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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 computationThu, 15 Dec 2016 11:31:45 +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/15/t14817980994fczybp0tjmr8qy.htm/, Retrieved Fri, 03 May 2024 04:39:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=299830, Retrieved Fri, 03 May 2024 04:39:13 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact50
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [CY Vrouwen] [2016-12-15 10:31:45] [ada7696de20b35d9f514c719a1db97fd] [Current]
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Dataseries X:
5
5
5
5
5
5
3
5
5
5
4
5
5
5
5
4
4
5
3
5
5
2
3
4
4
5
4
5
4
4
4
4
5
4
4
4
4
4
5
3
4
4
5
5
5
2
5
5
5
4
5
5
4
5
3
5
4
4
4
4
3
3
5
5
5
5
5
5
4
5
5
4
4
4
4
5
5
5
5
3
4
4
3
5
5
4
4




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=299830&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=299830&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299830&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 Mean4.356320.081710253.3143
Geometric Mean4.27786
Harmonic Mean4.18269
Quadratic Mean4.42173
Winsorized Mean ( 1 / 29 )4.356320.081710253.3143
Winsorized Mean ( 2 / 29 )4.379310.075352558.1177
Winsorized Mean ( 3 / 29 )4.379310.075352558.1177
Winsorized Mean ( 4 / 29 )4.379310.075352558.1177
Winsorized Mean ( 5 / 29 )4.379310.075352558.1177
Winsorized Mean ( 6 / 29 )4.379310.075352558.1177
Winsorized Mean ( 7 / 29 )4.379310.075352558.1177
Winsorized Mean ( 8 / 29 )4.379310.075352558.1177
Winsorized Mean ( 9 / 29 )4.379310.075352558.1177
Winsorized Mean ( 10 / 29 )4.379310.075352558.1177
Winsorized Mean ( 11 / 29 )4.505750.053912883.5747
Winsorized Mean ( 12 / 29 )4.505750.053912883.5747
Winsorized Mean ( 13 / 29 )4.505750.053912883.5747
Winsorized Mean ( 14 / 29 )4.505750.053912883.5747
Winsorized Mean ( 15 / 29 )4.505750.053912883.5747
Winsorized Mean ( 16 / 29 )4.505750.053912883.5747
Winsorized Mean ( 17 / 29 )4.505750.053912883.5747
Winsorized Mean ( 18 / 29 )4.505750.053912883.5747
Winsorized Mean ( 19 / 29 )4.505750.053912883.5747
Winsorized Mean ( 20 / 29 )4.505750.053912883.5747
Winsorized Mean ( 21 / 29 )4.505750.053912883.5747
Winsorized Mean ( 22 / 29 )4.505750.053912883.5747
Winsorized Mean ( 23 / 29 )4.505750.053912883.5747
Winsorized Mean ( 24 / 29 )4.505750.053912883.5747
Winsorized Mean ( 25 / 29 )4.505750.053912883.5747
Winsorized Mean ( 26 / 29 )4.505750.053912883.5747
Winsorized Mean ( 27 / 29 )4.505750.053912883.5747
Winsorized Mean ( 28 / 29 )4.505750.053912883.5747
Winsorized Mean ( 29 / 29 )4.505750.053912883.5747
Trimmed Mean ( 1 / 29 )4.376470.078459455.7801
Trimmed Mean ( 2 / 29 )4.397590.07460358.9466
Trimmed Mean ( 3 / 29 )4.407410.074074159.5
Trimmed Mean ( 4 / 29 )4.417720.073418860.1715
Trimmed Mean ( 5 / 29 )4.428570.072614960.9871
Trimmed Mean ( 6 / 29 )4.440.071634761.9811
Trimmed Mean ( 7 / 29 )4.452050.070444163.1999
Trimmed Mean ( 8 / 29 )4.464790.068999764.7074
Trimmed Mean ( 9 / 29 )4.478260.067246166.5951
Trimmed Mean ( 10 / 29 )4.492540.065110168.9991
Trimmed Mean ( 11 / 29 )4.507690.062492672.1316
Trimmed Mean ( 12 / 29 )4.507940.063492171
Trimmed Mean ( 13 / 29 )4.50820.06454169.8501
Trimmed Mean ( 14 / 29 )4.508470.065643868.6809
Trimmed Mean ( 15 / 29 )4.508770.06680567.4915
Trimmed Mean ( 16 / 29 )4.509090.068030166.2808
Trimmed Mean ( 17 / 29 )4.509430.069325265.0476
Trimmed Mean ( 18 / 29 )4.50980.070697163.7905
Trimmed Mean ( 19 / 29 )4.51020.072153862.5082
Trimmed Mean ( 20 / 29 )4.510640.073704361.1991
Trimmed Mean ( 21 / 29 )4.511110.075359259.8614
Trimmed Mean ( 22 / 29 )4.511630.077130858.4932
Trimmed Mean ( 23 / 29 )4.51220.079033457.0922
Trimmed Mean ( 24 / 29 )4.512820.08108455.6561
Trimmed Mean ( 25 / 29 )4.513510.083302954.182
Trimmed Mean ( 26 / 29 )4.514290.085714352.6667
Trimmed Mean ( 27 / 29 )4.515150.088347851.1066
Trimmed Mean ( 28 / 29 )4.516130.091239649.4975
Trimmed Mean ( 29 / 29 )4.517240.094434947.8344
Median5
Midrange3.5
Midmean - Weighted Average at Xnp4.57895
Midmean - Weighted Average at X(n+1)p4.57895
Midmean - Empirical Distribution Function4.57895
Midmean - Empirical Distribution Function - Averaging4.57895
Midmean - Empirical Distribution Function - Interpolation4.57895
Midmean - Closest Observation4.57895
Midmean - True Basic - Statistics Graphics Toolkit4.57895
Midmean - MS Excel (old versions)4.57895
Number of observations87

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 4.35632 & 0.0817102 & 53.3143 \tabularnewline
Geometric Mean & 4.27786 &  &  \tabularnewline
Harmonic Mean & 4.18269 &  &  \tabularnewline
Quadratic Mean & 4.42173 &  &  \tabularnewline
Winsorized Mean ( 1 / 29 ) & 4.35632 & 0.0817102 & 53.3143 \tabularnewline
Winsorized Mean ( 2 / 29 ) & 4.37931 & 0.0753525 & 58.1177 \tabularnewline
Winsorized Mean ( 3 / 29 ) & 4.37931 & 0.0753525 & 58.1177 \tabularnewline
Winsorized Mean ( 4 / 29 ) & 4.37931 & 0.0753525 & 58.1177 \tabularnewline
Winsorized Mean ( 5 / 29 ) & 4.37931 & 0.0753525 & 58.1177 \tabularnewline
Winsorized Mean ( 6 / 29 ) & 4.37931 & 0.0753525 & 58.1177 \tabularnewline
Winsorized Mean ( 7 / 29 ) & 4.37931 & 0.0753525 & 58.1177 \tabularnewline
Winsorized Mean ( 8 / 29 ) & 4.37931 & 0.0753525 & 58.1177 \tabularnewline
Winsorized Mean ( 9 / 29 ) & 4.37931 & 0.0753525 & 58.1177 \tabularnewline
Winsorized Mean ( 10 / 29 ) & 4.37931 & 0.0753525 & 58.1177 \tabularnewline
Winsorized Mean ( 11 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 12 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 13 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 14 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 15 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 16 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 17 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 18 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 19 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 20 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 21 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 22 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 23 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 24 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 25 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 26 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 27 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 28 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Winsorized Mean ( 29 / 29 ) & 4.50575 & 0.0539128 & 83.5747 \tabularnewline
Trimmed Mean ( 1 / 29 ) & 4.37647 & 0.0784594 & 55.7801 \tabularnewline
Trimmed Mean ( 2 / 29 ) & 4.39759 & 0.074603 & 58.9466 \tabularnewline
Trimmed Mean ( 3 / 29 ) & 4.40741 & 0.0740741 & 59.5 \tabularnewline
Trimmed Mean ( 4 / 29 ) & 4.41772 & 0.0734188 & 60.1715 \tabularnewline
Trimmed Mean ( 5 / 29 ) & 4.42857 & 0.0726149 & 60.9871 \tabularnewline
Trimmed Mean ( 6 / 29 ) & 4.44 & 0.0716347 & 61.9811 \tabularnewline
Trimmed Mean ( 7 / 29 ) & 4.45205 & 0.0704441 & 63.1999 \tabularnewline
Trimmed Mean ( 8 / 29 ) & 4.46479 & 0.0689997 & 64.7074 \tabularnewline
Trimmed Mean ( 9 / 29 ) & 4.47826 & 0.0672461 & 66.5951 \tabularnewline
Trimmed Mean ( 10 / 29 ) & 4.49254 & 0.0651101 & 68.9991 \tabularnewline
Trimmed Mean ( 11 / 29 ) & 4.50769 & 0.0624926 & 72.1316 \tabularnewline
Trimmed Mean ( 12 / 29 ) & 4.50794 & 0.0634921 & 71 \tabularnewline
Trimmed Mean ( 13 / 29 ) & 4.5082 & 0.064541 & 69.8501 \tabularnewline
Trimmed Mean ( 14 / 29 ) & 4.50847 & 0.0656438 & 68.6809 \tabularnewline
Trimmed Mean ( 15 / 29 ) & 4.50877 & 0.066805 & 67.4915 \tabularnewline
Trimmed Mean ( 16 / 29 ) & 4.50909 & 0.0680301 & 66.2808 \tabularnewline
Trimmed Mean ( 17 / 29 ) & 4.50943 & 0.0693252 & 65.0476 \tabularnewline
Trimmed Mean ( 18 / 29 ) & 4.5098 & 0.0706971 & 63.7905 \tabularnewline
Trimmed Mean ( 19 / 29 ) & 4.5102 & 0.0721538 & 62.5082 \tabularnewline
Trimmed Mean ( 20 / 29 ) & 4.51064 & 0.0737043 & 61.1991 \tabularnewline
Trimmed Mean ( 21 / 29 ) & 4.51111 & 0.0753592 & 59.8614 \tabularnewline
Trimmed Mean ( 22 / 29 ) & 4.51163 & 0.0771308 & 58.4932 \tabularnewline
Trimmed Mean ( 23 / 29 ) & 4.5122 & 0.0790334 & 57.0922 \tabularnewline
Trimmed Mean ( 24 / 29 ) & 4.51282 & 0.081084 & 55.6561 \tabularnewline
Trimmed Mean ( 25 / 29 ) & 4.51351 & 0.0833029 & 54.182 \tabularnewline
Trimmed Mean ( 26 / 29 ) & 4.51429 & 0.0857143 & 52.6667 \tabularnewline
Trimmed Mean ( 27 / 29 ) & 4.51515 & 0.0883478 & 51.1066 \tabularnewline
Trimmed Mean ( 28 / 29 ) & 4.51613 & 0.0912396 & 49.4975 \tabularnewline
Trimmed Mean ( 29 / 29 ) & 4.51724 & 0.0944349 & 47.8344 \tabularnewline
Median & 5 &  &  \tabularnewline
Midrange & 3.5 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 4.57895 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 4.57895 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 4.57895 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 4.57895 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 4.57895 &  &  \tabularnewline
Midmean - Closest Observation & 4.57895 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 4.57895 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 4.57895 &  &  \tabularnewline
Number of observations & 87 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299830&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]4.35632[/C][C]0.0817102[/C][C]53.3143[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]4.27786[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]4.18269[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]4.42173[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 29 )[/C][C]4.35632[/C][C]0.0817102[/C][C]53.3143[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 29 )[/C][C]4.37931[/C][C]0.0753525[/C][C]58.1177[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 29 )[/C][C]4.37931[/C][C]0.0753525[/C][C]58.1177[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 29 )[/C][C]4.37931[/C][C]0.0753525[/C][C]58.1177[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 29 )[/C][C]4.37931[/C][C]0.0753525[/C][C]58.1177[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 29 )[/C][C]4.37931[/C][C]0.0753525[/C][C]58.1177[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 29 )[/C][C]4.37931[/C][C]0.0753525[/C][C]58.1177[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 29 )[/C][C]4.37931[/C][C]0.0753525[/C][C]58.1177[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 29 )[/C][C]4.37931[/C][C]0.0753525[/C][C]58.1177[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 29 )[/C][C]4.37931[/C][C]0.0753525[/C][C]58.1177[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 21 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 22 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 23 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 24 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 25 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 26 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 27 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 28 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Winsorized Mean ( 29 / 29 )[/C][C]4.50575[/C][C]0.0539128[/C][C]83.5747[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 29 )[/C][C]4.37647[/C][C]0.0784594[/C][C]55.7801[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 29 )[/C][C]4.39759[/C][C]0.074603[/C][C]58.9466[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 29 )[/C][C]4.40741[/C][C]0.0740741[/C][C]59.5[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 29 )[/C][C]4.41772[/C][C]0.0734188[/C][C]60.1715[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 29 )[/C][C]4.42857[/C][C]0.0726149[/C][C]60.9871[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 29 )[/C][C]4.44[/C][C]0.0716347[/C][C]61.9811[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 29 )[/C][C]4.45205[/C][C]0.0704441[/C][C]63.1999[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 29 )[/C][C]4.46479[/C][C]0.0689997[/C][C]64.7074[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 29 )[/C][C]4.47826[/C][C]0.0672461[/C][C]66.5951[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 29 )[/C][C]4.49254[/C][C]0.0651101[/C][C]68.9991[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 29 )[/C][C]4.50769[/C][C]0.0624926[/C][C]72.1316[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 29 )[/C][C]4.50794[/C][C]0.0634921[/C][C]71[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 29 )[/C][C]4.5082[/C][C]0.064541[/C][C]69.8501[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 29 )[/C][C]4.50847[/C][C]0.0656438[/C][C]68.6809[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 29 )[/C][C]4.50877[/C][C]0.066805[/C][C]67.4915[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 29 )[/C][C]4.50909[/C][C]0.0680301[/C][C]66.2808[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 29 )[/C][C]4.50943[/C][C]0.0693252[/C][C]65.0476[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 29 )[/C][C]4.5098[/C][C]0.0706971[/C][C]63.7905[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 29 )[/C][C]4.5102[/C][C]0.0721538[/C][C]62.5082[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 29 )[/C][C]4.51064[/C][C]0.0737043[/C][C]61.1991[/C][/ROW]
[ROW][C]Trimmed Mean ( 21 / 29 )[/C][C]4.51111[/C][C]0.0753592[/C][C]59.8614[/C][/ROW]
[ROW][C]Trimmed Mean ( 22 / 29 )[/C][C]4.51163[/C][C]0.0771308[/C][C]58.4932[/C][/ROW]
[ROW][C]Trimmed Mean ( 23 / 29 )[/C][C]4.5122[/C][C]0.0790334[/C][C]57.0922[/C][/ROW]
[ROW][C]Trimmed Mean ( 24 / 29 )[/C][C]4.51282[/C][C]0.081084[/C][C]55.6561[/C][/ROW]
[ROW][C]Trimmed Mean ( 25 / 29 )[/C][C]4.51351[/C][C]0.0833029[/C][C]54.182[/C][/ROW]
[ROW][C]Trimmed Mean ( 26 / 29 )[/C][C]4.51429[/C][C]0.0857143[/C][C]52.6667[/C][/ROW]
[ROW][C]Trimmed Mean ( 27 / 29 )[/C][C]4.51515[/C][C]0.0883478[/C][C]51.1066[/C][/ROW]
[ROW][C]Trimmed Mean ( 28 / 29 )[/C][C]4.51613[/C][C]0.0912396[/C][C]49.4975[/C][/ROW]
[ROW][C]Trimmed Mean ( 29 / 29 )[/C][C]4.51724[/C][C]0.0944349[/C][C]47.8344[/C][/ROW]
[ROW][C]Median[/C][C]5[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]3.5[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]4.57895[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]4.57895[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]4.57895[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]4.57895[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]4.57895[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]4.57895[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]4.57895[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]4.57895[/C][C][/C][C][/C][/ROW]
[ROW][C]Number of observations[/C][C]87[/C][C][/C][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299830&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299830&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 Mean4.356320.081710253.3143
Geometric Mean4.27786
Harmonic Mean4.18269
Quadratic Mean4.42173
Winsorized Mean ( 1 / 29 )4.356320.081710253.3143
Winsorized Mean ( 2 / 29 )4.379310.075352558.1177
Winsorized Mean ( 3 / 29 )4.379310.075352558.1177
Winsorized Mean ( 4 / 29 )4.379310.075352558.1177
Winsorized Mean ( 5 / 29 )4.379310.075352558.1177
Winsorized Mean ( 6 / 29 )4.379310.075352558.1177
Winsorized Mean ( 7 / 29 )4.379310.075352558.1177
Winsorized Mean ( 8 / 29 )4.379310.075352558.1177
Winsorized Mean ( 9 / 29 )4.379310.075352558.1177
Winsorized Mean ( 10 / 29 )4.379310.075352558.1177
Winsorized Mean ( 11 / 29 )4.505750.053912883.5747
Winsorized Mean ( 12 / 29 )4.505750.053912883.5747
Winsorized Mean ( 13 / 29 )4.505750.053912883.5747
Winsorized Mean ( 14 / 29 )4.505750.053912883.5747
Winsorized Mean ( 15 / 29 )4.505750.053912883.5747
Winsorized Mean ( 16 / 29 )4.505750.053912883.5747
Winsorized Mean ( 17 / 29 )4.505750.053912883.5747
Winsorized Mean ( 18 / 29 )4.505750.053912883.5747
Winsorized Mean ( 19 / 29 )4.505750.053912883.5747
Winsorized Mean ( 20 / 29 )4.505750.053912883.5747
Winsorized Mean ( 21 / 29 )4.505750.053912883.5747
Winsorized Mean ( 22 / 29 )4.505750.053912883.5747
Winsorized Mean ( 23 / 29 )4.505750.053912883.5747
Winsorized Mean ( 24 / 29 )4.505750.053912883.5747
Winsorized Mean ( 25 / 29 )4.505750.053912883.5747
Winsorized Mean ( 26 / 29 )4.505750.053912883.5747
Winsorized Mean ( 27 / 29 )4.505750.053912883.5747
Winsorized Mean ( 28 / 29 )4.505750.053912883.5747
Winsorized Mean ( 29 / 29 )4.505750.053912883.5747
Trimmed Mean ( 1 / 29 )4.376470.078459455.7801
Trimmed Mean ( 2 / 29 )4.397590.07460358.9466
Trimmed Mean ( 3 / 29 )4.407410.074074159.5
Trimmed Mean ( 4 / 29 )4.417720.073418860.1715
Trimmed Mean ( 5 / 29 )4.428570.072614960.9871
Trimmed Mean ( 6 / 29 )4.440.071634761.9811
Trimmed Mean ( 7 / 29 )4.452050.070444163.1999
Trimmed Mean ( 8 / 29 )4.464790.068999764.7074
Trimmed Mean ( 9 / 29 )4.478260.067246166.5951
Trimmed Mean ( 10 / 29 )4.492540.065110168.9991
Trimmed Mean ( 11 / 29 )4.507690.062492672.1316
Trimmed Mean ( 12 / 29 )4.507940.063492171
Trimmed Mean ( 13 / 29 )4.50820.06454169.8501
Trimmed Mean ( 14 / 29 )4.508470.065643868.6809
Trimmed Mean ( 15 / 29 )4.508770.06680567.4915
Trimmed Mean ( 16 / 29 )4.509090.068030166.2808
Trimmed Mean ( 17 / 29 )4.509430.069325265.0476
Trimmed Mean ( 18 / 29 )4.50980.070697163.7905
Trimmed Mean ( 19 / 29 )4.51020.072153862.5082
Trimmed Mean ( 20 / 29 )4.510640.073704361.1991
Trimmed Mean ( 21 / 29 )4.511110.075359259.8614
Trimmed Mean ( 22 / 29 )4.511630.077130858.4932
Trimmed Mean ( 23 / 29 )4.51220.079033457.0922
Trimmed Mean ( 24 / 29 )4.512820.08108455.6561
Trimmed Mean ( 25 / 29 )4.513510.083302954.182
Trimmed Mean ( 26 / 29 )4.514290.085714352.6667
Trimmed Mean ( 27 / 29 )4.515150.088347851.1066
Trimmed Mean ( 28 / 29 )4.516130.091239649.4975
Trimmed Mean ( 29 / 29 )4.517240.094434947.8344
Median5
Midrange3.5
Midmean - Weighted Average at Xnp4.57895
Midmean - Weighted Average at X(n+1)p4.57895
Midmean - Empirical Distribution Function4.57895
Midmean - Empirical Distribution Function - Averaging4.57895
Midmean - Empirical Distribution Function - Interpolation4.57895
Midmean - Closest Observation4.57895
Midmean - True Basic - Statistics Graphics Toolkit4.57895
Midmean - MS Excel (old versions)4.57895
Number of observations87



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')