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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, 13 Dec 2017 14:14:03 +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/2017/Dec/13/t1513170873tvagyntk1w8bm9k.htm/, Retrieved Wed, 15 May 2024 03:38:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309283, Retrieved Wed, 15 May 2024 03:38:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact65
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [Central Tendency] [2017-12-13 13:14:03] [64452737148143cd44d8cd15ff28c062] [Current]
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Dataseries X:
3.5
3.7
0
-2.3
0.2
2.8
1.5
-3.1
0.3
2.9
7.1
1.6
-2.6
1.2
-0.8
4
6.2
4.3
2
2.9
-3.6
-2.5
3.4
5.9
6.9
3.9
1.6
6.7
2
-2.1
2
2.2
4.3
8.8
2.9
2.8
2.4
6.6
4.3
1.6
2
1.4
1.2
2.7
1.9
0
3.1
5.6
3.3
1.4
3.6
2
5
4
0.9
1.4
1
2
5.8
2.8
3.8
2
1.8
2.5
-1.6
2.4
4.2
4.1
5.3
5
3.7
2.3
4.1
1
2.5
3.8
6.5
1.2
3.9
4.7
5
0
4.9
4.1
3.9
6.9
2.7
2.2
4.8
-0.2
5.9
1.7
6.6
4.5
6.1
3.3
2.5
3.2
4.7
-10.4
3.2
6.9
7
3.2
4.9
3.5
2.7
4.6
1.6
7.8
4.1




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309283&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]3 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309283&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309283&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 time3 seconds
R ServerBig Analytics Cloud Computing Center







Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean2.89820.25815411.2266
Geometric MeanNaN
Harmonic Mean0
Quadratic Mean3.96615
Winsorized Mean ( 1 / 37 )2.950450.233912.6142
Winsorized Mean ( 2 / 37 )2.946850.22944512.8434
Winsorized Mean ( 3 / 37 )2.957660.22587213.0944
Winsorized Mean ( 4 / 37 )2.957660.2244913.175
Winsorized Mean ( 5 / 37 )2.966670.22252513.3318
Winsorized Mean ( 6 / 37 )2.977480.22022913.5199
Winsorized Mean ( 7 / 37 )2.99640.21178614.1482
Winsorized Mean ( 8 / 37 )3.046850.1998815.2433
Winsorized Mean ( 9 / 37 )3.09550.19181616.1378
Winsorized Mean ( 10 / 37 )3.10450.18756316.5518
Winsorized Mean ( 11 / 37 )3.074770.18280516.82
Winsorized Mean ( 12 / 37 )3.063960.18114116.9148
Winsorized Mean ( 13 / 37 )3.063960.17406717.6022
Winsorized Mean ( 14 / 37 )3.076580.172217.8663
Winsorized Mean ( 15 / 37 )3.144140.15899319.7754
Winsorized Mean ( 16 / 37 )3.129730.15284720.4762
Winsorized Mean ( 17 / 37 )3.083780.14630321.078
Winsorized Mean ( 18 / 37 )3.067570.13566422.6114
Winsorized Mean ( 19 / 37 )3.067570.13566422.6114
Winsorized Mean ( 20 / 37 )3.067570.13566422.6114
Winsorized Mean ( 21 / 37 )3.086490.128624.0006
Winsorized Mean ( 22 / 37 )3.086490.128624.0006
Winsorized Mean ( 23 / 37 )3.065770.12597524.3362
Winsorized Mean ( 24 / 37 )3.065770.12071725.3964
Winsorized Mean ( 25 / 37 )3.088290.11809826.1502
Winsorized Mean ( 26 / 37 )3.064860.11522626.5987
Winsorized Mean ( 27 / 37 )3.040540.11231627.0713
Winsorized Mean ( 28 / 37 )2.990090.10651228.0729
Winsorized Mean ( 29 / 37 )3.016220.1034529.1562
Winsorized Mean ( 30 / 37 )3.043240.10036530.3218
Winsorized Mean ( 31 / 37 )3.043240.094097932.3412
Winsorized Mean ( 32 / 37 )3.043240.087756834.6781
Winsorized Mean ( 33 / 37 )3.043240.087756834.6781
Winsorized Mean ( 34 / 37 )3.043240.087756834.6781
Winsorized Mean ( 35 / 37 )3.043240.087756834.6781
Winsorized Mean ( 36 / 37 )3.010810.084249835.7367
Winsorized Mean ( 37 / 37 )3.010810.084249835.7367
Trimmed Mean ( 1 / 37 )2.966060.22605213.1211
Trimmed Mean ( 2 / 37 )2.982240.21719313.7308
Trimmed Mean ( 3 / 37 )3.000950.20988614.298
Trimmed Mean ( 4 / 37 )3.01650.20316914.8473
Trimmed Mean ( 5 / 37 )3.032670.1960315.4704
Trimmed Mean ( 6 / 37 )3.047470.18849316.1676
Trimmed Mean ( 7 / 37 )3.047470.18048916.8845
Trimmed Mean ( 8 / 37 )3.071580.17335817.7181
Trimmed Mean ( 9 / 37 )3.075270.16780818.3261
Trimmed Mean ( 10 / 37 )3.072530.16306818.842
Trimmed Mean ( 11 / 37 )3.068540.15843319.368
Trimmed Mean ( 12 / 37 )3.067820.15396819.925
Trimmed Mean ( 13 / 37 )3.068240.14911720.576
Trimmed Mean ( 14 / 37 )3.068240.14470721.2031
Trimmed Mean ( 15 / 37 )3.06790.13990521.9284
Trimmed Mean ( 16 / 37 )3.060760.13641522.4372
Trimmed Mean ( 17 / 37 )3.054550.13329222.9162
Trimmed Mean ( 18 / 37 )3.0520.13063123.3636
Trimmed Mean ( 19 / 37 )3.050680.12901323.6464
Trimmed Mean ( 20 / 37 )3.04930.12707623.9958
Trimmed Mean ( 21 / 37 )3.047830.12476124.4294
Trimmed Mean ( 22 / 37 )3.044780.12298624.757
Trimmed Mean ( 23 / 37 )3.041540.12083225.1716
Trimmed Mean ( 24 / 37 )3.039680.1186125.6276
Trimmed Mean ( 25 / 37 )3.03770.11667426.0357
Trimmed Mean ( 26 / 37 )3.03390.11466426.4589
Trimmed Mean ( 27 / 37 )3.031580.11261726.9193
Trimmed Mean ( 28 / 37 )3.031580.1105227.4302
Trimmed Mean ( 29 / 37 )3.033960.10879127.8879
Trimmed Mean ( 30 / 37 )3.035290.10707628.3471
Trimmed Mean ( 31 / 37 )3.035290.10536428.8076
Trimmed Mean ( 32 / 37 )3.034040.10418129.1229
Trimmed Mean ( 33 / 37 )3.033330.10362129.2733
Trimmed Mean ( 34 / 37 )3.032560.10271629.5237
Trimmed Mean ( 35 / 37 )3.031710.10135929.9106
Trimmed Mean ( 36 / 37 )3.030770.099401330.4902
Trimmed Mean ( 37 / 37 )3.032430.097418231.128
Median2.9
Midrange-0.8
Midmean - Weighted Average at Xnp2.9569
Midmean - Weighted Average at X(n+1)p2.98305
Midmean - Empirical Distribution Function2.98305
Midmean - Empirical Distribution Function - Averaging2.98305
Midmean - Empirical Distribution Function - Interpolation2.9569
Midmean - Closest Observation2.9569
Midmean - True Basic - Statistics Graphics Toolkit2.98305
Midmean - MS Excel (old versions)2.98305
Number of observations111

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 2.8982 & 0.258154 & 11.2266 \tabularnewline
Geometric Mean & NaN &  &  \tabularnewline
Harmonic Mean & 0 &  &  \tabularnewline
Quadratic Mean & 3.96615 &  &  \tabularnewline
Winsorized Mean ( 1 / 37 ) & 2.95045 & 0.2339 & 12.6142 \tabularnewline
Winsorized Mean ( 2 / 37 ) & 2.94685 & 0.229445 & 12.8434 \tabularnewline
Winsorized Mean ( 3 / 37 ) & 2.95766 & 0.225872 & 13.0944 \tabularnewline
Winsorized Mean ( 4 / 37 ) & 2.95766 & 0.22449 & 13.175 \tabularnewline
Winsorized Mean ( 5 / 37 ) & 2.96667 & 0.222525 & 13.3318 \tabularnewline
Winsorized Mean ( 6 / 37 ) & 2.97748 & 0.220229 & 13.5199 \tabularnewline
Winsorized Mean ( 7 / 37 ) & 2.9964 & 0.211786 & 14.1482 \tabularnewline
Winsorized Mean ( 8 / 37 ) & 3.04685 & 0.19988 & 15.2433 \tabularnewline
Winsorized Mean ( 9 / 37 ) & 3.0955 & 0.191816 & 16.1378 \tabularnewline
Winsorized Mean ( 10 / 37 ) & 3.1045 & 0.187563 & 16.5518 \tabularnewline
Winsorized Mean ( 11 / 37 ) & 3.07477 & 0.182805 & 16.82 \tabularnewline
Winsorized Mean ( 12 / 37 ) & 3.06396 & 0.181141 & 16.9148 \tabularnewline
Winsorized Mean ( 13 / 37 ) & 3.06396 & 0.174067 & 17.6022 \tabularnewline
Winsorized Mean ( 14 / 37 ) & 3.07658 & 0.1722 & 17.8663 \tabularnewline
Winsorized Mean ( 15 / 37 ) & 3.14414 & 0.158993 & 19.7754 \tabularnewline
Winsorized Mean ( 16 / 37 ) & 3.12973 & 0.152847 & 20.4762 \tabularnewline
Winsorized Mean ( 17 / 37 ) & 3.08378 & 0.146303 & 21.078 \tabularnewline
Winsorized Mean ( 18 / 37 ) & 3.06757 & 0.135664 & 22.6114 \tabularnewline
Winsorized Mean ( 19 / 37 ) & 3.06757 & 0.135664 & 22.6114 \tabularnewline
Winsorized Mean ( 20 / 37 ) & 3.06757 & 0.135664 & 22.6114 \tabularnewline
Winsorized Mean ( 21 / 37 ) & 3.08649 & 0.1286 & 24.0006 \tabularnewline
Winsorized Mean ( 22 / 37 ) & 3.08649 & 0.1286 & 24.0006 \tabularnewline
Winsorized Mean ( 23 / 37 ) & 3.06577 & 0.125975 & 24.3362 \tabularnewline
Winsorized Mean ( 24 / 37 ) & 3.06577 & 0.120717 & 25.3964 \tabularnewline
Winsorized Mean ( 25 / 37 ) & 3.08829 & 0.118098 & 26.1502 \tabularnewline
Winsorized Mean ( 26 / 37 ) & 3.06486 & 0.115226 & 26.5987 \tabularnewline
Winsorized Mean ( 27 / 37 ) & 3.04054 & 0.112316 & 27.0713 \tabularnewline
Winsorized Mean ( 28 / 37 ) & 2.99009 & 0.106512 & 28.0729 \tabularnewline
Winsorized Mean ( 29 / 37 ) & 3.01622 & 0.10345 & 29.1562 \tabularnewline
Winsorized Mean ( 30 / 37 ) & 3.04324 & 0.100365 & 30.3218 \tabularnewline
Winsorized Mean ( 31 / 37 ) & 3.04324 & 0.0940979 & 32.3412 \tabularnewline
Winsorized Mean ( 32 / 37 ) & 3.04324 & 0.0877568 & 34.6781 \tabularnewline
Winsorized Mean ( 33 / 37 ) & 3.04324 & 0.0877568 & 34.6781 \tabularnewline
Winsorized Mean ( 34 / 37 ) & 3.04324 & 0.0877568 & 34.6781 \tabularnewline
Winsorized Mean ( 35 / 37 ) & 3.04324 & 0.0877568 & 34.6781 \tabularnewline
Winsorized Mean ( 36 / 37 ) & 3.01081 & 0.0842498 & 35.7367 \tabularnewline
Winsorized Mean ( 37 / 37 ) & 3.01081 & 0.0842498 & 35.7367 \tabularnewline
Trimmed Mean ( 1 / 37 ) & 2.96606 & 0.226052 & 13.1211 \tabularnewline
Trimmed Mean ( 2 / 37 ) & 2.98224 & 0.217193 & 13.7308 \tabularnewline
Trimmed Mean ( 3 / 37 ) & 3.00095 & 0.209886 & 14.298 \tabularnewline
Trimmed Mean ( 4 / 37 ) & 3.0165 & 0.203169 & 14.8473 \tabularnewline
Trimmed Mean ( 5 / 37 ) & 3.03267 & 0.19603 & 15.4704 \tabularnewline
Trimmed Mean ( 6 / 37 ) & 3.04747 & 0.188493 & 16.1676 \tabularnewline
Trimmed Mean ( 7 / 37 ) & 3.04747 & 0.180489 & 16.8845 \tabularnewline
Trimmed Mean ( 8 / 37 ) & 3.07158 & 0.173358 & 17.7181 \tabularnewline
Trimmed Mean ( 9 / 37 ) & 3.07527 & 0.167808 & 18.3261 \tabularnewline
Trimmed Mean ( 10 / 37 ) & 3.07253 & 0.163068 & 18.842 \tabularnewline
Trimmed Mean ( 11 / 37 ) & 3.06854 & 0.158433 & 19.368 \tabularnewline
Trimmed Mean ( 12 / 37 ) & 3.06782 & 0.153968 & 19.925 \tabularnewline
Trimmed Mean ( 13 / 37 ) & 3.06824 & 0.149117 & 20.576 \tabularnewline
Trimmed Mean ( 14 / 37 ) & 3.06824 & 0.144707 & 21.2031 \tabularnewline
Trimmed Mean ( 15 / 37 ) & 3.0679 & 0.139905 & 21.9284 \tabularnewline
Trimmed Mean ( 16 / 37 ) & 3.06076 & 0.136415 & 22.4372 \tabularnewline
Trimmed Mean ( 17 / 37 ) & 3.05455 & 0.133292 & 22.9162 \tabularnewline
Trimmed Mean ( 18 / 37 ) & 3.052 & 0.130631 & 23.3636 \tabularnewline
Trimmed Mean ( 19 / 37 ) & 3.05068 & 0.129013 & 23.6464 \tabularnewline
Trimmed Mean ( 20 / 37 ) & 3.0493 & 0.127076 & 23.9958 \tabularnewline
Trimmed Mean ( 21 / 37 ) & 3.04783 & 0.124761 & 24.4294 \tabularnewline
Trimmed Mean ( 22 / 37 ) & 3.04478 & 0.122986 & 24.757 \tabularnewline
Trimmed Mean ( 23 / 37 ) & 3.04154 & 0.120832 & 25.1716 \tabularnewline
Trimmed Mean ( 24 / 37 ) & 3.03968 & 0.11861 & 25.6276 \tabularnewline
Trimmed Mean ( 25 / 37 ) & 3.0377 & 0.116674 & 26.0357 \tabularnewline
Trimmed Mean ( 26 / 37 ) & 3.0339 & 0.114664 & 26.4589 \tabularnewline
Trimmed Mean ( 27 / 37 ) & 3.03158 & 0.112617 & 26.9193 \tabularnewline
Trimmed Mean ( 28 / 37 ) & 3.03158 & 0.11052 & 27.4302 \tabularnewline
Trimmed Mean ( 29 / 37 ) & 3.03396 & 0.108791 & 27.8879 \tabularnewline
Trimmed Mean ( 30 / 37 ) & 3.03529 & 0.107076 & 28.3471 \tabularnewline
Trimmed Mean ( 31 / 37 ) & 3.03529 & 0.105364 & 28.8076 \tabularnewline
Trimmed Mean ( 32 / 37 ) & 3.03404 & 0.104181 & 29.1229 \tabularnewline
Trimmed Mean ( 33 / 37 ) & 3.03333 & 0.103621 & 29.2733 \tabularnewline
Trimmed Mean ( 34 / 37 ) & 3.03256 & 0.102716 & 29.5237 \tabularnewline
Trimmed Mean ( 35 / 37 ) & 3.03171 & 0.101359 & 29.9106 \tabularnewline
Trimmed Mean ( 36 / 37 ) & 3.03077 & 0.0994013 & 30.4902 \tabularnewline
Trimmed Mean ( 37 / 37 ) & 3.03243 & 0.0974182 & 31.128 \tabularnewline
Median & 2.9 &  &  \tabularnewline
Midrange & -0.8 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 2.9569 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 2.98305 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 2.98305 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 2.98305 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 2.9569 &  &  \tabularnewline
Midmean - Closest Observation & 2.9569 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 2.98305 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 2.98305 &  &  \tabularnewline
Number of observations & 111 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309283&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]2.8982[/C][C]0.258154[/C][C]11.2266[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]NaN[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]0[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]3.96615[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 37 )[/C][C]2.95045[/C][C]0.2339[/C][C]12.6142[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 37 )[/C][C]2.94685[/C][C]0.229445[/C][C]12.8434[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 37 )[/C][C]2.95766[/C][C]0.225872[/C][C]13.0944[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 37 )[/C][C]2.95766[/C][C]0.22449[/C][C]13.175[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 37 )[/C][C]2.96667[/C][C]0.222525[/C][C]13.3318[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 37 )[/C][C]2.97748[/C][C]0.220229[/C][C]13.5199[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 37 )[/C][C]2.9964[/C][C]0.211786[/C][C]14.1482[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 37 )[/C][C]3.04685[/C][C]0.19988[/C][C]15.2433[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 37 )[/C][C]3.0955[/C][C]0.191816[/C][C]16.1378[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 37 )[/C][C]3.1045[/C][C]0.187563[/C][C]16.5518[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 37 )[/C][C]3.07477[/C][C]0.182805[/C][C]16.82[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 37 )[/C][C]3.06396[/C][C]0.181141[/C][C]16.9148[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 37 )[/C][C]3.06396[/C][C]0.174067[/C][C]17.6022[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 37 )[/C][C]3.07658[/C][C]0.1722[/C][C]17.8663[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 37 )[/C][C]3.14414[/C][C]0.158993[/C][C]19.7754[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 37 )[/C][C]3.12973[/C][C]0.152847[/C][C]20.4762[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 37 )[/C][C]3.08378[/C][C]0.146303[/C][C]21.078[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 37 )[/C][C]3.06757[/C][C]0.135664[/C][C]22.6114[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 37 )[/C][C]3.06757[/C][C]0.135664[/C][C]22.6114[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 37 )[/C][C]3.06757[/C][C]0.135664[/C][C]22.6114[/C][/ROW]
[ROW][C]Winsorized Mean ( 21 / 37 )[/C][C]3.08649[/C][C]0.1286[/C][C]24.0006[/C][/ROW]
[ROW][C]Winsorized Mean ( 22 / 37 )[/C][C]3.08649[/C][C]0.1286[/C][C]24.0006[/C][/ROW]
[ROW][C]Winsorized Mean ( 23 / 37 )[/C][C]3.06577[/C][C]0.125975[/C][C]24.3362[/C][/ROW]
[ROW][C]Winsorized Mean ( 24 / 37 )[/C][C]3.06577[/C][C]0.120717[/C][C]25.3964[/C][/ROW]
[ROW][C]Winsorized Mean ( 25 / 37 )[/C][C]3.08829[/C][C]0.118098[/C][C]26.1502[/C][/ROW]
[ROW][C]Winsorized Mean ( 26 / 37 )[/C][C]3.06486[/C][C]0.115226[/C][C]26.5987[/C][/ROW]
[ROW][C]Winsorized Mean ( 27 / 37 )[/C][C]3.04054[/C][C]0.112316[/C][C]27.0713[/C][/ROW]
[ROW][C]Winsorized Mean ( 28 / 37 )[/C][C]2.99009[/C][C]0.106512[/C][C]28.0729[/C][/ROW]
[ROW][C]Winsorized Mean ( 29 / 37 )[/C][C]3.01622[/C][C]0.10345[/C][C]29.1562[/C][/ROW]
[ROW][C]Winsorized Mean ( 30 / 37 )[/C][C]3.04324[/C][C]0.100365[/C][C]30.3218[/C][/ROW]
[ROW][C]Winsorized Mean ( 31 / 37 )[/C][C]3.04324[/C][C]0.0940979[/C][C]32.3412[/C][/ROW]
[ROW][C]Winsorized Mean ( 32 / 37 )[/C][C]3.04324[/C][C]0.0877568[/C][C]34.6781[/C][/ROW]
[ROW][C]Winsorized Mean ( 33 / 37 )[/C][C]3.04324[/C][C]0.0877568[/C][C]34.6781[/C][/ROW]
[ROW][C]Winsorized Mean ( 34 / 37 )[/C][C]3.04324[/C][C]0.0877568[/C][C]34.6781[/C][/ROW]
[ROW][C]Winsorized Mean ( 35 / 37 )[/C][C]3.04324[/C][C]0.0877568[/C][C]34.6781[/C][/ROW]
[ROW][C]Winsorized Mean ( 36 / 37 )[/C][C]3.01081[/C][C]0.0842498[/C][C]35.7367[/C][/ROW]
[ROW][C]Winsorized Mean ( 37 / 37 )[/C][C]3.01081[/C][C]0.0842498[/C][C]35.7367[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 37 )[/C][C]2.96606[/C][C]0.226052[/C][C]13.1211[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 37 )[/C][C]2.98224[/C][C]0.217193[/C][C]13.7308[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 37 )[/C][C]3.00095[/C][C]0.209886[/C][C]14.298[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 37 )[/C][C]3.0165[/C][C]0.203169[/C][C]14.8473[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 37 )[/C][C]3.03267[/C][C]0.19603[/C][C]15.4704[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 37 )[/C][C]3.04747[/C][C]0.188493[/C][C]16.1676[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 37 )[/C][C]3.04747[/C][C]0.180489[/C][C]16.8845[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 37 )[/C][C]3.07158[/C][C]0.173358[/C][C]17.7181[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 37 )[/C][C]3.07527[/C][C]0.167808[/C][C]18.3261[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 37 )[/C][C]3.07253[/C][C]0.163068[/C][C]18.842[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 37 )[/C][C]3.06854[/C][C]0.158433[/C][C]19.368[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 37 )[/C][C]3.06782[/C][C]0.153968[/C][C]19.925[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 37 )[/C][C]3.06824[/C][C]0.149117[/C][C]20.576[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 37 )[/C][C]3.06824[/C][C]0.144707[/C][C]21.2031[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 37 )[/C][C]3.0679[/C][C]0.139905[/C][C]21.9284[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 37 )[/C][C]3.06076[/C][C]0.136415[/C][C]22.4372[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 37 )[/C][C]3.05455[/C][C]0.133292[/C][C]22.9162[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 37 )[/C][C]3.052[/C][C]0.130631[/C][C]23.3636[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 37 )[/C][C]3.05068[/C][C]0.129013[/C][C]23.6464[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 37 )[/C][C]3.0493[/C][C]0.127076[/C][C]23.9958[/C][/ROW]
[ROW][C]Trimmed Mean ( 21 / 37 )[/C][C]3.04783[/C][C]0.124761[/C][C]24.4294[/C][/ROW]
[ROW][C]Trimmed Mean ( 22 / 37 )[/C][C]3.04478[/C][C]0.122986[/C][C]24.757[/C][/ROW]
[ROW][C]Trimmed Mean ( 23 / 37 )[/C][C]3.04154[/C][C]0.120832[/C][C]25.1716[/C][/ROW]
[ROW][C]Trimmed Mean ( 24 / 37 )[/C][C]3.03968[/C][C]0.11861[/C][C]25.6276[/C][/ROW]
[ROW][C]Trimmed Mean ( 25 / 37 )[/C][C]3.0377[/C][C]0.116674[/C][C]26.0357[/C][/ROW]
[ROW][C]Trimmed Mean ( 26 / 37 )[/C][C]3.0339[/C][C]0.114664[/C][C]26.4589[/C][/ROW]
[ROW][C]Trimmed Mean ( 27 / 37 )[/C][C]3.03158[/C][C]0.112617[/C][C]26.9193[/C][/ROW]
[ROW][C]Trimmed Mean ( 28 / 37 )[/C][C]3.03158[/C][C]0.11052[/C][C]27.4302[/C][/ROW]
[ROW][C]Trimmed Mean ( 29 / 37 )[/C][C]3.03396[/C][C]0.108791[/C][C]27.8879[/C][/ROW]
[ROW][C]Trimmed Mean ( 30 / 37 )[/C][C]3.03529[/C][C]0.107076[/C][C]28.3471[/C][/ROW]
[ROW][C]Trimmed Mean ( 31 / 37 )[/C][C]3.03529[/C][C]0.105364[/C][C]28.8076[/C][/ROW]
[ROW][C]Trimmed Mean ( 32 / 37 )[/C][C]3.03404[/C][C]0.104181[/C][C]29.1229[/C][/ROW]
[ROW][C]Trimmed Mean ( 33 / 37 )[/C][C]3.03333[/C][C]0.103621[/C][C]29.2733[/C][/ROW]
[ROW][C]Trimmed Mean ( 34 / 37 )[/C][C]3.03256[/C][C]0.102716[/C][C]29.5237[/C][/ROW]
[ROW][C]Trimmed Mean ( 35 / 37 )[/C][C]3.03171[/C][C]0.101359[/C][C]29.9106[/C][/ROW]
[ROW][C]Trimmed Mean ( 36 / 37 )[/C][C]3.03077[/C][C]0.0994013[/C][C]30.4902[/C][/ROW]
[ROW][C]Trimmed Mean ( 37 / 37 )[/C][C]3.03243[/C][C]0.0974182[/C][C]31.128[/C][/ROW]
[ROW][C]Median[/C][C]2.9[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]-0.8[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]2.9569[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]2.98305[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]2.98305[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]2.98305[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]2.9569[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]2.9569[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]2.98305[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]2.98305[/C][C][/C][C][/C][/ROW]
[ROW][C]Number of observations[/C][C]111[/C][C][/C][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309283&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309283&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 Mean2.89820.25815411.2266
Geometric MeanNaN
Harmonic Mean0
Quadratic Mean3.96615
Winsorized Mean ( 1 / 37 )2.950450.233912.6142
Winsorized Mean ( 2 / 37 )2.946850.22944512.8434
Winsorized Mean ( 3 / 37 )2.957660.22587213.0944
Winsorized Mean ( 4 / 37 )2.957660.2244913.175
Winsorized Mean ( 5 / 37 )2.966670.22252513.3318
Winsorized Mean ( 6 / 37 )2.977480.22022913.5199
Winsorized Mean ( 7 / 37 )2.99640.21178614.1482
Winsorized Mean ( 8 / 37 )3.046850.1998815.2433
Winsorized Mean ( 9 / 37 )3.09550.19181616.1378
Winsorized Mean ( 10 / 37 )3.10450.18756316.5518
Winsorized Mean ( 11 / 37 )3.074770.18280516.82
Winsorized Mean ( 12 / 37 )3.063960.18114116.9148
Winsorized Mean ( 13 / 37 )3.063960.17406717.6022
Winsorized Mean ( 14 / 37 )3.076580.172217.8663
Winsorized Mean ( 15 / 37 )3.144140.15899319.7754
Winsorized Mean ( 16 / 37 )3.129730.15284720.4762
Winsorized Mean ( 17 / 37 )3.083780.14630321.078
Winsorized Mean ( 18 / 37 )3.067570.13566422.6114
Winsorized Mean ( 19 / 37 )3.067570.13566422.6114
Winsorized Mean ( 20 / 37 )3.067570.13566422.6114
Winsorized Mean ( 21 / 37 )3.086490.128624.0006
Winsorized Mean ( 22 / 37 )3.086490.128624.0006
Winsorized Mean ( 23 / 37 )3.065770.12597524.3362
Winsorized Mean ( 24 / 37 )3.065770.12071725.3964
Winsorized Mean ( 25 / 37 )3.088290.11809826.1502
Winsorized Mean ( 26 / 37 )3.064860.11522626.5987
Winsorized Mean ( 27 / 37 )3.040540.11231627.0713
Winsorized Mean ( 28 / 37 )2.990090.10651228.0729
Winsorized Mean ( 29 / 37 )3.016220.1034529.1562
Winsorized Mean ( 30 / 37 )3.043240.10036530.3218
Winsorized Mean ( 31 / 37 )3.043240.094097932.3412
Winsorized Mean ( 32 / 37 )3.043240.087756834.6781
Winsorized Mean ( 33 / 37 )3.043240.087756834.6781
Winsorized Mean ( 34 / 37 )3.043240.087756834.6781
Winsorized Mean ( 35 / 37 )3.043240.087756834.6781
Winsorized Mean ( 36 / 37 )3.010810.084249835.7367
Winsorized Mean ( 37 / 37 )3.010810.084249835.7367
Trimmed Mean ( 1 / 37 )2.966060.22605213.1211
Trimmed Mean ( 2 / 37 )2.982240.21719313.7308
Trimmed Mean ( 3 / 37 )3.000950.20988614.298
Trimmed Mean ( 4 / 37 )3.01650.20316914.8473
Trimmed Mean ( 5 / 37 )3.032670.1960315.4704
Trimmed Mean ( 6 / 37 )3.047470.18849316.1676
Trimmed Mean ( 7 / 37 )3.047470.18048916.8845
Trimmed Mean ( 8 / 37 )3.071580.17335817.7181
Trimmed Mean ( 9 / 37 )3.075270.16780818.3261
Trimmed Mean ( 10 / 37 )3.072530.16306818.842
Trimmed Mean ( 11 / 37 )3.068540.15843319.368
Trimmed Mean ( 12 / 37 )3.067820.15396819.925
Trimmed Mean ( 13 / 37 )3.068240.14911720.576
Trimmed Mean ( 14 / 37 )3.068240.14470721.2031
Trimmed Mean ( 15 / 37 )3.06790.13990521.9284
Trimmed Mean ( 16 / 37 )3.060760.13641522.4372
Trimmed Mean ( 17 / 37 )3.054550.13329222.9162
Trimmed Mean ( 18 / 37 )3.0520.13063123.3636
Trimmed Mean ( 19 / 37 )3.050680.12901323.6464
Trimmed Mean ( 20 / 37 )3.04930.12707623.9958
Trimmed Mean ( 21 / 37 )3.047830.12476124.4294
Trimmed Mean ( 22 / 37 )3.044780.12298624.757
Trimmed Mean ( 23 / 37 )3.041540.12083225.1716
Trimmed Mean ( 24 / 37 )3.039680.1186125.6276
Trimmed Mean ( 25 / 37 )3.03770.11667426.0357
Trimmed Mean ( 26 / 37 )3.03390.11466426.4589
Trimmed Mean ( 27 / 37 )3.031580.11261726.9193
Trimmed Mean ( 28 / 37 )3.031580.1105227.4302
Trimmed Mean ( 29 / 37 )3.033960.10879127.8879
Trimmed Mean ( 30 / 37 )3.035290.10707628.3471
Trimmed Mean ( 31 / 37 )3.035290.10536428.8076
Trimmed Mean ( 32 / 37 )3.034040.10418129.1229
Trimmed Mean ( 33 / 37 )3.033330.10362129.2733
Trimmed Mean ( 34 / 37 )3.032560.10271629.5237
Trimmed Mean ( 35 / 37 )3.031710.10135929.9106
Trimmed Mean ( 36 / 37 )3.030770.099401330.4902
Trimmed Mean ( 37 / 37 )3.032430.097418231.128
Median2.9
Midrange-0.8
Midmean - Weighted Average at Xnp2.9569
Midmean - Weighted Average at X(n+1)p2.98305
Midmean - Empirical Distribution Function2.98305
Midmean - Empirical Distribution Function - Averaging2.98305
Midmean - Empirical Distribution Function - Interpolation2.9569
Midmean - Closest Observation2.9569
Midmean - True Basic - Statistics Graphics Toolkit2.98305
Midmean - MS Excel (old versions)2.98305
Number of observations111



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')