APO 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: Tue, 23 Nov 2010 15:27:36 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/23/t12905260146826irfp2c8b85m.htm/, Retrieved Tue, 23 Nov 2010 16:26:57 +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/2010/Nov/23/t12905260146826irfp2c8b85m.htm/}, year = {2010}, } @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 = {2010}, 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 «

-2.7089441800854E-14 -0.0067221197756271 0.0067221197756187 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -2.7089441800854E-14 -0.0067221197756271 1.8651746813703E-14 1.8651746813703E-14 0.0017733645364189 3.1974423109205E-14 3.1974423109205E-14 -0.0063988842582678 1.0658141036402E-14 0.0063988842583105 3.1974423109205E-14 3.1974423109205E-14 3.1974423109205E-14 3.1974423109205E-14 3.1974423109205E-14 3.1974423109205E-14 3.1974423109205E-14 -0.0063988842582678 0.0063988842583105 -0.0063988842582678 0.0063988842583105 3.1974423109205E-14 3.1974423109205E-14 3.1974423109205E-14 -0.0063988842582678 1.0658141036402E-14 0.0063988842583105 -0.0063988842582678 0.0063988842583105 -0.0063988842582678 1.0658141036402E-14 1.0658141036402E-14 1.0658141036402E-14 1.0658141 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 2 seconds R Server 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean -0.000166711983576282 0.000441915484534445 -0.377248567680112 Geometric Mean NaN Harmonic Mean -1.97281723523094e-13 Quadratic Mean 0.00623621381001801 Winsorized Mean ( 1 / 66 ) -0.000245714178302282 0.000416067402428724 -0.590563396382333 Winsorized Mean ( 2 / 66 ) -8.77097888504521e-05 0.000359652412913997 -0.243873767284931 Winsorized Mean ( 3 / 66 ) -0.000114387124779762 0.000334938522988833 -0.341516776747642 Winsorized Mean ( 4 / 66 ) -0.000106596241143282 0.000283307688281284 -0.37625608323572 Winsorized Mean ( 5 / 66 ) -0.000189221361781440 0.000268172310918283 -0.705596193482849 Winsorized Mean ( 6 / 66 ) -5.52605845621496e-05 0.000242467328649391 -0.227909404825656 Winsorized Mean ( 7 / 66 ) -5.7774151453324e-05 0.000242127746100323 -0.238610206322186 Winsorized Mean ( 8 / 66 ) -4.863053285118e-05 0.000240880684302308 -0.201886394469671 Winsorized Mean ( 9 / 66 ) -4.6426102482069e-05 0.000240309449932244 -0.193192995511242 Winsorized Mean ( 10 / 66 ) -4.64261024820690e-05 0.000240309449932244 -0.193192995511242 Winsorized Mean ( 11 / 66 ) -4.51705154352725e-05 0.000240142287649143 -0.188098963649702 Winsorized Mean ( 12 / 66 ) -4.51705154352725e-05 0.000240142287649143 -0.188098963649702 Winsorized Mean ( 13 / 66 ) -4.51705154352726e-05 0.000240142287649143 -0.188098963649703 Winsorized Mean ( 14 / 66 ) -4.51705154352725e-05 0.000240142287649143 -0.188098963649702 Winsorized Mean ( 15 / 66 ) -4.51705154352725e-05 0.000240142287649143 -0.188098963649702 Winsorized Mean ( 16 / 66 ) -4.51705154352725e-05 0.000240142287649143 -0.188098963649702 Winsorized Mean ( 17 / 66 ) -4.53638021726241e-05 0.000240116226533337 -0.188924350626199 Winsorized Mean ( 18 / 66 ) -9.22287093661162e-05 0.000233951277417707 -0.394221866980648 Winsorized Mean ( 19 / 66 ) -9.20126830169565e-05 0.000233922016790106 -0.393347681759762 Winsorized Mean ( 20 / 66 ) -3.99405639185466e-05 0.000227030187625529 -0.175926225213829 Winsorized Mean ( 21 / 66 ) -3.99405639185466e-05 0.000227030187625529 -0.175926225213828 Winsorized Mean ( 22 / 66 ) -3.99405639185466e-05 0.000227030187625529 -0.175926225213828 Winsorized Mean ( 23 / 66 ) -3.99405639185466e-05 0.000227030187625529 -0.175926225213828 Winsorized Mean ( 24 / 66 ) -3.99405639185466e-05 0.000227030187625529 -0.175926225213828 Winsorized Mean ( 25 / 66 ) -3.99405639185466e-05 0.000227030187625529 -0.175926225213828 Winsorized Mean ( 26 / 66 ) -3.99405639185466e-05 0.000227030187625529 -0.175926225213828 Winsorized Mean ( 27 / 66 ) -3.99405639185466e-05 0.000227030187625529 -0.175926225213828 Winsorized Mean ( 28 / 66 ) -8.69397861304466e-05 0.000220944624004965 -0.393491294581093 Winsorized Mean ( 29 / 66 ) -0.000633128082807472 0.000163606089741262 -3.86983200814067 Winsorized Mean ( 30 / 66 ) -0.000550231319738137 0.000102219161079201 -5.38285888799076 Winsorized Mean ( 31 / 66 ) -3.74589248508516e-15 2.20324421892674e-15 -1.70017125333019 Winsorized Mean ( 32 / 66 ) -3.74589248508516e-15 2.20324421892674e-15 -1.70017125333019 Winsorized Mean ( 33 / 66 ) -3.74589248508516e-15 2.20324421892674e-15 -1.70017125333019 Winsorized Mean ( 34 / 66 ) -3.74589248508516e-15 2.20324421892674e-15 -1.70017125333018 Winsorized Mean ( 35 / 66 ) -3.74589248508516e-15 2.20324421892674e-15 -1.70017125333018 Winsorized Mean ( 36 / 66 ) -3.74589248508516e-15 2.20324421892674e-15 -1.70017125333018 Winsorized Mean ( 37 / 66 ) -3.74589248508516e-15 2.20324421892674e-15 -1.70017125333019 Winsorized Mean ( 38 / 66 ) -3.74589248508516e-15 2.20324421892674e-15 -1.70017125333019 Winsorized Mean ( 39 / 66 ) -3.74589248508516e-15 2.20324421892674e-15 -1.70017125333019 Winsorized Mean ( 40 / 66 ) -3.74589248508516e-15 2.20324421892674e-15 -1.70017125333019 Winsorized Mean ( 41 / 66 ) -3.74589248508516e-15 2.20324421892674e-15 -1.70017125333019 Winsorized Mean ( 42 / 66 ) -4.58522109170166e-15 2.12898336511452e-15 -2.15371391192388 Winsorized Mean ( 43 / 66 ) -4.58522109170166e-15 2.12898336511452e-15 -2.15371391192388 Winsorized Mean ( 44 / 66 ) -4.58522109170166e-15 2.12898336511452e-15 -2.15371391192388 Winsorized Mean ( 45 / 66 ) -4.58522109170166e-15 2.12898336511452e-15 -2.15371391192388 Winsorized Mean ( 46 / 66 ) -4.58522109170166e-15 2.12898336511452e-15 -2.15371391192388 Winsorized Mean ( 47 / 66 ) -4.58522109170166e-15 2.12898336511452e-15 -2.15371391192388 Winsorized Mean ( 48 / 66 ) -9.61453139325177e-16 1.81415631926426e-15 -0.529972598896604 Winsorized Mean ( 49 / 66 ) -9.61453139325178e-16 1.81415631926426e-15 -0.529972598896605 Winsorized Mean ( 50 / 66 ) -9.61453139325178e-16 1.81415631926426e-15 -0.529972598896605 Winsorized Mean ( 51 / 66 ) -9.61453139325178e-16 1.81415631926426e-15 -0.529972598896605 Winsorized Mean ( 52 / 66 ) -9.61453139325177e-16 1.81415631926426e-15 -0.529972598896604 Winsorized Mean ( 53 / 66 ) -9.61453139325177e-16 1.81415631926426e-15 -0.529972598896604 Winsorized Mean ( 54 / 66 ) -9.61453139325178e-16 1.81415631926426e-15 -0.529972598896605 Winsorized Mean ( 55 / 66 ) -3.50830475781426e-16 1.76695528977598e-15 -0.198550850613717 Winsorized Mean ( 56 / 66 ) -3.50830475781428e-16 1.76695528977598e-15 -0.198550850613718 Winsorized Mean ( 57 / 66 ) -3.50830475781428e-16 1.76695528977598e-15 -0.198550850613718 Winsorized Mean ( 58 / 66 ) -3.50830475781426e-16 1.76695528977598e-15 -0.198550850613717 Winsorized Mean ( 59 / 66 ) -2.05391259555647e-15 1.61358962144812e-15 -1.27288411393795 Winsorized Mean ( 60 / 66 ) -2.05391259555647e-15 1.61358962144812e-15 -1.27288411393795 Winsorized Mean ( 61 / 66 ) -2.05391259555647e-15 1.61358962144812e-15 -1.27288411393795 Winsorized Mean ( 62 / 66 ) -2.05391259555647e-15 1.61358962144812e-15 -1.27288411393795 Winsorized Mean ( 63 / 66 ) -2.05391259555647e-15 1.61358962144812e-15 -1.27288411393795 Winsorized Mean ( 64 / 66 ) -4.46975789714079e-15 1.40711135225717e-15 -3.17654881397324 Winsorized Mean ( 65 / 66 ) -4.46975789714079e-15 1.40711135225717e-15 -3.17654881397324 Winsorized Mean ( 66 / 66 ) -7.10764780365012e-15 1.19915449738295e-15 -5.92721606695546 Trimmed Mean ( 1 / 66 ) -0.000168395943006234 0.000366316743051846 -0.459700371878445 Trimmed Mean ( 2 / 66 ) -8.94997845408796e-05 0.000305881397963237 -0.292596362959072 Trimmed Mean ( 3 / 66 ) -9.04224627318217e-05 0.000272971330959362 -0.331252598630158 Trimmed Mean ( 4 / 66 ) -8.21013995207313e-05 0.000246681076026229 -0.332824069212353 Trimmed Mean ( 5 / 66 ) -7.56553885674285e-05 0.000235452721474525 -0.321318811240048 Trimmed Mean ( 6 / 66 ) -5.14924155431708e-05 0.000227269248752873 -0.226570096155694 Trimmed Mean ( 7 / 66 ) -5.08171164358269e-05 0.000224127281023970 -0.226733292813168 Trimmed Mean ( 8 / 66 ) -4.97368314952218e-05 0.000220834954931949 -0.225221734079862 Trimmed Mean ( 9 / 66 ) -4.98887955946781e-05 0.000217524708128046 -0.229347718813216 Trimmed Mean ( 10 / 66 ) -5.03162885715434e-05 0.000214071241663484 -0.235044596278091 Trimmed Mean ( 11 / 66 ) -5.07533881321585e-05 0.000210372101154148 -0.241255317856856 Trimmed Mean ( 12 / 66 ) -5.13301311793574e-05 0.000206426471013753 -0.248660605043903 Trimmed Mean ( 13 / 66 ) -5.19201326874116e-05 0.000202186356927537 -0.256793452715604 Trimmed Mean ( 14 / 66 ) -5.25238551607692e-05 0.000197620525414127 -0.265781375951218 Trimmed Mean ( 15 / 66 ) -5.31417828687942e-05 0.000192692417269492 -0.275785542689374 Trimmed Mean ( 16 / 66 ) -5.37744231412959e-05 0.000187358758551205 -0.28701312688619 Trimmed Mean ( 17 / 66 ) -5.44223077577133e-05 0.000181567654840069 -0.299735698000012 Trimmed Mean ( 18 / 66 ) -5.50721288183367e-05 0.000175259040393152 -0.314232741973226 Trimmed Mean ( 19 / 66 ) -5.25236665036741e-05 0.000169084330572311 -0.310635919519531 Trimmed Mean ( 20 / 66 ) -4.99257048909582e-05 0.00016231872191407 -0.307578228205791 Trimmed Mean ( 21 / 66 ) -5.05576758385792e-05 0.000155681956313982 -0.324749746442121 Trimmed Mean ( 22 / 66 ) -5.12058511694725e-05 0.000148352199761094 -0.345164084199184 Trimmed Mean ( 23 / 66 ) -5.18708622232462e-05 0.000140195374296766 -0.369989826579055 Trimmed Mean ( 24 / 66 ) -5.25533735679086e-05 0.000131027965176995 -0.401085169085230 Trimmed Mean ( 25 / 66 ) -5.32540852150954e-05 0.000120585765237779 -0.441628289293395 Trimmed Mean ( 26 / 66 ) -5.39737350149089e-05 0.000108460753401749 -0.497633782931464 Trimmed Mean ( 27 / 66 ) -5.47131012475939e-05 9.39536684112853e-05 -0.582341298352349 Trimmed Mean ( 28 / 66 ) -5.54730054311869e-05 7.56400915927102e-05 -0.733380992316687 Trimmed Mean ( 29 / 66 ) -5.54730054311869e-05 5.12683089426565e-05 -1.08201355916056 Trimmed Mean ( 30 / 66 ) -2.53562820189529e-05 2.80196843425693e-05 -0.904945313050157 Trimmed Mean ( 31 / 66 ) -3.53340545228515e-15 2.21832098446478e-15 -1.59282875518470 Trimmed Mean ( 32 / 66 ) -3.52332542226617e-15 2.21162512343967e-15 -1.59309341575323 Trimmed Mean ( 33 / 66 ) -3.51294449582872e-15 2.2040013607257e-15 -1.59389397775691 Trimmed Mean ( 34 / 66 ) -3.50224899586286e-15 2.19536559397575e-15 -1.59529192106923 Trimmed Mean ( 35 / 66 ) -3.49122440359036e-15 2.1856243702037e-15 -1.59735792260816 Trimmed Mean ( 36 / 66 ) -3.47985529280934e-15 2.17467354982718e-15 -1.60017364127406 Trimmed Mean ( 37 / 66 ) -3.46812525787654e-15 2.16239672578734e-15 -1.60383393875783 Trimmed Mean ( 38 / 66 ) -3.45601683472011e-15 2.14866334087697e-15 -1.60844966680985 Trimmed Mean ( 39 / 66 ) -3.44351141408314e-15 2.13332642997437e-15 -1.61415119866326 Trimmed Mean ( 40 / 66 ) -3.43058914609160e-15 2.11621989164265e-15 -1.62109294957468 Trimmed Mean ( 41 / 66 ) -3.41722883511729e-15 2.09715516304719e-15 -1.62945922902148 Trimmed Mean ( 42 / 66 ) -3.40340782376456e-15 2.07591712966152e-15 -1.63947191105817 Trimmed Mean ( 43 / 66 ) -3.35404219018323e-15 2.05789706043149e-15 -1.62983963322246 Trimmed Mean ( 44 / 66 ) -3.30291349825971e-15 2.03770374409231e-15 -1.62089975436100 Trimmed Mean ( 45 / 66 ) -3.24992558117533e-15 2.01508285983192e-15 -1.61279997262565 Trimmed Mean ( 46 / 66 ) -3.19497514864338e-15 1.98973999146359e-15 -1.60572495016962 Trimmed Mean ( 47 / 66 ) -3.13795111488382e-15 1.96133188263204e-15 -1.59990827797731 Trimmed Mean ( 48 / 66 ) -3.07873384905657e-15 1.92945508781351e-15 -1.59564939785432 Trimmed Mean ( 49 / 66 ) -3.1652240741273e-15 1.91958912630497e-15 -1.64890706597201 Trimmed Mean ( 50 / 66 ) -3.25517390820085e-15 1.90803439929037e-15 -1.70603523155112 Trimmed Mean ( 51 / 66 ) -3.34879516407333e-15 1.89457744298833e-15 -1.76756837070289 Trimmed Mean ( 52 / 66 ) -3.44631730560716e-15 1.87897066340832e-15 -1.83415173675771 Trimmed Mean ( 53 / 66 ) -3.54798932550413e-15 1.86092511508746e-15 -1.9065728635391 Trimmed Mean ( 54 / 66 ) -3.65408186800532e-15 1.84010125762304e-15 -1.98580477724661 Trimmed Mean ( 55 / 66 ) -3.76488963461767e-15 1.81609694973120e-15 -2.07306643798665 Trimmed Mean ( 56 / 66 ) -3.90596645936297e-15 1.79236897186744e-15 -2.17922008284567 Trimmed Mean ( 57 / 66 ) -3.90596645936297e-15 1.76486502878265e-15 -2.21318140235188 Trimmed Mean ( 58 / 66 ) -4.05360499688712e-15 1.73296702061808e-15 -2.33911260206289 Trimmed Mean ( 59 / 66 ) -4.3704877115731e-15 1.69591503427617e-15 -2.57706761437991 Trimmed Mean ( 60 / 66 ) -4.46864767411618e-15 1.67122364784683e-15 -2.67387771820575 Trimmed Mean ( 61 / 66 ) -4.57184148089224e-15 1.64227131742060e-15 -2.78385272420936 Trimmed Mean ( 62 / 66 ) -4.68046654065651e-15 1.60828936848847e-15 -2.91021418928821 Trimmed Mean ( 63 / 66 ) -4.7949632252729e-15 1.56831219876659e-15 -3.05740351254292 Trimmed Mean ( 64 / 66 ) -4.91582083681242e-15 1.52110316405886e-15 -3.23174716414053 Trimmed Mean ( 65 / 66 ) -4.93573436090491e-15 1.49400369802698e-15 -3.30369621402086 Trimmed Mean ( 66 / 66 ) -4.95681926876754e-15 1.46165511961144e-15 -3.39123723665075 Median -1.1990408665952e-14 Midrange -1.10016162846449e-14 Midmean - Weighted Average at Xnp -1.16674346951505e-15 Midmean - Weighted Average at X(n+1)p -1.16674346951505e-15 Midmean - Empirical Distribution Function -1.16674346951505e-15 Midmean - Empirical Distribution Function - Averaging -1.16674346951505e-15 Midmean - Empirical Distribution Function - Interpolation -1.16674346951505e-15 Midmean - Closest Observation -1.16674346951505e-15 Midmean - True Basic - Statistics Graphics Toolkit -1.16674346951505e-15 Midmean - MS Excel (old versions) -1.16674346951505e-15 Number of observations 200

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t12905260146826irfp2c8b85m/16xv11290526053.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905260146826irfp2c8b85m/16xv11290526053.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905260146826irfp2c8b85m/2ypum1290526053.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905260146826irfp2c8b85m/2ypum1290526053.ps (open in new window)

Parameters (Session):

par1 = Apollo Oil Corp. ; par3 = Time series of Xycoon Stock Exchange ; par4 = No season ;

Parameters (R input):

par1 = Apollo Oil Corp. ; par3 = Time series of Xycoon Stock Exchange ; par4 = No season ;

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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