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Author's title

Author*Unverified author*
R Software Modulerwasp_centraltendency.wasp
Title produced by softwareCentral Tendency
Date of computationWed, 09 Aug 2017 14:05:03 +0200
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/Aug/09/t15022804919rsgar1ae34yaw0.htm/, Retrieved Tue, 14 May 2024 06:54:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=307016, Retrieved Tue, 14 May 2024 06:54:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact79
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [] [2017-08-09 12:05:03] [e040697baf0f5290ebe3dabde76ed401] [Current]
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Dataseries X:
982800
946400
1001000
800800
1037400
1019200
1092000
1128400
1255800
1092000
1037400
1292200
1092000
819000
964600
728000
1019200
837200
1110200
1001000
1055600
1183000
1164800
1383200
1001000
837200
928200
673400
964600
746200
1055600
1001000
891800
1274000
1146600
1310400
982800
910000
819000
673400
891800
800800
1092000
1055600
910000
1219400
1128400
1456000
1164800
709800
709800
709800
837200
837200
1128400
1037400
928200
1164800
1073800
1547000
1219400
709800
746200
618800
855400
982800
1237600
1219400
982800
1146600
1019200
1456000
1110200
891800
800800
600600
891800
1073800
1255800
1183000
873600
1255800
982800
1510600
1255800
910000
837200
564200
891800
855400
1292200
1292200
982800
1274000
946400
1474200
1255800
928200
709800
491400
964600
928200
1219400
1401400
1037400
1164800
873600
1510600




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=307016&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=307016&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307016&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 Mean102173021307.447.9518
Geometric Mean997178
Harmonic Mean971564
Quadratic Mean1045230
Winsorized Mean ( 1 / 36 )102206021085.248.4731
Winsorized Mean ( 2 / 36 )102274020953.348.8103
Winsorized Mean ( 3 / 36 )102223020646.149.5122
Winsorized Mean ( 4 / 36 )102358020160.550.7716
Winsorized Mean ( 5 / 36 )102358020160.550.7716
Winsorized Mean ( 6 / 36 )102257019259.353.095
Winsorized Mean ( 7 / 36 )102139019046.153.6273
Winsorized Mean ( 8 / 36 )101600018157.255.9558
Winsorized Mean ( 9 / 36 )101448017932.556.5724
Winsorized Mean ( 10 / 36 )101448017932.556.5724
Winsorized Mean ( 11 / 36 )101634017643.757.6034
Winsorized Mean ( 12 / 36 )101634017048.859.6133
Winsorized Mean ( 13 / 36 )101634017048.859.6133
Winsorized Mean ( 14 / 36 )102105015727.964.92
Winsorized Mean ( 15 / 36 )102105015727.964.92
Winsorized Mean ( 16 / 36 )102105015727.964.92
Winsorized Mean ( 17 / 36 )102392015361.766.6539
Winsorized Mean ( 18 / 36 )102392015361.766.6539
Winsorized Mean ( 19 / 36 )102392014523.470.5011
Winsorized Mean ( 20 / 36 )10205501406972.5389
Winsorized Mean ( 21 / 36 )10205501406972.5389
Winsorized Mean ( 22 / 36 )10205501406972.5389
Winsorized Mean ( 23 / 36 )10205501406972.5389
Winsorized Mean ( 24 / 36 )101650012541.881.0492
Winsorized Mean ( 25 / 36 )101650012541.881.0492
Winsorized Mean ( 26 / 36 )101650011487.388.4894
Winsorized Mean ( 27 / 36 )101650011487.388.4894
Winsorized Mean ( 28 / 36 )102122010952.193.2443
Winsorized Mean ( 29 / 36 )102122010952.193.2443
Winsorized Mean ( 30 / 36 )101617010344.298.2355
Winsorized Mean ( 31 / 36 )101617010344.298.2355
Winsorized Mean ( 32 / 36 )10107709721.14103.977
Winsorized Mean ( 33 / 36 )10163409098.94111.698
Winsorized Mean ( 34 / 36 )10163409098.94111.698
Winsorized Mean ( 35 / 36 )10104408432.92119.821
Winsorized Mean ( 36 / 36 )10165007772.57130.781
Trimmed Mean ( 1 / 36 )102178020526.249.7792
Trimmed Mean ( 2 / 36 )102148019897.551.3369
Trimmed Mean ( 3 / 36 )102081019267.752.9801
Trimmed Mean ( 4 / 36 )102029018687.754.5971
Trimmed Mean ( 5 / 36 )101939018195.256.025
Trimmed Mean ( 6 / 36 )101844017636.357.7468
Trimmed Mean ( 7 / 36 )101765017228.259.0691
Trimmed Mean ( 8 / 36 )101702016811.960.4941
Trimmed Mean ( 9 / 36 )101718016519.361.5753
Trimmed Mean ( 10 / 36 )10175501622662.7109
Trimmed Mean ( 11 / 36 )101793015889.264.0643
Trimmed Mean ( 12 / 36 )101812015551.865.466
Trimmed Mean ( 13 / 36 )101831015260.666.7282
Trimmed Mean ( 14 / 36 )101852014922.868.2524
Trimmed Mean ( 15 / 36 )101827014733.169.1141
Trimmed Mean ( 16 / 36 )101800014509.470.1618
Trimmed Mean ( 17 / 36 )101772014245.571.4421
Trimmed Mean ( 18 / 36 )101718013986.772.7246
Trimmed Mean ( 19 / 36 )101660013679.474.3164
Trimmed Mean ( 20 / 36 )101599013438.375.6038
Trimmed Mean ( 21 / 36 )101562013216.976.8419
Trimmed Mean ( 22 / 36 )101522012948.778.4032
Trimmed Mean ( 23 / 36 )101480012623.380.3911
Trimmed Mean ( 24 / 36 )101435012227.182.9592
Trimmed Mean ( 25 / 36 )101418011993.284.563
Trimmed Mean ( 26 / 36 )101400011702.686.6472
Trimmed Mean ( 27 / 36 )10138101151388.0576
Trimmed Mean ( 28 / 36 )10136001127189.9301
Trimmed Mean ( 29 / 36 )101301011050.691.6705
Trimmed Mean ( 30 / 36 )101238010764.994.0443
Trimmed Mean ( 31 / 36 )101208010515.196.2504
Trimmed Mean ( 32 / 36 )10117501018699.3277
Trimmed Mean ( 33 / 36 )10118309886.39102.346
Trimmed Mean ( 34 / 36 )10114609619.23105.15
Trimmed Mean ( 35 / 36 )10110609252.69109.272
Trimmed Mean ( 36 / 36 )10111108923.35113.311
Median1001000
Midrange1019200
Midmean - Weighted Average at Xnp1014000
Midmean - Weighted Average at X(n+1)p1014000
Midmean - Empirical Distribution Function1014000
Midmean - Empirical Distribution Function - Averaging1014000
Midmean - Empirical Distribution Function - Interpolation1014000
Midmean - Closest Observation1014000
Midmean - True Basic - Statistics Graphics Toolkit1014000
Midmean - MS Excel (old versions)1014000
Number of observations108

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 1021730 & 21307.4 & 47.9518 \tabularnewline
Geometric Mean & 997178 &  &  \tabularnewline
Harmonic Mean & 971564 &  &  \tabularnewline
Quadratic Mean & 1045230 &  &  \tabularnewline
Winsorized Mean ( 1 / 36 ) & 1022060 & 21085.2 & 48.4731 \tabularnewline
Winsorized Mean ( 2 / 36 ) & 1022740 & 20953.3 & 48.8103 \tabularnewline
Winsorized Mean ( 3 / 36 ) & 1022230 & 20646.1 & 49.5122 \tabularnewline
Winsorized Mean ( 4 / 36 ) & 1023580 & 20160.5 & 50.7716 \tabularnewline
Winsorized Mean ( 5 / 36 ) & 1023580 & 20160.5 & 50.7716 \tabularnewline
Winsorized Mean ( 6 / 36 ) & 1022570 & 19259.3 & 53.095 \tabularnewline
Winsorized Mean ( 7 / 36 ) & 1021390 & 19046.1 & 53.6273 \tabularnewline
Winsorized Mean ( 8 / 36 ) & 1016000 & 18157.2 & 55.9558 \tabularnewline
Winsorized Mean ( 9 / 36 ) & 1014480 & 17932.5 & 56.5724 \tabularnewline
Winsorized Mean ( 10 / 36 ) & 1014480 & 17932.5 & 56.5724 \tabularnewline
Winsorized Mean ( 11 / 36 ) & 1016340 & 17643.7 & 57.6034 \tabularnewline
Winsorized Mean ( 12 / 36 ) & 1016340 & 17048.8 & 59.6133 \tabularnewline
Winsorized Mean ( 13 / 36 ) & 1016340 & 17048.8 & 59.6133 \tabularnewline
Winsorized Mean ( 14 / 36 ) & 1021050 & 15727.9 & 64.92 \tabularnewline
Winsorized Mean ( 15 / 36 ) & 1021050 & 15727.9 & 64.92 \tabularnewline
Winsorized Mean ( 16 / 36 ) & 1021050 & 15727.9 & 64.92 \tabularnewline
Winsorized Mean ( 17 / 36 ) & 1023920 & 15361.7 & 66.6539 \tabularnewline
Winsorized Mean ( 18 / 36 ) & 1023920 & 15361.7 & 66.6539 \tabularnewline
Winsorized Mean ( 19 / 36 ) & 1023920 & 14523.4 & 70.5011 \tabularnewline
Winsorized Mean ( 20 / 36 ) & 1020550 & 14069 & 72.5389 \tabularnewline
Winsorized Mean ( 21 / 36 ) & 1020550 & 14069 & 72.5389 \tabularnewline
Winsorized Mean ( 22 / 36 ) & 1020550 & 14069 & 72.5389 \tabularnewline
Winsorized Mean ( 23 / 36 ) & 1020550 & 14069 & 72.5389 \tabularnewline
Winsorized Mean ( 24 / 36 ) & 1016500 & 12541.8 & 81.0492 \tabularnewline
Winsorized Mean ( 25 / 36 ) & 1016500 & 12541.8 & 81.0492 \tabularnewline
Winsorized Mean ( 26 / 36 ) & 1016500 & 11487.3 & 88.4894 \tabularnewline
Winsorized Mean ( 27 / 36 ) & 1016500 & 11487.3 & 88.4894 \tabularnewline
Winsorized Mean ( 28 / 36 ) & 1021220 & 10952.1 & 93.2443 \tabularnewline
Winsorized Mean ( 29 / 36 ) & 1021220 & 10952.1 & 93.2443 \tabularnewline
Winsorized Mean ( 30 / 36 ) & 1016170 & 10344.2 & 98.2355 \tabularnewline
Winsorized Mean ( 31 / 36 ) & 1016170 & 10344.2 & 98.2355 \tabularnewline
Winsorized Mean ( 32 / 36 ) & 1010770 & 9721.14 & 103.977 \tabularnewline
Winsorized Mean ( 33 / 36 ) & 1016340 & 9098.94 & 111.698 \tabularnewline
Winsorized Mean ( 34 / 36 ) & 1016340 & 9098.94 & 111.698 \tabularnewline
Winsorized Mean ( 35 / 36 ) & 1010440 & 8432.92 & 119.821 \tabularnewline
Winsorized Mean ( 36 / 36 ) & 1016500 & 7772.57 & 130.781 \tabularnewline
Trimmed Mean ( 1 / 36 ) & 1021780 & 20526.2 & 49.7792 \tabularnewline
Trimmed Mean ( 2 / 36 ) & 1021480 & 19897.5 & 51.3369 \tabularnewline
Trimmed Mean ( 3 / 36 ) & 1020810 & 19267.7 & 52.9801 \tabularnewline
Trimmed Mean ( 4 / 36 ) & 1020290 & 18687.7 & 54.5971 \tabularnewline
Trimmed Mean ( 5 / 36 ) & 1019390 & 18195.2 & 56.025 \tabularnewline
Trimmed Mean ( 6 / 36 ) & 1018440 & 17636.3 & 57.7468 \tabularnewline
Trimmed Mean ( 7 / 36 ) & 1017650 & 17228.2 & 59.0691 \tabularnewline
Trimmed Mean ( 8 / 36 ) & 1017020 & 16811.9 & 60.4941 \tabularnewline
Trimmed Mean ( 9 / 36 ) & 1017180 & 16519.3 & 61.5753 \tabularnewline
Trimmed Mean ( 10 / 36 ) & 1017550 & 16226 & 62.7109 \tabularnewline
Trimmed Mean ( 11 / 36 ) & 1017930 & 15889.2 & 64.0643 \tabularnewline
Trimmed Mean ( 12 / 36 ) & 1018120 & 15551.8 & 65.466 \tabularnewline
Trimmed Mean ( 13 / 36 ) & 1018310 & 15260.6 & 66.7282 \tabularnewline
Trimmed Mean ( 14 / 36 ) & 1018520 & 14922.8 & 68.2524 \tabularnewline
Trimmed Mean ( 15 / 36 ) & 1018270 & 14733.1 & 69.1141 \tabularnewline
Trimmed Mean ( 16 / 36 ) & 1018000 & 14509.4 & 70.1618 \tabularnewline
Trimmed Mean ( 17 / 36 ) & 1017720 & 14245.5 & 71.4421 \tabularnewline
Trimmed Mean ( 18 / 36 ) & 1017180 & 13986.7 & 72.7246 \tabularnewline
Trimmed Mean ( 19 / 36 ) & 1016600 & 13679.4 & 74.3164 \tabularnewline
Trimmed Mean ( 20 / 36 ) & 1015990 & 13438.3 & 75.6038 \tabularnewline
Trimmed Mean ( 21 / 36 ) & 1015620 & 13216.9 & 76.8419 \tabularnewline
Trimmed Mean ( 22 / 36 ) & 1015220 & 12948.7 & 78.4032 \tabularnewline
Trimmed Mean ( 23 / 36 ) & 1014800 & 12623.3 & 80.3911 \tabularnewline
Trimmed Mean ( 24 / 36 ) & 1014350 & 12227.1 & 82.9592 \tabularnewline
Trimmed Mean ( 25 / 36 ) & 1014180 & 11993.2 & 84.563 \tabularnewline
Trimmed Mean ( 26 / 36 ) & 1014000 & 11702.6 & 86.6472 \tabularnewline
Trimmed Mean ( 27 / 36 ) & 1013810 & 11513 & 88.0576 \tabularnewline
Trimmed Mean ( 28 / 36 ) & 1013600 & 11271 & 89.9301 \tabularnewline
Trimmed Mean ( 29 / 36 ) & 1013010 & 11050.6 & 91.6705 \tabularnewline
Trimmed Mean ( 30 / 36 ) & 1012380 & 10764.9 & 94.0443 \tabularnewline
Trimmed Mean ( 31 / 36 ) & 1012080 & 10515.1 & 96.2504 \tabularnewline
Trimmed Mean ( 32 / 36 ) & 1011750 & 10186 & 99.3277 \tabularnewline
Trimmed Mean ( 33 / 36 ) & 1011830 & 9886.39 & 102.346 \tabularnewline
Trimmed Mean ( 34 / 36 ) & 1011460 & 9619.23 & 105.15 \tabularnewline
Trimmed Mean ( 35 / 36 ) & 1011060 & 9252.69 & 109.272 \tabularnewline
Trimmed Mean ( 36 / 36 ) & 1011110 & 8923.35 & 113.311 \tabularnewline
Median & 1001000 &  &  \tabularnewline
Midrange & 1019200 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 1014000 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 1014000 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 1014000 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 1014000 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 1014000 &  &  \tabularnewline
Midmean - Closest Observation & 1014000 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 1014000 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 1014000 &  &  \tabularnewline
Number of observations & 108 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=307016&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]1021730[/C][C]21307.4[/C][C]47.9518[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]997178[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]971564[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]1045230[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 36 )[/C][C]1022060[/C][C]21085.2[/C][C]48.4731[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 36 )[/C][C]1022740[/C][C]20953.3[/C][C]48.8103[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 36 )[/C][C]1022230[/C][C]20646.1[/C][C]49.5122[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 36 )[/C][C]1023580[/C][C]20160.5[/C][C]50.7716[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 36 )[/C][C]1023580[/C][C]20160.5[/C][C]50.7716[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 36 )[/C][C]1022570[/C][C]19259.3[/C][C]53.095[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 36 )[/C][C]1021390[/C][C]19046.1[/C][C]53.6273[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 36 )[/C][C]1016000[/C][C]18157.2[/C][C]55.9558[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 36 )[/C][C]1014480[/C][C]17932.5[/C][C]56.5724[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 36 )[/C][C]1014480[/C][C]17932.5[/C][C]56.5724[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 36 )[/C][C]1016340[/C][C]17643.7[/C][C]57.6034[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 36 )[/C][C]1016340[/C][C]17048.8[/C][C]59.6133[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 36 )[/C][C]1016340[/C][C]17048.8[/C][C]59.6133[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 36 )[/C][C]1021050[/C][C]15727.9[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 36 )[/C][C]1021050[/C][C]15727.9[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 36 )[/C][C]1021050[/C][C]15727.9[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 36 )[/C][C]1023920[/C][C]15361.7[/C][C]66.6539[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 36 )[/C][C]1023920[/C][C]15361.7[/C][C]66.6539[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 36 )[/C][C]1023920[/C][C]14523.4[/C][C]70.5011[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 36 )[/C][C]1020550[/C][C]14069[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 21 / 36 )[/C][C]1020550[/C][C]14069[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 22 / 36 )[/C][C]1020550[/C][C]14069[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 23 / 36 )[/C][C]1020550[/C][C]14069[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 24 / 36 )[/C][C]1016500[/C][C]12541.8[/C][C]81.0492[/C][/ROW]
[ROW][C]Winsorized Mean ( 25 / 36 )[/C][C]1016500[/C][C]12541.8[/C][C]81.0492[/C][/ROW]
[ROW][C]Winsorized Mean ( 26 / 36 )[/C][C]1016500[/C][C]11487.3[/C][C]88.4894[/C][/ROW]
[ROW][C]Winsorized Mean ( 27 / 36 )[/C][C]1016500[/C][C]11487.3[/C][C]88.4894[/C][/ROW]
[ROW][C]Winsorized Mean ( 28 / 36 )[/C][C]1021220[/C][C]10952.1[/C][C]93.2443[/C][/ROW]
[ROW][C]Winsorized Mean ( 29 / 36 )[/C][C]1021220[/C][C]10952.1[/C][C]93.2443[/C][/ROW]
[ROW][C]Winsorized Mean ( 30 / 36 )[/C][C]1016170[/C][C]10344.2[/C][C]98.2355[/C][/ROW]
[ROW][C]Winsorized Mean ( 31 / 36 )[/C][C]1016170[/C][C]10344.2[/C][C]98.2355[/C][/ROW]
[ROW][C]Winsorized Mean ( 32 / 36 )[/C][C]1010770[/C][C]9721.14[/C][C]103.977[/C][/ROW]
[ROW][C]Winsorized Mean ( 33 / 36 )[/C][C]1016340[/C][C]9098.94[/C][C]111.698[/C][/ROW]
[ROW][C]Winsorized Mean ( 34 / 36 )[/C][C]1016340[/C][C]9098.94[/C][C]111.698[/C][/ROW]
[ROW][C]Winsorized Mean ( 35 / 36 )[/C][C]1010440[/C][C]8432.92[/C][C]119.821[/C][/ROW]
[ROW][C]Winsorized Mean ( 36 / 36 )[/C][C]1016500[/C][C]7772.57[/C][C]130.781[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 36 )[/C][C]1021780[/C][C]20526.2[/C][C]49.7792[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 36 )[/C][C]1021480[/C][C]19897.5[/C][C]51.3369[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 36 )[/C][C]1020810[/C][C]19267.7[/C][C]52.9801[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 36 )[/C][C]1020290[/C][C]18687.7[/C][C]54.5971[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 36 )[/C][C]1019390[/C][C]18195.2[/C][C]56.025[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 36 )[/C][C]1018440[/C][C]17636.3[/C][C]57.7468[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 36 )[/C][C]1017650[/C][C]17228.2[/C][C]59.0691[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 36 )[/C][C]1017020[/C][C]16811.9[/C][C]60.4941[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 36 )[/C][C]1017180[/C][C]16519.3[/C][C]61.5753[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 36 )[/C][C]1017550[/C][C]16226[/C][C]62.7109[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 36 )[/C][C]1017930[/C][C]15889.2[/C][C]64.0643[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 36 )[/C][C]1018120[/C][C]15551.8[/C][C]65.466[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 36 )[/C][C]1018310[/C][C]15260.6[/C][C]66.7282[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 36 )[/C][C]1018520[/C][C]14922.8[/C][C]68.2524[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 36 )[/C][C]1018270[/C][C]14733.1[/C][C]69.1141[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 36 )[/C][C]1018000[/C][C]14509.4[/C][C]70.1618[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 36 )[/C][C]1017720[/C][C]14245.5[/C][C]71.4421[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 36 )[/C][C]1017180[/C][C]13986.7[/C][C]72.7246[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 36 )[/C][C]1016600[/C][C]13679.4[/C][C]74.3164[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 36 )[/C][C]1015990[/C][C]13438.3[/C][C]75.6038[/C][/ROW]
[ROW][C]Trimmed Mean ( 21 / 36 )[/C][C]1015620[/C][C]13216.9[/C][C]76.8419[/C][/ROW]
[ROW][C]Trimmed Mean ( 22 / 36 )[/C][C]1015220[/C][C]12948.7[/C][C]78.4032[/C][/ROW]
[ROW][C]Trimmed Mean ( 23 / 36 )[/C][C]1014800[/C][C]12623.3[/C][C]80.3911[/C][/ROW]
[ROW][C]Trimmed Mean ( 24 / 36 )[/C][C]1014350[/C][C]12227.1[/C][C]82.9592[/C][/ROW]
[ROW][C]Trimmed Mean ( 25 / 36 )[/C][C]1014180[/C][C]11993.2[/C][C]84.563[/C][/ROW]
[ROW][C]Trimmed Mean ( 26 / 36 )[/C][C]1014000[/C][C]11702.6[/C][C]86.6472[/C][/ROW]
[ROW][C]Trimmed Mean ( 27 / 36 )[/C][C]1013810[/C][C]11513[/C][C]88.0576[/C][/ROW]
[ROW][C]Trimmed Mean ( 28 / 36 )[/C][C]1013600[/C][C]11271[/C][C]89.9301[/C][/ROW]
[ROW][C]Trimmed Mean ( 29 / 36 )[/C][C]1013010[/C][C]11050.6[/C][C]91.6705[/C][/ROW]
[ROW][C]Trimmed Mean ( 30 / 36 )[/C][C]1012380[/C][C]10764.9[/C][C]94.0443[/C][/ROW]
[ROW][C]Trimmed Mean ( 31 / 36 )[/C][C]1012080[/C][C]10515.1[/C][C]96.2504[/C][/ROW]
[ROW][C]Trimmed Mean ( 32 / 36 )[/C][C]1011750[/C][C]10186[/C][C]99.3277[/C][/ROW]
[ROW][C]Trimmed Mean ( 33 / 36 )[/C][C]1011830[/C][C]9886.39[/C][C]102.346[/C][/ROW]
[ROW][C]Trimmed Mean ( 34 / 36 )[/C][C]1011460[/C][C]9619.23[/C][C]105.15[/C][/ROW]
[ROW][C]Trimmed Mean ( 35 / 36 )[/C][C]1011060[/C][C]9252.69[/C][C]109.272[/C][/ROW]
[ROW][C]Trimmed Mean ( 36 / 36 )[/C][C]1011110[/C][C]8923.35[/C][C]113.311[/C][/ROW]
[ROW][C]Median[/C][C]1001000[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]1019200[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]1014000[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]1014000[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]1014000[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]1014000[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]1014000[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]1014000[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]1014000[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]1014000[/C][C][/C][C][/C][/ROW]
[ROW][C]Number of observations[/C][C]108[/C][C][/C][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=307016&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307016&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 Mean102173021307.447.9518
Geometric Mean997178
Harmonic Mean971564
Quadratic Mean1045230
Winsorized Mean ( 1 / 36 )102206021085.248.4731
Winsorized Mean ( 2 / 36 )102274020953.348.8103
Winsorized Mean ( 3 / 36 )102223020646.149.5122
Winsorized Mean ( 4 / 36 )102358020160.550.7716
Winsorized Mean ( 5 / 36 )102358020160.550.7716
Winsorized Mean ( 6 / 36 )102257019259.353.095
Winsorized Mean ( 7 / 36 )102139019046.153.6273
Winsorized Mean ( 8 / 36 )101600018157.255.9558
Winsorized Mean ( 9 / 36 )101448017932.556.5724
Winsorized Mean ( 10 / 36 )101448017932.556.5724
Winsorized Mean ( 11 / 36 )101634017643.757.6034
Winsorized Mean ( 12 / 36 )101634017048.859.6133
Winsorized Mean ( 13 / 36 )101634017048.859.6133
Winsorized Mean ( 14 / 36 )102105015727.964.92
Winsorized Mean ( 15 / 36 )102105015727.964.92
Winsorized Mean ( 16 / 36 )102105015727.964.92
Winsorized Mean ( 17 / 36 )102392015361.766.6539
Winsorized Mean ( 18 / 36 )102392015361.766.6539
Winsorized Mean ( 19 / 36 )102392014523.470.5011
Winsorized Mean ( 20 / 36 )10205501406972.5389
Winsorized Mean ( 21 / 36 )10205501406972.5389
Winsorized Mean ( 22 / 36 )10205501406972.5389
Winsorized Mean ( 23 / 36 )10205501406972.5389
Winsorized Mean ( 24 / 36 )101650012541.881.0492
Winsorized Mean ( 25 / 36 )101650012541.881.0492
Winsorized Mean ( 26 / 36 )101650011487.388.4894
Winsorized Mean ( 27 / 36 )101650011487.388.4894
Winsorized Mean ( 28 / 36 )102122010952.193.2443
Winsorized Mean ( 29 / 36 )102122010952.193.2443
Winsorized Mean ( 30 / 36 )101617010344.298.2355
Winsorized Mean ( 31 / 36 )101617010344.298.2355
Winsorized Mean ( 32 / 36 )10107709721.14103.977
Winsorized Mean ( 33 / 36 )10163409098.94111.698
Winsorized Mean ( 34 / 36 )10163409098.94111.698
Winsorized Mean ( 35 / 36 )10104408432.92119.821
Winsorized Mean ( 36 / 36 )10165007772.57130.781
Trimmed Mean ( 1 / 36 )102178020526.249.7792
Trimmed Mean ( 2 / 36 )102148019897.551.3369
Trimmed Mean ( 3 / 36 )102081019267.752.9801
Trimmed Mean ( 4 / 36 )102029018687.754.5971
Trimmed Mean ( 5 / 36 )101939018195.256.025
Trimmed Mean ( 6 / 36 )101844017636.357.7468
Trimmed Mean ( 7 / 36 )101765017228.259.0691
Trimmed Mean ( 8 / 36 )101702016811.960.4941
Trimmed Mean ( 9 / 36 )101718016519.361.5753
Trimmed Mean ( 10 / 36 )10175501622662.7109
Trimmed Mean ( 11 / 36 )101793015889.264.0643
Trimmed Mean ( 12 / 36 )101812015551.865.466
Trimmed Mean ( 13 / 36 )101831015260.666.7282
Trimmed Mean ( 14 / 36 )101852014922.868.2524
Trimmed Mean ( 15 / 36 )101827014733.169.1141
Trimmed Mean ( 16 / 36 )101800014509.470.1618
Trimmed Mean ( 17 / 36 )101772014245.571.4421
Trimmed Mean ( 18 / 36 )101718013986.772.7246
Trimmed Mean ( 19 / 36 )101660013679.474.3164
Trimmed Mean ( 20 / 36 )101599013438.375.6038
Trimmed Mean ( 21 / 36 )101562013216.976.8419
Trimmed Mean ( 22 / 36 )101522012948.778.4032
Trimmed Mean ( 23 / 36 )101480012623.380.3911
Trimmed Mean ( 24 / 36 )101435012227.182.9592
Trimmed Mean ( 25 / 36 )101418011993.284.563
Trimmed Mean ( 26 / 36 )101400011702.686.6472
Trimmed Mean ( 27 / 36 )10138101151388.0576
Trimmed Mean ( 28 / 36 )10136001127189.9301
Trimmed Mean ( 29 / 36 )101301011050.691.6705
Trimmed Mean ( 30 / 36 )101238010764.994.0443
Trimmed Mean ( 31 / 36 )101208010515.196.2504
Trimmed Mean ( 32 / 36 )10117501018699.3277
Trimmed Mean ( 33 / 36 )10118309886.39102.346
Trimmed Mean ( 34 / 36 )10114609619.23105.15
Trimmed Mean ( 35 / 36 )10110609252.69109.272
Trimmed Mean ( 36 / 36 )10111108923.35113.311
Median1001000
Midrange1019200
Midmean - Weighted Average at Xnp1014000
Midmean - Weighted Average at X(n+1)p1014000
Midmean - Empirical Distribution Function1014000
Midmean - Empirical Distribution Function - Averaging1014000
Midmean - Empirical Distribution Function - Interpolation1014000
Midmean - Closest Observation1014000
Midmean - True Basic - Statistics Graphics Toolkit1014000
Midmean - MS Excel (old versions)1014000
Number of observations108



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')