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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 computationTue, 22 Nov 2016 13:23:08 +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/22/t14798174283ozzy1816l6tvuw.htm/, Retrieved Sun, 05 May 2024 13:53:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=296907, Retrieved Sun, 05 May 2024 13:53:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [Central tendency] [2016-11-22 12:23:08] [67fe698233d7575d27222b521501ef35] [Current]
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Dataseries X:
1680
1920
120
1080
840
1440
480
720
4080
1560
480
720
6120
2040
3960
2160
120
1200
1080
1080
1080
2160
240
1440
1200
1560
2520
600
1560
3240
7440
480
2640
960
3120
1200
960
480
600
120
2640
720
600
840
1320
2160
1200
1800
1320
600




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=296907&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=296907&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=296907&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 Mean1593.6201.2387.91898
Geometric Mean1128.29
Harmonic Mean706.3
Quadratic Mean2126.95
Winsorized Mean ( 1 / 16 )1567.2186.7998.38975
Winsorized Mean ( 2 / 16 )1485.6151.639.79753
Winsorized Mean ( 3 / 16 )1485.6147.86210.0472
Winsorized Mean ( 4 / 16 )1447.2126.41211.4483
Winsorized Mean ( 5 / 16 )1435.2122.99711.6685
Winsorized Mean ( 6 / 16 )1377.6107.99912.7556
Winsorized Mean ( 7 / 16 )1377.6107.99912.7556
Winsorized Mean ( 8 / 16 )1377.6100.32513.7313
Winsorized Mean ( 9 / 16 )1312.886.220915.226
Winsorized Mean ( 10 / 16 )1312.886.220915.226
Winsorized Mean ( 11 / 16 )1312.886.220915.226
Winsorized Mean ( 12 / 16 )1312.875.771417.3258
Winsorized Mean ( 13 / 16 )1281.669.798218.3615
Winsorized Mean ( 14 / 16 )124863.682919.5971
Winsorized Mean ( 15 / 16 )124851.428624.2667
Winsorized Mean ( 16 / 16 )1209.645.07526.8353
Trimmed Mean ( 1 / 16 )1502.5166.4019.0294
Trimmed Mean ( 2 / 16 )1432.17137.27310.433
Trimmed Mean ( 3 / 16 )1401.82126.41111.0894
Trimmed Mean ( 4 / 16 )1368.57113.7812.0282
Trimmed Mean ( 5 / 16 )1344107.34812.52
Trimmed Mean ( 6 / 16 )132099.980113.2026
Trimmed Mean ( 7 / 16 )1306.6795.949713.6182
Trimmed Mean ( 8 / 16 )1291.7690.213114.319
Trimmed Mean ( 9 / 16 )127584.8115.0336
Trimmed Mean ( 10 / 16 )126882.314815.4043
Trimmed Mean ( 11 / 16 )126078.437116.0638
Trimmed Mean ( 12 / 16 )1250.7772.3617.2854
Trimmed Mean ( 13 / 16 )124067.243918.4403
Trimmed Mean ( 14 / 16 )1232.7361.75719.9609
Trimmed Mean ( 15 / 16 )123055.658722.099
Trimmed Mean ( 16 / 16 )1226.6751.841923.6617
Median1200
Midrange3780
Midmean - Weighted Average at Xnp1219.2
Midmean - Weighted Average at X(n+1)p1250.77
Midmean - Empirical Distribution Function1250.77
Midmean - Empirical Distribution Function - Averaging1250.77
Midmean - Empirical Distribution Function - Interpolation1219.2
Midmean - Closest Observation1250.77
Midmean - True Basic - Statistics Graphics Toolkit1250.77
Midmean - MS Excel (old versions)1250.77
Number of observations50

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 1593.6 & 201.238 & 7.91898 \tabularnewline
Geometric Mean & 1128.29 &  &  \tabularnewline
Harmonic Mean & 706.3 &  &  \tabularnewline
Quadratic Mean & 2126.95 &  &  \tabularnewline
Winsorized Mean ( 1 / 16 ) & 1567.2 & 186.799 & 8.38975 \tabularnewline
Winsorized Mean ( 2 / 16 ) & 1485.6 & 151.63 & 9.79753 \tabularnewline
Winsorized Mean ( 3 / 16 ) & 1485.6 & 147.862 & 10.0472 \tabularnewline
Winsorized Mean ( 4 / 16 ) & 1447.2 & 126.412 & 11.4483 \tabularnewline
Winsorized Mean ( 5 / 16 ) & 1435.2 & 122.997 & 11.6685 \tabularnewline
Winsorized Mean ( 6 / 16 ) & 1377.6 & 107.999 & 12.7556 \tabularnewline
Winsorized Mean ( 7 / 16 ) & 1377.6 & 107.999 & 12.7556 \tabularnewline
Winsorized Mean ( 8 / 16 ) & 1377.6 & 100.325 & 13.7313 \tabularnewline
Winsorized Mean ( 9 / 16 ) & 1312.8 & 86.2209 & 15.226 \tabularnewline
Winsorized Mean ( 10 / 16 ) & 1312.8 & 86.2209 & 15.226 \tabularnewline
Winsorized Mean ( 11 / 16 ) & 1312.8 & 86.2209 & 15.226 \tabularnewline
Winsorized Mean ( 12 / 16 ) & 1312.8 & 75.7714 & 17.3258 \tabularnewline
Winsorized Mean ( 13 / 16 ) & 1281.6 & 69.7982 & 18.3615 \tabularnewline
Winsorized Mean ( 14 / 16 ) & 1248 & 63.6829 & 19.5971 \tabularnewline
Winsorized Mean ( 15 / 16 ) & 1248 & 51.4286 & 24.2667 \tabularnewline
Winsorized Mean ( 16 / 16 ) & 1209.6 & 45.075 & 26.8353 \tabularnewline
Trimmed Mean ( 1 / 16 ) & 1502.5 & 166.401 & 9.0294 \tabularnewline
Trimmed Mean ( 2 / 16 ) & 1432.17 & 137.273 & 10.433 \tabularnewline
Trimmed Mean ( 3 / 16 ) & 1401.82 & 126.411 & 11.0894 \tabularnewline
Trimmed Mean ( 4 / 16 ) & 1368.57 & 113.78 & 12.0282 \tabularnewline
Trimmed Mean ( 5 / 16 ) & 1344 & 107.348 & 12.52 \tabularnewline
Trimmed Mean ( 6 / 16 ) & 1320 & 99.9801 & 13.2026 \tabularnewline
Trimmed Mean ( 7 / 16 ) & 1306.67 & 95.9497 & 13.6182 \tabularnewline
Trimmed Mean ( 8 / 16 ) & 1291.76 & 90.2131 & 14.319 \tabularnewline
Trimmed Mean ( 9 / 16 ) & 1275 & 84.81 & 15.0336 \tabularnewline
Trimmed Mean ( 10 / 16 ) & 1268 & 82.3148 & 15.4043 \tabularnewline
Trimmed Mean ( 11 / 16 ) & 1260 & 78.4371 & 16.0638 \tabularnewline
Trimmed Mean ( 12 / 16 ) & 1250.77 & 72.36 & 17.2854 \tabularnewline
Trimmed Mean ( 13 / 16 ) & 1240 & 67.2439 & 18.4403 \tabularnewline
Trimmed Mean ( 14 / 16 ) & 1232.73 & 61.757 & 19.9609 \tabularnewline
Trimmed Mean ( 15 / 16 ) & 1230 & 55.6587 & 22.099 \tabularnewline
Trimmed Mean ( 16 / 16 ) & 1226.67 & 51.8419 & 23.6617 \tabularnewline
Median & 1200 &  &  \tabularnewline
Midrange & 3780 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 1219.2 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 1250.77 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 1250.77 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 1250.77 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 1219.2 &  &  \tabularnewline
Midmean - Closest Observation & 1250.77 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 1250.77 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 1250.77 &  &  \tabularnewline
Number of observations & 50 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=296907&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]1593.6[/C][C]201.238[/C][C]7.91898[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]1128.29[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]706.3[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]2126.95[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 16 )[/C][C]1567.2[/C][C]186.799[/C][C]8.38975[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 16 )[/C][C]1485.6[/C][C]151.63[/C][C]9.79753[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 16 )[/C][C]1485.6[/C][C]147.862[/C][C]10.0472[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 16 )[/C][C]1447.2[/C][C]126.412[/C][C]11.4483[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 16 )[/C][C]1435.2[/C][C]122.997[/C][C]11.6685[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 16 )[/C][C]1377.6[/C][C]107.999[/C][C]12.7556[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 16 )[/C][C]1377.6[/C][C]107.999[/C][C]12.7556[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 16 )[/C][C]1377.6[/C][C]100.325[/C][C]13.7313[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 16 )[/C][C]1312.8[/C][C]86.2209[/C][C]15.226[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 16 )[/C][C]1312.8[/C][C]86.2209[/C][C]15.226[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 16 )[/C][C]1312.8[/C][C]86.2209[/C][C]15.226[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 16 )[/C][C]1312.8[/C][C]75.7714[/C][C]17.3258[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 16 )[/C][C]1281.6[/C][C]69.7982[/C][C]18.3615[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 16 )[/C][C]1248[/C][C]63.6829[/C][C]19.5971[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 16 )[/C][C]1248[/C][C]51.4286[/C][C]24.2667[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 16 )[/C][C]1209.6[/C][C]45.075[/C][C]26.8353[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 16 )[/C][C]1502.5[/C][C]166.401[/C][C]9.0294[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 16 )[/C][C]1432.17[/C][C]137.273[/C][C]10.433[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 16 )[/C][C]1401.82[/C][C]126.411[/C][C]11.0894[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 16 )[/C][C]1368.57[/C][C]113.78[/C][C]12.0282[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 16 )[/C][C]1344[/C][C]107.348[/C][C]12.52[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 16 )[/C][C]1320[/C][C]99.9801[/C][C]13.2026[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 16 )[/C][C]1306.67[/C][C]95.9497[/C][C]13.6182[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 16 )[/C][C]1291.76[/C][C]90.2131[/C][C]14.319[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 16 )[/C][C]1275[/C][C]84.81[/C][C]15.0336[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 16 )[/C][C]1268[/C][C]82.3148[/C][C]15.4043[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 16 )[/C][C]1260[/C][C]78.4371[/C][C]16.0638[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 16 )[/C][C]1250.77[/C][C]72.36[/C][C]17.2854[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 16 )[/C][C]1240[/C][C]67.2439[/C][C]18.4403[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 16 )[/C][C]1232.73[/C][C]61.757[/C][C]19.9609[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 16 )[/C][C]1230[/C][C]55.6587[/C][C]22.099[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 16 )[/C][C]1226.67[/C][C]51.8419[/C][C]23.6617[/C][/ROW]
[ROW][C]Median[/C][C]1200[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]3780[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]1219.2[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]1250.77[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]1250.77[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]1250.77[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]1219.2[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]1250.77[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]1250.77[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]1250.77[/C][C][/C][C][/C][/ROW]
[ROW][C]Number of observations[/C][C]50[/C][C][/C][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=296907&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=296907&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 Mean1593.6201.2387.91898
Geometric Mean1128.29
Harmonic Mean706.3
Quadratic Mean2126.95
Winsorized Mean ( 1 / 16 )1567.2186.7998.38975
Winsorized Mean ( 2 / 16 )1485.6151.639.79753
Winsorized Mean ( 3 / 16 )1485.6147.86210.0472
Winsorized Mean ( 4 / 16 )1447.2126.41211.4483
Winsorized Mean ( 5 / 16 )1435.2122.99711.6685
Winsorized Mean ( 6 / 16 )1377.6107.99912.7556
Winsorized Mean ( 7 / 16 )1377.6107.99912.7556
Winsorized Mean ( 8 / 16 )1377.6100.32513.7313
Winsorized Mean ( 9 / 16 )1312.886.220915.226
Winsorized Mean ( 10 / 16 )1312.886.220915.226
Winsorized Mean ( 11 / 16 )1312.886.220915.226
Winsorized Mean ( 12 / 16 )1312.875.771417.3258
Winsorized Mean ( 13 / 16 )1281.669.798218.3615
Winsorized Mean ( 14 / 16 )124863.682919.5971
Winsorized Mean ( 15 / 16 )124851.428624.2667
Winsorized Mean ( 16 / 16 )1209.645.07526.8353
Trimmed Mean ( 1 / 16 )1502.5166.4019.0294
Trimmed Mean ( 2 / 16 )1432.17137.27310.433
Trimmed Mean ( 3 / 16 )1401.82126.41111.0894
Trimmed Mean ( 4 / 16 )1368.57113.7812.0282
Trimmed Mean ( 5 / 16 )1344107.34812.52
Trimmed Mean ( 6 / 16 )132099.980113.2026
Trimmed Mean ( 7 / 16 )1306.6795.949713.6182
Trimmed Mean ( 8 / 16 )1291.7690.213114.319
Trimmed Mean ( 9 / 16 )127584.8115.0336
Trimmed Mean ( 10 / 16 )126882.314815.4043
Trimmed Mean ( 11 / 16 )126078.437116.0638
Trimmed Mean ( 12 / 16 )1250.7772.3617.2854
Trimmed Mean ( 13 / 16 )124067.243918.4403
Trimmed Mean ( 14 / 16 )1232.7361.75719.9609
Trimmed Mean ( 15 / 16 )123055.658722.099
Trimmed Mean ( 16 / 16 )1226.6751.841923.6617
Median1200
Midrange3780
Midmean - Weighted Average at Xnp1219.2
Midmean - Weighted Average at X(n+1)p1250.77
Midmean - Empirical Distribution Function1250.77
Midmean - Empirical Distribution Function - Averaging1250.77
Midmean - Empirical Distribution Function - Interpolation1219.2
Midmean - Closest Observation1250.77
Midmean - True Basic - Statistics Graphics Toolkit1250.77
Midmean - MS Excel (old versions)1250.77
Number of observations50



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')