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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 computationWed, 14 Dec 2016 17:20:35 +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/14/t1481732803elfc994hx5lh2a5.htm/, Retrieved Fri, 01 Nov 2024 05:23:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=299605, Retrieved Fri, 01 Nov 2024 05:23:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [Central tendency F1] [2016-12-14 16:20:35] [71d167f7de04005af677e6526bf8917e] [Current]
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Dataseries X:
7600.00
2800.00
8800.00
6800.00
6000.00
3200.00
4400.00
4800.00
5200.00
4400.00
6800.00
2800.00
5600.00
4400.00
4800.00
2800.00
3600.00
800.00
4400.00
1600.00
7600.00
8000.00
8400.00
5600.00
7200.00
9600.00
6400.00
6000.00
6800.00
6800.00
4000.00
4000.00
3200.00
6000.00
7200.00
6400.00
8000.00
8000.00
8800.00
7600.00
4400.00
5600.00
5200.00
4400.00
4800.00
2800.00
3600.00
8000.00
6400.00
6800.00
11200.00
6400.00
8000.00
2800.00
1600.00
6000.00
3200.00
6000.00
6000.00
8400.00
4000.00
5200.00
7200.00
3600.00
4000.00
7600.00
7200.00
4800.00
5600.00
6800.00
5200.00
6800.00
8000.00
4800.00
9200.00
5600.00
10000.00
4400.00




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=299605&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=299605&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299605&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 Mean5753.85236.55124.3239
Geometric Mean5297.91
Harmonic Mean4669.26
Quadratic Mean6116.81
Winsorized Mean ( 1 / 26 )5748.72229.79625.0166
Winsorized Mean ( 2 / 26 )5738.46227.43325.2315
Winsorized Mean ( 3 / 26 )5769.23214.49626.8967
Winsorized Mean ( 4 / 26 )5748.72210.43227.3187
Winsorized Mean ( 5 / 26 )5748.72210.43227.3187
Winsorized Mean ( 6 / 26 )5717.95204.91627.9039
Winsorized Mean ( 7 / 26 )5717.95204.91627.9039
Winsorized Mean ( 8 / 26 )5717.95190.91729.9499
Winsorized Mean ( 9 / 26 )5717.95190.91729.9499
Winsorized Mean ( 10 / 26 )5717.95190.91729.9499
Winsorized Mean ( 11 / 26 )5774.36181.69131.7811
Winsorized Mean ( 12 / 26 )5774.36181.69131.7811
Winsorized Mean ( 13 / 26 )5774.36181.69131.7811
Winsorized Mean ( 14 / 26 )5774.36159.85236.1232
Winsorized Mean ( 15 / 26 )5774.36159.85236.1232
Winsorized Mean ( 16 / 26 )5774.36159.85236.1232
Winsorized Mean ( 17 / 26 )5774.36159.85236.1232
Winsorized Mean ( 18 / 26 )5774.36133.71743.1834
Winsorized Mean ( 19 / 26 )5774.36133.71743.1834
Winsorized Mean ( 20 / 26 )5774.36133.71743.1834
Winsorized Mean ( 21 / 26 )5774.36133.71743.1834
Winsorized Mean ( 22 / 26 )5661.54118.84247.6394
Winsorized Mean ( 23 / 26 )5661.54118.84247.6394
Winsorized Mean ( 24 / 26 )5661.54118.84247.6394
Winsorized Mean ( 25 / 26 )5789.74101.85756.8417
Winsorized Mean ( 26 / 26 )5789.74101.85756.8417
Trimmed Mean ( 1 / 26 )5747.37222.37525.8454
Trimmed Mean ( 2 / 26 )5745.95213.65126.8941
Trimmed Mean ( 3 / 26 )5750204.86128.0678
Trimmed Mean ( 4 / 26 )5742.86200.37828.6601
Trimmed Mean ( 5 / 26 )5741.18196.50429.2166
Trimmed Mean ( 6 / 26 )5739.39191.86129.9143
Trimmed Mean ( 7 / 26 )5743.75187.79630.5851
Trimmed Mean ( 8 / 26 )5748.39182.8731.4344
Trimmed Mean ( 9 / 26 )5753.33180.16731.9333
Trimmed Mean ( 10 / 26 )5758.62176.80132.5712
Trimmed Mean ( 11 / 26 )5764.29172.61233.3944
Trimmed Mean ( 12 / 26 )5762.96169.35634.0288
Trimmed Mean ( 13 / 26 )5761.54165.23134.8696
Trimmed Mean ( 14 / 26 )576016036
Trimmed Mean ( 15 / 26 )5758.33157.88536.4717
Trimmed Mean ( 16 / 26 )5756.52155.03337.1309
Trimmed Mean ( 17 / 26 )5754.55151.22738.0525
Trimmed Mean ( 18 / 26 )5752.38146.15639.3577
Trimmed Mean ( 19 / 26 )5750145.13539.6183
Trimmed Mean ( 20 / 26 )5747.37143.45540.0638
Trimmed Mean ( 21 / 26 )5744.44140.88440.7743
Trimmed Mean ( 22 / 26 )5741.18137.07741.883
Trimmed Mean ( 23 / 26 )5750135.30142.4977
Trimmed Mean ( 24 / 26 )5760132.31843.5315
Trimmed Mean ( 25 / 26 )5771.43127.53845.2525
Trimmed Mean ( 26 / 26 )5769.23125.85345.8409
Median5800
Midrange6000
Midmean - Weighted Average at Xnp5752.38
Midmean - Weighted Average at X(n+1)p5752.38
Midmean - Empirical Distribution Function5752.38
Midmean - Empirical Distribution Function - Averaging5752.38
Midmean - Empirical Distribution Function - Interpolation5752.38
Midmean - Closest Observation5752.38
Midmean - True Basic - Statistics Graphics Toolkit5752.38
Midmean - MS Excel (old versions)5752.38
Number of observations78

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 5753.85 & 236.551 & 24.3239 \tabularnewline
Geometric Mean & 5297.91 &  &  \tabularnewline
Harmonic Mean & 4669.26 &  &  \tabularnewline
Quadratic Mean & 6116.81 &  &  \tabularnewline
Winsorized Mean ( 1 / 26 ) & 5748.72 & 229.796 & 25.0166 \tabularnewline
Winsorized Mean ( 2 / 26 ) & 5738.46 & 227.433 & 25.2315 \tabularnewline
Winsorized Mean ( 3 / 26 ) & 5769.23 & 214.496 & 26.8967 \tabularnewline
Winsorized Mean ( 4 / 26 ) & 5748.72 & 210.432 & 27.3187 \tabularnewline
Winsorized Mean ( 5 / 26 ) & 5748.72 & 210.432 & 27.3187 \tabularnewline
Winsorized Mean ( 6 / 26 ) & 5717.95 & 204.916 & 27.9039 \tabularnewline
Winsorized Mean ( 7 / 26 ) & 5717.95 & 204.916 & 27.9039 \tabularnewline
Winsorized Mean ( 8 / 26 ) & 5717.95 & 190.917 & 29.9499 \tabularnewline
Winsorized Mean ( 9 / 26 ) & 5717.95 & 190.917 & 29.9499 \tabularnewline
Winsorized Mean ( 10 / 26 ) & 5717.95 & 190.917 & 29.9499 \tabularnewline
Winsorized Mean ( 11 / 26 ) & 5774.36 & 181.691 & 31.7811 \tabularnewline
Winsorized Mean ( 12 / 26 ) & 5774.36 & 181.691 & 31.7811 \tabularnewline
Winsorized Mean ( 13 / 26 ) & 5774.36 & 181.691 & 31.7811 \tabularnewline
Winsorized Mean ( 14 / 26 ) & 5774.36 & 159.852 & 36.1232 \tabularnewline
Winsorized Mean ( 15 / 26 ) & 5774.36 & 159.852 & 36.1232 \tabularnewline
Winsorized Mean ( 16 / 26 ) & 5774.36 & 159.852 & 36.1232 \tabularnewline
Winsorized Mean ( 17 / 26 ) & 5774.36 & 159.852 & 36.1232 \tabularnewline
Winsorized Mean ( 18 / 26 ) & 5774.36 & 133.717 & 43.1834 \tabularnewline
Winsorized Mean ( 19 / 26 ) & 5774.36 & 133.717 & 43.1834 \tabularnewline
Winsorized Mean ( 20 / 26 ) & 5774.36 & 133.717 & 43.1834 \tabularnewline
Winsorized Mean ( 21 / 26 ) & 5774.36 & 133.717 & 43.1834 \tabularnewline
Winsorized Mean ( 22 / 26 ) & 5661.54 & 118.842 & 47.6394 \tabularnewline
Winsorized Mean ( 23 / 26 ) & 5661.54 & 118.842 & 47.6394 \tabularnewline
Winsorized Mean ( 24 / 26 ) & 5661.54 & 118.842 & 47.6394 \tabularnewline
Winsorized Mean ( 25 / 26 ) & 5789.74 & 101.857 & 56.8417 \tabularnewline
Winsorized Mean ( 26 / 26 ) & 5789.74 & 101.857 & 56.8417 \tabularnewline
Trimmed Mean ( 1 / 26 ) & 5747.37 & 222.375 & 25.8454 \tabularnewline
Trimmed Mean ( 2 / 26 ) & 5745.95 & 213.651 & 26.8941 \tabularnewline
Trimmed Mean ( 3 / 26 ) & 5750 & 204.861 & 28.0678 \tabularnewline
Trimmed Mean ( 4 / 26 ) & 5742.86 & 200.378 & 28.6601 \tabularnewline
Trimmed Mean ( 5 / 26 ) & 5741.18 & 196.504 & 29.2166 \tabularnewline
Trimmed Mean ( 6 / 26 ) & 5739.39 & 191.861 & 29.9143 \tabularnewline
Trimmed Mean ( 7 / 26 ) & 5743.75 & 187.796 & 30.5851 \tabularnewline
Trimmed Mean ( 8 / 26 ) & 5748.39 & 182.87 & 31.4344 \tabularnewline
Trimmed Mean ( 9 / 26 ) & 5753.33 & 180.167 & 31.9333 \tabularnewline
Trimmed Mean ( 10 / 26 ) & 5758.62 & 176.801 & 32.5712 \tabularnewline
Trimmed Mean ( 11 / 26 ) & 5764.29 & 172.612 & 33.3944 \tabularnewline
Trimmed Mean ( 12 / 26 ) & 5762.96 & 169.356 & 34.0288 \tabularnewline
Trimmed Mean ( 13 / 26 ) & 5761.54 & 165.231 & 34.8696 \tabularnewline
Trimmed Mean ( 14 / 26 ) & 5760 & 160 & 36 \tabularnewline
Trimmed Mean ( 15 / 26 ) & 5758.33 & 157.885 & 36.4717 \tabularnewline
Trimmed Mean ( 16 / 26 ) & 5756.52 & 155.033 & 37.1309 \tabularnewline
Trimmed Mean ( 17 / 26 ) & 5754.55 & 151.227 & 38.0525 \tabularnewline
Trimmed Mean ( 18 / 26 ) & 5752.38 & 146.156 & 39.3577 \tabularnewline
Trimmed Mean ( 19 / 26 ) & 5750 & 145.135 & 39.6183 \tabularnewline
Trimmed Mean ( 20 / 26 ) & 5747.37 & 143.455 & 40.0638 \tabularnewline
Trimmed Mean ( 21 / 26 ) & 5744.44 & 140.884 & 40.7743 \tabularnewline
Trimmed Mean ( 22 / 26 ) & 5741.18 & 137.077 & 41.883 \tabularnewline
Trimmed Mean ( 23 / 26 ) & 5750 & 135.301 & 42.4977 \tabularnewline
Trimmed Mean ( 24 / 26 ) & 5760 & 132.318 & 43.5315 \tabularnewline
Trimmed Mean ( 25 / 26 ) & 5771.43 & 127.538 & 45.2525 \tabularnewline
Trimmed Mean ( 26 / 26 ) & 5769.23 & 125.853 & 45.8409 \tabularnewline
Median & 5800 &  &  \tabularnewline
Midrange & 6000 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 5752.38 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 5752.38 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 5752.38 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 5752.38 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 5752.38 &  &  \tabularnewline
Midmean - Closest Observation & 5752.38 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 5752.38 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 5752.38 &  &  \tabularnewline
Number of observations & 78 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299605&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]5753.85[/C][C]236.551[/C][C]24.3239[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]5297.91[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]4669.26[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]6116.81[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 26 )[/C][C]5748.72[/C][C]229.796[/C][C]25.0166[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 26 )[/C][C]5738.46[/C][C]227.433[/C][C]25.2315[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 26 )[/C][C]5769.23[/C][C]214.496[/C][C]26.8967[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 26 )[/C][C]5748.72[/C][C]210.432[/C][C]27.3187[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 26 )[/C][C]5748.72[/C][C]210.432[/C][C]27.3187[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 26 )[/C][C]5717.95[/C][C]204.916[/C][C]27.9039[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 26 )[/C][C]5717.95[/C][C]204.916[/C][C]27.9039[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 26 )[/C][C]5717.95[/C][C]190.917[/C][C]29.9499[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 26 )[/C][C]5717.95[/C][C]190.917[/C][C]29.9499[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 26 )[/C][C]5717.95[/C][C]190.917[/C][C]29.9499[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 26 )[/C][C]5774.36[/C][C]181.691[/C][C]31.7811[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 26 )[/C][C]5774.36[/C][C]181.691[/C][C]31.7811[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 26 )[/C][C]5774.36[/C][C]181.691[/C][C]31.7811[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 26 )[/C][C]5774.36[/C][C]159.852[/C][C]36.1232[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 26 )[/C][C]5774.36[/C][C]159.852[/C][C]36.1232[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 26 )[/C][C]5774.36[/C][C]159.852[/C][C]36.1232[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 26 )[/C][C]5774.36[/C][C]159.852[/C][C]36.1232[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 26 )[/C][C]5774.36[/C][C]133.717[/C][C]43.1834[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 26 )[/C][C]5774.36[/C][C]133.717[/C][C]43.1834[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 26 )[/C][C]5774.36[/C][C]133.717[/C][C]43.1834[/C][/ROW]
[ROW][C]Winsorized Mean ( 21 / 26 )[/C][C]5774.36[/C][C]133.717[/C][C]43.1834[/C][/ROW]
[ROW][C]Winsorized Mean ( 22 / 26 )[/C][C]5661.54[/C][C]118.842[/C][C]47.6394[/C][/ROW]
[ROW][C]Winsorized Mean ( 23 / 26 )[/C][C]5661.54[/C][C]118.842[/C][C]47.6394[/C][/ROW]
[ROW][C]Winsorized Mean ( 24 / 26 )[/C][C]5661.54[/C][C]118.842[/C][C]47.6394[/C][/ROW]
[ROW][C]Winsorized Mean ( 25 / 26 )[/C][C]5789.74[/C][C]101.857[/C][C]56.8417[/C][/ROW]
[ROW][C]Winsorized Mean ( 26 / 26 )[/C][C]5789.74[/C][C]101.857[/C][C]56.8417[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 26 )[/C][C]5747.37[/C][C]222.375[/C][C]25.8454[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 26 )[/C][C]5745.95[/C][C]213.651[/C][C]26.8941[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 26 )[/C][C]5750[/C][C]204.861[/C][C]28.0678[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 26 )[/C][C]5742.86[/C][C]200.378[/C][C]28.6601[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 26 )[/C][C]5741.18[/C][C]196.504[/C][C]29.2166[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 26 )[/C][C]5739.39[/C][C]191.861[/C][C]29.9143[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 26 )[/C][C]5743.75[/C][C]187.796[/C][C]30.5851[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 26 )[/C][C]5748.39[/C][C]182.87[/C][C]31.4344[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 26 )[/C][C]5753.33[/C][C]180.167[/C][C]31.9333[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 26 )[/C][C]5758.62[/C][C]176.801[/C][C]32.5712[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 26 )[/C][C]5764.29[/C][C]172.612[/C][C]33.3944[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 26 )[/C][C]5762.96[/C][C]169.356[/C][C]34.0288[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 26 )[/C][C]5761.54[/C][C]165.231[/C][C]34.8696[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 26 )[/C][C]5760[/C][C]160[/C][C]36[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 26 )[/C][C]5758.33[/C][C]157.885[/C][C]36.4717[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 26 )[/C][C]5756.52[/C][C]155.033[/C][C]37.1309[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 26 )[/C][C]5754.55[/C][C]151.227[/C][C]38.0525[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 26 )[/C][C]5752.38[/C][C]146.156[/C][C]39.3577[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 26 )[/C][C]5750[/C][C]145.135[/C][C]39.6183[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 26 )[/C][C]5747.37[/C][C]143.455[/C][C]40.0638[/C][/ROW]
[ROW][C]Trimmed Mean ( 21 / 26 )[/C][C]5744.44[/C][C]140.884[/C][C]40.7743[/C][/ROW]
[ROW][C]Trimmed Mean ( 22 / 26 )[/C][C]5741.18[/C][C]137.077[/C][C]41.883[/C][/ROW]
[ROW][C]Trimmed Mean ( 23 / 26 )[/C][C]5750[/C][C]135.301[/C][C]42.4977[/C][/ROW]
[ROW][C]Trimmed Mean ( 24 / 26 )[/C][C]5760[/C][C]132.318[/C][C]43.5315[/C][/ROW]
[ROW][C]Trimmed Mean ( 25 / 26 )[/C][C]5771.43[/C][C]127.538[/C][C]45.2525[/C][/ROW]
[ROW][C]Trimmed Mean ( 26 / 26 )[/C][C]5769.23[/C][C]125.853[/C][C]45.8409[/C][/ROW]
[ROW][C]Median[/C][C]5800[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]6000[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]5752.38[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]5752.38[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]5752.38[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]5752.38[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]5752.38[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]5752.38[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]5752.38[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]5752.38[/C][C][/C][C][/C][/ROW]
[ROW][C]Number of observations[/C][C]78[/C][C][/C][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299605&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299605&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 Mean5753.85236.55124.3239
Geometric Mean5297.91
Harmonic Mean4669.26
Quadratic Mean6116.81
Winsorized Mean ( 1 / 26 )5748.72229.79625.0166
Winsorized Mean ( 2 / 26 )5738.46227.43325.2315
Winsorized Mean ( 3 / 26 )5769.23214.49626.8967
Winsorized Mean ( 4 / 26 )5748.72210.43227.3187
Winsorized Mean ( 5 / 26 )5748.72210.43227.3187
Winsorized Mean ( 6 / 26 )5717.95204.91627.9039
Winsorized Mean ( 7 / 26 )5717.95204.91627.9039
Winsorized Mean ( 8 / 26 )5717.95190.91729.9499
Winsorized Mean ( 9 / 26 )5717.95190.91729.9499
Winsorized Mean ( 10 / 26 )5717.95190.91729.9499
Winsorized Mean ( 11 / 26 )5774.36181.69131.7811
Winsorized Mean ( 12 / 26 )5774.36181.69131.7811
Winsorized Mean ( 13 / 26 )5774.36181.69131.7811
Winsorized Mean ( 14 / 26 )5774.36159.85236.1232
Winsorized Mean ( 15 / 26 )5774.36159.85236.1232
Winsorized Mean ( 16 / 26 )5774.36159.85236.1232
Winsorized Mean ( 17 / 26 )5774.36159.85236.1232
Winsorized Mean ( 18 / 26 )5774.36133.71743.1834
Winsorized Mean ( 19 / 26 )5774.36133.71743.1834
Winsorized Mean ( 20 / 26 )5774.36133.71743.1834
Winsorized Mean ( 21 / 26 )5774.36133.71743.1834
Winsorized Mean ( 22 / 26 )5661.54118.84247.6394
Winsorized Mean ( 23 / 26 )5661.54118.84247.6394
Winsorized Mean ( 24 / 26 )5661.54118.84247.6394
Winsorized Mean ( 25 / 26 )5789.74101.85756.8417
Winsorized Mean ( 26 / 26 )5789.74101.85756.8417
Trimmed Mean ( 1 / 26 )5747.37222.37525.8454
Trimmed Mean ( 2 / 26 )5745.95213.65126.8941
Trimmed Mean ( 3 / 26 )5750204.86128.0678
Trimmed Mean ( 4 / 26 )5742.86200.37828.6601
Trimmed Mean ( 5 / 26 )5741.18196.50429.2166
Trimmed Mean ( 6 / 26 )5739.39191.86129.9143
Trimmed Mean ( 7 / 26 )5743.75187.79630.5851
Trimmed Mean ( 8 / 26 )5748.39182.8731.4344
Trimmed Mean ( 9 / 26 )5753.33180.16731.9333
Trimmed Mean ( 10 / 26 )5758.62176.80132.5712
Trimmed Mean ( 11 / 26 )5764.29172.61233.3944
Trimmed Mean ( 12 / 26 )5762.96169.35634.0288
Trimmed Mean ( 13 / 26 )5761.54165.23134.8696
Trimmed Mean ( 14 / 26 )576016036
Trimmed Mean ( 15 / 26 )5758.33157.88536.4717
Trimmed Mean ( 16 / 26 )5756.52155.03337.1309
Trimmed Mean ( 17 / 26 )5754.55151.22738.0525
Trimmed Mean ( 18 / 26 )5752.38146.15639.3577
Trimmed Mean ( 19 / 26 )5750145.13539.6183
Trimmed Mean ( 20 / 26 )5747.37143.45540.0638
Trimmed Mean ( 21 / 26 )5744.44140.88440.7743
Trimmed Mean ( 22 / 26 )5741.18137.07741.883
Trimmed Mean ( 23 / 26 )5750135.30142.4977
Trimmed Mean ( 24 / 26 )5760132.31843.5315
Trimmed Mean ( 25 / 26 )5771.43127.53845.2525
Trimmed Mean ( 26 / 26 )5769.23125.85345.8409
Median5800
Midrange6000
Midmean - Weighted Average at Xnp5752.38
Midmean - Weighted Average at X(n+1)p5752.38
Midmean - Empirical Distribution Function5752.38
Midmean - Empirical Distribution Function - Averaging5752.38
Midmean - Empirical Distribution Function - Interpolation5752.38
Midmean - Closest Observation5752.38
Midmean - True Basic - Statistics Graphics Toolkit5752.38
Midmean - MS Excel (old versions)5752.38
Number of observations78



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')