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 08:56:30 +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/t1482307060kij4uk7qvysq7ql.htm/, Retrieved Mon, 06 May 2024 22:09:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=301887, Retrieved Mon, 06 May 2024 22:09:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact60
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [TEST2] [2016-12-21 07:56:30] [2afcbc313e2a613e91c73c4ef04af8e0] [Current]
Feedback Forum

Post a new message
Dataseries X:
26
20
19
20
25
22
26
22
19
24
26
13
22
21
7
17
25
25
19
23
22
21
18
22
18
23
20
15
21
18
19
22
16
18
20
24
24
18
21
17
22
16
21
24
24
16
16
18
20
24
17
19
20
15
22
23
16
19
19
21
24
22
18
24
24
22
23
22
20
18
25
16
20
15
19
19
16
17
28
25
20
16
23
21
23
18
20
9
25
20
21
22
27
18
16
22
20
20




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=301887&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=301887&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301887&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 Mean20.28570.36464955.6308
Geometric Mean19.9095
Harmonic Mean19.4298
Quadratic Mean20.6012
Winsorized Mean ( 1 / 32 )20.29590.35535957.1138
Winsorized Mean ( 2 / 32 )20.35710.3286161.9493
Winsorized Mean ( 3 / 32 )20.41840.31610264.5941
Winsorized Mean ( 4 / 32 )20.41840.31610264.5941
Winsorized Mean ( 5 / 32 )20.36730.30748766.2381
Winsorized Mean ( 6 / 32 )20.42860.29726368.7222
Winsorized Mean ( 7 / 32 )20.42860.29726368.7222
Winsorized Mean ( 8 / 32 )20.42860.29726368.7222
Winsorized Mean ( 9 / 32 )20.42860.29726368.7222
Winsorized Mean ( 10 / 32 )20.42860.29726368.7222
Winsorized Mean ( 11 / 32 )20.31630.28073672.3681
Winsorized Mean ( 12 / 32 )20.31630.28073672.3681
Winsorized Mean ( 13 / 32 )20.31630.28073672.3681
Winsorized Mean ( 14 / 32 )20.31630.28073672.3681
Winsorized Mean ( 15 / 32 )20.46940.25792979.3606
Winsorized Mean ( 16 / 32 )20.46940.25792979.3606
Winsorized Mean ( 17 / 32 )20.46940.25792979.3606
Winsorized Mean ( 18 / 32 )20.46940.25792979.3606
Winsorized Mean ( 19 / 32 )20.66330.23295988.6993
Winsorized Mean ( 20 / 32 )20.45920.20470499.9452
Winsorized Mean ( 21 / 32 )20.45920.20470499.9452
Winsorized Mean ( 22 / 32 )20.45920.20470499.9452
Winsorized Mean ( 23 / 32 )20.45920.20470499.9452
Winsorized Mean ( 24 / 32 )20.45920.20470499.9452
Winsorized Mean ( 25 / 32 )20.45920.20470499.9452
Winsorized Mean ( 26 / 32 )20.19390.173246116.562
Winsorized Mean ( 27 / 32 )20.19390.173246116.562
Winsorized Mean ( 28 / 32 )20.19390.173246116.562
Winsorized Mean ( 29 / 32 )20.48980.137028149.531
Winsorized Mean ( 30 / 32 )20.48980.137028149.531
Winsorized Mean ( 31 / 32 )20.48980.137028149.531
Winsorized Mean ( 32 / 32 )20.48980.137028149.531
Trimmed Mean ( 1 / 32 )20.28570.33568160.4316
Trimmed Mean ( 2 / 32 )20.34380.31263365.0724
Trimmed Mean ( 3 / 32 )20.4130.30293767.3838
Trimmed Mean ( 4 / 32 )20.4130.29725968.6708
Trimmed Mean ( 5 / 32 )20.40910.29076570.1909
Trimmed Mean ( 6 / 32 )20.41860.28576171.4534
Trimmed Mean ( 7 / 32 )20.41670.28250272.2709
Trimmed Mean ( 8 / 32 )20.41670.27869873.2572
Trimmed Mean ( 9 / 32 )20.41250.27426274.4271
Trimmed Mean ( 10 / 32 )20.41030.26908475.8509
Trimmed Mean ( 11 / 32 )20.40790.26303177.5875
Trimmed Mean ( 12 / 32 )20.41890.25887278.8765
Trimmed Mean ( 13 / 32 )20.43060.25394180.4539
Trimmed Mean ( 14 / 32 )20.44290.24808682.4022
Trimmed Mean ( 15 / 32 )20.45590.24111484.839
Trimmed Mean ( 16 / 32 )20.45590.23670486.4196
Trimmed Mean ( 17 / 32 )20.45310.2313888.3964
Trimmed Mean ( 18 / 32 )20.45160.22493690.9219
Trimmed Mean ( 19 / 32 )20.450.21710194.1959
Trimmed Mean ( 20 / 32 )20.4310.21174396.49
Trimmed Mean ( 21 / 32 )20.42860.20991297.3197
Trimmed Mean ( 22 / 32 )20.42590.2074898.4475
Trimmed Mean ( 23 / 32 )20.42310.20430299.9653
Trimmed Mean ( 24 / 32 )20.420.200184102.006
Trimmed Mean ( 25 / 32 )20.41670.194866104.773
Trimmed Mean ( 26 / 32 )20.4130.187987108.587
Trimmed Mean ( 27 / 32 )20.4130.18506110.305
Trimmed Mean ( 28 / 32 )20.45240.180984113.006
Trimmed Mean ( 29 / 32 )20.4750.175366116.756
Trimmed Mean ( 30 / 32 )20.47370.175841116.433
Trimmed Mean ( 31 / 32 )20.47220.17587116.405
Trimmed Mean ( 32 / 32 )20.47220.175278116.798
Median20
Midrange17.5
Midmean - Weighted Average at Xnp20.3898
Midmean - Weighted Average at X(n+1)p20.3898
Midmean - Empirical Distribution Function20.3898
Midmean - Empirical Distribution Function - Averaging20.3898
Midmean - Empirical Distribution Function - Interpolation20.3898
Midmean - Closest Observation20.3898
Midmean - True Basic - Statistics Graphics Toolkit20.3898
Midmean - MS Excel (old versions)20.3898
Number of observations98

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 20.2857 & 0.364649 & 55.6308 \tabularnewline
Geometric Mean & 19.9095 &  &  \tabularnewline
Harmonic Mean & 19.4298 &  &  \tabularnewline
Quadratic Mean & 20.6012 &  &  \tabularnewline
Winsorized Mean ( 1 / 32 ) & 20.2959 & 0.355359 & 57.1138 \tabularnewline
Winsorized Mean ( 2 / 32 ) & 20.3571 & 0.32861 & 61.9493 \tabularnewline
Winsorized Mean ( 3 / 32 ) & 20.4184 & 0.316102 & 64.5941 \tabularnewline
Winsorized Mean ( 4 / 32 ) & 20.4184 & 0.316102 & 64.5941 \tabularnewline
Winsorized Mean ( 5 / 32 ) & 20.3673 & 0.307487 & 66.2381 \tabularnewline
Winsorized Mean ( 6 / 32 ) & 20.4286 & 0.297263 & 68.7222 \tabularnewline
Winsorized Mean ( 7 / 32 ) & 20.4286 & 0.297263 & 68.7222 \tabularnewline
Winsorized Mean ( 8 / 32 ) & 20.4286 & 0.297263 & 68.7222 \tabularnewline
Winsorized Mean ( 9 / 32 ) & 20.4286 & 0.297263 & 68.7222 \tabularnewline
Winsorized Mean ( 10 / 32 ) & 20.4286 & 0.297263 & 68.7222 \tabularnewline
Winsorized Mean ( 11 / 32 ) & 20.3163 & 0.280736 & 72.3681 \tabularnewline
Winsorized Mean ( 12 / 32 ) & 20.3163 & 0.280736 & 72.3681 \tabularnewline
Winsorized Mean ( 13 / 32 ) & 20.3163 & 0.280736 & 72.3681 \tabularnewline
Winsorized Mean ( 14 / 32 ) & 20.3163 & 0.280736 & 72.3681 \tabularnewline
Winsorized Mean ( 15 / 32 ) & 20.4694 & 0.257929 & 79.3606 \tabularnewline
Winsorized Mean ( 16 / 32 ) & 20.4694 & 0.257929 & 79.3606 \tabularnewline
Winsorized Mean ( 17 / 32 ) & 20.4694 & 0.257929 & 79.3606 \tabularnewline
Winsorized Mean ( 18 / 32 ) & 20.4694 & 0.257929 & 79.3606 \tabularnewline
Winsorized Mean ( 19 / 32 ) & 20.6633 & 0.232959 & 88.6993 \tabularnewline
Winsorized Mean ( 20 / 32 ) & 20.4592 & 0.204704 & 99.9452 \tabularnewline
Winsorized Mean ( 21 / 32 ) & 20.4592 & 0.204704 & 99.9452 \tabularnewline
Winsorized Mean ( 22 / 32 ) & 20.4592 & 0.204704 & 99.9452 \tabularnewline
Winsorized Mean ( 23 / 32 ) & 20.4592 & 0.204704 & 99.9452 \tabularnewline
Winsorized Mean ( 24 / 32 ) & 20.4592 & 0.204704 & 99.9452 \tabularnewline
Winsorized Mean ( 25 / 32 ) & 20.4592 & 0.204704 & 99.9452 \tabularnewline
Winsorized Mean ( 26 / 32 ) & 20.1939 & 0.173246 & 116.562 \tabularnewline
Winsorized Mean ( 27 / 32 ) & 20.1939 & 0.173246 & 116.562 \tabularnewline
Winsorized Mean ( 28 / 32 ) & 20.1939 & 0.173246 & 116.562 \tabularnewline
Winsorized Mean ( 29 / 32 ) & 20.4898 & 0.137028 & 149.531 \tabularnewline
Winsorized Mean ( 30 / 32 ) & 20.4898 & 0.137028 & 149.531 \tabularnewline
Winsorized Mean ( 31 / 32 ) & 20.4898 & 0.137028 & 149.531 \tabularnewline
Winsorized Mean ( 32 / 32 ) & 20.4898 & 0.137028 & 149.531 \tabularnewline
Trimmed Mean ( 1 / 32 ) & 20.2857 & 0.335681 & 60.4316 \tabularnewline
Trimmed Mean ( 2 / 32 ) & 20.3438 & 0.312633 & 65.0724 \tabularnewline
Trimmed Mean ( 3 / 32 ) & 20.413 & 0.302937 & 67.3838 \tabularnewline
Trimmed Mean ( 4 / 32 ) & 20.413 & 0.297259 & 68.6708 \tabularnewline
Trimmed Mean ( 5 / 32 ) & 20.4091 & 0.290765 & 70.1909 \tabularnewline
Trimmed Mean ( 6 / 32 ) & 20.4186 & 0.285761 & 71.4534 \tabularnewline
Trimmed Mean ( 7 / 32 ) & 20.4167 & 0.282502 & 72.2709 \tabularnewline
Trimmed Mean ( 8 / 32 ) & 20.4167 & 0.278698 & 73.2572 \tabularnewline
Trimmed Mean ( 9 / 32 ) & 20.4125 & 0.274262 & 74.4271 \tabularnewline
Trimmed Mean ( 10 / 32 ) & 20.4103 & 0.269084 & 75.8509 \tabularnewline
Trimmed Mean ( 11 / 32 ) & 20.4079 & 0.263031 & 77.5875 \tabularnewline
Trimmed Mean ( 12 / 32 ) & 20.4189 & 0.258872 & 78.8765 \tabularnewline
Trimmed Mean ( 13 / 32 ) & 20.4306 & 0.253941 & 80.4539 \tabularnewline
Trimmed Mean ( 14 / 32 ) & 20.4429 & 0.248086 & 82.4022 \tabularnewline
Trimmed Mean ( 15 / 32 ) & 20.4559 & 0.241114 & 84.839 \tabularnewline
Trimmed Mean ( 16 / 32 ) & 20.4559 & 0.236704 & 86.4196 \tabularnewline
Trimmed Mean ( 17 / 32 ) & 20.4531 & 0.23138 & 88.3964 \tabularnewline
Trimmed Mean ( 18 / 32 ) & 20.4516 & 0.224936 & 90.9219 \tabularnewline
Trimmed Mean ( 19 / 32 ) & 20.45 & 0.217101 & 94.1959 \tabularnewline
Trimmed Mean ( 20 / 32 ) & 20.431 & 0.211743 & 96.49 \tabularnewline
Trimmed Mean ( 21 / 32 ) & 20.4286 & 0.209912 & 97.3197 \tabularnewline
Trimmed Mean ( 22 / 32 ) & 20.4259 & 0.20748 & 98.4475 \tabularnewline
Trimmed Mean ( 23 / 32 ) & 20.4231 & 0.204302 & 99.9653 \tabularnewline
Trimmed Mean ( 24 / 32 ) & 20.42 & 0.200184 & 102.006 \tabularnewline
Trimmed Mean ( 25 / 32 ) & 20.4167 & 0.194866 & 104.773 \tabularnewline
Trimmed Mean ( 26 / 32 ) & 20.413 & 0.187987 & 108.587 \tabularnewline
Trimmed Mean ( 27 / 32 ) & 20.413 & 0.18506 & 110.305 \tabularnewline
Trimmed Mean ( 28 / 32 ) & 20.4524 & 0.180984 & 113.006 \tabularnewline
Trimmed Mean ( 29 / 32 ) & 20.475 & 0.175366 & 116.756 \tabularnewline
Trimmed Mean ( 30 / 32 ) & 20.4737 & 0.175841 & 116.433 \tabularnewline
Trimmed Mean ( 31 / 32 ) & 20.4722 & 0.17587 & 116.405 \tabularnewline
Trimmed Mean ( 32 / 32 ) & 20.4722 & 0.175278 & 116.798 \tabularnewline
Median & 20 &  &  \tabularnewline
Midrange & 17.5 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 20.3898 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 20.3898 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 20.3898 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 20.3898 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 20.3898 &  &  \tabularnewline
Midmean - Closest Observation & 20.3898 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 20.3898 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 20.3898 &  &  \tabularnewline
Number of observations & 98 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301887&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]20.2857[/C][C]0.364649[/C][C]55.6308[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]19.9095[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]19.4298[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]20.6012[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 32 )[/C][C]20.2959[/C][C]0.355359[/C][C]57.1138[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 32 )[/C][C]20.3571[/C][C]0.32861[/C][C]61.9493[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 32 )[/C][C]20.4184[/C][C]0.316102[/C][C]64.5941[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 32 )[/C][C]20.4184[/C][C]0.316102[/C][C]64.5941[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 32 )[/C][C]20.3673[/C][C]0.307487[/C][C]66.2381[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 32 )[/C][C]20.4286[/C][C]0.297263[/C][C]68.7222[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 32 )[/C][C]20.4286[/C][C]0.297263[/C][C]68.7222[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 32 )[/C][C]20.4286[/C][C]0.297263[/C][C]68.7222[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 32 )[/C][C]20.4286[/C][C]0.297263[/C][C]68.7222[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 32 )[/C][C]20.4286[/C][C]0.297263[/C][C]68.7222[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 32 )[/C][C]20.3163[/C][C]0.280736[/C][C]72.3681[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 32 )[/C][C]20.3163[/C][C]0.280736[/C][C]72.3681[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 32 )[/C][C]20.3163[/C][C]0.280736[/C][C]72.3681[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 32 )[/C][C]20.3163[/C][C]0.280736[/C][C]72.3681[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 32 )[/C][C]20.4694[/C][C]0.257929[/C][C]79.3606[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 32 )[/C][C]20.4694[/C][C]0.257929[/C][C]79.3606[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 32 )[/C][C]20.4694[/C][C]0.257929[/C][C]79.3606[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 32 )[/C][C]20.4694[/C][C]0.257929[/C][C]79.3606[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 32 )[/C][C]20.6633[/C][C]0.232959[/C][C]88.6993[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 32 )[/C][C]20.4592[/C][C]0.204704[/C][C]99.9452[/C][/ROW]
[ROW][C]Winsorized Mean ( 21 / 32 )[/C][C]20.4592[/C][C]0.204704[/C][C]99.9452[/C][/ROW]
[ROW][C]Winsorized Mean ( 22 / 32 )[/C][C]20.4592[/C][C]0.204704[/C][C]99.9452[/C][/ROW]
[ROW][C]Winsorized Mean ( 23 / 32 )[/C][C]20.4592[/C][C]0.204704[/C][C]99.9452[/C][/ROW]
[ROW][C]Winsorized Mean ( 24 / 32 )[/C][C]20.4592[/C][C]0.204704[/C][C]99.9452[/C][/ROW]
[ROW][C]Winsorized Mean ( 25 / 32 )[/C][C]20.4592[/C][C]0.204704[/C][C]99.9452[/C][/ROW]
[ROW][C]Winsorized Mean ( 26 / 32 )[/C][C]20.1939[/C][C]0.173246[/C][C]116.562[/C][/ROW]
[ROW][C]Winsorized Mean ( 27 / 32 )[/C][C]20.1939[/C][C]0.173246[/C][C]116.562[/C][/ROW]
[ROW][C]Winsorized Mean ( 28 / 32 )[/C][C]20.1939[/C][C]0.173246[/C][C]116.562[/C][/ROW]
[ROW][C]Winsorized Mean ( 29 / 32 )[/C][C]20.4898[/C][C]0.137028[/C][C]149.531[/C][/ROW]
[ROW][C]Winsorized Mean ( 30 / 32 )[/C][C]20.4898[/C][C]0.137028[/C][C]149.531[/C][/ROW]
[ROW][C]Winsorized Mean ( 31 / 32 )[/C][C]20.4898[/C][C]0.137028[/C][C]149.531[/C][/ROW]
[ROW][C]Winsorized Mean ( 32 / 32 )[/C][C]20.4898[/C][C]0.137028[/C][C]149.531[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 32 )[/C][C]20.2857[/C][C]0.335681[/C][C]60.4316[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 32 )[/C][C]20.3438[/C][C]0.312633[/C][C]65.0724[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 32 )[/C][C]20.413[/C][C]0.302937[/C][C]67.3838[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 32 )[/C][C]20.413[/C][C]0.297259[/C][C]68.6708[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 32 )[/C][C]20.4091[/C][C]0.290765[/C][C]70.1909[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 32 )[/C][C]20.4186[/C][C]0.285761[/C][C]71.4534[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 32 )[/C][C]20.4167[/C][C]0.282502[/C][C]72.2709[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 32 )[/C][C]20.4167[/C][C]0.278698[/C][C]73.2572[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 32 )[/C][C]20.4125[/C][C]0.274262[/C][C]74.4271[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 32 )[/C][C]20.4103[/C][C]0.269084[/C][C]75.8509[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 32 )[/C][C]20.4079[/C][C]0.263031[/C][C]77.5875[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 32 )[/C][C]20.4189[/C][C]0.258872[/C][C]78.8765[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 32 )[/C][C]20.4306[/C][C]0.253941[/C][C]80.4539[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 32 )[/C][C]20.4429[/C][C]0.248086[/C][C]82.4022[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 32 )[/C][C]20.4559[/C][C]0.241114[/C][C]84.839[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 32 )[/C][C]20.4559[/C][C]0.236704[/C][C]86.4196[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 32 )[/C][C]20.4531[/C][C]0.23138[/C][C]88.3964[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 32 )[/C][C]20.4516[/C][C]0.224936[/C][C]90.9219[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 32 )[/C][C]20.45[/C][C]0.217101[/C][C]94.1959[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 32 )[/C][C]20.431[/C][C]0.211743[/C][C]96.49[/C][/ROW]
[ROW][C]Trimmed Mean ( 21 / 32 )[/C][C]20.4286[/C][C]0.209912[/C][C]97.3197[/C][/ROW]
[ROW][C]Trimmed Mean ( 22 / 32 )[/C][C]20.4259[/C][C]0.20748[/C][C]98.4475[/C][/ROW]
[ROW][C]Trimmed Mean ( 23 / 32 )[/C][C]20.4231[/C][C]0.204302[/C][C]99.9653[/C][/ROW]
[ROW][C]Trimmed Mean ( 24 / 32 )[/C][C]20.42[/C][C]0.200184[/C][C]102.006[/C][/ROW]
[ROW][C]Trimmed Mean ( 25 / 32 )[/C][C]20.4167[/C][C]0.194866[/C][C]104.773[/C][/ROW]
[ROW][C]Trimmed Mean ( 26 / 32 )[/C][C]20.413[/C][C]0.187987[/C][C]108.587[/C][/ROW]
[ROW][C]Trimmed Mean ( 27 / 32 )[/C][C]20.413[/C][C]0.18506[/C][C]110.305[/C][/ROW]
[ROW][C]Trimmed Mean ( 28 / 32 )[/C][C]20.4524[/C][C]0.180984[/C][C]113.006[/C][/ROW]
[ROW][C]Trimmed Mean ( 29 / 32 )[/C][C]20.475[/C][C]0.175366[/C][C]116.756[/C][/ROW]
[ROW][C]Trimmed Mean ( 30 / 32 )[/C][C]20.4737[/C][C]0.175841[/C][C]116.433[/C][/ROW]
[ROW][C]Trimmed Mean ( 31 / 32 )[/C][C]20.4722[/C][C]0.17587[/C][C]116.405[/C][/ROW]
[ROW][C]Trimmed Mean ( 32 / 32 )[/C][C]20.4722[/C][C]0.175278[/C][C]116.798[/C][/ROW]
[ROW][C]Median[/C][C]20[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]17.5[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]20.3898[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]20.3898[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]20.3898[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]20.3898[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]20.3898[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]20.3898[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]20.3898[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]20.3898[/C][C][/C][C][/C][/ROW]
[ROW][C]Number of observations[/C][C]98[/C][C][/C][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301887&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301887&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 Mean20.28570.36464955.6308
Geometric Mean19.9095
Harmonic Mean19.4298
Quadratic Mean20.6012
Winsorized Mean ( 1 / 32 )20.29590.35535957.1138
Winsorized Mean ( 2 / 32 )20.35710.3286161.9493
Winsorized Mean ( 3 / 32 )20.41840.31610264.5941
Winsorized Mean ( 4 / 32 )20.41840.31610264.5941
Winsorized Mean ( 5 / 32 )20.36730.30748766.2381
Winsorized Mean ( 6 / 32 )20.42860.29726368.7222
Winsorized Mean ( 7 / 32 )20.42860.29726368.7222
Winsorized Mean ( 8 / 32 )20.42860.29726368.7222
Winsorized Mean ( 9 / 32 )20.42860.29726368.7222
Winsorized Mean ( 10 / 32 )20.42860.29726368.7222
Winsorized Mean ( 11 / 32 )20.31630.28073672.3681
Winsorized Mean ( 12 / 32 )20.31630.28073672.3681
Winsorized Mean ( 13 / 32 )20.31630.28073672.3681
Winsorized Mean ( 14 / 32 )20.31630.28073672.3681
Winsorized Mean ( 15 / 32 )20.46940.25792979.3606
Winsorized Mean ( 16 / 32 )20.46940.25792979.3606
Winsorized Mean ( 17 / 32 )20.46940.25792979.3606
Winsorized Mean ( 18 / 32 )20.46940.25792979.3606
Winsorized Mean ( 19 / 32 )20.66330.23295988.6993
Winsorized Mean ( 20 / 32 )20.45920.20470499.9452
Winsorized Mean ( 21 / 32 )20.45920.20470499.9452
Winsorized Mean ( 22 / 32 )20.45920.20470499.9452
Winsorized Mean ( 23 / 32 )20.45920.20470499.9452
Winsorized Mean ( 24 / 32 )20.45920.20470499.9452
Winsorized Mean ( 25 / 32 )20.45920.20470499.9452
Winsorized Mean ( 26 / 32 )20.19390.173246116.562
Winsorized Mean ( 27 / 32 )20.19390.173246116.562
Winsorized Mean ( 28 / 32 )20.19390.173246116.562
Winsorized Mean ( 29 / 32 )20.48980.137028149.531
Winsorized Mean ( 30 / 32 )20.48980.137028149.531
Winsorized Mean ( 31 / 32 )20.48980.137028149.531
Winsorized Mean ( 32 / 32 )20.48980.137028149.531
Trimmed Mean ( 1 / 32 )20.28570.33568160.4316
Trimmed Mean ( 2 / 32 )20.34380.31263365.0724
Trimmed Mean ( 3 / 32 )20.4130.30293767.3838
Trimmed Mean ( 4 / 32 )20.4130.29725968.6708
Trimmed Mean ( 5 / 32 )20.40910.29076570.1909
Trimmed Mean ( 6 / 32 )20.41860.28576171.4534
Trimmed Mean ( 7 / 32 )20.41670.28250272.2709
Trimmed Mean ( 8 / 32 )20.41670.27869873.2572
Trimmed Mean ( 9 / 32 )20.41250.27426274.4271
Trimmed Mean ( 10 / 32 )20.41030.26908475.8509
Trimmed Mean ( 11 / 32 )20.40790.26303177.5875
Trimmed Mean ( 12 / 32 )20.41890.25887278.8765
Trimmed Mean ( 13 / 32 )20.43060.25394180.4539
Trimmed Mean ( 14 / 32 )20.44290.24808682.4022
Trimmed Mean ( 15 / 32 )20.45590.24111484.839
Trimmed Mean ( 16 / 32 )20.45590.23670486.4196
Trimmed Mean ( 17 / 32 )20.45310.2313888.3964
Trimmed Mean ( 18 / 32 )20.45160.22493690.9219
Trimmed Mean ( 19 / 32 )20.450.21710194.1959
Trimmed Mean ( 20 / 32 )20.4310.21174396.49
Trimmed Mean ( 21 / 32 )20.42860.20991297.3197
Trimmed Mean ( 22 / 32 )20.42590.2074898.4475
Trimmed Mean ( 23 / 32 )20.42310.20430299.9653
Trimmed Mean ( 24 / 32 )20.420.200184102.006
Trimmed Mean ( 25 / 32 )20.41670.194866104.773
Trimmed Mean ( 26 / 32 )20.4130.187987108.587
Trimmed Mean ( 27 / 32 )20.4130.18506110.305
Trimmed Mean ( 28 / 32 )20.45240.180984113.006
Trimmed Mean ( 29 / 32 )20.4750.175366116.756
Trimmed Mean ( 30 / 32 )20.47370.175841116.433
Trimmed Mean ( 31 / 32 )20.47220.17587116.405
Trimmed Mean ( 32 / 32 )20.47220.175278116.798
Median20
Midrange17.5
Midmean - Weighted Average at Xnp20.3898
Midmean - Weighted Average at X(n+1)p20.3898
Midmean - Empirical Distribution Function20.3898
Midmean - Empirical Distribution Function - Averaging20.3898
Midmean - Empirical Distribution Function - Interpolation20.3898
Midmean - Closest Observation20.3898
Midmean - True Basic - Statistics Graphics Toolkit20.3898
Midmean - MS Excel (old versions)20.3898
Number of observations98



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')