*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 14:44:40 +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/t1290436975txc5hwncvu3ki09.htm/, Retrieved Mon, 22 Nov 2010 15:42:55 +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/t1290436975txc5hwncvu3ki09.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-15 -3.5527136788005E-15 -3.5527136788005E-15 -3.5527136788005E-15 -3.5527136788005E-15 -3.5527136788005E-15 -3.5527136788005E-15 -3.5527136788005E-15 -0.027018803106504 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 0.027018803106547 -0.027018803106504 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 0.027018803106547 -0.027018803106504 0.027018803106547 -3.5527136788005E-15 -3.5527136788005E-15 -3.5527136788005E-15 -3.5527136788005E-15 -3.5527136788005E-15 -3.5527136788005E-15 -3.5527136788005E-15 -0.027018803106504 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 4.7517545453957E-14 0.027018803106547 -3.5527136788005E-15 -3.5527136788005E-15 -3.5527136788005E-15 -3.5527136788005E-15 -3.5527136788005E-15 -3.5527136788005E-15 -3.5527136788005E-15 -0.027018803106504 4.7517545453957E-14 4.75175 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean -3.0713737046503e-05 0.000845800934981046 -0.0363131982671446 Geometric Mean NaN Harmonic Mean -3.27027139976659e-14 Quadratic Mean 0.0119315300123241 Winsorized Mean ( 1 / 66 ) -3.0713737046503e-05 0.000845800934981046 -0.0363131982671446 Winsorized Mean ( 2 / 66 ) -3.0713737046503e-05 0.000845800934981046 -0.0363131982671446 Winsorized Mean ( 3 / 66 ) -3.0713737046503e-05 0.000845800934981046 -0.0363131982671446 Winsorized Mean ( 4 / 66 ) -3.0713737046503e-05 0.000845800934981046 -0.0363131982671446 Winsorized Mean ( 5 / 66 ) -3.0713737046503e-05 0.000845800934981046 -0.0363131982671446 Winsorized Mean ( 6 / 66 ) -3.0713737046503e-05 0.000845800934981046 -0.0363131982671446 Winsorized Mean ( 7 / 66 ) -3.07137370487079e-05 0.00083965158079259 -0.0365791451493673 Winsorized Mean ( 8 / 66 ) -3.07137370487079e-05 0.00083965158079259 -0.0365791451493673 Winsorized Mean ( 9 / 66 ) -3.07137370487080e-05 0.00083965158079259 -0.0365791451493675 Winsorized Mean ( 10 / 66 ) -3.07137370487079e-05 0.00083965158079259 -0.0365791451493673 Winsorized Mean ( 11 / 66 ) -3.07137370487079e-05 0.00083965158079259 -0.0365791451493673 Winsorized Mean ( 12 / 66 ) -3.07137370487079e-05 0.00083965158079259 -0.0365791451493673 Winsorized Mean ( 13 / 66 ) -3.07137370487082e-05 0.00083965158079259 -0.0365791451493677 Winsorized Mean ( 14 / 66 ) -3.07137370487079e-05 0.00083965158079259 -0.0365791451493673 Winsorized Mean ( 15 / 66 ) -3.07137370487079e-05 0.00083965158079259 -0.0365791451493673 Winsorized Mean ( 16 / 66 ) -3.07137370487079e-05 0.00083965158079259 -0.0365791451493673 Winsorized Mean ( 17 / 66 ) -3.07137370487079e-05 0.00083965158079259 -0.0365791451493673 Winsorized Mean ( 18 / 66 ) -3.07137370487082e-05 0.00083965158079259 -0.0365791451493677 Winsorized Mean ( 19 / 66 ) -3.07137370487076e-05 0.00083965158079259 -0.036579145149367 Winsorized Mean ( 20 / 66 ) -0.000644988477982336 0.000133488018499891 -4.83180801715821 Winsorized Mean ( 21 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 22 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 23 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 24 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 25 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437772 Winsorized Mean ( 26 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 27 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 28 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 29 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437772 Winsorized Mean ( 30 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437772 Winsorized Mean ( 31 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 32 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 33 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437772 Winsorized Mean ( 34 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 35 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 36 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 37 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437772 Winsorized Mean ( 38 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437772 Winsorized Mean ( 39 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 40 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 41 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 42 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437772 Winsorized Mean ( 43 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 44 / 66 ) 2.40474307133802e-15 1.91385850846780e-15 1.25648947437771 Winsorized Mean ( 45 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 46 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 47 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 48 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 49 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 50 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 51 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 52 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 53 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 54 / 66 ) -7.08766378920713e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 55 / 66 ) -7.08766378920713e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 56 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 57 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 58 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 59 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 60 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 61 / 66 ) -7.08766378920713e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 62 / 66 ) -7.08766378920713e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 63 / 66 ) -7.08766378920713e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 64 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 65 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Winsorized Mean ( 66 / 66 ) -7.08766378920712e-15 9.5846992528132e-16 -7.39476910256399 Trimmed Mean ( 1 / 66 ) -3.10239768148667e-05 0.00083217208232862 -0.037280722910163 Trimmed Mean ( 2 / 66 ) -3.13405480070745e-05 0.000817649793519687 -0.0383300384290012 Trimmed Mean ( 3 / 66 ) -3.16636464403588e-05 0.000802152319055892 -0.0394733589720539 Trimmed Mean ( 4 / 66 ) -3.19934760910031e-05 0.000785586080221846 -0.040725614794458 Trimmed Mean ( 5 / 66 ) -3.23302495237663e-05 0.000767843089226429 -0.0421052816355197 Trimmed Mean ( 6 / 66 ) -3.26741883487159e-05 0.000748797578276337 -0.0436355422301564 Trimmed Mean ( 7 / 66 ) -3.30255237075355e-05 0.00072830151424199 -0.0453459495301313 Trimmed Mean ( 8 / 66 ) -3.33844967912044e-05 0.000707416552262843 -0.047192134089054 Trimmed Mean ( 9 / 66 ) -3.37513593931956e-05 0.000684832464732508 -0.0492841112700151 Trimmed Mean ( 10 / 66 ) -3.41263744974534e-05 0.000660317659606473 -0.0516817534727021 Trimmed Mean ( 11 / 66 ) -3.45098169074248e-05 0.000633584720431139 -0.054467564943709 Trimmed Mean ( 12 / 66 ) -3.49019739176228e-05 0.00060426880607869 -0.0577590197715381 Trimmed Mean ( 13 / 66 ) -3.53031460315035e-05 0.000571893676966547 -0.0617302611540651 Trimmed Mean ( 14 / 66 ) -3.5713647729428e-05 0.000535815286503167 -0.0666529093682677 Trimmed Mean ( 15 / 66 ) -3.61338082908330e-05 0.000495121422156445 -0.0729796907866687 Trimmed Mean ( 16 / 66 ) -3.65639726751287e-05 0.000448436017110739 -0.0815366546842275 Trimmed Mean ( 17 / 66 ) -3.70045024662748e-05 0.000393485308943417 -0.0940429073848754 Trimmed Mean ( 18 / 66 ) -3.74557768864733e-05 0.000325927560747908 -0.114920557195358 Trimmed Mean ( 19 / 66 ) -3.79181938849483e-05 0.000234855088671104 -0.161453575902925 Trimmed Mean ( 20 / 66 ) -3.83921713083852e-05 3.83921713087762e-05 -0.999999999989815 Trimmed Mean ( 21 / 66 ) 9.27528096521589e-17 1.86266294913295e-15 0.0497958096473302 Trimmed Mean ( 22 / 66 ) -4.83943369709096e-17 1.85458846299792e-15 -0.0260943804711697 Trimmed Mean ( 23 / 66 ) -1.93207643246525e-16 1.84565985707984e-15 -0.10468215067115 Trimmed Mean ( 24 / 66 ) -3.41831826003078e-16 1.83580458129308e-15 -0.18620273066445 Trimmed Mean ( 25 / 66 ) -4.94419320299806e-16 1.82494227927006e-15 -0.270923264760770 Trimmed Mean ( 26 / 66 ) -6.51130800928878e-16 1.81298368506931e-15 -0.359148737129416 Trimmed Mean ( 27 / 66 ) -8.12135746780664e-16 1.79982931637080e-15 -0.45122931346527 Trimmed Mean ( 28 / 66 ) -9.77613052239444e-16 1.78536791608268e-15 -0.547569519667658 Trimmed Mean ( 29 / 66 ) -9.77613052239444e-16 1.76947458006780e-15 -0.552487762893993 Trimmed Mean ( 30 / 66 ) -1.32275143219633e-15 1.75200848933609e-15 -0.754991451381365 Trimmed Mean ( 31 / 66 ) -1.50282363043470e-15 1.73281013829649e-15 -0.867275414207879 Trimmed Mean ( 32 / 66 ) -1.68819206979773e-15 1.71169791311892e-15 -0.98626752820049 Trimmed Mean ( 33 / 66 ) -1.87909389541041e-15 1.68846382066027e-15 -1.11290148620158 Trimmed Mean ( 34 / 66 ) -2.07578062482953e-15 1.66286809042950e-15 -1.24831346321245 Trimmed Mean ( 35 / 66 ) -2.27851925361539e-15 1.63463225620294e-15 -1.39390327394378 Trimmed Mean ( 36 / 66 ) -2.48759346455081e-15 1.60343014765037e-15 -1.55141991573258 Trimmed Mean ( 37 / 66 ) -2.70330495202386e-15 1.56887594703828e-15 -1.72308394244118 Trimmed Mean ( 38 / 66 ) -2.92597487457669e-15 1.53050802299143e-15 -1.91176709342417 Trimmed Mean ( 39 / 66 ) -3.15594545032797e-15 1.48776651525890e-15 -2.12126393352708 Trimmed Mean ( 40 / 66 ) -3.39358171193763e-15 1.43996136535542e-15 -2.35671719643673 Trimmed Mean ( 41 / 66 ) -3.63927344004253e-15 1.38622516579781e-15 -2.62531191168213 Trimmed Mean ( 42 / 66 ) -3.89343729670278e-15 1.32544074543348e-15 -2.93746612975101 Trimmed Mean ( 43 / 66 ) -4.15651918342127e-15 1.25612426926675e-15 -3.30900316562437 Trimmed Mean ( 44 / 66 ) -4.42899685180829e-15 1.17622426171372e-15 -3.76543572171805 Trimmed Mean ( 45 / 66 ) -4.71138279904574e-15 1.08274647267576e-15 -4.35132592711445 Trimmed Mean ( 46 / 66 ) -4.61359345788683e-15 1.08204780654547e-15 -4.26376120350552 Trimmed Mean ( 47 / 66 ) -4.51211395291061e-15 1.08081634335356e-15 -4.17472772377813 Trimmed Mean ( 48 / 66 ) -4.40673139005069e-15 1.07898404138305e-15 -4.08414881132265 Trimmed Mean ( 49 / 66 ) -4.29721617766684e-15 1.07647294870018e-15 -3.99194070120912 Trimmed Mean ( 50 / 66 ) -4.18332035678765e-15 1.07319340898344e-15 -3.89801159955895 Trimmed Mean ( 51 / 66 ) -4.06477572689298e-15 1.06904185870959e-15 -3.80226058856053 Trimmed Mean ( 52 / 66 ) -3.94129173741936e-15 1.06389809887498e-15 -3.70457635142601 Trimmed Mean ( 53 / 66 ) -3.8125531100958e-15 1.05762188283426e-15 -3.60483569031187 Trimmed Mean ( 54 / 66 ) -3.67821715114948e-15 1.05004860198928e-15 -3.50290181252681 Trimmed Mean ( 55 / 66 ) -3.53791070513888e-15 1.04098376319580e-15 -3.39862237070591 Trimmed Mean ( 56 / 66 ) -3.39122669340052e-15 1.03019581981719e-15 -3.29182727027790 Trimmed Mean ( 57 / 66 ) -3.39122669340052e-15 1.01740671512290e-15 -3.33320651711136 Trimmed Mean ( 58 / 66 ) -3.23772016948828e-15 1.00227917449719e-15 -3.2303576207822 Trimmed Mean ( 59 / 66 ) -2.90824275231081e-15 9.84399254798228e-16 -2.95433254153256 Trimmed Mean ( 60 / 66 ) -2.73114864057791e-15 9.63251759847354e-16 -2.83534248721302 Trimmed Mean ( 61 / 66 ) -2.54497277952538e-15 9.38184529348652e-16 -2.71265694531572 Trimmed Mean ( 62 / 66 ) -2.34899818894377e-15 9.08354600622093e-16 -2.58599250483791 Trimmed Mean ( 63 / 66 ) -2.14243037724964e-15 8.72643220404622e-16 -2.45510459160646 Trimmed Mean ( 64 / 66 ) -1.92438657601695e-15 8.29513637548432e-16 -2.31989745425323 Trimmed Mean ( 65 / 66 ) -1.69388312899953e-15 7.76754368509327e-16 -2.18071915353404 Trimmed Mean ( 66 / 66 ) -1.44982065568697e-15 7.1096525804368e-16 -2.03922855481905 Median -3.5527136788005e-15 Midrange 2.15001627612565e-14 Midmean - Weighted Average at Xnp -8.40123990276066e-15 Midmean - Weighted Average at X(n+1)p -8.40123990276066e-15 Midmean - Empirical Distribution Function -8.40123990276066e-15 Midmean - Empirical Distribution Function - Averaging -8.40123990276066e-15 Midmean - Empirical Distribution Function - Interpolation -8.40123990276066e-15 Midmean - Closest Observation -8.40123990276066e-15 Midmean - True Basic - Statistics Graphics Toolkit -8.40123990276066e-15 Midmean - MS Excel (old versions) -8.40123990276066e-15 Number of observations 200

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/22/t1290436975txc5hwncvu3ki09/19qik1290437071.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290436975txc5hwncvu3ki09/19qik1290437071.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290436975txc5hwncvu3ki09/29qik1290437071.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/22/t1290436975txc5hwncvu3ki09/29qik1290437071.ps (open in new window)

Parameters (Session):

Parameters (R input):

R code (references can be found in the software module):

geomean <- function(x) { return(exp(mean(log(x)))) } harmean <- function(x) { return(1/mean(1/x)) } quamean <- function(x) { return(sqrt(mean(x*x))) } winmean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) win <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { win[j,1] <- (j*x[j+1]+sum(x[(j+1):(n-j)])+j*x[n-j])/n win[j,2] <- sd(c(rep(x[j+1],j),x[(j+1):(n-j)],rep(x[n-j],j)))/sqrtn } return(win) } trimean <- function(x) { x <-sort(x[!is.na(x)]) n<-length(x) denom <- 3 nodenom <- n/denom if (nodenom>40) denom <- n/40 sqrtn = sqrt(n) roundnodenom = floor(nodenom) tri <- array(NA,dim=c(roundnodenom,2)) for (j in 1:roundnodenom) { tri[j,1] <- mean(x,trim=j/n) tri[j,2] <- sd(x[(j+1):(n-j)]) / sqrt(n-j*2) } return(tri) } midrange <- function(x) { return((max(x)+min(x))/2) } q1 <- function(data,n,p,i,f) { np <- n*p; i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q2 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q3 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- data[i+1] } } q4 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- (data[i]+data[i+1])/2 } else { qvalue <- data[i+1] } } q5 <- function(data,n,p,i,f) { np <- (n-1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i+1] } else { qvalue <- data[i+1] + f*(data[i+2]-data[i+1]) } } q6 <- function(data,n,p,i,f) { np <- n*p+0.5 i <<- floor(np) f <<- np - i qvalue <- data[i] } q7 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- f*data[i] + (1-f)*data[i+1] } } q8 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { if (f == 0.5) { qvalue <- (data[i]+data[i+1])/2 } else { if (f < 0.5) { qvalue <- data[i] } else { qvalue <- data[i+1] } } } } midmean <- function(x,def) { x <-sort(x[!is.na(x)]) n<-length(x) if (def==1) { qvalue1 <- q1(x,n,0.25,i,f) qvalue3 <- q1(x,n,0.75,i,f) } if (def==2) { qvalue1 <- q2(x,n,0.25,i,f) qvalue3 <- q2(x,n,0.75,i,f) } if (def==3) { qvalue1 <- q3(x,n,0.25,i,f) qvalue3 <- q3(x,n,0.75,i,f) } if (def==4) { qvalue1 <- q4(x,n,0.25,i,f) qvalue3 <- q4(x,n,0.75,i,f) } if (def==5) { qvalue1 <- q5(x,n,0.25,i,f) qvalue3 <- q5(x,n,0.75,i,f) } if (def==6) { qvalue1 <- q6(x,n,0.25,i,f) qvalue3 <- q6(x,n,0.75,i,f) } if (def==7) { qvalue1 <- q7(x,n,0.25,i,f) qvalue3 <- q7(x,n,0.75,i,f) } if (def==8) { qvalue1 <- q8(x,n,0.25,i,f) qvalue3 <- q8(x,n,0.75,i,f) } midm <- 0 myn <- 0 roundno4 <- round(n/4) round3no4 <- round(3*n/4) for (i in 1:n) { if ((x[i]>=qvalue1) & (x[i]<=qvalue3)){ midm = midm + x[i] myn = myn + 1 } } midm = midm / myn return(midm) } (arm <- mean(x)) sqrtn <- sqrt(length(x)) (armse <- sd(x) / sqrtn) (armose <- arm / armse) (geo <- geomean(x)) (har <- harmean(x)) (qua <- quamean(x)) (win <- winmean(x)) (tri <- trimean(x)) (midr <- midrange(x)) midm <- array(NA,dim=8) for (j in 1:8) midm[j] <- midmean(x,j) midm bitmap(file='test1.png') lb <- win[,1] - 2*win[,2] ub <- win[,1] + 2*win[,2] if ((ylimmin == '') | (ylimmax == '')) plot(win[,1],type='b',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(win[,1],type='l',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() bitmap(file='test2.png') lb <- tri[,1] - 2*tri[,2] ub <- tri[,1] + 2*tri[,2] if ((ylimmin == '') | (ylimmax == '')) plot(tri[,1],type='b',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(tri[,1],type='l',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(ylimmin,ylimmax)) lines(ub,lty=3) lines(lb,lty=3) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Central Tendency - Ungrouped Data',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Measure',header=TRUE) a<-table.element(a,'Value',header=TRUE) a<-table.element(a,'S.E.',header=TRUE) a<-table.element(a,'Value/S.E.',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', 'Arithmetic Mean', 'click to view the definition of the Arithmetic Mean'),header=TRUE) a<-table.element(a,arm) a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean_standard_error.htm', armse, 'click to view the definition of the Standard Error of the Arithmetic Mean')) a<-table.element(a,armose) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/geometric_mean.htm', 'Geometric Mean', 'click to view the definition of the Geometric Mean'),header=TRUE) a<-table.element(a,geo) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/harmonic_mean.htm', 'Harmonic Mean', 'click to view the definition of the Harmonic Mean'),header=TRUE) a<-table.element(a,har) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/quadratic_mean.htm', 'Quadratic Mean', 'click to view the definition of the Quadratic Mean'),header=TRUE) a<-table.element(a,qua) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) for (j in 1:length(win[,1])) { a<-table.row.start(a) mylabel <- paste('Winsorized Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(win[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/winsorized_mean.htm', mylabel, 'click to view the definition of the Winsorized Mean'),header=TRUE) a<-table.element(a,win[j,1]) a<-table.element(a,win[j,2]) a<-table.element(a,win[j,1]/win[j,2]) a<-table.row.end(a) } for (j in 1:length(tri[,1])) { a<-table.row.start(a) mylabel <- paste('Trimmed Mean (',j) mylabel <- paste(mylabel,'/') mylabel <- paste(mylabel,length(tri[,1])) mylabel <- paste(mylabel,')') a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', mylabel, 'click to view the definition of the Trimmed Mean'),header=TRUE) a<-table.element(a,tri[j,1]) a<-table.element(a,tri[j,2]) a<-table.element(a,tri[j,1]/tri[j,2]) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/median_1.htm', 'Median', 'click to view the definition of the Median'),header=TRUE) a<-table.element(a,median(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/midrange.htm', 'Midrange', 'click to view the definition of the Midrange'),header=TRUE) a<-table.element(a,midr) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_1.htm','Weighted Average at Xnp',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[1]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_2.htm','Weighted Average at X(n+1)p',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[2]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_3.htm','Empirical Distribution Function',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[3]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_4.htm','Empirical Distribution Function - Averaging',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[4]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_5.htm','Empirical Distribution Function - Interpolation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[5]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_6.htm','Closest Observation',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[6]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_7.htm','True Basic - Statistics Graphics Toolkit',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[7]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean') mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_8.htm','MS Excel (old versions)',''),sep=' - ') a<-table.element(a,mylabel,header=TRUE) a<-table.element(a,midm[8]) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of observations',header=TRUE) a<-table.element(a,length(x)) a<-table.element(a,'') a<-table.element(a,'') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')

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Software written by Ed van Stee & Patrick Wessa

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