Free Statistics

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 computationWed, 21 Dec 2016 18:35:35 +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/21/t14823417581u5mwk2sm0edj7u.htm/, Retrieved Tue, 07 May 2024 04:02:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=302439, Retrieved Tue, 07 May 2024 04:02:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact59
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [Centran Tendency ...] [2016-12-21 17:35:35] [bde5266f17215258f6d7c4cd7e531432] [Current]
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Dataseries X:
1549.5
1746.5
1869.5
1784
1795
1942.5
2100
2072.5
2075
2278
2451
2290.5
2388
2574.5
2939.5
2924
3087.5
3259.5
3474.5
3376
3496
3771.5
3743
3474.5
3405
3684.5
3804
3470.5
3453.5
3842
4156.5
4055
4133.5
4552
4588
4423.5
4462.5
4846
4869.5
4637
4841
5114.5
5374.5
5166.5
5236.5
5740.5
5992
5842
5844.5
6384.5
6487
6372
6583.5
6990
6874
6710
6924
7428.5
7415.5
7228.5
6734
7158.5
7192




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=302439&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=302439&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302439&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 Mean4356.82222.78419.5562
Geometric Mean3978.72
Harmonic Mean3595.34
Quadratic Mean4696.71
Winsorized Mean ( 1 / 21 )4359.74222.12419.6275
Winsorized Mean ( 2 / 21 )4354.99220.61719.7401
Winsorized Mean ( 3 / 21 )4353.78220.15519.776
Winsorized Mean ( 4 / 21 )4356.38218.83719.9069
Winsorized Mean ( 5 / 21 )4348.8215.07820.2197
Winsorized Mean ( 6 / 21 )4354.9211.64620.5764
Winsorized Mean ( 7 / 21 )4349.62210.51620.6617
Winsorized Mean ( 8 / 21 )4335.02206.57820.9849
Winsorized Mean ( 9 / 21 )4357.02201.59921.6122
Winsorized Mean ( 10 / 21 )4338.92197.53821.965
Winsorized Mean ( 11 / 21 )4339.1191.64922.6408
Winsorized Mean ( 12 / 21 )4331.57186.18523.2649
Winsorized Mean ( 13 / 21 )4354.48181.63423.9739
Winsorized Mean ( 14 / 21 )4347.7154.88828.0699
Winsorized Mean ( 15 / 21 )4316.27148.41429.0827
Winsorized Mean ( 16 / 21 )4353.22142.80630.4834
Winsorized Mean ( 17 / 21 )4372.25131.72333.1926
Winsorized Mean ( 18 / 21 )4300.96110.17639.0373
Winsorized Mean ( 19 / 21 )4268.09102.54641.6211
Winsorized Mean ( 20 / 21 )4261.2697.104343.8833
Winsorized Mean ( 21 / 21 )4249.693.751545.3283
Trimmed Mean ( 1 / 21 )4352.48219.6319.8174
Trimmed Mean ( 2 / 21 )4344.74216.48320.0697
Trimmed Mean ( 3 / 21 )4339.07213.49820.3237
Trimmed Mean ( 4 / 21 )4333.45209.92420.6429
Trimmed Mean ( 5 / 21 )4326.64205.8921.0144
Trimmed Mean ( 6 / 21 )4321.17201.99921.392
Trimmed Mean ( 7 / 21 )4313.94198.00821.7867
Trimmed Mean ( 8 / 21 )4307.11193.13722.3008
Trimmed Mean ( 9 / 21 )4302.22187.8722.9
Trimmed Mean ( 10 / 21 )4293.3182.24923.5573
Trimmed Mean ( 11 / 21 )4286.29175.87424.3714
Trimmed Mean ( 12 / 21 )4278.54168.90325.3314
Trimmed Mean ( 13 / 21 )4271.01160.94126.5377
Trimmed Mean ( 14 / 21 )4259.46151.19928.1713
Trimmed Mean ( 15 / 21 )4247.42145.69229.1535
Trimmed Mean ( 16 / 21 )4238.1139.70330.3365
Trimmed Mean ( 17 / 21 )4222.47132.46131.8771
Trimmed Mean ( 18 / 21 )4201.91125.20133.5612
Trimmed Mean ( 19 / 21 )4188.04122.0834.3056
Trimmed Mean ( 20 / 21 )4176.5119.50734.9476
Trimmed Mean ( 21 / 21 )4163.79116.70635.6777
Median4133.5
Midrange4489
Midmean - Weighted Average at Xnp4197.52
Midmean - Weighted Average at X(n+1)p4247.42
Midmean - Empirical Distribution Function4247.42
Midmean - Empirical Distribution Function - Averaging4247.42
Midmean - Empirical Distribution Function - Interpolation4238.1
Midmean - Closest Observation4197.52
Midmean - True Basic - Statistics Graphics Toolkit4247.42
Midmean - MS Excel (old versions)4247.42
Number of observations63

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 4356.82 & 222.784 & 19.5562 \tabularnewline
Geometric Mean & 3978.72 &  &  \tabularnewline
Harmonic Mean & 3595.34 &  &  \tabularnewline
Quadratic Mean & 4696.71 &  &  \tabularnewline
Winsorized Mean ( 1 / 21 ) & 4359.74 & 222.124 & 19.6275 \tabularnewline
Winsorized Mean ( 2 / 21 ) & 4354.99 & 220.617 & 19.7401 \tabularnewline
Winsorized Mean ( 3 / 21 ) & 4353.78 & 220.155 & 19.776 \tabularnewline
Winsorized Mean ( 4 / 21 ) & 4356.38 & 218.837 & 19.9069 \tabularnewline
Winsorized Mean ( 5 / 21 ) & 4348.8 & 215.078 & 20.2197 \tabularnewline
Winsorized Mean ( 6 / 21 ) & 4354.9 & 211.646 & 20.5764 \tabularnewline
Winsorized Mean ( 7 / 21 ) & 4349.62 & 210.516 & 20.6617 \tabularnewline
Winsorized Mean ( 8 / 21 ) & 4335.02 & 206.578 & 20.9849 \tabularnewline
Winsorized Mean ( 9 / 21 ) & 4357.02 & 201.599 & 21.6122 \tabularnewline
Winsorized Mean ( 10 / 21 ) & 4338.92 & 197.538 & 21.965 \tabularnewline
Winsorized Mean ( 11 / 21 ) & 4339.1 & 191.649 & 22.6408 \tabularnewline
Winsorized Mean ( 12 / 21 ) & 4331.57 & 186.185 & 23.2649 \tabularnewline
Winsorized Mean ( 13 / 21 ) & 4354.48 & 181.634 & 23.9739 \tabularnewline
Winsorized Mean ( 14 / 21 ) & 4347.7 & 154.888 & 28.0699 \tabularnewline
Winsorized Mean ( 15 / 21 ) & 4316.27 & 148.414 & 29.0827 \tabularnewline
Winsorized Mean ( 16 / 21 ) & 4353.22 & 142.806 & 30.4834 \tabularnewline
Winsorized Mean ( 17 / 21 ) & 4372.25 & 131.723 & 33.1926 \tabularnewline
Winsorized Mean ( 18 / 21 ) & 4300.96 & 110.176 & 39.0373 \tabularnewline
Winsorized Mean ( 19 / 21 ) & 4268.09 & 102.546 & 41.6211 \tabularnewline
Winsorized Mean ( 20 / 21 ) & 4261.26 & 97.1043 & 43.8833 \tabularnewline
Winsorized Mean ( 21 / 21 ) & 4249.6 & 93.7515 & 45.3283 \tabularnewline
Trimmed Mean ( 1 / 21 ) & 4352.48 & 219.63 & 19.8174 \tabularnewline
Trimmed Mean ( 2 / 21 ) & 4344.74 & 216.483 & 20.0697 \tabularnewline
Trimmed Mean ( 3 / 21 ) & 4339.07 & 213.498 & 20.3237 \tabularnewline
Trimmed Mean ( 4 / 21 ) & 4333.45 & 209.924 & 20.6429 \tabularnewline
Trimmed Mean ( 5 / 21 ) & 4326.64 & 205.89 & 21.0144 \tabularnewline
Trimmed Mean ( 6 / 21 ) & 4321.17 & 201.999 & 21.392 \tabularnewline
Trimmed Mean ( 7 / 21 ) & 4313.94 & 198.008 & 21.7867 \tabularnewline
Trimmed Mean ( 8 / 21 ) & 4307.11 & 193.137 & 22.3008 \tabularnewline
Trimmed Mean ( 9 / 21 ) & 4302.22 & 187.87 & 22.9 \tabularnewline
Trimmed Mean ( 10 / 21 ) & 4293.3 & 182.249 & 23.5573 \tabularnewline
Trimmed Mean ( 11 / 21 ) & 4286.29 & 175.874 & 24.3714 \tabularnewline
Trimmed Mean ( 12 / 21 ) & 4278.54 & 168.903 & 25.3314 \tabularnewline
Trimmed Mean ( 13 / 21 ) & 4271.01 & 160.941 & 26.5377 \tabularnewline
Trimmed Mean ( 14 / 21 ) & 4259.46 & 151.199 & 28.1713 \tabularnewline
Trimmed Mean ( 15 / 21 ) & 4247.42 & 145.692 & 29.1535 \tabularnewline
Trimmed Mean ( 16 / 21 ) & 4238.1 & 139.703 & 30.3365 \tabularnewline
Trimmed Mean ( 17 / 21 ) & 4222.47 & 132.461 & 31.8771 \tabularnewline
Trimmed Mean ( 18 / 21 ) & 4201.91 & 125.201 & 33.5612 \tabularnewline
Trimmed Mean ( 19 / 21 ) & 4188.04 & 122.08 & 34.3056 \tabularnewline
Trimmed Mean ( 20 / 21 ) & 4176.5 & 119.507 & 34.9476 \tabularnewline
Trimmed Mean ( 21 / 21 ) & 4163.79 & 116.706 & 35.6777 \tabularnewline
Median & 4133.5 &  &  \tabularnewline
Midrange & 4489 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 4197.52 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 4247.42 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 4247.42 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 4247.42 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 4238.1 &  &  \tabularnewline
Midmean - Closest Observation & 4197.52 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 4247.42 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 4247.42 &  &  \tabularnewline
Number of observations & 63 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302439&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]4356.82[/C][C]222.784[/C][C]19.5562[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]3978.72[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]3595.34[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]4696.71[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 21 )[/C][C]4359.74[/C][C]222.124[/C][C]19.6275[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 21 )[/C][C]4354.99[/C][C]220.617[/C][C]19.7401[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 21 )[/C][C]4353.78[/C][C]220.155[/C][C]19.776[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 21 )[/C][C]4356.38[/C][C]218.837[/C][C]19.9069[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 21 )[/C][C]4348.8[/C][C]215.078[/C][C]20.2197[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 21 )[/C][C]4354.9[/C][C]211.646[/C][C]20.5764[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 21 )[/C][C]4349.62[/C][C]210.516[/C][C]20.6617[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 21 )[/C][C]4335.02[/C][C]206.578[/C][C]20.9849[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 21 )[/C][C]4357.02[/C][C]201.599[/C][C]21.6122[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 21 )[/C][C]4338.92[/C][C]197.538[/C][C]21.965[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 21 )[/C][C]4339.1[/C][C]191.649[/C][C]22.6408[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 21 )[/C][C]4331.57[/C][C]186.185[/C][C]23.2649[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 21 )[/C][C]4354.48[/C][C]181.634[/C][C]23.9739[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 21 )[/C][C]4347.7[/C][C]154.888[/C][C]28.0699[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 21 )[/C][C]4316.27[/C][C]148.414[/C][C]29.0827[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 21 )[/C][C]4353.22[/C][C]142.806[/C][C]30.4834[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 21 )[/C][C]4372.25[/C][C]131.723[/C][C]33.1926[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 21 )[/C][C]4300.96[/C][C]110.176[/C][C]39.0373[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 21 )[/C][C]4268.09[/C][C]102.546[/C][C]41.6211[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 21 )[/C][C]4261.26[/C][C]97.1043[/C][C]43.8833[/C][/ROW]
[ROW][C]Winsorized Mean ( 21 / 21 )[/C][C]4249.6[/C][C]93.7515[/C][C]45.3283[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 21 )[/C][C]4352.48[/C][C]219.63[/C][C]19.8174[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 21 )[/C][C]4344.74[/C][C]216.483[/C][C]20.0697[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 21 )[/C][C]4339.07[/C][C]213.498[/C][C]20.3237[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 21 )[/C][C]4333.45[/C][C]209.924[/C][C]20.6429[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 21 )[/C][C]4326.64[/C][C]205.89[/C][C]21.0144[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 21 )[/C][C]4321.17[/C][C]201.999[/C][C]21.392[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 21 )[/C][C]4313.94[/C][C]198.008[/C][C]21.7867[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 21 )[/C][C]4307.11[/C][C]193.137[/C][C]22.3008[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 21 )[/C][C]4302.22[/C][C]187.87[/C][C]22.9[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 21 )[/C][C]4293.3[/C][C]182.249[/C][C]23.5573[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 21 )[/C][C]4286.29[/C][C]175.874[/C][C]24.3714[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 21 )[/C][C]4278.54[/C][C]168.903[/C][C]25.3314[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 21 )[/C][C]4271.01[/C][C]160.941[/C][C]26.5377[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 21 )[/C][C]4259.46[/C][C]151.199[/C][C]28.1713[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 21 )[/C][C]4247.42[/C][C]145.692[/C][C]29.1535[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 21 )[/C][C]4238.1[/C][C]139.703[/C][C]30.3365[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 21 )[/C][C]4222.47[/C][C]132.461[/C][C]31.8771[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 21 )[/C][C]4201.91[/C][C]125.201[/C][C]33.5612[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 21 )[/C][C]4188.04[/C][C]122.08[/C][C]34.3056[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 21 )[/C][C]4176.5[/C][C]119.507[/C][C]34.9476[/C][/ROW]
[ROW][C]Trimmed Mean ( 21 / 21 )[/C][C]4163.79[/C][C]116.706[/C][C]35.6777[/C][/ROW]
[ROW][C]Median[/C][C]4133.5[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]4489[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]4197.52[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]4247.42[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]4247.42[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]4247.42[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]4238.1[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]4197.52[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]4247.42[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]4247.42[/C][C][/C][C][/C][/ROW]
[ROW][C]Number of observations[/C][C]63[/C][C][/C][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302439&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302439&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 Mean4356.82222.78419.5562
Geometric Mean3978.72
Harmonic Mean3595.34
Quadratic Mean4696.71
Winsorized Mean ( 1 / 21 )4359.74222.12419.6275
Winsorized Mean ( 2 / 21 )4354.99220.61719.7401
Winsorized Mean ( 3 / 21 )4353.78220.15519.776
Winsorized Mean ( 4 / 21 )4356.38218.83719.9069
Winsorized Mean ( 5 / 21 )4348.8215.07820.2197
Winsorized Mean ( 6 / 21 )4354.9211.64620.5764
Winsorized Mean ( 7 / 21 )4349.62210.51620.6617
Winsorized Mean ( 8 / 21 )4335.02206.57820.9849
Winsorized Mean ( 9 / 21 )4357.02201.59921.6122
Winsorized Mean ( 10 / 21 )4338.92197.53821.965
Winsorized Mean ( 11 / 21 )4339.1191.64922.6408
Winsorized Mean ( 12 / 21 )4331.57186.18523.2649
Winsorized Mean ( 13 / 21 )4354.48181.63423.9739
Winsorized Mean ( 14 / 21 )4347.7154.88828.0699
Winsorized Mean ( 15 / 21 )4316.27148.41429.0827
Winsorized Mean ( 16 / 21 )4353.22142.80630.4834
Winsorized Mean ( 17 / 21 )4372.25131.72333.1926
Winsorized Mean ( 18 / 21 )4300.96110.17639.0373
Winsorized Mean ( 19 / 21 )4268.09102.54641.6211
Winsorized Mean ( 20 / 21 )4261.2697.104343.8833
Winsorized Mean ( 21 / 21 )4249.693.751545.3283
Trimmed Mean ( 1 / 21 )4352.48219.6319.8174
Trimmed Mean ( 2 / 21 )4344.74216.48320.0697
Trimmed Mean ( 3 / 21 )4339.07213.49820.3237
Trimmed Mean ( 4 / 21 )4333.45209.92420.6429
Trimmed Mean ( 5 / 21 )4326.64205.8921.0144
Trimmed Mean ( 6 / 21 )4321.17201.99921.392
Trimmed Mean ( 7 / 21 )4313.94198.00821.7867
Trimmed Mean ( 8 / 21 )4307.11193.13722.3008
Trimmed Mean ( 9 / 21 )4302.22187.8722.9
Trimmed Mean ( 10 / 21 )4293.3182.24923.5573
Trimmed Mean ( 11 / 21 )4286.29175.87424.3714
Trimmed Mean ( 12 / 21 )4278.54168.90325.3314
Trimmed Mean ( 13 / 21 )4271.01160.94126.5377
Trimmed Mean ( 14 / 21 )4259.46151.19928.1713
Trimmed Mean ( 15 / 21 )4247.42145.69229.1535
Trimmed Mean ( 16 / 21 )4238.1139.70330.3365
Trimmed Mean ( 17 / 21 )4222.47132.46131.8771
Trimmed Mean ( 18 / 21 )4201.91125.20133.5612
Trimmed Mean ( 19 / 21 )4188.04122.0834.3056
Trimmed Mean ( 20 / 21 )4176.5119.50734.9476
Trimmed Mean ( 21 / 21 )4163.79116.70635.6777
Median4133.5
Midrange4489
Midmean - Weighted Average at Xnp4197.52
Midmean - Weighted Average at X(n+1)p4247.42
Midmean - Empirical Distribution Function4247.42
Midmean - Empirical Distribution Function - Averaging4247.42
Midmean - Empirical Distribution Function - Interpolation4238.1
Midmean - Closest Observation4197.52
Midmean - True Basic - Statistics Graphics Toolkit4247.42
Midmean - MS Excel (old versions)4247.42
Number of observations63



Parameters (Session):
par1 = 12 ; par2 = Double ; par3 = multiplicative ; par4 = 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')