*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 14:55:55 +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/t1290437634ivh5yd9gwgw7y4t.htm/, Retrieved Mon, 22 Nov 2010 15:53:56 +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/t1290437634ivh5yd9gwgw7y4t.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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-3.8635761256955E-14 -3.8635761256955E-14 -3.8635761256955E-14 -3.8635761256955E-14 -3.8635761256955E-14 -3.8635761256955E-14 -0.024154942035039 0.024154942035005 -3.8635761256955E-14 -0.024154942035039 4.8849813083507E-15 4.8849813083507E-15 4.8849813083507E-15 4.8849813083507E-15 4.8849813083507E-15 4.8849813083507E-15 4.8849813083507E-15 4.8849813083507E-15 4.8849813083507E-15 4.8849813083507E-15 4.8849813083507E-15 4.8849813083507E-15 0.024154942035005 -3.8635761256955E-14 -3.8635761256955E-14 -0.010814569746239 1.8651746813703E-14 1.8651746813703E-14 1.8651746813703E-14 1.8651746813703E-14 1.8651746813703E-14 0.024170360927819 5.7731597280508E-15 5.7731597280508E-15 -0.024170360927795 1.8651746813703E-14 0.024170360927819 -0.024170360927795 1.8651746813703E-14 0.024170360927819 5.7731597280508E-15 5.7731597280508E-15 5.7731597280508E-15 5.7731597280508E-15 5.7731597280508E-15 5.7731597280508E-15 5.7731597280508E-15 5.7731597280508E-15 5.7731597280508E-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 3 seconds R Server 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 0.000183158574720759 0.000804119977408575 0.227775182642548 Geometric Mean NaN Harmonic Mean 1.73817319388260e-14 Quadratic Mean 0.0113449868108755 Winsorized Mean ( 1 / 66 ) 0.000183158574720759 0.000804119977408575 0.227775182642548 Winsorized Mean ( 2 / 66 ) 0.000183158574720759 0.000804119977408575 0.227775182642548 Winsorized Mean ( 3 / 66 ) 0.000183158574720759 0.000804119977408575 0.227775182642548 Winsorized Mean ( 4 / 66 ) 0.000183158574720759 0.000804119977408575 0.227775182642548 Winsorized Mean ( 5 / 66 ) 0.000183158574720760 0.000804119977408575 0.227775182642548 Winsorized Mean ( 6 / 66 ) 0.000183158574720759 0.000804119977408575 0.227775182642548 Winsorized Mean ( 7 / 66 ) 0.000183158574720760 0.000804119977408575 0.227775182642548 Winsorized Mean ( 8 / 66 ) 0.000183158574720759 0.000804119977408575 0.227775182642548 Winsorized Mean ( 9 / 66 ) 0.000183158574720759 0.000804119977408575 0.227775182642548 Winsorized Mean ( 10 / 66 ) 0.000183158574720760 0.000804119977408575 0.227775182642548 Winsorized Mean ( 11 / 66 ) 0.000183158574720759 0.000804119977408575 0.227775182642548 Winsorized Mean ( 12 / 66 ) 0.000183158574720759 0.000804119977408575 0.227775182642547 Winsorized Mean ( 13 / 66 ) 0.000183158574720759 0.000804119977408575 0.227775182642547 Winsorized Mean ( 14 / 66 ) 0.000183158574720760 0.000804119977408575 0.227775182642548 Winsorized Mean ( 15 / 66 ) 0.000183158574720760 0.000804119977408575 0.227775182642548 Winsorized Mean ( 16 / 66 ) 0.000184392086141239 0.000803932281343996 0.229362709298073 Winsorized Mean ( 17 / 66 ) 0.000183081480252049 0.000803735817714511 0.227788131643376 Winsorized Mean ( 18 / 66 ) 0.000202471822052209 0.000800791750515568 0.252839545264862 Winsorized Mean ( 19 / 66 ) 0.000182004239041784 0.000797721978209304 0.228154976311848 Winsorized Mean ( 20 / 66 ) 0.000182004239041784 0.000797721978209304 0.228154976311848 Winsorized Mean ( 21 / 66 ) 0.00156012126393226 0.000619367853641108 2.51889285948682 Winsorized Mean ( 22 / 66 ) 0.00267673123260919 0.000543542142417128 4.92460661965562 Winsorized Mean ( 23 / 66 ) 3.7747582837276e-16 1.41834126142623e-15 0.266138931890894 Winsorized Mean ( 24 / 66 ) 3.7747582837276e-16 1.41834126142623e-15 0.266138931890894 Winsorized Mean ( 25 / 66 ) 3.77475828372759e-16 1.41834126142623e-15 0.266138931890893 Winsorized Mean ( 26 / 66 ) 3.7747582837276e-16 1.41834126142623e-15 0.266138931890894 Winsorized Mean ( 27 / 66 ) 3.7747582837276e-16 1.41834126142623e-15 0.266138931890894 Winsorized Mean ( 28 / 66 ) 3.77475828372759e-16 1.41834126142623e-15 0.266138931890893 Winsorized Mean ( 29 / 66 ) 3.77475828372759e-16 1.41834126142623e-15 0.266138931890893 Winsorized Mean ( 30 / 66 ) 3.7747582837276e-16 1.41834126142623e-15 0.266138931890894 Winsorized Mean ( 31 / 66 ) 3.7747582837276e-16 1.41834126142623e-15 0.266138931890894 Winsorized Mean ( 32 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 33 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 34 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 35 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 36 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 37 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 38 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 39 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 40 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 41 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 42 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 43 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 44 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 45 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 46 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 47 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 48 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 49 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 50 / 66 ) 4.21440660147724e-15 9.46390161460444e-16 4.45313864524297 Winsorized Mean ( 51 / 66 ) 5.46007683510664e-15 8.259564826138e-16 6.61061078887331 Winsorized Mean ( 52 / 66 ) 5.46007683510664e-15 8.259564826138e-16 6.61061078887331 Winsorized Mean ( 53 / 66 ) 5.46007683510664e-15 8.259564826138e-16 6.61061078887331 Winsorized Mean ( 54 / 66 ) 5.46007683510664e-15 8.259564826138e-16 6.61061078887331 Winsorized Mean ( 55 / 66 ) 5.46007683510664e-15 8.259564826138e-16 6.61061078887331 Winsorized Mean ( 56 / 66 ) 5.46007683510664e-15 8.259564826138e-16 6.61061078887331 Winsorized Mean ( 57 / 66 ) 5.46007683510664e-15 8.259564826138e-16 6.61061078887331 Winsorized Mean ( 58 / 66 ) 5.46007683510664e-15 8.259564826138e-16 6.61061078887331 Winsorized Mean ( 59 / 66 ) 5.46007683510664e-15 8.259564826138e-16 6.61061078887331 Winsorized Mean ( 60 / 66 ) 5.46007683510664e-15 8.259564826138e-16 6.61061078887331 Winsorized Mean ( 61 / 66 ) 5.46007683510664e-15 8.259564826138e-16 6.61061078887331 Winsorized Mean ( 62 / 66 ) 5.46007683510664e-15 8.259564826138e-16 6.61061078887331 Winsorized Mean ( 63 / 66 ) 1.00763841714981e-14 4.56593042827617e-16 22.0686327349545 Winsorized Mean ( 64 / 66 ) 1.00763841714981e-14 4.56593042827617e-16 22.0686327349545 Winsorized Mean ( 65 / 66 ) 1.00763841714981e-14 4.56593042827617e-16 22.0686327349545 Winsorized Mean ( 66 / 66 ) 1.00763841714981e-14 4.56593042827617e-16 22.0686327349545 Trimmed Mean ( 1 / 66 ) 0.000185008661333979 0.000793608706460219 0.23312327577552 Trimmed Mean ( 2 / 66 ) 0.000186896504816857 0.000782428584784082 0.238867173888378 Trimmed Mean ( 3 / 66 ) 0.000188823272907628 0.000770524067005336 0.245058241518004 Trimmed Mean ( 4 / 66 ) 0.000190790182000291 0.000757832598614982 0.251757686788586 Trimmed Mean ( 5 / 66 ) 0.000192798499705431 0.000744283311548899 0.259039127592698 Trimmed Mean ( 6 / 66 ) 0.00019484954757451 0.000729795382346976 0.266992025830427 Trimmed Mean ( 7 / 66 ) 0.000196944703999913 0.00071427593767878 0.275726359535413 Trimmed Mean ( 8 / 66 ) 0.000199085407304130 0.000697617341917446 0.285379097309896 Trimmed Mean ( 9 / 66 ) 0.000201273159032615 0.000679693624217752 0.296123358909327 Trimmed Mean ( 10 / 66 ) 0.000203509527466177 0.000660355680197682 0.308181686883128 Trimmed Mean ( 11 / 66 ) 0.000205796151370157 0.000639424683277452 0.321845804130242 Trimmed Mean ( 12 / 66 ) 0.000208134743999227 0.000616682801656247 0.337506970261262 Trimmed Mean ( 13 / 66 ) 0.00021052709737839 0.000591859717582261 0.355704385894668 Trimmed Mean ( 14 / 66 ) 0.000212975086882650 0.000564612332641634 0.377205871303254 Trimmed Mean ( 15 / 66 ) 0.000215480676139952 0.00053449285029782 0.403149782115674 Trimmed Mean ( 16 / 66 ) 0.000218045922284333 0.000500895784238102 0.435311953395647 Trimmed Mean ( 17 / 66 ) 0.000220580096692698 0.000462993325833181 0.476421763306744 Trimmed Mean ( 18 / 66 ) 0.000223270097872085 0.000419503238912197 0.532224967919296 Trimmed Mean ( 19 / 66 ) 0.000224696591412542 0.000368816099683323 0.60923748069966 Trimmed Mean ( 20 / 66 ) 0.000227505298805355 0.00030724425055429 0.740470483645894 Trimmed Mean ( 21 / 66 ) 0.000230385112714442 0.000224946602160493 1.02417689576866 Trimmed Mean ( 22 / 66 ) 0.000149204639502121 0.000153544664903162 0.971734443500341 Trimmed Mean ( 23 / 66 ) 3.47485388226849e-15 1.23436359560971e-15 2.81509750824441 Trimmed Mean ( 24 / 66 ) 3.65204942310921e-15 1.21517198247071e-15 3.00537658520055 Trimmed Mean ( 25 / 66 ) 3.83397017837235e-15 1.19420102915398e-15 3.21048976242174 Trimmed Mean ( 26 / 66 ) 4.02080771080476e-15 1.17124656776267e-15 3.43293019717049 Trimmed Mean ( 27 / 66 ) 4.21276407974216e-15 1.14606918564183e-15 3.67583749089538 Trimmed Mean ( 28 / 66 ) 4.41005257003894e-15 1.11838514570433e-15 3.94323242487418 Trimmed Mean ( 29 / 66 ) 4.41005257003894e-15 1.08785397644945e-15 4.05390122710449 Trimmed Mean ( 30 / 66 ) 4.82153999265794e-15 1.05406107053873e-15 4.57425108223917 Trimmed Mean ( 31 / 66 ) 5.03622908272003e-15 1.01649253881271e-15 4.95451652660677 Trimmed Mean ( 32 / 66 ) 5.25723255778394e-15 9.74497544794268e-16 5.39481354865171 Trimmed Mean ( 33 / 66 ) 5.30587182813407e-15 9.7261533102950e-16 5.4552623826296 Trimmed Mean ( 34 / 66 ) 5.35598501576753e-15 9.70367929924419e-16 5.51954042440861 Trimmed Mean ( 35 / 66 ) 5.40764014763587e-15 9.67720719400792e-16 5.58801732692492 Trimmed Mean ( 36 / 66 ) 5.46090950237509e-15 9.64635171529701e-16 5.6611138216278 Trimmed Mean ( 37 / 66 ) 5.51586994774096e-15 9.61068296336007e-16 5.73931110699391 Trimmed Mean ( 38 / 66 ) 5.57260331069927e-15 9.56971985347188e-16 5.82316242902089 Trimmed Mean ( 39 / 66 ) 5.63119678391852e-15 9.52292232221026e-16 5.91330748417942 Trimmed Mean ( 40 / 66 ) 5.69174337291173e-15 9.46968201447476e-16 6.01049049399092 Trimmed Mean ( 41 / 66 ) 5.75434238865048e-15 9.40931107621166e-16 6.11558310915922 Trimmed Mean ( 42 / 66 ) 5.81909999113884e-15 9.34102856238824e-16 6.22961374357586 Trimmed Mean ( 43 / 66 ) 5.88612979020575e-15 9.26394381079245e-16 6.35380558261637 Trimmed Mean ( 44 / 66 ) 5.95555351066789e-15 9.17703590988933e-16 6.48962646452117 Trimmed Mean ( 45 / 66 ) 6.02750173005594e-15 9.07912807265136e-16 6.63885527533454 Trimmed Mean ( 46 / 66 ) 6.1021146983102e-15 8.96885526974563e-16 6.80367172262695 Trimmed Mean ( 47 / 66 ) 6.17954325027218e-15 8.84462279673014e-16 6.98677986873195 Trimmed Mean ( 48 / 66 ) 6.25994982346346e-15 8.70455242148726e-16 7.1915815085572 Trimmed Mean ( 49 / 66 ) 6.34350959560342e-15 8.5464111583037e-16 7.42242501338127 Trimmed Mean ( 50 / 66 ) 6.43041175862898e-15 8.3675151509533e-16 7.68497175400557 Trimmed Mean ( 51 / 66 ) 6.5208609487168e-15 8.16459689498503e-16 7.98675186612353 Trimmed Mean ( 52 / 66 ) 6.5641936330963e-15 8.0595583756803e-16 8.14460709522714 Trimmed Mean ( 53 / 66 ) 6.60937026149195e-15 7.93975084778534e-16 8.32440512076714 Trimmed Mean ( 54 / 66 ) 6.6565110911222e-15 7.80298175414206e-16 8.53072748451413 Trimmed Mean ( 55 / 66 ) 6.705747068736e-15 7.6466004423432e-16 8.76957952661265 Trimmed Mean ( 56 / 66 ) 6.75722104533226e-15 7.4673600397011e-16 9.0490093010203 Trimmed Mean ( 57 / 66 ) 6.75722104533226e-15 7.2612201385162e-16 9.30590302515339 Trimmed Mean ( 58 / 66 ) 6.81108916037485e-15 7.02305519197437e-16 9.69818543951963 Trimmed Mean ( 59 / 66 ) 6.92670852924676e-15 6.74620536645962e-16 10.2675625080798 Trimmed Mean ( 60 / 66 ) 6.9888539400154e-15 6.42174825121688e-16 10.8831017140715 Trimmed Mean ( 61 / 66 ) 7.05418629492604e-15 6.03723796153939e-16 11.6844595821221 Trimmed Mean ( 62 / 66 ) 7.12295719483197e-15 5.57432490289477e-16 12.7781521868827 Trimmed Mean ( 63 / 66 ) 7.19544544067875e-15 5.00368991351792e-16 14.3802784845632 Trimmed Mean ( 64 / 66 ) 7.06841992344686e-15 4.87469590245162e-16 14.5002274293499 Trimmed Mean ( 65 / 66 ) 6.93413580523029e-15 4.71670320498798e-16 14.7012341117782 Trimmed Mean ( 66 / 66 ) 6.79195262123627e-15 4.52208494101194e-16 15.019515798207 Median 5.7731597280508e-15 Midrange 1.20008170068076e-14 Midmean - Weighted Average at Xnp 6.08861620125478e-15 Midmean - Weighted Average at X(n+1)p 6.08861620125478e-15 Midmean - Empirical Distribution Function 6.08861620125478e-15 Midmean - Empirical Distribution Function - Averaging 6.08861620125478e-15 Midmean - Empirical Distribution Function - Interpolation 6.08861620125478e-15 Midmean - Closest Observation 6.08861620125478e-15 Midmean - True Basic - Statistics Graphics Toolkit 6.08861620125478e-15 Midmean - MS Excel (old versions) 6.08861620125478e-15 Number of observations 200

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290437634ivh5yd9gwgw7y4t/1bw2v1290437751.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290437634ivh5yd9gwgw7y4t/1bw2v1290437751.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290437634ivh5yd9gwgw7y4t/2bw2v1290437751.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290437634ivh5yd9gwgw7y4t/2bw2v1290437751.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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