569181081453 Winsorized Mean ( 117 / 120 ) 0.250611111111111 0.74275959276182 0.337405418325542 Winsorized Mean ( 118 / 120 ) -0.0542222222222228 0.719793105903758 -0.0753302883529878 Winsorized Mean ( 119 / 120 ) -0.0641388888888887 0.717983144099422 -0.0893320259897454 Winsorized Mean ( 120 / 120 ) -0.0941388888888892 0.714922085373121 -0.131677130718039 Trimmed Mean ( 1 / 120 ) 0.122988826815643 1.78596013678202 0.068864262019446 Trimmed Mean ( 2 / 120 ) 0.106713483146067 1.77016296716641 0.0602845529623124 Trimmed Mean ( 3 / 120 ) 0.0866666666666668 1.75478529234938 0.0493887582968249 Trimmed Mean ( 4 / 120 ) 0.071193181818182 1.73961803432782 0.0409246055245057 Trimmed Mean ( 5 / 120 ) 0.0473428571428572 1.72653205211073 0.0274207809145388 Trimmed Mean ( 6 / 120 ) 0.0177298850574713 1.71415956324656 0.0103431940862562 Trimmed Mean ( 7 / 120 ) -0.0115317919075143 1.70149285478746 -0.0067774553828231 Trimmed Mean ( 8 / 120 ) -0.0320058139534882 1.68965421888076 -0.0189422271112306 Trimmed Mean ( 9 / 120 ) -0.0393274853801169 1.67919115984149 -0.0234204933426574 Trimmed Mean ( 10 / 120 ) -0.0428529411764706 1.66926491746421 -0.0256717437287119 Trimmed Mean ( 11 / 120 ) -0.0468639053254438 1.65969217241445 -0.0282365043978415 Trimmed Mean ( 12 / 120 ) -0.0503273809523809 1.64987172474173 -0.0305038144467014 Trimmed Mean ( 13 / 120 ) -0.0503273809523809 1.63982313509245 -0.0306907372358444 Trimmed Mean ( 14 / 120 ) -0.0535542168674697 1.62987502255304 -0.0328578670919088 Trimmed Mean ( 15 / 120 ) -0.0464545454545454 1.62075104617803 -0.0286623572226543 Trimmed Mean ( 16 / 120 ) -0.0369207317073170 1.61169944313627 -0.0229079508990035 Trimmed Mean ( 17 / 120 ) -0.0257975460122698 1.60279502961612 -0.0160953493962659 Trimmed Mean ( 18 / 120 ) -0.01179012345679 1.59422116129715 -0.00739553817438785 Trimmed Mean ( 19 / 120 ) -0.00118012422360236 1.58591895829396 -0.000744126436871573 Trimmed Mean ( 20 / 120 ) 0.00737500000000003 1.57761791620399 0.00467476942563222 Trimmed Mean ( 21 / 120 ) 0.0148427672955976 1.56917895452852 0.00945893854411096 Trimmed Mean ( 22 / 120 ) 0.0199683544303798 1.56076857486964 0.0127939239371523 Trimmed Mean ( 23 / 120 ) 0.0244585987261147 1.55224208580636 0.0157569485776498 Trimmed Mean ( 24 / 120 ) 0.0321794871794873 1.54444421346233 0.0208356422970742 Trimmed Mean ( 25 / 120 ) 0.0409677419354839 1.53661483663656 0.0266610349963539 Trimmed Mean ( 26 / 120 ) 0.0409677419354839 1.52930453421421 0.0267884786966476 Trimmed Mean ( 27 / 120 ) 0.0729411764705883 1.52171907247337 0.047933404916869 Trimmed Mean ( 28 / 120 ) 0.0895394736842106 1.51389270759104 0.0591451912247393 Trimmed Mean ( 29 / 120 ) 0.106357615894040 1.50578388016490 0.0706327231251754 Trimmed Mean ( 30 / 120 ) 0.1237 1.49747868884879 0.082605516139329 Trimmed Mean ( 31 / 120 ) 0.139932885906040 1.48923734788004 0.0939627831018595 Trimmed Mean ( 32 / 120 ) 0.156013513513514 1.48081976674555 0.105356179743866 Trimmed Mean ( 33 / 120 ) 0.173265306122449 1.47240723587305 0.117674853736855 Trimmed Mean ( 34 / 120 ) 0.186780821917808 1.46415107404484 0.127569364411154 Trimmed Mean ( 35 / 120 ) 0.199172413793104 1.45571612959490 0.136820915660617 Trimmed Mean ( 36 / 120 ) 0.2128125 1.44706465699994 0.147064955923395 Trimmed Mean ( 37 / 120 ) 0.227622377622378 1.43824810272634 0.158263638374281 Trimmed Mean ( 38 / 120 ) 0.243133802816902 1.42936850489490 0.170098754788765 Trimmed Mean ( 39 / 120 ) 0.261418439716312 1.42039501685084 0.184046294597613 Trimmed Mean ( 40 / 120 ) 0.279892857142857 1.41122318190089 0.198333517144926 Trimmed Mean ( 41 / 120 ) 0.296187050359712 1.40211470066621 0.211243096031288 Trimmed Mean ( 42 / 120 ) 0.311666666666667 1.39281137871252 0.223768035952409 Trimmed Mean ( 43 / 120 ) 0.323102189781022 1.38380692362439 0.233487912413945 Trimmed Mean ( 44 / 120 ) 0.333970588235294 1.37453274571540 0.242970267006243 Trimmed Mean ( 45 / 120 ) 0.347222222222222 1.36532883622066 0.254313988696937 Trimmed Mean ( 46 / 120 ) 0.355820895522388 1.35650576246741 0.262306954653233 Trimmed Mean ( 47 / 120 ) 0.365563909774436 1.34749496922618 0.271291483918762 Trimmed Mean ( 48 / 120 ) 0.372727272727273 1.33877942423753 0.278408276956863 Trimmed Mean ( 49 / 120 ) 0.372727272727273 1.32998955193532 0.280248271262659 Trimmed Mean ( 50 / 120 ) 0.388846153846154 1.32180585995839 0.29417796185167 Trimmed Mean ( 51 / 120 ) 0.39422480620155 1.31387733479030 0.300046888520587 Trimmed Mean ( 52 / 120 ) 0.39422480620155 1.30609372224541 0.301835005778764 Trimmed Mean ( 53 / 120 ) 0.399015748031496 1.29823045579749 0.307353556719931 Trimmed Mean ( 54 / 120 ) 0.403452380952381 1.29022430956571 0.312699410452267 Trimmed Mean ( 55 / 120 ) 0.4106 1.28218514793079 0.320234562584533 Trimmed Mean ( 56 / 120 ) 0.413951612903226 1.2753676044841 0.324574351306872 Trimmed Mean ( 57 / 120 ) 0.416341463414634 1.26837152940580 0.328248824387976 Trimmed Mean ( 58 / 120 ) 0.418073770491803 1.26123691758505 0.331479173074247 Trimmed Mean ( 59 / 120 ) 0.420909090909091 1.25387226565751 0.335687376168555 Trimmed Mean ( 60 / 120 ) 0.423666666666667 1.24621197704274 0.339963565164914 Trimmed Mean ( 61 / 120 ) 0.42546218487395 1.23840109735669 0.343557661392645 Trimmed Mean ( 62 / 120 ) 0.425593220338983 1.23058923710648 0.345845069586089 Trimmed Mean ( 63 / 120 ) 0.425593220338983 1.22269155384684 0.348078972983807 Trimmed Mean ( 64 / 120 ) 0.431077586206897 1.21513709145975 0.354756339211933 Trimmed Mean ( 65 / 120 ) 0.433478260869565 1.20733738223233 0.359036560325893 Trimmed Mean ( 66 / 120 ) 0.434429824561404 1.19941207892446 0.36220230911045 Trimmed Mean ( 67 / 120 ) 0.434115044247788 1.19151645502529 0.36433827029151 Trimmed Mean ( 68 / 120 ) 0.433794642857143 1.18363658156801 0.366493102369713 Trimmed Mean ( 69 / 120 ) 0.433198198198198 1.17594523424519 0.368382970212263 Trimmed Mean ( 70 / 120 ) 0.431727272727273 1.16851512591593 0.369466567571273 Trimmed Mean ( 71 / 120 ) 0.423944954128441 1.16160955245907 0.36496338484043 Trimmed Mean ( 72 / 120 ) 0.413981481481482 1.15459448992887 0.358551409254503 Trimmed Mean ( 73 / 120 ) 0.399766355140187 1.14783113540042 0.34827976242405 Trimmed Mean ( 74 / 120 ) 0.385377358490566 1.14124269715421 0.337682212075958 Trimmed Mean ( 75 / 120 ) 0.367904761904762 1.13465807888678 0.324242843505523 Trimmed Mean ( 76 / 120 ) 0.346971153846154 1.12815546559337 0.307556151991569 Trimmed Mean ( 77 / 120 ) 0.325 1.12154182199739 0.289779652996976 Trimmed Mean ( 78 / 120 ) 0.30421568627451 1.11493321068717 0.2728555247601 Trimmed Mean ( 79 / 120 ) 0.283267326732673 1.10819225341439 0.2556120798173 Trimmed Mean ( 80 / 120 ) 0.26355 1.10127440771724 0.239313651668611 Trimmed Mean ( 81 / 120 ) 0.244141414141414 1.09403655435281 0.223156541863300 Trimmed Mean ( 82 / 120 ) 0.217551020408163 1.08729036635887 0.200085485109833 Trimmed Mean ( 83 / 120 ) 0.19 1.08057440503465 0.175832408314269 Trimmed Mean ( 84 / 120 ) 0.1615625 1.07356740670838 0.150491249073367 Trimmed Mean ( 85 / 120 ) 0.131105263157895 1.06636762814168 0.122945651854012 Trimmed Mean ( 86 / 120 ) 0.100212765957447 1.05883820173470 0.0946440785695758 Trimmed Mean ( 87 / 120 ) 0.070483870967742 1.05106923674771 0.0670592083789245 Trimmed Mean ( 88 / 120 ) 0.0401630434782610 1.04299791808841 0.0385073093452298 Trimmed Mean ( 89 / 120 ) 0.00873626373626382 1.03452869795865 0.00844467993348309 Trimmed Mean ( 90 / 120 ) -0.0232777777777777 1.02589317893758 -0.0226902549463134 Trimmed Mean ( 91 / 120 ) -0.0540449438202247 1.01696024549346 -0.0531436150623570 Trimmed Mean ( 92 / 120 ) -0.0814772727272727 1.00822816514124 -0.0808123354854495 Trimmed Mean ( 93 / 120 ) -0.106724137931034 0.999806237170422 -0.106744821109615 Trimmed Mean ( 94 / 120 ) -0.127034883720930 0.99185862062932 -0.128077612150337 Trimmed Mean ( 95 / 120 ) -0.147764705882353 0.983718092815494 -0.150210417965818 Trimmed Mean ( 96 / 120 ) -0.167083333333333 0.975418543060968 -0.171293989151578 Trimmed Mean ( 97 / 120 ) -0.195481927710843 0.967971624465408 -0.201950060074131 Trimmed Mean ( 98 / 120 ) -0.195481927710843 0.960105961036306 -0.203604534961794 Trimmed Mean ( 99 / 120 ) -0.253827160493827 0.951736373746212 -0.266699022434874 Trimmed Mean ( 100 / 120 ) -0.28425 0.942936495148877 -0.301451902076524 Trimmed Mean ( 101 / 120 ) -0.312721518987342 0.934664321004732 -0.33458163744945 Trimmed Mean ( 102 / 120 ) -0.338333333333333 0.926445272152601 -0.365195164251002 Trimmed Mean ( 103 / 120 ) -0.364480519480519 0.919097561744389 -0.396563471225795 Trimmed Mean ( 104 / 120 ) -0.364480519480519 0.911640682197748 -0.399807211983831 Trimmed Mean ( 105 / 120 ) -0.4262 0.90429706800563 -0.471305298976538 Trimmed Mean ( 106 / 120 ) -0.458243243243243 0.896563072027675 -0.511110994351882 Trimmed Mean ( 107 / 120 ) -0.491917808219178 0.888600255421672 -0.553587291043199 Trimmed Mean ( 108 / 120 ) -0.526527777777778 0.879989864272462 -0.59833391173555 Trimmed Mean ( 109 / 120 ) -0.526527777777778 0.87087673007174 -0.604595070228142 Trimmed Mean ( 110 / 120 ) -0.595785714285714 0.861775898095977 -0.69134645747468 Trimmed Mean ( 111 / 120 ) -0.630072463768116 0.85240177223005 -0.739173103922254 Trimmed Mean ( 112 / 120 ) -0.667205882352941 0.842463433722073 -0.791970138579393 Trimmed Mean ( 113 / 120 ) -0.70134328358209 0.833025367191224 -0.841923080862307 Trimmed Mean ( 114 / 120 ) -0.73969696969697 0.823685565292864 -0.898033182642904 Trimmed Mean ( 115 / 120 ) -0.73969696969697 0.815314380251579 -0.907253677371327 Trimmed Mean ( 116 / 120 ) -0.7984375 0.80713169330863 -0.989228284082129 Trimmed Mean ( 117 / 120 ) -0.825396825396825 0.79856025724708 -1.03360619052376 Trimmed Mean ( 118 / 120 ) -0.852096774193548 0.789907201358725 -1.07873022644666 Trimmed Mean ( 119 / 120 ) -0.872049180327869 0.782018368805682 -1.11512621073043 Trimmed Mean ( 120 / 120 ) -0.892416666666667 0.773407897459036 -1.15387581326571 Median -1.48 Midrange 1.475 Midmean - Weighted Average at Xnp -0.14961325966851 Midmean - Weighted Average at X(n+1)p -0.14961325966851 Midmean - Empirical Distribution Function -0.14961325966851 Midmean - Empirical Distribution Function - Averaging -0.14961325966851 Midmean - Empirical Distribution Function - Interpolation -0.14961325966851 Midmean - Closest Observation -0.14961325966851 Midmean - True Basic - Statistics Graphics Toolkit -0.14961325966851 Midmean - MS Excel (old versions) 0.00873626373626208 Number of observations 360

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Oct/28/t1256748219gcyob033lvp5glh/1wkrv1256748143.png (open in new window) http://www.freestatistics.org/blog/date/2009/Oct/28/t1256748219gcyob033lvp5glh/1wkrv1256748143.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Oct/28/t1256748219gcyob033lvp5glh/2yol81256748143.png (open in new window) http://www.freestatistics.org/blog/date/2009/Oct/28/t1256748219gcyob033lvp5glh/2yol81256748143.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

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