*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, 22 Nov 2010 14:52:08 +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/22/t1290437409d2dxwzrbayg1333.htm/, Retrieved Mon, 22 Nov 2010 15:50:09 +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/22/t1290437409d2dxwzrbayg1333.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:

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-3.4194869158455E-14 0.048019748106666 -0.048019748106707 -3.4194869158455E-14 0.048019748106666 -7.105427357601E-15 -7.105427357601E-15 -7.105427357601E-15 -7.105427357601E-15 -7.105427357601E-15 -0.048019748106707 0.048019748106666 -7.105427357601E-15 -7.105427357601E-15 -7.105427357601E-15 -7.105427357601E-15 -7.105427357601E-15 -7.105427357601E-15 -0.048019748106707 -3.4194869158455E-14 -3.4194869158455E-14 -3.4194869158455E-14 0.048019748106666 -7.105427357601E-15 -7.105427357601E-15 -7.105427357601E-15 -7.105427357601E-15 -7.105427357601E-15 -7.105427357601E-15 -0.048019748106707 -3.4194869158455E-14 -3.4194869158455E-14 -3.4194869158455E-14 -3.4194869158455E-14 0.048019748106666 -7.105427357601E-15 -7.105427357601E-15 -7.105427357601E-15 -7.105427357601E-15 -7.105427357601E-15 -7.105427357601E-15 -0.048019748106707 -3.4194869158455E-14 -3.4194869158455E-14 -3.4194869158455E-14 -3.4194869158455E-14 -3.4194869158455E-14 0.048019748106666 -7.105427357601E-15 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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 0.000453833355158754 0.0015765659387376 0.287861956171748 Geometric Mean NaN Harmonic Mean -1.26684479810764e-14 Quadratic Mean 0.0222448294305122 Winsorized Mean ( 1 / 66 ) 0.000453833355158754 0.0015765659387376 0.287861956171748 Winsorized Mean ( 2 / 66 ) 0.000453833355158754 0.0015765659387376 0.287861956171748 Winsorized Mean ( 3 / 66 ) 0.000453833355158754 0.0015765659387376 0.287861956171748 Winsorized Mean ( 4 / 66 ) 0.000453833355158754 0.0015765659387376 0.287861956171748 Winsorized Mean ( 5 / 66 ) 0.000453833355158754 0.0015765659387376 0.287861956171748 Winsorized Mean ( 6 / 66 ) 0.000453833355158754 0.0015765659387376 0.287861956171748 Winsorized Mean ( 7 / 66 ) 0.000453833355158754 0.0015765659387376 0.287861956171749 Winsorized Mean ( 8 / 66 ) 0.000453833355158754 0.0015765659387376 0.287861956171748 Winsorized Mean ( 9 / 66 ) 0.000453833355158754 0.0015765659387376 0.287861956171748 Winsorized Mean ( 10 / 66 ) 0.000453833355158754 0.0015765659387376 0.287861956171748 Winsorized Mean ( 11 / 66 ) 0.000453833355158754 0.0015765659387376 0.287861956171748 Winsorized Mean ( 12 / 66 ) 0.000453833355158754 0.0015765659387376 0.287861956171748 Winsorized Mean ( 13 / 66 ) 0.000545601428917153 0.00156251786817296 0.349180921402916 Winsorized Mean ( 14 / 66 ) 0.000446774272558244 0.00154756616301879 0.288694779735126 Winsorized Mean ( 15 / 66 ) 0.000446774272558244 0.00154756616301879 0.288694779735126 Winsorized Mean ( 16 / 66 ) 0.000446774272558244 0.00154756616301879 0.288694779735126 Winsorized Mean ( 17 / 66 ) 0.000446774272558244 0.00154756616301879 0.288694779735126 Winsorized Mean ( 18 / 66 ) 0.000454686708279013 0.00154635776170025 0.294037201183688 Winsorized Mean ( 19 / 66 ) 0.000446334692805289 0.00154510568241338 0.288870009272204 Winsorized Mean ( 20 / 66 ) 0.000446334692805289 0.00154510568241338 0.288870009272204 Winsorized Mean ( 21 / 66 ) 0.00512842903929642 0.00105654269725912 4.85397235019519 Winsorized Mean ( 22 / 66 ) 0.00513768984113897 0.00101033188627832 5.0851506429875 Winsorized Mean ( 23 / 66 ) -2.31903385383704e-14 1.45913458695186e-15 -15.8932142009019 Winsorized Mean ( 24 / 66 ) -2.31903385383704e-14 1.45913458695186e-15 -15.8932142009019 Winsorized Mean ( 25 / 66 ) -2.31903385383704e-14 1.45913458695186e-15 -15.8932142009019 Winsorized Mean ( 26 / 66 ) -2.31903385383704e-14 1.45913458695186e-15 -15.8932142009019 Winsorized Mean ( 27 / 66 ) -2.31903385383704e-14 1.45913458695186e-15 -15.8932142009019 Winsorized Mean ( 28 / 66 ) -2.41229258790555e-14 1.37426920845765e-15 -17.5532753921838 Winsorized Mean ( 29 / 66 ) -2.41229258790555e-14 1.37426920845765e-15 -17.5532753921838 Winsorized Mean ( 30 / 66 ) -2.41229258790555e-14 1.37426920845765e-15 -17.5532753921838 Winsorized Mean ( 31 / 66 ) -2.41229258790555e-14 1.37426920845765e-15 -17.5532753921838 Winsorized Mean ( 32 / 66 ) -2.41229258790555e-14 1.37426920845765e-15 -17.5532753921838 Winsorized Mean ( 33 / 66 ) -2.41229258790555e-14 1.37426920845765e-15 -17.5532753921838 Winsorized Mean ( 34 / 66 ) -2.41229258790555e-14 1.37426920845765e-15 -17.5532753921838 Winsorized Mean ( 35 / 66 ) -2.41229258790555e-14 1.37426920845765e-15 -17.5532753921838 Winsorized Mean ( 36 / 66 ) -2.51620946301047e-14 1.29433883581763e-15 -19.4401140828088 Winsorized Mean ( 37 / 66 ) -2.51620946301047e-14 1.29433883581763e-15 -19.4401140828088 Winsorized Mean ( 38 / 66 ) -2.51620946301047e-14 1.29433883581763e-15 -19.4401140828088 Winsorized Mean ( 39 / 66 ) -2.51620946301047e-14 1.29433883581763e-15 -19.4401140828088 Winsorized Mean ( 40 / 66 ) -2.51620946301047e-14 1.29433883581763e-15 -19.4401140828088 Winsorized Mean ( 41 / 66 ) -2.51620946301047e-14 1.29433883581763e-15 -19.4401140828088 Winsorized Mean ( 42 / 66 ) -2.51620946301047e-14 1.29433883581763e-15 -19.4401140828088 Winsorized Mean ( 43 / 66 ) -2.51620946301047e-14 1.29433883581763e-15 -19.4401140828088 Winsorized Mean ( 44 / 66 ) -2.51620946301047e-14 1.29433883581763e-15 -19.4401140828088 Winsorized Mean ( 45 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 46 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 47 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 48 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 49 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 50 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 51 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 52 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 53 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 54 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 55 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 56 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 57 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 58 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 59 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 60 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 61 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 62 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 63 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 64 / 66 ) -2.49622544856722e-14 1.27714705601842e-15 -19.5453251589473 Winsorized Mean ( 65 / 66 ) -2.09210426760365e-14 9.59967756171123e-16 -21.7934847723231 Winsorized Mean ( 66 / 66 ) -2.09210426760365e-14 9.59967756171123e-16 -21.7934847723231 Trimmed Mean ( 1 / 66 ) 0.000458417530463595 0.00155496375054855 0.294809142851 Trimmed Mean ( 2 / 66 ) 0.000463095260366494 0.00153197960425748 0.302285525916611 Trimmed Mean ( 3 / 66 ) 0.000467869438308628 0.00150749638547427 0.310361897260161 Trimmed Mean ( 4 / 66 ) 0.000472743078291223 0.00148138176597098 0.319123057371616 Trimmed Mean ( 5 / 66 ) 0.00047771932122082 0.001453485274489 0.328671593448906 Trimmed Mean ( 6 / 66 ) 0.000482801441659557 0.00142363458166845 0.339132982491708 Trimmed Mean ( 7 / 66 ) 0.000487992855010956 0.00139163072023592 0.350662606045537 Trimmed Mean ( 8 / 66 ) 0.000493297125174341 0.00135724183527137 0.363455584962653 Trimmed Mean ( 9 / 66 ) 0.000498717972703955 0.00132019486164588 0.377760879997825 Trimmed Mean ( 10 / 66 ) 0.000504259283512004 0.00128016420687662 0.393902032882415 Trimmed Mean ( 11 / 66 ) 0.000509925118158437 0.00123675598573354 0.412308591218171 Trimmed Mean ( 12 / 66 ) 0.000515719721774107 0.00118948543003630 0.433565396222103 Trimmed Mean ( 13 / 66 ) 0.000521647534668297 0.0011377434204762 0.458493123563802 Trimmed Mean ( 14 / 66 ) 0.000519504968098632 0.00108286749044249 0.479749343926051 Trimmed Mean ( 15 / 66 ) 0.000525616791253287 0.00102430310257278 0.513145757279341 Trimmed Mean ( 16 / 66 ) 0.000531874134006862 0.000958920042435058 0.554659523703596 Trimmed Mean ( 17 / 66 ) 0.000538282256103896 0.000885011628682243 0.608220546102183 Trimmed Mean ( 18 / 66 ) 0.000544846673861834 0.000799986705416172 0.68106966049941 Trimmed Mean ( 19 / 66 ) 0.000551030484944332 0.000699748443870888 0.787469396710803 Trimmed Mean ( 20 / 66 ) 0.000557918366006111 0.000576060600989016 0.968506377711378 Trimmed Mean ( 21 / 66 ) 0.000564980623803631 0.000407208658494748 1.38744747199652 Trimmed Mean ( 22 / 66 ) 0.000286381819438992 0.000286381819462961 0.999999999916303 Trimmed Mean ( 23 / 66 ) -2.38164466503356e-14 1.49068155494157e-15 -15.9768842455887 Trimmed Mean ( 24 / 66 ) -2.38522651922101e-14 1.48986444533806e-15 -16.0096881745492 Trimmed Mean ( 25 / 66 ) -2.38890388952012e-14 1.48872970196872e-15 -16.0465925168349 Trimmed Mean ( 26 / 66 ) -2.39268064820569e-14 1.48725124163147e-15 -16.0879384816044 Trimmed Mean ( 27 / 66 ) -2.39656087973196e-14 1.48540054598158e-15 -16.1341052836914 Trimmed Mean ( 28 / 66 ) -2.40054889546730e-14 1.48314638231921e-15 -16.1855156313939 Trimmed Mean ( 29 / 66 ) -2.40054889546730e-14 1.48629075068446e-15 -16.1512738632183 Trimmed Mean ( 30 / 66 ) -2.39935055950421e-14 1.48930576818804e-15 -16.1105302265993 Trimmed Mean ( 31 / 66 ) -2.39872534074086e-14 1.49217747230362e-15 -16.0753354427588 Trimmed Mean ( 32 / 66 ) -2.39808173319035e-14 1.49489053728490e-15 -16.0418550614807 Trimmed Mean ( 33 / 66 ) -2.39741891347416e-14 1.49742812189111e-15 -16.0102436866649 Trimmed Mean ( 34 / 66 ) -2.39673600831202e-14 1.49977169705069e-15 -15.9806723451657 Trimmed Mean ( 35 / 66 ) -2.39603209068335e-14 1.50190085034219e-15 -15.9533306751737 Trimmed Mean ( 36 / 66 ) -2.39530617562879e-14 1.50379306360070e-15 -15.9284294734905 Trimmed Mean ( 37 / 66 ) -2.38997534285182e-14 1.51021344576495e-15 -15.8254142787164 Trimmed Mean ( 38 / 66 ) -2.38447254772720e-14 1.51661623026666e-15 -15.7223198601004 Trimmed Mean ( 39 / 66 ) -2.37878933309031e-14 1.52299145208339e-15 -15.6191903102031 Trimmed Mean ( 40 / 66 ) -2.37291667796551e-14 1.52932785403903e-15 -15.5160757171755 Trimmed Mean ( 41 / 66 ) -2.36684494978564e-14 1.53561271573358e-15 -15.4130330228151 Trimmed Mean ( 42 / 66 ) -2.36056385166854e-14 1.54183165689077e-15 -15.3101270240410 Trimmed Mean ( 43 / 66 ) -2.35406236414381e-14 1.54796841072383e-15 -15.2074315459904 Trimmed Mean ( 44 / 66 ) -2.34732868063606e-14 1.55400456204883e-15 -15.1050308214108 Trimmed Mean ( 45 / 66 ) -2.34035013590984e-14 1.55991924380634e-15 -15.0030211192163 Trimmed Mean ( 46 / 66 ) -2.33393551398979e-14 1.5668536443552e-15 -14.8956829656561 Trimmed Mean ( 47 / 66 ) -2.3272788308652e-14 1.57370280228179e-15 -14.7885536423444 Trimmed Mean ( 48 / 66 ) -2.32036612146659e-14 1.58044451092026e-15 -14.6817310284148 Trimmed Mean ( 49 / 66 ) -2.3131823254249e-14 1.58705335072516e-15 -14.5753280717876 Trimmed Mean ( 50 / 66 ) -2.30571117754154e-14 1.59350018934032e-15 -14.4694753911266 Trimmed Mean ( 51 / 66 ) -2.29793508484661e-14 1.59975159195327e-15 -14.3643244138977 Trimmed Mean ( 52 / 66 ) -2.28983498828940e-14 1.60576912314823e-15 -14.2600511822024 Trimmed Mean ( 53 / 66 ) -2.2813902067723e-14 1.61150851688199e-15 -14.1568609962201 Trimmed Mean ( 54 / 66 ) -2.27257826084142e-14 1.61691868531527e-15 -14.0549941161593 Trimmed Mean ( 55 / 66 ) -2.26337467286916e-14 1.62194052960872e-15 -13.9547328126463 Trimmed Mean ( 56 / 66 ) -2.25375273998908e-14 1.6265055058465e-15 -13.8564101497839 Trimmed Mean ( 57 / 66 ) -2.25375273998908e-14 1.63053388615737e-15 -13.8221766448561 Trimmed Mean ( 58 / 66 ) -2.24368327534713e-14 1.63393263769729e-15 -13.7317978941235 Trimmed Mean ( 59 / 66 ) -2.22207076587173e-14 1.63659281877418e-15 -13.5774197490129 Trimmed Mean ( 60 / 66 ) -2.21045404202870e-14 1.63838635961284e-15 -13.4916531077019 Trimmed Mean ( 61 / 66 ) -2.19824158875782e-14 1.63916205150955e-15 -13.4107642788179 Trimmed Mean ( 62 / 66 ) -2.18538637478848e-14 1.63874050705261e-15 -13.3357683256335 Trimmed Mean ( 63 / 66 ) -2.17183628438836e-14 1.63690776749463e-15 -13.2679209392015 Trimmed Mean ( 64 / 66 ) -2.15753341118823e-14 1.63340710844725e-15 -13.2087916112917 Trimmed Mean ( 65 / 66 ) -2.14241323094810e-14 1.62792841133844e-15 -13.1603651366135 Trimmed Mean ( 66 / 66 ) -2.14468965462884e-14 1.65188565447400e-15 -12.9832815535393 Median -3.4194869158455e-14 Midrange -2.04974925921420e-14 Midmean - Weighted Average at Xnp -2.2170873913607e-14 Midmean - Weighted Average at X(n+1)p -2.2170873913607e-14 Midmean - Empirical Distribution Function -2.2170873913607e-14 Midmean - Empirical Distribution Function - Averaging -2.2170873913607e-14 Midmean - Empirical Distribution Function - Interpolation -2.2170873913607e-14 Midmean - Closest Observation -2.2170873913607e-14 Midmean - True Basic - Statistics Graphics Toolkit -2.2170873913607e-14 Midmean - MS Excel (old versions) -2.2170873913607e-14 Number of observations 200

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290437409d2dxwzrbayg1333/103cr1290437523.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290437409d2dxwzrbayg1333/103cr1290437523.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290437409d2dxwzrbayg1333/203cr1290437523.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290437409d2dxwzrbayg1333/203cr1290437523.ps (open in new window)

Parameters (Session):

par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

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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