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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_centraltendency.wasp
Title produced by softwareCentral Tendency
Date of computationMon, 05 Dec 2016 20:17:01 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/05/t1480966363f80nl2tdvlsktks.htm/, Retrieved Wed, 01 May 2024 16:07:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297744, Retrieved Wed, 01 May 2024 16:07:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact55
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [Central Tendency ...] [2016-12-05 19:17:01] [bde5266f17215258f6d7c4cd7e531432] [Current]
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Dataseries X:
3618
3723.5
3884
4075
4090.5
4033
4231.5
4360
4493
4680
4730
4740
4690
4762
4895.5
5000
5110.5




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297744&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=297744&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297744&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center







Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean4418.62110.69139.9186
Geometric Mean4395.91
Harmonic Mean4372.74
Quadratic Mean4440.75
Winsorized Mean ( 1 / 5 )4418.32105.61741.8333
Winsorized Mean ( 2 / 5 )4424.9194.276846.9353
Winsorized Mean ( 3 / 5 )4427.6578.520256.3886
Winsorized Mean ( 4 / 5 )4432.3574.116459.8026
Winsorized Mean ( 5 / 5 )4433.9771.990261.5913
Trimmed Mean ( 1 / 5 )4425.87102.63643.1219
Trimmed Mean ( 2 / 5 )4435.7394.325247.026
Trimmed Mean ( 3 / 5 )4444.0989.004949.9309
Trimmed Mean ( 4 / 5 )4454.4491.315948.7806
Trimmed Mean ( 5 / 5 )4467.8694.314547.3719
Median4493
Midrange4364.25
Midmean - Weighted Average at Xnp4418.75
Midmean - Weighted Average at X(n+1)p4454.44
Midmean - Empirical Distribution Function4454.44
Midmean - Empirical Distribution Function - Averaging4454.44
Midmean - Empirical Distribution Function - Interpolation4454.44
Midmean - Closest Observation4412.3
Midmean - True Basic - Statistics Graphics Toolkit4454.44
Midmean - MS Excel (old versions)4454.44
Number of observations17

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 4418.62 & 110.691 & 39.9186 \tabularnewline
Geometric Mean & 4395.91 &  &  \tabularnewline
Harmonic Mean & 4372.74 &  &  \tabularnewline
Quadratic Mean & 4440.75 &  &  \tabularnewline
Winsorized Mean ( 1 / 5 ) & 4418.32 & 105.617 & 41.8333 \tabularnewline
Winsorized Mean ( 2 / 5 ) & 4424.91 & 94.2768 & 46.9353 \tabularnewline
Winsorized Mean ( 3 / 5 ) & 4427.65 & 78.5202 & 56.3886 \tabularnewline
Winsorized Mean ( 4 / 5 ) & 4432.35 & 74.1164 & 59.8026 \tabularnewline
Winsorized Mean ( 5 / 5 ) & 4433.97 & 71.9902 & 61.5913 \tabularnewline
Trimmed Mean ( 1 / 5 ) & 4425.87 & 102.636 & 43.1219 \tabularnewline
Trimmed Mean ( 2 / 5 ) & 4435.73 & 94.3252 & 47.026 \tabularnewline
Trimmed Mean ( 3 / 5 ) & 4444.09 & 89.0049 & 49.9309 \tabularnewline
Trimmed Mean ( 4 / 5 ) & 4454.44 & 91.3159 & 48.7806 \tabularnewline
Trimmed Mean ( 5 / 5 ) & 4467.86 & 94.3145 & 47.3719 \tabularnewline
Median & 4493 &  &  \tabularnewline
Midrange & 4364.25 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 4418.75 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 4454.44 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 4454.44 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 4454.44 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 4454.44 &  &  \tabularnewline
Midmean - Closest Observation & 4412.3 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 4454.44 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 4454.44 &  &  \tabularnewline
Number of observations & 17 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297744&T=1

[TABLE]
[ROW][C]Central Tendency - Ungrouped Data[/C][/ROW]
[ROW][C]Measure[/C][C]Value[/C][C]S.E.[/C][C]Value/S.E.[/C][/ROW]
[ROW][C]Arithmetic Mean[/C][C]4418.62[/C][C]110.691[/C][C]39.9186[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]4395.91[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]4372.74[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]4440.75[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 5 )[/C][C]4418.32[/C][C]105.617[/C][C]41.8333[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 5 )[/C][C]4424.91[/C][C]94.2768[/C][C]46.9353[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 5 )[/C][C]4427.65[/C][C]78.5202[/C][C]56.3886[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 5 )[/C][C]4432.35[/C][C]74.1164[/C][C]59.8026[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 5 )[/C][C]4433.97[/C][C]71.9902[/C][C]61.5913[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 5 )[/C][C]4425.87[/C][C]102.636[/C][C]43.1219[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 5 )[/C][C]4435.73[/C][C]94.3252[/C][C]47.026[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 5 )[/C][C]4444.09[/C][C]89.0049[/C][C]49.9309[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 5 )[/C][C]4454.44[/C][C]91.3159[/C][C]48.7806[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 5 )[/C][C]4467.86[/C][C]94.3145[/C][C]47.3719[/C][/ROW]
[ROW][C]Median[/C][C]4493[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]4364.25[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]4418.75[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]4454.44[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]4454.44[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]4454.44[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]4454.44[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]4412.3[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]4454.44[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]4454.44[/C][C][/C][C][/C][/ROW]
[ROW][C]Number of observations[/C][C]17[/C][C][/C][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297744&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297744&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean4418.62110.69139.9186
Geometric Mean4395.91
Harmonic Mean4372.74
Quadratic Mean4440.75
Winsorized Mean ( 1 / 5 )4418.32105.61741.8333
Winsorized Mean ( 2 / 5 )4424.9194.276846.9353
Winsorized Mean ( 3 / 5 )4427.6578.520256.3886
Winsorized Mean ( 4 / 5 )4432.3574.116459.8026
Winsorized Mean ( 5 / 5 )4433.9771.990261.5913
Trimmed Mean ( 1 / 5 )4425.87102.63643.1219
Trimmed Mean ( 2 / 5 )4435.7394.325247.026
Trimmed Mean ( 3 / 5 )4444.0989.004949.9309
Trimmed Mean ( 4 / 5 )4454.4491.315948.7806
Trimmed Mean ( 5 / 5 )4467.8694.314547.3719
Median4493
Midrange4364.25
Midmean - Weighted Average at Xnp4418.75
Midmean - Weighted Average at X(n+1)p4454.44
Midmean - Empirical Distribution Function4454.44
Midmean - Empirical Distribution Function - Averaging4454.44
Midmean - Empirical Distribution Function - Interpolation4454.44
Midmean - Closest Observation4412.3
Midmean - True Basic - Statistics Graphics Toolkit4454.44
Midmean - MS Excel (old versions)4454.44
Number of observations17



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,'Arithmetic Mean',header=TRUE)
a<-table.element(a,signif(arm,6))
a<-table.element(a, signif(armse,6))
a<-table.element(a,signif(armose,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Geometric Mean',header=TRUE)
a<-table.element(a,signif(geo,6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Harmonic Mean',header=TRUE)
a<-table.element(a,signif(har,6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Quadratic Mean',header=TRUE)
a<-table.element(a,signif(qua,6))
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, mylabel,header=TRUE)
a<-table.element(a,signif(win[j,1],6))
a<-table.element(a,signif(win[j,2],6))
a<-table.element(a,signif(win[j,1]/win[j,2],6))
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, mylabel,header=TRUE)
a<-table.element(a,signif(tri[j,1],6))
a<-table.element(a,signif(tri[j,2],6))
a<-table.element(a,signif(tri[j,1]/tri[j,2],6))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a, 'Median',header=TRUE)
a<-table.element(a,signif(median(x),6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Midrange',header=TRUE)
a<-table.element(a,signif(midr,6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'Weighted Average at Xnp',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[1],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'Weighted Average at X(n+1)p',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[2],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'Empirical Distribution Function',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[3],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'Empirical Distribution Function - Averaging',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[4],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'Empirical Distribution Function - Interpolation',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[5],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'Closest Observation',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[6],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'True Basic - Statistics Graphics Toolkit',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[7],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'MS Excel (old versions)',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[8],6))
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,signif(length(x),6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')