*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 15:02:28 +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/t12904380299wc3jlwbdupqzij.htm/, Retrieved Mon, 22 Nov 2010 16:00:32 +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/t12904380299wc3jlwbdupqzij.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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-3.5527136788005E-14 0.016941797155665 1.1546319456102E-14 1.1546319456102E-14 -0.016941797155689 0.016941797155665 1.1546319456102E-14 1.1546319456102E-14 1.1546319456102E-14 1.1546319456102E-14 1.1546319456102E-14 -0.016941797155689 -3.5527136788005E-14 -3.5527136788005E-14 -3.5527136788005E-14 0.016941797155665 0.012632696758011 2.6645352591004E-14 -0.016271545440073 -4.2632564145606E-14 -4.2632564145606E-14 0.016271545440057 2.6645352591004E-14 2.6645352591004E-14 -0.016271545440073 -4.2632564145606E-14 0.016271545440057 2.6645352591004E-14 2.6645352591004E-14 -0.016271545440073 0.016271545440057 2.6645352591004E-14 2.6645352591004E-14 -0.016271545440073 -4.2632564145606E-14 -4.2632564145606E-14 -4.2632564145606E-14 0.016271545440057 2.6645352591004E-14 2.6645352591004E-14 2.6645352591004E-14 -0.016271545440073 0.016271545440057 2.6645352591004E-14 -0.016271545440073 0.016271545440057 2.6645352591004E-14 -0.016271545440073 -4.2632564145606E-14 -4.2632564145 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 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean -1.61887283010518e-05 0.000613567105488939 -0.0263846092077432 Geometric Mean NaN Harmonic Mean 1.59314095872071e-13 Quadratic Mean 0.00865544430226438 Winsorized Mean ( 1 / 66 ) -1.61887283010518e-05 0.000613567105488939 -0.0263846092077432 Winsorized Mean ( 2 / 66 ) -1.44558618814118e-05 0.000613328064547284 -0.0235695424961225 Winsorized Mean ( 3 / 66 ) -1.70551615106768e-05 0.000612968666109104 -0.0278238716816255 Winsorized Mean ( 4 / 66 ) -1.70551615106768e-05 0.000612968666109104 -0.0278238716816255 Winsorized Mean ( 5 / 66 ) -1.70551615106768e-05 0.000612968666109104 -0.0278238716816253 Winsorized Mean ( 6 / 66 ) -1.70551615106768e-05 0.000612968666109104 -0.0278238716816255 Winsorized Mean ( 7 / 66 ) -1.70551615106768e-05 0.000612968666109104 -0.0278238716816255 Winsorized Mean ( 8 / 66 ) -1.70551615106768e-05 0.000612968666109104 -0.0278238716816253 Winsorized Mean ( 9 / 66 ) 5.30826680366317e-06 0.000609933517749149 0.00870302524650946 Winsorized Mean ( 10 / 66 ) -1.95399868791868e-05 0.000606540643922569 -0.0322154616924258 Winsorized Mean ( 11 / 66 ) -1.95399868791868e-05 0.000606540643922569 -0.0322154616924258 Winsorized Mean ( 12 / 66 ) -1.95399868791868e-05 0.000606540643922569 -0.0322154616924258 Winsorized Mean ( 13 / 66 ) -1.95399868791867e-05 0.000606540643922569 -0.0322154616924256 Winsorized Mean ( 14 / 66 ) -1.95399868791868e-05 0.000606540643922569 -0.0322154616924258 Winsorized Mean ( 15 / 66 ) -1.95399868791868e-05 0.000606540643922569 -0.0322154616924258 Winsorized Mean ( 16 / 66 ) -1.95399868791868e-05 0.000606540643922569 -0.0322154616924258 Winsorized Mean ( 17 / 66 ) -1.95399868762114e-05 0.000600929532683472 -0.0325162699009897 Winsorized Mean ( 18 / 66 ) -1.95399868762114e-05 0.000600929532683472 -0.0325162699009897 Winsorized Mean ( 19 / 66 ) -1.95399868762117e-05 0.000600929532683472 -0.0325162699009902 Winsorized Mean ( 20 / 66 ) -1.95399868762117e-05 0.000600929532683472 -0.0325162699009902 Winsorized Mean ( 21 / 66 ) -1.95399868792565e-05 0.000598364578633585 -0.032655654390301 Winsorized Mean ( 22 / 66 ) -1.95399868792567e-05 0.000598364578633585 -0.0326556543903015 Winsorized Mean ( 23 / 66 ) -1.95399868792567e-05 0.000598364578633585 -0.0326556543903015 Winsorized Mean ( 24 / 66 ) -1.95399868792567e-05 0.000598364578633585 -0.0326556543903015 Winsorized Mean ( 25 / 66 ) -1.95399868792565e-05 0.000598364578633585 -0.032655654390301 Winsorized Mean ( 26 / 66 ) -1.95399868792565e-05 0.000598364578633585 -0.032655654390301 Winsorized Mean ( 27 / 66 ) -0.000465320998277782 0.000541257673095319 -0.859703282572839 Winsorized Mean ( 28 / 66 ) 0.00043006187353345 0.000283824800814646 1.51523711916319 Winsorized Mean ( 29 / 66 ) -0.000554913927661358 9.36403267233084e-05 -5.92601443287396 Winsorized Mean ( 30 / 66 ) -5.09814412907876e-15 2.29665515247865e-15 -2.21981263646683 Winsorized Mean ( 31 / 66 ) -5.09814412907876e-15 2.29665515247865e-15 -2.21981263646683 Winsorized Mean ( 32 / 66 ) -5.09814412907876e-15 2.29665515247865e-15 -2.21981263646683 Winsorized Mean ( 33 / 66 ) -5.09814412907876e-15 2.29665515247865e-15 -2.21981263646683 Winsorized Mean ( 34 / 66 ) -5.09814412907876e-15 2.29665515247865e-15 -2.21981263646683 Winsorized Mean ( 35 / 66 ) -5.09814412907876e-15 2.29665515247865e-15 -2.21981263646683 Winsorized Mean ( 36 / 66 ) -5.09814412907876e-15 2.29665515247865e-15 -2.21981263646683 Winsorized Mean ( 37 / 66 ) -7.06990022081295e-15 2.12613128069834e-15 -3.32524161842481 Winsorized Mean ( 38 / 66 ) -7.06990022081295e-15 2.12613128069834e-15 -3.32524161842481 Winsorized Mean ( 39 / 66 ) -7.06990022081295e-15 2.12613128069834e-15 -3.32524161842481 Winsorized Mean ( 40 / 66 ) -7.06990022081295e-15 2.12613128069834e-15 -3.32524161842481 Winsorized Mean ( 41 / 66 ) -7.06990022081295e-15 2.12613128069834e-15 -3.32524161842481 Winsorized Mean ( 42 / 66 ) -7.06990022081295e-15 2.12613128069834e-15 -3.32524161842481 Winsorized Mean ( 43 / 66 ) -7.06990022081294e-15 2.12613128069834e-15 -3.32524161842481 Winsorized Mean ( 44 / 66 ) -7.06990022081295e-15 2.12613128069834e-15 -3.32524161842481 Winsorized Mean ( 45 / 66 ) -7.06990022081295e-15 2.12613128069834e-15 -3.32524161842481 Winsorized Mean ( 46 / 66 ) -7.06990022081295e-15 2.12613128069834e-15 -3.32524161842481 Winsorized Mean ( 47 / 66 ) -5.50448575609146e-15 2.00025504146554e-15 -2.75189195476715 Winsorized Mean ( 48 / 66 ) -5.50448575609146e-15 2.00025504146554e-15 -2.75189195476715 Winsorized Mean ( 49 / 66 ) -5.50448575609146e-15 2.00025504146554e-15 -2.75189195476715 Winsorized Mean ( 50 / 66 ) -5.50448575609146e-15 2.00025504146554e-15 -2.75189195476715 Winsorized Mean ( 51 / 66 ) -5.50448575609146e-15 2.00025504146554e-15 -2.75189195476715 Winsorized Mean ( 52 / 66 ) -5.50448575609146e-15 2.00025504146554e-15 -2.75189195476715 Winsorized Mean ( 53 / 66 ) -5.50448575609146e-15 2.00025504146554e-15 -2.75189195476715 Winsorized Mean ( 54 / 66 ) -5.50448575609146e-15 2.00025504146554e-15 -2.75189195476715 Winsorized Mean ( 55 / 66 ) -5.62661028880021e-15 1.99041645823697e-15 -2.82685076558503 Winsorized Mean ( 56 / 66 ) -5.62661028880021e-15 1.99041645823697e-15 -2.82685076558503 Winsorized Mean ( 57 / 66 ) -5.62661028880021e-15 1.99041645823697e-15 -2.82685076558503 Winsorized Mean ( 58 / 66 ) -5.62661028880021e-15 1.99041645823697e-15 -2.82685076558503 Winsorized Mean ( 59 / 66 ) -5.62661028880021e-15 1.99041645823697e-15 -2.82685076558503 Winsorized Mean ( 60 / 66 ) -5.62661028880021e-15 1.99041645823697e-15 -2.82685076558503 Winsorized Mean ( 61 / 66 ) -5.62661028880021e-15 1.99041645823697e-15 -2.82685076558503 Winsorized Mean ( 62 / 66 ) -5.62661028880021e-15 1.99041645823697e-15 -2.82685076558503 Winsorized Mean ( 63 / 66 ) -5.62661028880021e-15 1.99041645823697e-15 -2.82685076558503 Winsorized Mean ( 64 / 66 ) -5.62661028880021e-15 1.99041645823697e-15 -2.82685076558503 Winsorized Mean ( 65 / 66 ) -5.62661028880021e-15 1.99041645823697e-15 -2.82685076558503 Winsorized Mean ( 66 / 66 ) -5.62661028880021e-15 1.99041645823697e-15 -2.82685076558503 Trimmed Mean ( 1 / 66 ) -1.63522508090220e-05 0.000607791817057613 -0.0269043615759506 Trimmed Mean ( 2 / 66 ) -1.65191105110324e-05 0.00060165461209918 -0.0274561354285926 Trimmed Mean ( 3 / 66 ) -1.75826407324864e-05 0.00059525970171727 -0.0295377642426693 Trimmed Mean ( 4 / 66 ) -1.77657932400591e-05 0.000588590697050157 -0.0301836120229152 Trimmed Mean ( 5 / 66 ) -1.79528015898966e-05 0.000581489382940065 -0.0308738252435936 Trimmed Mean ( 6 / 66 ) -1.81437888407944e-05 0.000573921201943927 -0.0316137281204104 Trimmed Mean ( 7 / 66 ) -1.83388833443997e-05 0.000565847529261499 -0.0324095845542248 Trimmed Mean ( 8 / 66 ) -1.85382190328660e-05 0.000557224979010552 -0.0332688227038634 Trimmed Mean ( 9 / 66 ) -1.87419357254744e-05 0.000548004547323641 -0.0342003288421726 Trimmed Mean ( 10 / 66 ) -2.17110965315408e-05 0.000538603255280199 -0.0403099987211291 Trimmed Mean ( 11 / 66 ) -2.19550414362997e-05 0.000529018408045093 -0.0415014697077011 Trimmed Mean ( 12 / 66 ) -2.22045305434395e-05 0.000518732120687976 -0.0428053896373148 Trimmed Mean ( 13 / 66 ) -2.24597550323526e-05 0.000507672493615008 -0.0442406380389497 Trimmed Mean ( 14 / 66 ) -2.27209149744962e-05 0.000495755889997604 -0.045830852306376 Trimmed Mean ( 15 / 66 ) -2.29882198564549e-05 0.000482884021805075 -0.0476060892852126 Trimmed Mean ( 16 / 66 ) -2.32618891403651e-05 0.000468940008204605 -0.0496052559674449 Trimmed Mean ( 17 / 66 ) -2.35421528648514e-05 0.000453782917540264 -0.0518797688385053 Trimmed Mean ( 18 / 66 ) -2.38292522901484e-05 0.000437918018787326 -0.0544148705187695 Trimmed Mean ( 19 / 66 ) -2.41234405901441e-05 0.000420543899177126 -0.0573624790119324 Trimmed Mean ( 20 / 66 ) -2.44249835976397e-05 0.000401408841273203 -0.060848145546988 Trimmed Mean ( 21 / 66 ) -2.4734160605325e-05 0.000380184084047266 -0.065058379987969 Trimmed Mean ( 22 / 66 ) -2.50512652284061e-05 0.000356762747842267 -0.0702182763753177 Trimmed Mean ( 23 / 66 ) -2.53766063352036e-05 0.000330249713540151 -0.0768406611566018 Trimmed Mean ( 24 / 66 ) -2.57105090500747e-05 0.000299729089307711 -0.0857791584709333 Trimmed Mean ( 25 / 66 ) -2.60533158373423e-05 0.000263695578927215 -0.0988007305368346 Trimmed Mean ( 26 / 66 ) -2.64053876729145e-05 0.000219297835937683 -0.120408792727066 Trimmed Mean ( 27 / 66 ) -2.67671053122011e-05 0.000159481302089186 -0.167838517503652 Trimmed Mean ( 28 / 66 ) -4.20774867816912e-06 7.7925866175952e-05 -0.0539968162646826 Trimmed Mean ( 29 / 66 ) -4.20774867816912e-06 2.6052297073323e-05 -0.161511618968823 Trimmed Mean ( 30 / 66 ) -6.14111935906944e-15 2.41585878776437e-15 -2.54200261628389 Trimmed Mean ( 31 / 66 ) -6.19150463588059e-15 2.41601139354947e-15 -2.56269678711423 Trimmed Mean ( 32 / 66 ) -6.24337183259794e-15 2.41563861412803e-15 -2.58456368269788 Trimmed Mean ( 33 / 66 ) -6.29678730384417e-15 2.41469202280261e-15 -2.60769789454799 Trimmed Mean ( 34 / 66 ) -6.35182142573423e-15 2.41311825501298e-15 -2.63220478836420 Trimmed Mean ( 35 / 66 ) -6.4085489052209e-15 2.41085839647873e-15 -2.65820212194177 Trimmed Mean ( 36 / 66 ) -6.46704911844153e-15 2.40784727833123e-15 -2.68582196912569 Trimmed Mean ( 37 / 66 ) -6.52740648128821e-15 2.40401266197768e-15 -2.71521302051645 Trimmed Mean ( 38 / 66 ) -6.50375810554607e-15 2.41046769740441e-15 -2.69813120190299 Trimmed Mean ( 39 / 66 ) -6.47933437322222e-15 2.41668235899261e-15 -2.68108646927151 Trimmed Mean ( 40 / 66 ) -6.45409651648758e-15 2.42262507313045e-15 -2.66409218168776 Trimmed Mean ( 41 / 66 ) -6.42800313918565e-15 2.42826067598474e-15 -2.64716354498426 Trimmed Mean ( 42 / 66 ) -6.40100999025263e-15 2.43354994931959e-15 -2.63031789918359 Trimmed Mean ( 43 / 66 ) -6.37306971328687e-15 2.43844908561233e-15 -2.61357505920059 Trimmed Mean ( 44 / 66 ) -6.34413156928662e-15 2.44290906974321e-15 -2.59695772055629 Trimmed Mean ( 45 / 66 ) -6.3141411291409e-15 2.44687496184112e-15 -2.58049194487237 Trimmed Mean ( 46 / 66 ) -6.28303993195275e-15 2.45028506250609e-15 -2.56420774386414 Trimmed Mean ( 47 / 66 ) -6.25076510468203e-15 2.45306993740798e-15 -2.54813978572778 Trimmed Mean ( 48 / 66 ) -6.28130026624465e-15 2.46350884239426e-15 -2.5497372520652 Trimmed Mean ( 49 / 66 ) -6.31303288512346e-15 2.47378095298204e-15 -2.55197731937961 Trimmed Mean ( 50 / 66 ) -6.34603480875742e-15 2.48384815436793e-15 -2.55492059673524 Trimmed Mean ( 51 / 66 ) -6.38038374968256e-15 2.49366686051896e-15 -2.55863517725649 Trimmed Mean ( 52 / 66 ) -6.41616389647958e-15 2.50318715802167e-15 -2.56319783197930 Trimmed Mean ( 53 / 66 ) -6.45346660271478e-15 2.5123517945279e-15 -2.56869544176533 Trimmed Mean ( 54 / 66 ) -6.4923911657428e-15 2.52109497875718e-15 -2.57522672507298 Trimmed Mean ( 55 / 66 ) -6.53304570934986e-15 2.52934095079718e-15 -2.58290433612393 Trimmed Mean ( 56 / 66 ) -6.57050171846348e-15 2.53775657857618e-15 -2.58909848719607 Trimmed Mean ( 57 / 66 ) -6.57050171846348e-15 2.54555519369909e-15 -2.58116647194576 Trimmed Mean ( 58 / 66 ) -6.60969986753587e-15 2.55262771752861e-15 -2.58937087541117 Trimmed Mean ( 59 / 66 ) -6.69383248017906e-15 2.55884715326454e-15 -2.61595635817449 Trimmed Mean ( 60 / 66 ) -6.73905375947478e-15 2.56406515715611e-15 -2.62826930925160 Trimmed Mean ( 61 / 66 ) -6.78659407873437e-15 2.5681078210206e-15 -2.64264374851571 Trimmed Mean ( 62 / 66 ) -6.83663652006026e-15 2.57077044722537e-15 -2.65937261237736 Trimmed Mean ( 63 / 66 ) -6.88938395821458e-15 2.57181102420605e-15 -2.6788064493741 Trimmed Mean ( 64 / 66 ) -6.9450618095997e-15 2.57094200823772e-15 -2.70136852070042 Trimmed Mean ( 65 / 66 ) -7.00392125249253e-15 2.56781987154629e-15 -2.72757498689926 Trimmed Mean ( 66 / 66 ) -7.06624301555553e-15 2.56203166597014e-15 -2.75806232585334 Median -1.7763568394003e-14 Midrange -1.20008170068076e-14 Midmean - Weighted Average at Xnp -3.41489288953668e-15 Midmean - Weighted Average at X(n+1)p -3.41489288953668e-15 Midmean - Empirical Distribution Function -3.41489288953668e-15 Midmean - Empirical Distribution Function - Averaging -3.41489288953668e-15 Midmean - Empirical Distribution Function - Interpolation -3.41489288953668e-15 Midmean - Closest Observation -3.41489288953668e-15 Midmean - True Basic - Statistics Graphics Toolkit -3.41489288953668e-15 Midmean - MS Excel (old versions) -3.41489288953668e-15 Number of observations 200

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/22/t12904380299wc3jlwbdupqzij/1mgb31290438144.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t12904380299wc3jlwbdupqzij/1mgb31290438144.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t12904380299wc3jlwbdupqzij/2xpa61290438144.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t12904380299wc3jlwbdupqzij/2xpa61290438144.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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