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Central tendancy, question 6

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

Title produced by software: Central Tendency

Date of computation: Mon, 22 Oct 2007 12:59:05 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Oct/22/goutbfff5tzrglh1193083388.htm/, Retrieved Mon, 22 Oct 2007 22:03:10 +0200

User-defined keywords:

michael, junior, peter, yannick, gemiddelde

Dataseries X:

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0.0257493744938832 0.0237478877648397 0.021734532514729 0.0198465789395556 0.0181409407214812 0.0166215258042675 0.0152701505942662 0.0140703182613428 0.0130169628370724 0.0121149909956676 0.0113755336274530 0.0108066015941988 0.0104102672587416 0.0101793297879612 0.0100957980377989 0.0101353192153298 0.0102701844619835 0.0104732151930134 0.0107193845356072 0.0109874944000426 0.0112601657760742 0.011526196109277 0.0117752995862039 0.0120023630740173 0.0122043778163178 0.0123794859228747 0.0125298799458765 0.0126567941304853 0.0127639590477691 0.0128558221765743 0.012937217037477 0.0130144421835237 0.0130890257027324 0.0131688330441186 0.0132543391742880 0.0133498482950950 0.0134562040704081 0.0135731710298020 0.0137014134900641 0.0138365601431088 0.0139761760838112 0.0141174358517317 0.014254906668871 0.0143846557028513 0.0145009100196785 0.0145991622719790 0.0146742460172555 0.0147237824554493 0.0147425838993809 0.0147280297587216 0.0146806157352704 0.0145984773988804 0.0144813558459163 0.0143309797285947 0.0141518828509622 0.0139450299536084 0.0137166291746623 0.0134705795297250 0.0132144865802642 0.0129541498747749 0.0126954985073174 0.0124458847090164 0.0122127590106538 0.0120017778516538 0.0118197530780406 0.0116707726396800 0.0115605302029674 0.0114911913408600 0.0114653295564385 0.0114847599378424 0.0115477666075857 0.0116540564540377 0.0118008984086779 0.0119849795430896 0.0122015373336842 0.0124467534622199 0.0127161972913673 0.0130032727209724 0.0133047190084541 0.0136141559586320 0.0139270942039374 0.014239355935933 0.0145468382768111 0.0148447068575053 0.0151326313986313 0.0154054385698285 0.0156627183912659 0.0159026498787941 0.0161249314936152 0.0163293765922274 0.0165157734928721 0.0166846565470461 0.0168385143727890 0.016977358744869 0.0171040905359246 0.0172187792349239 0.0173246160284153 0.0174228142653241 0.0175143229302128 0.0176023510894760 0.0176871470530748 0.0177692702667512 0.0178499332357475 0.0179306594638612 0.0180101805121891 0.0180887588131363 0.0181670326317408 0.0182426657825205 0.0183152217343903 0.018383189674388 0.0184445154323164 0.0184986869432896 0.0185417822082271 0.0185711418697827 0.0185853019135757 0.0185807007579698 0.0185546256675338 0.0185049726254956 0.0184274237550889 0.0183201309933662 0.0181815126038245 0.0180089438351744 0.0178003184355686 0.0175555885089826 0.0172750557804465 0.0169579792264604 0.0166064564640451 0.016221689059496 0.0158060406733841 0.0153625193885056 0.0148957772281856 0.0144091565224269 0.0139084949432564 0.0133979903424574 0.0128841617006653 0.0123737931848146 0.0118732217969068 0.0113881931568975 0.0109276070046339 0.010499492187601 0.0101118222886584 0.0097739816098994 0.0094955906524746 0.00928730737487212 0.00916083841819465 0.00912687379165225 0.00919703433683982 0.00937991935349045 0.00968438506916855 0.0101152973121683 0.0106742913471305 0.0113594068413920 0.0121649595096515 0.0130812991346697 0.0140954137567437 0.0151890292663688 0.0163422947029298 0.0175302199442381 0.0187253547553665 0.0198976618965236 0.0210153504000639 0.0220458127317437 0.0229581518854807 0.0237213485977124 0.0243101462873557 0.0247034605007399 0.024886670194248 0.0248523903562826 0.0246046329518060 0.0241546036192985 0.0235246397989794 0.0227469681506455 0.0218629790162297 0.0209219326696186 0.0199805985887911 0.0190973891840825 0.0183298783311204 0.0177293033734479 0.0173304254644116 0.0171442690578422 0.0171531920939682 0.0173115013737246 0.0175490373660681 0.0177851170554244 0.0179383444747054 0.0179413450399507 0.0177538738190025 0.0173787755922235 0.0168765909408637 0.0163837185740052 0.0161209786732948 0.0163772628548821 0.0174324825582754 0.0194346308279013 0.0223196274919401 0.0258175092461661 0.0295050170940449

Text written by user:

Output produced by software:

Summary of compuational 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 0.0155075621707526 0.000280893466368102 55.2079846187324 Geometric Mean 0.0150358617366249 Harmonic Mean 0.0145889642845935 Quadratic Mean 0.0159984095400643 Winsorized Mean ( 1 / 65 ) 0.0154890162660757 0.000276707053113123 55.9762250069698 Winsorized Mean ( 2 / 65 ) 0.0154886920139576 0.000276532944810327 56.0102957156921 Winsorized Mean ( 3 / 65 ) 0.0154769291013444 0.000273978912217675 56.4894902898511 Winsorized Mean ( 4 / 65 ) 0.0154781135102916 0.00027364182361412 56.5634057903309 Winsorized Mean ( 5 / 65 ) 0.0154772693844906 0.000272653973064638 56.7652442784006 Winsorized Mean ( 6 / 65 ) 0.0154800094921382 0.00027150112805074 57.0163726511115 Winsorized Mean ( 7 / 65 ) 0.0154727291324121 0.000269383011218337 57.4376574915903 Winsorized Mean ( 8 / 65 ) 0.0154794813663143 0.000266952974472976 57.9857984233898 Winsorized Mean ( 9 / 65 ) 0.0154616325113275 0.000263848158406998 58.6004943323397 Winsorized Mean ( 10 / 65 ) 0.015460461742616 0.000263614286378599 58.6480420124572 Winsorized Mean ( 11 / 65 ) 0.0154505959768734 0.000261756154026981 59.0266770777846 Winsorized Mean ( 12 / 65 ) 0.0154187699459576 0.000256170040168352 60.1895910069134 Winsorized Mean ( 13 / 65 ) 0.0154108294495588 0.000253471679439458 60.7990189816836 Winsorized Mean ( 14 / 65 ) 0.015390415185258 0.000248039282478312 62.0482974772503 Winsorized Mean ( 15 / 65 ) 0.0153743593355683 0.000244612189493584 62.8519754775818 Winsorized Mean ( 16 / 65 ) 0.0153616440688941 0.000242344407139513 63.3876566421057 Winsorized Mean ( 17 / 65 ) 0.0153656440444600 0.000239306777976632 64.2089796803016 Winsorized Mean ( 18 / 65 ) 0.0153040521628794 0.000230266513793417 66.4623436154912 Winsorized Mean ( 19 / 65 ) 0.0153034541285381 0.000228280747226709 67.0378659368065 Winsorized Mean ( 20 / 65 ) 0.0152201720300211 0.000215639949959075 70.5814114356346 Winsorized Mean ( 21 / 65 ) 0.0152177149983763 0.000214002721532950 71.1099134131021 Winsorized Mean ( 22 / 65 ) 0.0152424609131956 0.000210359330840858 72.4591623878422 Winsorized Mean ( 23 / 65 ) 0.0152059519686994 0.000204011791044470 74.5346721914956 Winsorized Mean ( 24 / 65 ) 0.0151668313768914 0.000199584370267034 75.992079723472 Winsorized Mean ( 25 / 65 ) 0.0151212254252072 0.000194826309453004 77.6138780622682 Winsorized Mean ( 26 / 65 ) 0.0151129217323214 0.000192110829546946 78.6677240838643 Winsorized Mean ( 27 / 65 ) 0.0151149541642836 0.000191795111573373 78.8078175730836 Winsorized Mean ( 28 / 65 ) 0.0151145096486250 0.000191581709846039 78.8932808918525 Winsorized Mean ( 29 / 65 ) 0.0151172313157259 0.000190862155897409 79.2049699147882 Winsorized Mean ( 30 / 65 ) 0.0151185603064368 0.000190367731714081 79.4176627010709 Winsorized Mean ( 31 / 65 ) 0.0151147764201026 0.000189645732897100 79.7000606826401 Winsorized Mean ( 32 / 65 ) 0.0151289474769740 0.000188110224689882 80.4259710067091 Winsorized Mean ( 33 / 65 ) 0.0151226732346596 0.000187020558333619 80.8610206781805 Winsorized Mean ( 34 / 65 ) 0.0151377635857057 0.000185067498397263 81.7959053685984 Winsorized Mean ( 35 / 65 ) 0.0151344527530258 0.000183935964198042 82.2810961358852 Winsorized Mean ( 36 / 65 ) 0.0151281561019569 0.000182743149628084 82.783711087094 Winsorized Mean ( 37 / 65 ) 0.0151363677319119 0.000181655478248547 83.3245871682541 Winsorized Mean ( 38 / 65 ) 0.0151569781000026 0.000179611506916403 84.3875671454458 Winsorized Mean ( 39 / 65 ) 0.0151459397848304 0.000178030139876505 85.0751439915555 Winsorized Mean ( 40 / 65 ) 0.0151336417226310 0.000176922742697911 85.538136543989 Winsorized Mean ( 41 / 65 ) 0.0151540684532514 0.000174558110476570 86.8138891506018 Winsorized Mean ( 42 / 65 ) 0.0151591588966850 0.000173125585610936 87.5616324599885 Winsorized Mean ( 43 / 65 ) 0.0151557529290433 0.000171424739742313 88.410534861103 Winsorized Mean ( 44 / 65 ) 0.0151388368681001 0.000169845930564947 89.1327617785416 Winsorized Mean ( 45 / 65 ) 0.0151404688644426 0.000169652901740201 89.2437954738201 Winsorized Mean ( 46 / 65 ) 0.01516228626257 0.00016501715308923 91.8830920223868 Winsorized Mean ( 47 / 65 ) 0.0151629285576070 0.000164838597920739 91.9865174107947 Winsorized Mean ( 48 / 65 ) 0.0151772344520958 0.000163290346284036 92.9463057521828 Winsorized Mean ( 49 / 65 ) 0.0151573714253415 0.000161554406937711 93.822085776871 Winsorized Mean ( 50 / 65 ) 0.0151658769287622 0.000158688531989462 95.5700877601496 Winsorized Mean ( 51 / 65 ) 0.0151947975025068 0.000155595034151366 97.656056861847 Winsorized Mean ( 52 / 65 ) 0.0152008309775539 0.000154391835204989 98.4561842753626 Winsorized Mean ( 53 / 65 ) 0.0152022574944268 0.000153580008717121 98.985913735868 Winsorized Mean ( 54 / 65 ) 0.0152086145034915 0.000151931461614992 100.101811315628 Winsorized Mean ( 55 / 65 ) 0.0152224920388405 0.000148842979684374 102.272153319695 Winsorized Mean ( 56 / 65 ) 0.0152064435078129 0.000146163430987981 104.037264348723 Winsorized Mean ( 57 / 65 ) 0.0152082642545649 0.000143793450477482 105.764652034319 Winsorized Mean ( 58 / 65 ) 0.0152113207940281 0.000143231548390343 106.200910099591 Winsorized Mean ( 59 / 65 ) 0.0152203970378742 0.000141583189265972 107.501442203578 Winsorized Mean ( 60 / 65 ) 0.0152189571744811 0.000140908817491073 108.005712101340 Winsorized Mean ( 61 / 65 ) 0.0151943962464521 0.000138750500423800 109.508767175919 Winsorized Mean ( 62 / 65 ) 0.0152116014052747 0.000136885401421634 111.12654269406 Winsorized Mean ( 63 / 65 ) 0.0151999889046788 0.000135531192708736 112.151222171743 Winsorized Mean ( 64 / 65 ) 0.0152102085070583 0.000132192654652031 115.060920344598 Winsorized Mean ( 65 / 65 ) 0.0152233550375644 0.000130852346064915 116.339947241085 Trimmed Mean ( 1 / 65 ) 0.0155075621707526 0.000272523155088951 56.9036497676351 Trimmed Mean ( 2 / 65 ) 0.0154685018295003 0.000268055779770188 57.7062798002786 Trimmed Mean ( 3 / 65 ) 0.0154263513102486 0.000263376541961236 58.5714703191716 Trimmed Mean ( 4 / 65 ) 0.0154263513102486 0.000259356702796966 59.4792852619077 Trimmed Mean ( 5 / 65 ) 0.015390517716463 0.000255157857899817 60.317631771724 Trimmed Mean ( 6 / 65 ) 0.0153720419558128 0.000250906488798995 61.2660199797685 Trimmed Mean ( 7 / 65 ) 0.0153526707311716 0.000246594383794276 62.258801254694 Trimmed Mean ( 8 / 65 ) 0.0153526707311716 0.000242371414785443 63.3435702174795 Trimmed Mean ( 9 / 65 ) 0.0153139900237433 0.000238248562349444 64.2773659271108 Trimmed Mean ( 10 / 65 ) 0.0152957316621274 0.000234312003386779 65.2793345669052 Trimmed Mean ( 11 / 65 ) 0.0152771877616381 0.000230115455079413 66.3892295124893 Trimmed Mean ( 12 / 65 ) 0.0152592364119789 0.000225857952506324 67.5612093470637 Trimmed Mean ( 13 / 65 ) 0.0152439205707539 0.000222007965672795 68.6638451217602 Trimmed Mean ( 14 / 65 ) 0.0152289542306881 0.000218195055817120 69.7951389120944 Trimmed Mean ( 15 / 65 ) 0.0152153495223689 0.000214726289002957 70.8592766773859 Trimmed Mean ( 16 / 65 ) 0.0152153495223689 0.000211374559328849 71.9828799202715 Trimmed Mean ( 17 / 65 ) 0.0151906863258099 0.000208007927042161 73.0293625912195 Trimmed Mean ( 18 / 65 ) 0.0151780934611500 0.000204687971271721 74.1523469447125 Trimmed Mean ( 19 / 65 ) 0.0151694233478584 0.000202022393571609 75.0878310056326 Trimmed Mean ( 20 / 65 ) 0.0151605718346858 0.000199334133820053 76.0560750140857 Trimmed Mean ( 21 / 65 ) 0.0151567843384016 0.000197599187733243 76.7046894892254 Trimmed Mean ( 22 / 65 ) 0.0151530484716057 0.00019586295636705 77.365565968527 Trimmed Mean ( 23 / 65 ) 0.0151477461684771 0.00019428542140255 77.9664581064562 Trimmed Mean ( 24 / 65 ) 0.0151444002266493 0.000193096318491514 78.4292540891446 Trimmed Mean ( 25 / 65 ) 0.0151431476936001 0.000192149084930146 78.8093666910006 Trimmed Mean ( 26 / 65 ) 0.0151443390554962 0.000191469440999109 79.0953322706294 Trimmed Mean ( 27 / 65 ) 0.0151460037173212 0.000190911451671487 79.3352288965037 Trimmed Mean ( 28 / 65 ) 0.0151476104317810 0.000190303744786774 79.5970171199372 Trimmed Mean ( 29 / 65 ) 0.0151492858825215 0.000189633859297559 79.887030399726 Trimmed Mean ( 30 / 65 ) 0.0151508752984940 0.000188932796474313 80.1918755304817 Trimmed Mean ( 31 / 65 ) 0.0151524471635397 0.000188180234407253 80.5209283072063 Trimmed Mean ( 32 / 65 ) 0.0151524471635397 0.000187386746829481 80.861893489875 Trimmed Mean ( 33 / 65 ) 0.0151554360370387 0.000186606898285916 81.2158402301816 Trimmed Mean ( 34 / 65 ) 0.0151569521920936 0.000185806260118334 81.5739587161412 Trimmed Mean ( 35 / 65 ) 0.0151578276333762 0.000185043845000121 81.9147896184619 Trimmed Mean ( 36 / 65 ) 0.0151588801708457 0.000184260685428042 82.2686626592714 Trimmed Mean ( 37 / 65 ) 0.0151602470727362 0.000183457321937905 82.6363696613183 Trimmed Mean ( 38 / 65 ) 0.0151612978277378 0.000182623451391500 83.0194463647263 Trimmed Mean ( 39 / 65 ) 0.0151614860157882 0.000181823401650781 83.3857791578894 Trimmed Mean ( 40 / 65 ) 0.015162157198672 0.000181023452634661 83.757971566657 Trimmed Mean ( 41 / 65 ) 0.0151633784049286 0.000180186898960554 84.1536121238653 Trimmed Mean ( 42 / 65 ) 0.0151637742737998 0.000179401467499569 84.52424879878 Trimmed Mean ( 43 / 65 ) 0.0151639693036779 0.000178600325168236 84.9044887762317 Trimmed Mean ( 44 / 65 ) 0.0151643146473525 0.000177799700788028 85.2887523440287 Trimmed Mean ( 45 / 65 ) 0.0151653807311487 0.000176987865734605 85.6859913429851 Trimmed Mean ( 46 / 65 ) 0.0151664193846389 0.000176057989828475 86.1444538780369 Trimmed Mean ( 47 / 65 ) 0.0151665912345856 0.000175335712860396 86.500296985483 Trimmed Mean ( 48 / 65 ) 0.0151667432352945 0.000174495399650496 86.9177254281347 Trimmed Mean ( 49 / 65 ) 0.0151663083090087 0.000173629743550533 87.348561363248 Trimmed Mean ( 50 / 65 ) 0.0151666787205556 0.000172749132827174 87.795976004864 Trimmed Mean ( 51 / 65 ) 0.0151667119738153 0.000171940504444757 88.2090698918953 Trimmed Mean ( 52 / 65 ) 0.0151655454443714 0.000171225584766796 88.5705571689323 Trimmed Mean ( 53 / 65 ) 0.0151655454443714 0.000170457194425161 88.9698172935132 Trimmed Mean ( 54 / 65 ) 0.015162481870136 0.000169594551368121 89.4042983564042 Trimmed Mean ( 55 / 65 ) 0.0151605474025389 0.000168695728057726 89.86918386784 Trimmed Mean ( 56 / 65 ) 0.0151579371151910 0.000167878746589731 90.2909833621441 Trimmed Mean ( 57 / 65 ) 0.0151579371151910 0.000167118941275034 90.7014908037566 Trimmed Mean ( 58 / 65 ) 0.0151536461290696 0.000166389370057309 91.073402849294 Trimmed Mean ( 59 / 65 ) 0.0151511664457442 0.000165522869512977 91.5351847773297 Trimmed Mean ( 60 / 65 ) 0.0151481643707608 0.000164601121007470 92.0295334445104 Trimmed Mean ( 61 / 65 ) 0.0151450652191313 0.000163519365394573 92.6193982136991 Trimmed Mean ( 62 / 65 ) 0.0151428828224589 0.000162410078941793 93.2385657412678 Trimmed Mean ( 63 / 65 ) 0.0151398075019649 0.000161225689554262 93.9044363452353 Trimmed Mean ( 64 / 65 ) 0.0151398075019649 0.000159903000906509 94.6811968264232 Trimmed Mean ( 65 / 65 ) 0.0151337204900898 0.000158637979126471 95.397839618373 Median 0.0147280297587216 Midrange 0.0193159454428486 Midmean - Weighted Average at Xnp 0.0151389243811849 Midmean - Weighted Average at X(n+1)p 0.0151663083090087 Midmean - Empirical Distribution Function 0.0151663083090087 Midmean - Empirical Distribution Function - Averaging 0.0151663083090087 Midmean - Empirical Distribution Function - Interpolation 0.0151663083090087 Midmean - Closest Observation 0.0151391040730088 Midmean - True Basic - Statistics Graphics Toolkit 0.0151663083090087 Midmean - MS Excel (old versions) 0.0151663083090087 Number of observations 197

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/22/goutbfff5tzrglh1193083388/1jfzd1193083143.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/22/goutbfff5tzrglh1193083388/1jfzd1193083143.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/22/goutbfff5tzrglh1193083388/256qt1193083143.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/22/goutbfff5tzrglh1193083388/256qt1193083143.ps (open in new window)

Parameters:

par1 = 0.15603665073 ; par2 = 0.1802905110447 ; par3 = 0.01 ;

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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Software written by Ed van Stee & Patrick Wessa

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