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of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_centraltendency.wasp
Title produced by softwareCentral Tendency
Date of computationTue, 29 Nov 2016 20:44:17 +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/Nov/29/t148044870366t3tgtxr0dzjek.htm/, Retrieved Wed, 08 May 2024 02:48:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297284, Retrieved Wed, 08 May 2024 02:48:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact62
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [HPC Retail Sales] [2008-03-02 15:42:48] [74be16979710d4c4e7c6647856088456]
- RMPD    [Central Tendency] [Central tendens N...] [2016-11-29 19:44:17] [fd005a509166a1985dac46f39e8d81c5] [Current]
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Dataseries X:
4822.2
4627.82
4756.38
5042.92
5091.66
4887.44
5057.84
4941.12
4868.4
4965.74
4934.96
4863.56
4797.96
4878.32
4843.66
4713.62
4647.88
4718.48
4673.04
4534.54
4471.42
4212.42
4277.6
4312.66
4315.3
4495.32
4575.88
4561.5
4587.5
4658.34
4839.54
5057.32
5210.58
5027.7
5028.72
4996.66
4942.1
4865.68
4964.62
5007.74
5089.92
5340.76
5177.72
5191.68




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=297284&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=297284&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297284&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 Mean4815.3740.1916119.81
Geometric Mean4808.02
Harmonic Mean4800.53
Quadratic Mean4822.58
Winsorized Mean ( 1 / 14 )4813.8938.8931123.772
Winsorized Mean ( 2 / 14 )4814.6338.1934126.059
Winsorized Mean ( 3 / 14 )4813.8537.923126.938
Winsorized Mean ( 4 / 14 )4820.2232.2936149.262
Winsorized Mean ( 5 / 14 )4822.7431.5862152.685
Winsorized Mean ( 6 / 14 )4823.7229.4958163.539
Winsorized Mean ( 7 / 14 )4827.9228.5253169.251
Winsorized Mean ( 8 / 14 )4827.9227.4792175.693
Winsorized Mean ( 9 / 14 )4827.3926.4536182.485
Winsorized Mean ( 10 / 14 )4836.3224.5382197.094
Winsorized Mean ( 11 / 14 )4836.3522.6655213.379
Winsorized Mean ( 12 / 14 )4836.1821.5894224.007
Winsorized Mean ( 13 / 14 )4831.3919.2226251.339
Winsorized Mean ( 14 / 14 )4843.9416.7545289.112
Trimmed Mean ( 1 / 14 )4817.2237.4612128.592
Trimmed Mean ( 2 / 14 )4820.8735.537135.658
Trimmed Mean ( 3 / 14 )4824.4933.4568144.2
Trimmed Mean ( 4 / 14 )4828.8230.7202157.187
Trimmed Mean ( 5 / 14 )4831.629.7347162.49
Trimmed Mean ( 6 / 14 )4834.0428.6105168.96
Trimmed Mean ( 7 / 14 )4836.5627.7931174.02
Trimmed Mean ( 8 / 14 )4838.526.912179.79
Trimmed Mean ( 9 / 14 )4840.7425.9185186.767
Trimmed Mean ( 10 / 14 )4843.4624.7087196.022
Trimmed Mean ( 11 / 14 )4844.8823.5634205.611
Trimmed Mean ( 12 / 14 )4846.5922.4269216.106
Trimmed Mean ( 13 / 14 )4848.7120.8548232.499
Trimmed Mean ( 14 / 14 )4852.3819.1828252.955
Median4864.62
Midrange4776.59
Midmean - Weighted Average at Xnp4835.45
Midmean - Weighted Average at X(n+1)p4844.88
Midmean - Empirical Distribution Function4835.45
Midmean - Empirical Distribution Function - Averaging4844.88
Midmean - Empirical Distribution Function - Interpolation4844.88
Midmean - Closest Observation4835.45
Midmean - True Basic - Statistics Graphics Toolkit4844.88
Midmean - MS Excel (old versions)4843.46
Number of observations44

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 4815.37 & 40.1916 & 119.81 \tabularnewline
Geometric Mean & 4808.02 &  &  \tabularnewline
Harmonic Mean & 4800.53 &  &  \tabularnewline
Quadratic Mean & 4822.58 &  &  \tabularnewline
Winsorized Mean ( 1 / 14 ) & 4813.89 & 38.8931 & 123.772 \tabularnewline
Winsorized Mean ( 2 / 14 ) & 4814.63 & 38.1934 & 126.059 \tabularnewline
Winsorized Mean ( 3 / 14 ) & 4813.85 & 37.923 & 126.938 \tabularnewline
Winsorized Mean ( 4 / 14 ) & 4820.22 & 32.2936 & 149.262 \tabularnewline
Winsorized Mean ( 5 / 14 ) & 4822.74 & 31.5862 & 152.685 \tabularnewline
Winsorized Mean ( 6 / 14 ) & 4823.72 & 29.4958 & 163.539 \tabularnewline
Winsorized Mean ( 7 / 14 ) & 4827.92 & 28.5253 & 169.251 \tabularnewline
Winsorized Mean ( 8 / 14 ) & 4827.92 & 27.4792 & 175.693 \tabularnewline
Winsorized Mean ( 9 / 14 ) & 4827.39 & 26.4536 & 182.485 \tabularnewline
Winsorized Mean ( 10 / 14 ) & 4836.32 & 24.5382 & 197.094 \tabularnewline
Winsorized Mean ( 11 / 14 ) & 4836.35 & 22.6655 & 213.379 \tabularnewline
Winsorized Mean ( 12 / 14 ) & 4836.18 & 21.5894 & 224.007 \tabularnewline
Winsorized Mean ( 13 / 14 ) & 4831.39 & 19.2226 & 251.339 \tabularnewline
Winsorized Mean ( 14 / 14 ) & 4843.94 & 16.7545 & 289.112 \tabularnewline
Trimmed Mean ( 1 / 14 ) & 4817.22 & 37.4612 & 128.592 \tabularnewline
Trimmed Mean ( 2 / 14 ) & 4820.87 & 35.537 & 135.658 \tabularnewline
Trimmed Mean ( 3 / 14 ) & 4824.49 & 33.4568 & 144.2 \tabularnewline
Trimmed Mean ( 4 / 14 ) & 4828.82 & 30.7202 & 157.187 \tabularnewline
Trimmed Mean ( 5 / 14 ) & 4831.6 & 29.7347 & 162.49 \tabularnewline
Trimmed Mean ( 6 / 14 ) & 4834.04 & 28.6105 & 168.96 \tabularnewline
Trimmed Mean ( 7 / 14 ) & 4836.56 & 27.7931 & 174.02 \tabularnewline
Trimmed Mean ( 8 / 14 ) & 4838.5 & 26.912 & 179.79 \tabularnewline
Trimmed Mean ( 9 / 14 ) & 4840.74 & 25.9185 & 186.767 \tabularnewline
Trimmed Mean ( 10 / 14 ) & 4843.46 & 24.7087 & 196.022 \tabularnewline
Trimmed Mean ( 11 / 14 ) & 4844.88 & 23.5634 & 205.611 \tabularnewline
Trimmed Mean ( 12 / 14 ) & 4846.59 & 22.4269 & 216.106 \tabularnewline
Trimmed Mean ( 13 / 14 ) & 4848.71 & 20.8548 & 232.499 \tabularnewline
Trimmed Mean ( 14 / 14 ) & 4852.38 & 19.1828 & 252.955 \tabularnewline
Median & 4864.62 &  &  \tabularnewline
Midrange & 4776.59 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 4835.45 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 4844.88 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 4835.45 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 4844.88 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 4844.88 &  &  \tabularnewline
Midmean - Closest Observation & 4835.45 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 4844.88 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 4843.46 &  &  \tabularnewline
Number of observations & 44 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297284&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]4815.37[/C][C]40.1916[/C][C]119.81[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]4808.02[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]4800.53[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]4822.58[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 14 )[/C][C]4813.89[/C][C]38.8931[/C][C]123.772[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 14 )[/C][C]4814.63[/C][C]38.1934[/C][C]126.059[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 14 )[/C][C]4813.85[/C][C]37.923[/C][C]126.938[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 14 )[/C][C]4820.22[/C][C]32.2936[/C][C]149.262[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 14 )[/C][C]4822.74[/C][C]31.5862[/C][C]152.685[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 14 )[/C][C]4823.72[/C][C]29.4958[/C][C]163.539[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 14 )[/C][C]4827.92[/C][C]28.5253[/C][C]169.251[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 14 )[/C][C]4827.92[/C][C]27.4792[/C][C]175.693[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 14 )[/C][C]4827.39[/C][C]26.4536[/C][C]182.485[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 14 )[/C][C]4836.32[/C][C]24.5382[/C][C]197.094[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 14 )[/C][C]4836.35[/C][C]22.6655[/C][C]213.379[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 14 )[/C][C]4836.18[/C][C]21.5894[/C][C]224.007[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 14 )[/C][C]4831.39[/C][C]19.2226[/C][C]251.339[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 14 )[/C][C]4843.94[/C][C]16.7545[/C][C]289.112[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 14 )[/C][C]4817.22[/C][C]37.4612[/C][C]128.592[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 14 )[/C][C]4820.87[/C][C]35.537[/C][C]135.658[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 14 )[/C][C]4824.49[/C][C]33.4568[/C][C]144.2[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 14 )[/C][C]4828.82[/C][C]30.7202[/C][C]157.187[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 14 )[/C][C]4831.6[/C][C]29.7347[/C][C]162.49[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 14 )[/C][C]4834.04[/C][C]28.6105[/C][C]168.96[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 14 )[/C][C]4836.56[/C][C]27.7931[/C][C]174.02[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 14 )[/C][C]4838.5[/C][C]26.912[/C][C]179.79[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 14 )[/C][C]4840.74[/C][C]25.9185[/C][C]186.767[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 14 )[/C][C]4843.46[/C][C]24.7087[/C][C]196.022[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 14 )[/C][C]4844.88[/C][C]23.5634[/C][C]205.611[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 14 )[/C][C]4846.59[/C][C]22.4269[/C][C]216.106[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 14 )[/C][C]4848.71[/C][C]20.8548[/C][C]232.499[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 14 )[/C][C]4852.38[/C][C]19.1828[/C][C]252.955[/C][/ROW]
[ROW][C]Median[/C][C]4864.62[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]4776.59[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]4835.45[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]4844.88[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]4835.45[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]4844.88[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]4844.88[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]4835.45[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]4844.88[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]4843.46[/C][C][/C][C][/C][/ROW]
[ROW][C]Number of observations[/C][C]44[/C][C][/C][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297284&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297284&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 Mean4815.3740.1916119.81
Geometric Mean4808.02
Harmonic Mean4800.53
Quadratic Mean4822.58
Winsorized Mean ( 1 / 14 )4813.8938.8931123.772
Winsorized Mean ( 2 / 14 )4814.6338.1934126.059
Winsorized Mean ( 3 / 14 )4813.8537.923126.938
Winsorized Mean ( 4 / 14 )4820.2232.2936149.262
Winsorized Mean ( 5 / 14 )4822.7431.5862152.685
Winsorized Mean ( 6 / 14 )4823.7229.4958163.539
Winsorized Mean ( 7 / 14 )4827.9228.5253169.251
Winsorized Mean ( 8 / 14 )4827.9227.4792175.693
Winsorized Mean ( 9 / 14 )4827.3926.4536182.485
Winsorized Mean ( 10 / 14 )4836.3224.5382197.094
Winsorized Mean ( 11 / 14 )4836.3522.6655213.379
Winsorized Mean ( 12 / 14 )4836.1821.5894224.007
Winsorized Mean ( 13 / 14 )4831.3919.2226251.339
Winsorized Mean ( 14 / 14 )4843.9416.7545289.112
Trimmed Mean ( 1 / 14 )4817.2237.4612128.592
Trimmed Mean ( 2 / 14 )4820.8735.537135.658
Trimmed Mean ( 3 / 14 )4824.4933.4568144.2
Trimmed Mean ( 4 / 14 )4828.8230.7202157.187
Trimmed Mean ( 5 / 14 )4831.629.7347162.49
Trimmed Mean ( 6 / 14 )4834.0428.6105168.96
Trimmed Mean ( 7 / 14 )4836.5627.7931174.02
Trimmed Mean ( 8 / 14 )4838.526.912179.79
Trimmed Mean ( 9 / 14 )4840.7425.9185186.767
Trimmed Mean ( 10 / 14 )4843.4624.7087196.022
Trimmed Mean ( 11 / 14 )4844.8823.5634205.611
Trimmed Mean ( 12 / 14 )4846.5922.4269216.106
Trimmed Mean ( 13 / 14 )4848.7120.8548232.499
Trimmed Mean ( 14 / 14 )4852.3819.1828252.955
Median4864.62
Midrange4776.59
Midmean - Weighted Average at Xnp4835.45
Midmean - Weighted Average at X(n+1)p4844.88
Midmean - Empirical Distribution Function4835.45
Midmean - Empirical Distribution Function - Averaging4844.88
Midmean - Empirical Distribution Function - Interpolation4844.88
Midmean - Closest Observation4835.45
Midmean - True Basic - Statistics Graphics Toolkit4844.88
Midmean - MS Excel (old versions)4843.46
Number of observations44



Parameters (Session):
par1 = additive ; par2 = 12 ;
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')