Q2 residus in central tendency

*The author of this computation has been verified*

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

Title produced by software: Central Tendency

Date of computation: Mon, 01 Dec 2008 11:44:04 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/01/t1228157094byfc63pz7syyk7j.htm/, Retrieved Mon, 01 Dec 2008 18:44:54 +0000

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Dec/01/t1228157094byfc63pz7syyk7j.htm/}, year = {2008}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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-183.923544514060 -177.072609114252 -228.635109114254 -237.447609114242 -127.760109114251 -193.010109114250 -220.635109114248 -164.510109114256 -268.322609114246 -333.697609114249 -34.2601091142503 -154.885109114249 -97.7452805255622 101.105654874272 2.54315487427317 -43.2693451257275 -163.581845125727 -162.831845125727 46.5431548742728 26.6681548742733 -107.144345125727 42.4806548742729 76.918154874273 196.293154874273 201.43298346296 12.2839188627955 -0.278581137204542 42.9089188627948 87.5964188627952 84.3464188627952 57.721418862795 173.846418862796 -185.966081137205 47.6589188627952 89.0964188627953 -68.5285811372049 272.611247451482 146.462182851318 162.899682851318 10.0871828513171 279.774682851318 212.524682851317 248.899682851317 -41.9753171486821 -5.78781714868267 52.8371828513174 274.274682851318 414.649682851317 310.789511440004 362.64044683984 26.0779468398400 403.265446839839 327.95294683984 193.702946839840 317.07794683984 202.20294683984 321.39044683984 178.015446839840 16.4529468398398 -68.1720531601603 -157.032224571473 -76.1812891716376 -81.7437891716377 -134.556289171638 77.1312108283621 199.881210828362 105.256210828362 198.381210828362 262.568710828362 196.193710828362 11.6312108283621 -145.993789171638 -166.853960582951 -202.003025183115 43.4344748168846 -113.378025183116 -113.690525183116 -155.940525183116 -210.565525183116 -124.440525183115 -64.2530251831158 -298.628025183116 -154.190525183116 23.1844748168843 -249.675696594429 118.175238805407 -180.387261194593 -79.1997611945937 -81.5122611945933 -246.762261194593 -105.387261194593 -319.262261194593 -72.0747611945935 -90.4497611945934 -80.0122611945933 119.362738805407 -53.4974326059064 -114.646497206071 -155.208997206071 -50.0214972060714 -196.333997206071 -14.5839972060711 -82.2089972060712 17.9160027939294 -162.896497206071 -132.271497206071 -16.8339972060710 81.5410027939288 275.680831382616 -32.4682332175485 17.9692667824515 27.1567667824508 -123.155733217549 108.594266782451 67.9692667824511 34.0942667824517 -13.7182332175489 -113.093233217549 54.3442667824512 149.719266782451 153.859095371138 -28.2899692290262 238.147530770974 50.3350307709731 8.02253077097358 -61.2274692290265 -140.852469229027 -28.7274692290261 9.46003077097336 -121.914969229026 41.5225307709736 115.897530770973 27.0373593596605 -91.111705240504 3.32579475949605 -29.4867052405046 -73.7992052405041 50.9507947594957 -86.6742052405044 -9.54920524050376 -66.3617052405043 73.2632947594957 -216.299205240504 -128.924205240504 -142.784376651817 27.0665587480184 60.5040587480183 35.6915587480177 16.3790587480182 -64.870941251982 115.504058748018 -30.3709412519815 87.816558748018 205.441558748018 -64.1209412519819 -322.745941251982 -139.606112663295 35.2448227365406 -4.31767726345942 17.8698227365400 2.55732273654045 129.307322736540 -16.3176772634598 164.807322736541 21.9948227365402 138.619822736540 87.0573227365404 51.4323227365403 -80.4278486747727 -105.191879672279 5.24562032772056 68.4331203277199 -0.879379672279542 -105.129379672280 -82.7543796722797 -132.629379672279 102.558120327720 23.1831203277203 -180.379379672280 -267.00437967228 30.1354489164073 23.986384316243 90.4238843162428 31.6113843162422 81.2988843162427 25.0488843162426 -13.5761156837575 33.548884316243 140.736384316242 132.361384316243 94.7988843162427 4.17388431624253

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean -1.32394411912184e-15 10.6188693306243 -1.24678445312783e-16 Geometric Mean NaN Harmonic Mean -66.3453559687308 Quadratic Mean 146.755693925063 Winsorized Mean ( 1 / 64 ) -0.00225295911047496 10.5976619072822 -0.000212590204347511 Winsorized Mean ( 2 / 64 ) -0.389141708512662 10.5114574693141 -0.0370207185491333 Winsorized Mean ( 3 / 64 ) -0.608723958333333 10.3675898728177 -0.0587141240925551 Winsorized Mean ( 4 / 64 ) -0.114079873565210 10.2542623573272 -0.0111251174965008 Winsorized Mean ( 5 / 64 ) -0.192055669347345 10.2312339215092 -0.0187715060393239 Winsorized Mean ( 6 / 64 ) 0.152952070590623 10.1275534910648 0.0151025685251199 Winsorized Mean ( 7 / 64 ) -0.871577889419533 9.93937503722615 -0.0876894056371949 Winsorized Mean ( 8 / 64 ) -0.654044530600823 9.86473213619288 -0.0663012965350765 Winsorized Mean ( 9 / 64 ) -0.30687180550598 9.80392154025991 -0.0313009242521788 Winsorized Mean ( 10 / 64 ) 0.0231576007528721 9.74110968726661 0.00237730623063851 Winsorized Mean ( 11 / 64 ) -0.303784900513458 9.6285167015812 -0.0315505399147899 Winsorized Mean ( 12 / 64 ) -0.79974414549202 9.46661829076064 -0.08448044707502 Winsorized Mean ( 13 / 64 ) -0.948001838431845 9.30090307524163 -0.101925784062341 Winsorized Mean ( 14 / 64 ) -2.40296787591404 9.0117021152939 -0.266649723345374 Winsorized Mean ( 15 / 64 ) -2.69665818931076 8.91418257367838 -0.302513232932137 Winsorized Mean ( 16 / 64 ) -2.37954018357185 8.81648245707183 -0.269896775177404 Winsorized Mean ( 17 / 64 ) -2.26686442739213 8.78856082209186 -0.257933519865266 Winsorized Mean ( 18 / 64 ) -2.08081655068567 8.73527438827513 -0.238208493310597 Winsorized Mean ( 19 / 64 ) -2.22847410837344 8.71726451078163 -0.255639152123609 Winsorized Mean ( 20 / 64 ) -2.10152467046313 8.65444935992049 -0.242825924916202 Winsorized Mean ( 21 / 64 ) -0.994736679873597 8.53712761954928 -0.116518895371288 Winsorized Mean ( 22 / 64 ) -1.01157040610378 8.47548455556993 -0.119352516009128 Winsorized Mean ( 23 / 64 ) -2.77960388664457 8.24320502072544 -0.337199412080127 Winsorized Mean ( 24 / 64 ) -3.21506389381806 8.1749801388497 -0.393280942486847 Winsorized Mean ( 25 / 64 ) -4.38361129562939 8.04255112494204 -0.545052338185912 Winsorized Mean ( 26 / 64 ) -3.85657224669812 7.93390877306192 -0.486087294045071 Winsorized Mean ( 27 / 64 ) -4.97438463461072 7.78044088913562 -0.639344827046602 Winsorized Mean ( 28 / 64 ) -5.47142847380852 7.70549918353407 -0.710068010324424 Winsorized Mean ( 29 / 64 ) -5.91446326203778 7.64892240990871 -0.77324137245476 Winsorized Mean ( 30 / 64 ) -6.70059054390388 7.54541021545293 -0.888035289344657 Winsorized Mean ( 31 / 64 ) -5.71889571377338 7.37680556641522 -0.775253687017332 Winsorized Mean ( 32 / 64 ) -6.22706669718605 7.2178171400917 -0.862735447064392 Winsorized Mean ( 33 / 64 ) -6.41993694290547 7.13245319119481 -0.900102218803347 Winsorized Mean ( 34 / 64 ) -7.96024803886189 6.93823462844992 -1.14730165022400 Winsorized Mean ( 35 / 64 ) -7.25617865236192 6.82675811930629 -1.06290255573011 Winsorized Mean ( 36 / 64 ) -7.32195337768847 6.75061613151928 -1.08463482962712 Winsorized Mean ( 37 / 64 ) -7.32881178185327 6.73667398238876 -1.08789764815879 Winsorized Mean ( 38 / 64 ) -8.03388990685327 6.54335984899064 -1.22779276889266 Winsorized Mean ( 39 / 64 ) -8.47547549688246 6.45758888557676 -1.31248297887354 Winsorized Mean ( 40 / 64 ) -8.34599769886287 6.33932914728463 -1.31654272951544 Winsorized Mean ( 41 / 64 ) -8.381802974921 6.28470994364587 -1.33368175302909 Winsorized Mean ( 42 / 64 ) -9.48999191200051 6.1343786211271 -1.54701763587212 Winsorized Mean ( 43 / 64 ) -8.84197474019286 5.89291733811778 -1.50044099261318 Winsorized Mean ( 44 / 64 ) -8.92710865134738 5.84557313749744 -1.52715712238427 Winsorized Mean ( 45 / 64 ) -9.15383367824831 5.81243028032327 -1.57487199618319 Winsorized Mean ( 46 / 64 ) -9.13834411733252 5.80142125627267 -1.57519058066191 Winsorized Mean ( 47 / 64 ) -7.81407212576138 5.65457334822606 -1.38190304458829 Winsorized Mean ( 48 / 64 ) -8.05252711141418 5.55494447373351 -1.44961432998844 Winsorized Mean ( 49 / 64 ) -8.71862967798224 5.48836172302983 -1.58856688351969 Winsorized Mean ( 50 / 64 ) -8.76540532321326 5.48143725981741 -1.59910711511186 Winsorized Mean ( 51 / 64 ) -7.91104225758463 5.20760440228386 -1.51913272331422 Winsorized Mean ( 52 / 64 ) -6.17215160544716 5.04225979320218 -1.22408441028133 Winsorized Mean ( 53 / 64 ) -6.99832114529058 4.93967985969698 -1.4167560133583 Winsorized Mean ( 54 / 64 ) -7.29493259213999 4.73135234062323 -1.54182822731377 Winsorized Mean ( 55 / 64 ) -6.30494060639741 4.62225515603724 -1.36403993149586 Winsorized Mean ( 56 / 64 ) -8.32322306379616 4.42817324495429 -1.87960646600268 Winsorized Mean ( 57 / 64 ) -9.01121064449939 4.34929759179581 -2.07187722943987 Winsorized Mean ( 58 / 64 ) -9.96145125903775 4.26188282496368 -2.33733578987420 Winsorized Mean ( 59 / 64 ) -10.0913346614725 4.19614070550209 -2.40490855043076 Winsorized Mean ( 60 / 64 ) -10.4004823597843 4.150382049734 -2.50590963317483 Winsorized Mean ( 61 / 64 ) -10.2953297899913 4.11580888170771 -2.50141104358559 Winsorized Mean ( 62 / 64 ) -9.51945532053884 4.01521937288728 -2.37084314366952 Winsorized Mean ( 63 / 64 ) -9.61593325050653 3.87918120446654 -2.47885642450387 Winsorized Mean ( 64 / 64 ) -9.4130398980438 3.80076565453255 -2.47661675400019 Trimmed Mean ( 1 / 64 ) -0.426063545984570 10.3568008048484 -0.0411385285874295 Trimmed Mean ( 2 / 64 ) -0.8588913793879 10.0981272768677 -0.085054521084856 Trimmed Mean ( 3 / 64 ) -1.10134282242028 9.86896178988233 -0.111596624434130 Trimmed Mean ( 4 / 64 ) -1.27268851427661 9.67916990272539 -0.131487361733185 Trimmed Mean ( 5 / 64 ) -1.57825562831039 9.50981514215358 -0.165960705304833 Trimmed Mean ( 6 / 64 ) -1.8739782862225 9.33405392160606 -0.200767887346858 Trimmed Mean ( 7 / 64 ) -2.23837026048104 9.16768192555447 -0.244158804663771 Trimmed Mean ( 8 / 64 ) -2.45137686376335 9.02491922118324 -0.271623136305696 Trimmed Mean ( 9 / 64 ) -2.69928477178577 8.88442780371991 -0.303822016613786 Trimmed Mean ( 10 / 64 ) -2.99601816295226 8.7430222681185 -0.342675343957119 Trimmed Mean ( 11 / 64 ) -3.3370074256766 8.60010010618902 -0.388019602617781 Trimmed Mean ( 12 / 64 ) -3.65214742829095 8.4617525302153 -0.431606504119547 Trimmed Mean ( 13 / 64 ) -3.92707786518724 8.33366932094747 -0.471230344515369 Trimmed Mean ( 14 / 64 ) -4.19536238542037 8.2157944549557 -0.51064597689512 Trimmed Mean ( 15 / 64 ) -4.34709948675424 8.12189114760549 -0.535232424043981 Trimmed Mean ( 16 / 64 ) -4.47913479054971 8.03122431717008 -0.557715064809447 Trimmed Mean ( 17 / 64 ) -4.63859767209234 7.94349181357195 -0.583949449556554 Trimmed Mean ( 18 / 64 ) -4.81030686627878 7.8515542668365 -0.612656641322167 Trimmed Mean ( 19 / 64 ) -4.99936247255363 7.757384361721 -0.644464969045894 Trimmed Mean ( 20 / 64 ) -5.18357665742987 7.65727337643666 -0.676948099225636 Trimmed Mean ( 21 / 64 ) -5.38082798459574 7.55477905479316 -0.712241608334244 Trimmed Mean ( 22 / 64 ) -5.6517834319917 7.4542870900285 -0.758192348072026 Trimmed Mean ( 23 / 64 ) -5.92915606616682 7.35082475749704 -0.806597390329531 Trimmed Mean ( 24 / 64 ) -6.11173880121159 7.25954284263488 -0.841890313714756 Trimmed Mean ( 25 / 64 ) -6.2749317537408 7.16630380536148 -0.875616206648427 Trimmed Mean ( 26 / 64 ) -6.37868419030006 7.07639752892477 -0.901402749665658 Trimmed Mean ( 27 / 64 ) -6.51364670233227 6.98759367506286 -0.932173077776116 Trimmed Mean ( 28 / 64 ) -6.59413099345497 6.90372821218323 -0.955155068506042 Trimmed Mean ( 29 / 64 ) -6.65158272153922 6.81835340033402 -0.97554091596563 Trimmed Mean ( 30 / 64 ) -6.68855423047973 6.72966381590379 -0.99389128691289 Trimmed Mean ( 31 / 64 ) -6.68796167351116 6.6411361374184 -1.00705083213532 Trimmed Mean ( 32 / 64 ) -6.73485196188556 6.55793987886668 -1.02697677720239 Trimmed Mean ( 33 / 64 ) -6.75903221258554 6.47955309456549 -1.04313246823372 Trimmed Mean ( 34 / 64 ) -6.77494284107493 6.40020758527953 -1.05855048462136 Trimmed Mean ( 35 / 64 ) -6.72007818389675 6.3284488355608 -1.06188394004788 Trimmed Mean ( 36 / 64 ) -6.69557073390977 6.25813887915211 -1.06989807404481 Trimmed Mean ( 37 / 64 ) -6.66725965396497 6.1864046762266 -1.07772769531004 Trimmed Mean ( 38 / 64 ) -6.63766552336046 6.10794571464969 -1.08672634523261 Trimmed Mean ( 39 / 64 ) -6.57578300220842 6.03669984162274 -1.08930097151240 Trimmed Mean ( 40 / 64 ) -6.49228003540957 5.96435237097033 -1.08851382876182 Trimmed Mean ( 41 / 64 ) -6.41139053736797 5.8934467218927 -1.08788470311460 Trimmed Mean ( 42 / 64 ) -6.32595259969629 5.81870513468569 -1.08717531706270 Trimmed Mean ( 43 / 64 ) -6.18949807409826 5.74750441527631 -1.07690183893503 Trimmed Mean ( 44 / 64 ) -6.07561714389027 5.68799044755345 -1.06814826781285 Trimmed Mean ( 45 / 64 ) -5.95362820239478 5.62470607189824 -1.05847810112956 Trimmed Mean ( 46 / 64 ) -5.81708610209169 5.55589541016406 -1.04701144867663 Trimmed Mean ( 47 / 64 ) -5.67563056994301 5.47892242155392 -1.03590270736728 Trimmed Mean ( 48 / 64 ) -5.58463305692947 5.40529132321099 -1.03317892098588 Trimmed Mean ( 49 / 64 ) -5.47961628865352 5.33065271819272 -1.02794471490375 Trimmed Mean ( 50 / 64 ) -5.3416636332074 5.25160726300928 -1.01714834443780 Trimmed Mean ( 51 / 64 ) -5.19558398776715 5.16190388588883 -1.00652474409111 Trimmed Mean ( 52 / 64 ) -5.07941465002095 5.08655658562375 -0.998595919364588 Trimmed Mean ( 53 / 64 ) -5.03249928878619 5.01650879999215 -1.00318757315726 Trimmed Mean ( 54 / 64 ) -4.94771990952994 4.94565767162165 -1.00041697950914 Trimmed Mean ( 55 / 64 ) -4.84594374985037 4.884498926618 -0.992106626012848 Trimmed Mean ( 56 / 64 ) -4.78227843247377 4.82436660036829 -0.991275918399049 Trimmed Mean ( 57 / 64 ) -4.62663251461344 4.77317384668495 -0.96929897448146 Trimmed Mean ( 58 / 64 ) -4.43230218198692 4.7202182730701 -0.939003648893569 Trimmed Mean ( 59 / 64 ) -4.18495907322789 4.66576487372522 -0.896950272139742 Trimmed Mean ( 60 / 64 ) -3.91800424438067 4.60761724723275 -0.850331968596948 Trimmed Mean ( 61 / 64 ) -3.62166238767651 4.54260523162629 -0.79726549039788 Trimmed Mean ( 62 / 64 ) -3.31275589268026 4.46849340063741 -0.741358573385766 Trimmed Mean ( 63 / 64 ) -3.02153245911505 4.39267562390747 -0.687856950481417 Trimmed Mean ( 64 / 64 ) -2.70751337381069 4.31847906988873 -0.626959938902853 Median 4.70975232198155 Midrange 40.476036868534 Midmean - Weighted Average at Xnp -6.63164039784491 Midmean - Weighted Average at X(n+1)p -5.58463305692948 Midmean - Empirical Distribution Function -6.63164039784491 Midmean - Empirical Distribution Function - Averaging -5.58463305692948 Midmean - Empirical Distribution Function - Interpolation -5.58463305692948 Midmean - Closest Observation -6.63164039784491 Midmean - True Basic - Statistics Graphics Toolkit -5.58463305692948 Midmean - MS Excel (old versions) -5.67563056994302 Number of observations 192

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157094byfc63pz7syyk7j/1kdxw1228157038.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157094byfc63pz7syyk7j/1kdxw1228157038.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157094byfc63pz7syyk7j/22kpo1228157038.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t1228157094byfc63pz7syyk7j/22kpo1228157038.ps (open in new window)

Parameters (Session):

Parameters (R input):

R code (references can be found in the software module):

geomean <- function(x) { return(exp(mean(log(x)))) } harmean <- function(x) { return(1/mean(1/x)) } quamean <- function(x) { return(sqrt(mean(x*x))) } winmean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) win <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { win[j,1] <- (j*x[j+1]+sum(x[(j+1):(n-j)])+j*x[n-j])/n win[j,2] <- sd(c(rep(x[j+1],j),x[(j+1):(n-j)],rep(x[n-j],j)))/sqrtn } return(win) } trimean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) tri <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { tri[j,1] <- mean(x,trim=j/n) tri[j,2] <- sd(x[(j+1):(n-j)]) / sqrt(n-j*2) } return(tri) } midrange <- function(x) { return((max(x)+min(x))/2) } q1 <- function(data,n,p,i,f) { np <- n*p; i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q2 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q3 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- data[i+1] } } q4 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- (data[i]+data[i+1])/2 } else { qvalue <- data[i+1] } } q5 <- function(data,n,p,i,f) { np <- (n-1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i+1] } else { qvalue <- data[i+1] + f*(data[i+2]-data[i+1]) } } q6 <- function(data,n,p,i,f) { np <- n*p+0.5 i <<- floor(np) f <<- np - i qvalue <- data[i] } q7 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- f*data[i] + (1-f)*data[i+1] } } q8 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { if (f == 0.5) { qvalue <- (data[i]+data[i+1])/2 } else { if (f < 0.5) { qvalue <- data[i] } else { qvalue <- data[i+1] } } } } midmean <- function(x,def) { x <-sort(x[!is.na(x)]) n<-length(x) if (def==1) { qvalue1 <- q1(x,n,0.25,i,f) qvalue3 <- q1(x,n,0.75,i,f) } if (def==2) { qvalue1 <- q2(x,n,0.25,i,f) qvalue3 <- q2(x,n,0.75,i,f) } if (def==3) { qvalue1 <- q3(x,n,0.25,i,f) qvalue3 <- q3(x,n,0.75,i,f) } if (def==4) { qvalue1 <- q4(x,n,0.25,i,f) qvalue3 <- q4(x,n,0.75,i,f) } if (def==5) { qvalue1 <- q5(x,n,0.25,i,f) qvalue3 <- q5(x,n,0.75,i,f) } if (def==6) { qvalue1 <- q6(x,n,0.25,i,f) qvalue3 <- q6(x,n,0.75,i,f) } if (def==7) { qvalue1 <- q7(x,n,0.25,i,f) qvalue3 <- q7(x,n,0.75,i,f) } if (def==8) { qvalue1 <- q8(x,n,0.25,i,f) qvalue3 <- q8(x,n,0.75,i,f) } midm <- 0 myn <- 0 roundno4 <- round(n/4) round3no4 <- round(3*n/4) for (i in 1:n) { if ((x[i]>=qvalue1) & (x[i]<=qvalue3)){ midm = midm + x[i] myn = myn + 1 } } midm = midm / myn return(midm) } (arm <- mean(x)) sqrtn <- sqrt(length(x)) (armse <- sd(x) / sqrtn) (armose <- arm / armse) (geo <- geomean(x)) (har <- harmean(x)) (qua <- quamean(x)) (win <- winmean(x)) (tri <- trimean(x)) (midr <- midrange(x)) midm <- array(NA,dim=8) for (j in 1:8) midm[j] <- midmean(x,j) midm bitmap(file='test1.png') lb <- win[,1] - 2*win[,2] ub <- win[,1] + 2*win[,2] if ((ylimmin == '') | (ylimmax == '')) plot(win[,1],type='b',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(win[,1],type='l',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() bitmap(file='test2.png') lb <- tri[,1] - 2*tri[,2] ub <- tri[,1] + 2*tri[,2] if ((ylimmin == '') | (ylimmax == '')) plot(tri[,1],type='b',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(tri[,1],type='l',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Central Tendency - Ungrouped Data',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Measure',header=TRUE) a<-table.element(a,'Value',header=TRUE) a<-table.element(a,'S.E.',header=TRUE) a<-table.element(a,'Value/S.E.',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', 'Arithmetic Mean', 'click to view the definition of the Arithmetic Mean'),header=TRUE) a<-table.element(a,arm) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean_standard_error.htm', armse, 'click to view the definition of the Standard Error of the Arithmetic Mean')) a<-table.element(a,armose) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/geometric_mean.htm', 'Geometric Mean', 'click to view the definition of the Geometric Mean'),header=TRUE) a<-table.element(a,geo) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/harmonic_mean.htm', 'Harmonic Mean', 'click to view the definition of the Harmonic Mean'),header=TRUE) a<-table.element(a,har) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/quadratic_mean.htm', 'Quadratic Mean', 'click to view the definition of the Quadratic Mean'),header=TRUE) a<-table.element(a,qua) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) for (j in 1:length(win[,1])) { a<-table.row.start(a) mylabel <- paste('Winsorized Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(win[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/winsorized_mean.htm', mylabel, 'click to view the definition of the Winsorized Mean'),header=TRUE) a<-table.element(a,win[j,1]) a<-table.element(a,win[j,2]) a<-table.element(a,win[j,1]/win[j,2]) a<-table.row.end(a) } for (j in 1:length(tri[,1])) { a<-table.row.start(a) mylabel <- paste('Trimmed Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(tri[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', mylabel, 'click to view the definition of the Trimmed Mean'),header=TRUE) a<-table.element(a,tri[j,1]) a<-table.element(a,tri[j,2]) a<-table.element(a,tri[j,1]/tri[j,2]) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/median_1.htm', 'Median', 'click to view the definition of the Median'),header=TRUE) a<-table.element(a,median(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/midrange.htm', 'Midrange', 'click to view the definition of the Midrange'),header=TRUE) a<-table.element(a,midr) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_1.htm','Weighted Average at Xnp',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[1]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_2.htm','Weighted Average at X(n+1)p',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[2]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_3.htm','Empirical Distribution Function',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[3]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_4.htm','Empirical Distribution Function - Averaging',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[4]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_5.htm','Empirical Distribution Function - Interpolation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[5]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_6.htm','Closest Observation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[6]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_7.htm','True Basic - Statistics Graphics Toolkit',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[7]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_8.htm','MS Excel (old versions)',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[8]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of observations',header=TRUE) a<-table.element(a,length(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')

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Software written by Ed van Stee & Patrick Wessa

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