TRIMMED MEAN PLOT - LAURA VERBEEK

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Thu, 04 Jun 2009 03:59:26 -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/Jun/04/t12441096137c8seh0lupeo273.htm/, Retrieved Thu, 04 Jun 2009 12:00:13 +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/Jun/04/t12441096137c8seh0lupeo273.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 «

12.182 12.242 12.368 12.552 12.377 12.350 12.333 12.427 12.281 12.381 12.569 12.684 12.689 12.580 12.264 12.259 12.205 12.255 12.039 11.893 11.741 12.149 12.096 12.146 11.946 11.976 12.392 12.102 12.362 12.547 12.532 12.422 12.304 12.216 12.267 12.652 12.605 12.626 12.613 12.603 12.575 12.527 12.580 12.325 12.304 12.372 12.617 12.627 12.851 12.825 12.721 12.765 12.848 12.891 12.870 12.831 12.818 13.013 13.057 12.969 13.011 12.815 12.861 12.746 12.872 12.825 12.891 12.993 12.985 13.026 12.825 12.598 12.621 12.480 12.543 12.594 12.647 12.638 12.503 12.392 12.389 12.603 12.210 12.278 12.286 12.090 12.145 12.307 12.270 12.159 12.023 12.062 11.844 11.842 11.805 11.809 11.453 11.346 11.345 11.382 11.216 11.289 11.225 11.382 11.148 11.226 11.104 11.051 10.962 11.238 11.437 11.495 11.458 11.603 11.630 11.341 11.369 11.133 11.398 11.578 11.380 11.326 11.286 11.604 11.655 11.432 11.730 11.782 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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 10.84238 0.107210487626109 101.13171052642 Geometric Mean 10.7019594408487 Harmonic Mean 10.5546833508341 Quadratic Mean 10.9735697081670 Winsorized Mean ( 1 / 83 ) 10.843796 0.107021358420498 101.323662491683 Winsorized Mean ( 2 / 83 ) 10.843788 0.107002415598901 101.341525229187 Winsorized Mean ( 3 / 83 ) 10.843836 0.106992647442913 101.351226081081 Winsorized Mean ( 4 / 83 ) 10.844204 0.106898644529972 101.443793302351 Winsorized Mean ( 5 / 83 ) 10.844144 0.106875058444551 101.465619367368 Winsorized Mean ( 6 / 83 ) 10.844888 0.106724649416131 101.615587957705 Winsorized Mean ( 7 / 83 ) 10.842928 0.106529493678312 101.783343049978 Winsorized Mean ( 8 / 83 ) 10.843376 0.106482528812313 101.832442570111 Winsorized Mean ( 9 / 83 ) 10.84298 0.106399833838986 101.907865912729 Winsorized Mean ( 10 / 83 ) 10.84414 0.106264912894378 102.048170977927 Winsorized Mean ( 11 / 83 ) 10.844096 0.106198307619760 102.111759057658 Winsorized Mean ( 12 / 83 ) 10.845152 0.106004111174651 102.308786704807 Winsorized Mean ( 13 / 83 ) 10.845256 0.105965724519525 102.346830063920 Winsorized Mean ( 14 / 83 ) 10.845536 0.105768438606405 102.540381071137 Winsorized Mean ( 15 / 83 ) 10.845356 0.105723094202008 102.582657855979 Winsorized Mean ( 16 / 83 ) 10.846444 0.105613166828222 102.69973267293 Winsorized Mean ( 17 / 83 ) 10.847804 0.105476491101269 102.845704163451 Winsorized Mean ( 18 / 83 ) 10.847876 0.105380940882596 102.939638886746 Winsorized Mean ( 19 / 83 ) 10.851372 0.104993984635801 103.352320970014 Winsorized Mean ( 20 / 83 ) 10.847932 0.104641398569986 103.667689349017 Winsorized Mean ( 21 / 83 ) 10.854148 0.103764161506933 104.604015898830 Winsorized Mean ( 22 / 83 ) 10.852212 0.103578562834883 104.772760916752 Winsorized Mean ( 23 / 83 ) 10.849268 0.103366685928025 104.959038810187 Winsorized Mean ( 24 / 83 ) 10.851956 0.103029340735860 105.328791997433 Winsorized Mean ( 25 / 83 ) 10.849556 0.102726201513370 105.616248242061 Winsorized Mean ( 26 / 83 ) 10.849452 0.102650052214612 105.693584814909 Winsorized Mean ( 27 / 83 ) 10.851072 0.102336284669239 106.03347615239 Winsorized Mean ( 28 / 83 ) 10.849952 0.102239511960925 106.122885290638 Winsorized Mean ( 29 / 83 ) 10.851692 0.102056515824483 106.330222155171 Winsorized Mean ( 30 / 83 ) 10.851332 0.101992099500629 106.393848671907 Winsorized Mean ( 31 / 83 ) 10.851456 0.101899311829183 106.491945874872 Winsorized Mean ( 32 / 83 ) 10.851712 0.101791644607362 106.60709964809 Winsorized Mean ( 33 / 83 ) 10.852372 0.101557683247356 106.859192263846 Winsorized Mean ( 34 / 83 ) 10.853052 0.101449897525902 106.979427921344 Winsorized Mean ( 35 / 83 ) 10.855992 0.101176137636560 107.297948445080 Winsorized Mean ( 36 / 83 ) 10.855704 0.101086138948354 107.390628556367 Winsorized Mean ( 37 / 83 ) 10.85526 0.101031452425057 107.444362517229 Winsorized Mean ( 38 / 83 ) 10.859668 0.100280651430419 108.292754834517 Winsorized Mean ( 39 / 83 ) 10.860136 0.100237620443748 108.343912713835 Winsorized Mean ( 40 / 83 ) 10.859816 0.100138433140044 108.448031984009 Winsorized Mean ( 41 / 83 ) 10.859488 0.100010564298524 108.583408924533 Winsorized Mean ( 42 / 83 ) 10.857304 0.0997534073576127 108.841434970506 Winsorized Mean ( 43 / 83 ) 10.858164 0.0995370098291027 109.086700702006 Winsorized Mean ( 44 / 83 ) 10.859572 0.0992957402681788 109.365940277704 Winsorized Mean ( 45 / 83 ) 10.859032 0.099029579593381 109.654428955344 Winsorized Mean ( 46 / 83 ) 10.86216 0.098598221478457 110.165881667280 Winsorized Mean ( 47 / 83 ) 10.85934 0.0981396379233534 110.651926477262 Winsorized Mean ( 48 / 83 ) 10.8603 0.0973559334355149 111.552522961464 Winsorized Mean ( 49 / 83 ) 10.853244 0.0963667272576245 112.624391310760 Winsorized Mean ( 50 / 83 ) 10.852644 0.096265023179799 112.737146281365 Winsorized Mean ( 51 / 83 ) 10.848156 0.0957188848363438 113.333497549075 Winsorized Mean ( 52 / 83 ) 10.84878 0.0956624749987766 113.406850493244 Winsorized Mean ( 53 / 83 ) 10.854292 0.0950669227552497 114.175274484738 Winsorized Mean ( 54 / 83 ) 10.855588 0.0946832555659249 114.651613266948 Winsorized Mean ( 55 / 83 ) 10.857128 0.0944092509509767 115.000679389329 Winsorized Mean ( 56 / 83 ) 10.860488 0.093935872551609 115.615980402302 Winsorized Mean ( 57 / 83 ) 10.860032 0.0938362223365454 115.733900295456 Winsorized Mean ( 58 / 83 ) 10.87024 0.0927133931136737 117.245628004060 Winsorized Mean ( 59 / 83 ) 10.86788 0.0924888799046896 117.504720688578 Winsorized Mean ( 60 / 83 ) 10.86452 0.0921631384380273 117.883572371025 Winsorized Mean ( 61 / 83 ) 10.868668 0.091497518955456 118.786477754563 Winsorized Mean ( 62 / 83 ) 10.86718 0.0909496022586356 119.485734188224 Winsorized Mean ( 63 / 83 ) 10.88104 0.0896133963020437 121.422024485326 Winsorized Mean ( 64 / 83 ) 10.884112 0.0893438986317189 121.822666871355 Winsorized Mean ( 65 / 83 ) 10.882812 0.0887497792215366 122.623538846608 Winsorized Mean ( 66 / 83 ) 10.883604 0.0884810665923353 123.004891545270 Winsorized Mean ( 67 / 83 ) 10.88816 0.0879613618717293 123.783440459662 Winsorized Mean ( 68 / 83 ) 10.891968 0.087301176679944 124.763129366872 Winsorized Mean ( 69 / 83 ) 10.893348 0.0870562854187314 125.129942629693 Winsorized Mean ( 70 / 83 ) 10.898668 0.0864669764428285 126.044282434302 Winsorized Mean ( 71 / 83 ) 10.897532 0.0863523046477389 126.198507896863 Winsorized Mean ( 72 / 83 ) 10.906172 0.0854291448442467 127.663363830739 Winsorized Mean ( 73 / 83 ) 10.9234 0.0833707253620212 131.022009854985 Winsorized Mean ( 74 / 83 ) 10.929024 0.0817362086526875 133.710924205446 Winsorized Mean ( 75 / 83 ) 10.952124 0.079488341814594 137.782771032584 Winsorized Mean ( 76 / 83 ) 10.950908 0.0793658503321155 137.980100435826 Winsorized Mean ( 77 / 83 ) 10.955836 0.0778920179624656 140.654155413965 Winsorized Mean ( 78 / 83 ) 10.955524 0.0768542834801875 142.549295939039 Winsorized Mean ( 79 / 83 ) 10.952996 0.07660195190777 142.985860375824 Winsorized Mean ( 80 / 83 ) 10.972196 0.0748252157094107 146.637679503810 Winsorized Mean ( 81 / 83 ) 10.987424 0.0734851018484066 149.51906881297 Winsorized Mean ( 82 / 83 ) 11.03236 0.0676068972503789 163.183942004352 Winsorized Mean ( 83 / 83 ) 11.142584 0.0581502193617814 191.617230722320 Trimmed Mean ( 1 / 83 ) 10.8467177419355 0.106881789971857 101.483309222194 Trimmed Mean ( 2 / 83 ) 10.8496869918699 0.106730006981246 101.655450971500 Trimmed Mean ( 3 / 83 ) 10.8527090163934 0.106575392948111 101.831283152551 Trimmed Mean ( 4 / 83 ) 10.8557644628099 0.106411195231843 102.017127419328 Trimmed Mean ( 5 / 83 ) 10.858775 0.106259118556127 102.191465048379 Trimmed Mean ( 6 / 83 ) 10.8618487394958 0.106098966463315 102.374689420292 Trimmed Mean ( 7 / 83 ) 10.8648432203390 0.105953492678984 102.543511739222 Trimmed Mean ( 8 / 83 ) 10.8681880341880 0.105826407683848 102.698260973351 Trimmed Mean ( 9 / 83 ) 10.8715301724138 0.105693295051477 102.859222688809 Trimmed Mean ( 10 / 83 ) 10.8749782608696 0.105557701311990 103.024015545082 Trimmed Mean ( 11 / 83 ) 10.8783596491228 0.105424988369686 103.185779930811 Trimmed Mean ( 12 / 83 ) 10.8818053097345 0.105286021583483 103.354701280133 Trimmed Mean ( 13 / 83 ) 10.8852142857143 0.105153400622337 103.517472771128 Trimmed Mean ( 14 / 83 ) 10.8886756756757 0.105010468898591 103.691334681982 Trimmed Mean ( 15 / 83 ) 10.8921772727273 0.104871235569842 103.862390993509 Trimmed Mean ( 16 / 83 ) 10.8957568807339 0.104721190182946 104.045388155914 Trimmed Mean ( 17 / 83 ) 10.8993240740741 0.104565169862536 104.234747463258 Trimmed Mean ( 18 / 83 ) 10.9028644859813 0.104404557552388 104.429009054614 Trimmed Mean ( 19 / 83 ) 10.9064669811321 0.104235097829099 104.633345276982 Trimmed Mean ( 20 / 83 ) 10.9099190476190 0.104078488971407 104.823956952491 Trimmed Mean ( 21 / 83 ) 10.9136442307692 0.103930374099972 105.009188365581 Trimmed Mean ( 22 / 83 ) 10.9170825242718 0.103828617463277 105.145217099063 Trimmed Mean ( 23 / 83 ) 10.9206960784314 0.103724427740039 105.285672009698 Trimmed Mean ( 24 / 83 ) 10.9245396039604 0.103618617512760 105.430277552344 Trimmed Mean ( 25 / 83 ) 10.92832 0.103519689764045 105.567549757048 Trimmed Mean ( 26 / 83 ) 10.9322979797980 0.103424029871486 105.703655073027 Trimmed Mean ( 27 / 83 ) 10.9363622448980 0.103317395837248 105.852089633827 Trimmed Mean ( 28 / 83 ) 10.9404329896907 0.103213822980919 105.997749852878 Trimmed Mean ( 29 / 83 ) 10.944640625 0.103099200750198 106.156406115292 Trimmed Mean ( 30 / 83 ) 10.9488578947368 0.102978364783901 106.321924199447 Trimmed Mean ( 31 / 83 ) 10.9531808510638 0.102843167974766 106.503728606954 Trimmed Mean ( 32 / 83 ) 10.9575913978495 0.102694305584505 106.701061324502 Trimmed Mean ( 33 / 83 ) 10.9620869565217 0.102531605180270 106.914223543544 Trimmed Mean ( 34 / 83 ) 10.9666538461538 0.102361257935955 107.136763139579 Trimmed Mean ( 35 / 83 ) 10.9712944444444 0.102175285937032 107.377183668522 Trimmed Mean ( 36 / 83 ) 10.9759213483146 0.101982391329439 107.625651891791 Trimmed Mean ( 37 / 83 ) 10.9806647727273 0.101770638677395 107.896195950338 Trimmed Mean ( 38 / 83 ) 10.9855344827586 0.101536571802305 108.192883487812 Trimmed Mean ( 39 / 83 ) 10.9903488372093 0.101318465369945 108.473305404698 Trimmed Mean ( 40 / 83 ) 10.9952588235294 0.101076405263893 108.781656755824 Trimmed Mean ( 41 / 83 ) 11.0002976190476 0.100811459082084 109.117532066378 Trimmed Mean ( 42 / 83 ) 11.0054698795181 0.100523400494987 109.481671186272 Trimmed Mean ( 43 / 83 ) 11.0108475609756 0.100216869280635 109.870200895442 Trimmed Mean ( 44 / 83 ) 11.0163271604938 0.0998889540752632 110.285739424135 Trimmed Mean ( 45 / 83 ) 11.02189375 0.099539407668519 110.728946536477 Trimmed Mean ( 46 / 83 ) 11.0276202531646 0.0991670114240043 111.202506708750 Trimmed Mean ( 47 / 83 ) 11.0333846153846 0.0987803804495024 111.696113794834 Trimmed Mean ( 48 / 83 ) 11.0393961038961 0.098377182205429 112.215006126562 Trimmed Mean ( 49 / 83 ) 11.0455328947368 0.097975456767345 112.737753506634 Trimmed Mean ( 50 / 83 ) 11.0520733333333 0.0975824511567061 113.258820641685 Trimmed Mean ( 51 / 83 ) 11.0588108108108 0.0971504578899399 113.831792983818 Trimmed Mean ( 52 / 83 ) 11.0658835616438 0.0966994210733743 114.435882229814 Trimmed Mean ( 53 / 83 ) 11.0731319444444 0.0962015255215712 115.103496378148 Trimmed Mean ( 54 / 83 ) 11.0804014084507 0.0956868080992217 115.798631269641 Trimmed Mean ( 55 / 83 ) 11.0878357142857 0.0951385858905485 116.544045830591 Trimmed Mean ( 56 / 83 ) 11.0954347826087 0.0945471682935036 117.353433031067 Trimmed Mean ( 57 / 83 ) 11.1031470588235 0.0939216220134388 118.217156186196 Trimmed Mean ( 58 / 83 ) 11.1111044776119 0.093233041274328 119.175609051717 Trimmed Mean ( 59 / 83 ) 11.1189696969697 0.0925463691065894 120.144850676568 Trimmed Mean ( 60 / 83 ) 11.1271538461538 0.0917958440133336 121.216313938326 Trimmed Mean ( 61 / 83 ) 11.135703125 0.0909803786599298 122.396755091815 Trimmed Mean ( 62 / 83 ) 11.1443888888889 0.0901211382086812 123.660099177657 Trimmed Mean ( 63 / 83 ) 11.1534032258065 0.0892007609638925 125.037086066129 Trimmed Mean ( 64 / 83 ) 11.1622622950820 0.088279454761267 126.442356551340 Trimmed Mean ( 65 / 83 ) 11.1713166666667 0.0872733478936593 128.003759868118 Trimmed Mean ( 66 / 83 ) 11.1807203389831 0.0861917594394988 129.719133379928 Trimmed Mean ( 67 / 83 ) 11.1904224137931 0.085003605312782 131.646444555104 Trimmed Mean ( 68 / 83 ) 11.2003157894737 0.0837186851602456 133.785137308776 Trimmed Mean ( 69 / 83 ) 11.2104375 0.0823350587644504 136.156306538525 Trimmed Mean ( 70 / 83 ) 11.2208818181818 0.0808034195799671 138.866422689909 Trimmed Mean ( 71 / 83 ) 11.2315370370370 0.0791371043081826 141.925044329373 Trimmed Mean ( 72 / 83 ) 11.2426320754717 0.0772664382792522 145.504727872135 Trimmed Mean ( 73 / 83 ) 11.2538653846154 0.0752479546461827 149.557093445679 Trimmed Mean ( 74 / 83 ) 11.2649607843137 0.0731805620736688 153.933783304009 Trimmed Mean ( 75 / 83 ) 11.27631 0.0709977147092735 158.826379781026 Trimmed Mean ( 76 / 83 ) 11.2873367346939 0.0687735957922699 164.123114469501 Trimmed Mean ( 77 / 83 ) 11.2988645833333 0.066233858708579 170.590462395479 Trimmed Mean ( 78 / 83 ) 11.3107127659574 0.0634803846586348 178.176500139070 Trimmed Mean ( 79 / 83 ) 11.3230869565217 0.0604049969222003 187.452819029285 Trimmed Mean ( 80 / 83 ) 11.3361 0.0568214674532924 199.503823256912 Trimmed Mean ( 81 / 83 ) 11.3490227272727 0.0528694493772014 214.661262051401 Trimmed Mean ( 82 / 83 ) 11.362 0.0483602447211947 234.945047642003 Trimmed Mean ( 83 / 83 ) 11.3739642857143 0.0439894906294359 258.560945420525 Median 11.405 Midrange 10.3045 Midmean - Weighted Average at Xnp 11.135088 Midmean - Weighted Average at X(n+1)p 11.1443888888889 Midmean - Empirical Distribution Function 11.1443888888889 Midmean - Empirical Distribution Function - Averaging 11.1443888888889 Midmean - Empirical Distribution Function - Interpolation 11.1534032258065 Midmean - Closest Observation 11.1443888888889 Midmean - True Basic - Statistics Graphics Toolkit 11.1443888888889 Midmean - MS Excel (old versions) 11.1443888888889 Number of observations 250

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t12441096137c8seh0lupeo273/177xu1244109560.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t12441096137c8seh0lupeo273/177xu1244109560.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t12441096137c8seh0lupeo273/2o33d1244109560.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t12441096137c8seh0lupeo273/2o33d1244109560.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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