Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_centraltendency.wasp
Title produced by softwareCentral Tendency
Date of computationTue, 15 Aug 2017 22:00:33 +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/15/t1502827535n0liz57g7774t25.htm/, Retrieved Fri, 17 May 2024 08:09:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=307328, Retrieved Fri, 17 May 2024 08:09:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact47
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [] [2017-08-15 20:00:33] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum

Post a new message
Dataseries X:
1684800
1622400
1716000
1372800
1778400
1747200
1872000
1934400
2152800
1872000
1778400
2215200
1872000
1404000
1653600
1248000
1747200
1435200
1903200
1716000
1809600
2028000
1996800
2371200
1716000
1435200
1591200
1154400
1653600
1279200
1809600
1716000
1528800
2184000
1965600
2246400
1684800
1560000
1404000
1154400
1528800
1372800
1872000
1809600
1560000
2090400
1934400
2496000
1996800
1216800
1216800
1216800
1435200
1435200
1934400
1778400
1591200
1996800
1840800
2652000
2090400
1216800
1279200
1060800
1466400
1684800
2121600
2090400
1684800
1965600
1747200
2496000
1903200
1528800
1372800
1029600
1528800
1840800
2152800
2028000
1497600
2152800
1684800
2589600
2152800
1560000
1435200
967200
1528800
1466400
2215200
2215200
1684800
2184000
1622400
2527200
2152800
1591200
1216800
842400
1653600
1591200
2090400
2402400
1778400
1996800
1497600
2589600




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 time2 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=307328&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]2 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=307328&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307328&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 time2 seconds
R ServerBig Analytics Cloud Computing Center







Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean17515303652747.9518
Geometric Mean1709450
Harmonic Mean1665540
Quadratic Mean1791820
Winsorized Mean ( 1 / 36 )175211036146.148.4731
Winsorized Mean ( 2 / 36 )17532703592048.8103
Winsorized Mean ( 3 / 36 )175240035393.349.5122
Winsorized Mean ( 4 / 36 )175471034560.950.7716
Winsorized Mean ( 5 / 36 )175471034560.950.7716
Winsorized Mean ( 6 / 36 )175298033015.953.095
Winsorized Mean ( 7 / 36 )175096032650.553.6273
Winsorized Mean ( 8 / 36 )174171031126.655.9558
Winsorized Mean ( 9 / 36 )173911030741.356.5724
Winsorized Mean ( 10 / 36 )173911030741.356.5724
Winsorized Mean ( 11 / 36 )174229030246.357.6034
Winsorized Mean ( 12 / 36 )174229029226.559.6133
Winsorized Mean ( 13 / 36 )174229029226.559.6133
Winsorized Mean ( 14 / 36 )175038026962.164.92
Winsorized Mean ( 15 / 36 )175038026962.164.92
Winsorized Mean ( 16 / 36 )175038026962.164.92
Winsorized Mean ( 17 / 36 )175529026334.466.6539
Winsorized Mean ( 18 / 36 )175529026334.466.6539
Winsorized Mean ( 19 / 36 )175529024897.370.5011
Winsorized Mean ( 20 / 36 )174951024118.272.5389
Winsorized Mean ( 21 / 36 )174951024118.272.5389
Winsorized Mean ( 22 / 36 )174951024118.272.5389
Winsorized Mean ( 23 / 36 )174951024118.272.5389
Winsorized Mean ( 24 / 36 )174258021500.281.0492
Winsorized Mean ( 25 / 36 )174258021500.281.0492
Winsorized Mean ( 26 / 36 )174258019692.588.4894
Winsorized Mean ( 27 / 36 )174258019692.588.4894
Winsorized Mean ( 28 / 36 )175067018775.193.2443
Winsorized Mean ( 29 / 36 )175067018775.193.2443
Winsorized Mean ( 30 / 36 )174200017732.998.2355
Winsorized Mean ( 31 / 36 )174200017732.998.2355
Winsorized Mean ( 32 / 36 )173276016664.8103.977
Winsorized Mean ( 33 / 36 )174229015598.2111.698
Winsorized Mean ( 34 / 36 )174229015598.2111.698
Winsorized Mean ( 35 / 36 )173218014456.4119.821
Winsorized Mean ( 36 / 36 )174258013324.4130.781
Trimmed Mean ( 1 / 36 )175162035187.749.7792
Trimmed Mean ( 2 / 36 )17511003411051.3369
Trimmed Mean ( 3 / 36 )174995033030.452.9801
Trimmed Mean ( 4 / 36 )17490703203654.5971
Trimmed Mean ( 5 / 36 )174752031191.856.025
Trimmed Mean ( 6 / 36 )174590030233.757.7468
Trimmed Mean ( 7 / 36 )17445402953459.0691
Trimmed Mean ( 8 / 36 )174347028820.560.4941
Trimmed Mean ( 9 / 36 )174373028318.761.5753
Trimmed Mean ( 10 / 36 )174436027815.962.7109
Trimmed Mean ( 11 / 36 )174502027238.664.0643
Trimmed Mean ( 12 / 36 )174534026660.365.466
Trimmed Mean ( 13 / 36 )17456802616166.7282
Trimmed Mean ( 14 / 36 )17460302558268.2524
Trimmed Mean ( 15 / 36 )174560025256.869.1141
Trimmed Mean ( 16 / 36 )174515024873.270.1618
Trimmed Mean ( 17 / 36 )174467024420.871.4421
Trimmed Mean ( 18 / 36 )174373023977.272.7246
Trimmed Mean ( 19 / 36 )174274023450.374.3164
Trimmed Mean ( 20 / 36 )174169023037.175.6038
Trimmed Mean ( 21 / 36 )174105022657.676.8419
Trimmed Mean ( 22 / 36 )174038022197.878.4032
Trimmed Mean ( 23 / 36 )173965021639.980.3911
Trimmed Mean ( 24 / 36 )173888020960.782.9592
Trimmed Mean ( 25 / 36 )173859020559.784.563
Trimmed Mean ( 26 / 36 )173829020061.686.6472
Trimmed Mean ( 27 / 36 )173796019736.688.0576
Trimmed Mean ( 28 / 36 )173760019321.789.9301
Trimmed Mean ( 29 / 36 )173659018943.991.6705
Trimmed Mean ( 30 / 36 )173550018454.194.0443
Trimmed Mean ( 31 / 36 )173499018025.896.2504
Trimmed Mean ( 32 / 36 )173444017461.899.3277
Trimmed Mean ( 33 / 36 )173457016948.1102.346
Trimmed Mean ( 34 / 36 )173394016490.1105.15
Trimmed Mean ( 35 / 36 )173324015861.8109.272
Trimmed Mean ( 36 / 36 )173333015297.2113.311
Median1716000
Midrange1747200
Midmean - Weighted Average at Xnp1738290
Midmean - Weighted Average at X(n+1)p1738290
Midmean - Empirical Distribution Function1738290
Midmean - Empirical Distribution Function - Averaging1738290
Midmean - Empirical Distribution Function - Interpolation1738290
Midmean - Closest Observation1738290
Midmean - True Basic - Statistics Graphics Toolkit1738290
Midmean - MS Excel (old versions)1738290
Number of observations108

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 1751530 & 36527 & 47.9518 \tabularnewline
Geometric Mean & 1709450 &  &  \tabularnewline
Harmonic Mean & 1665540 &  &  \tabularnewline
Quadratic Mean & 1791820 &  &  \tabularnewline
Winsorized Mean ( 1 / 36 ) & 1752110 & 36146.1 & 48.4731 \tabularnewline
Winsorized Mean ( 2 / 36 ) & 1753270 & 35920 & 48.8103 \tabularnewline
Winsorized Mean ( 3 / 36 ) & 1752400 & 35393.3 & 49.5122 \tabularnewline
Winsorized Mean ( 4 / 36 ) & 1754710 & 34560.9 & 50.7716 \tabularnewline
Winsorized Mean ( 5 / 36 ) & 1754710 & 34560.9 & 50.7716 \tabularnewline
Winsorized Mean ( 6 / 36 ) & 1752980 & 33015.9 & 53.095 \tabularnewline
Winsorized Mean ( 7 / 36 ) & 1750960 & 32650.5 & 53.6273 \tabularnewline
Winsorized Mean ( 8 / 36 ) & 1741710 & 31126.6 & 55.9558 \tabularnewline
Winsorized Mean ( 9 / 36 ) & 1739110 & 30741.3 & 56.5724 \tabularnewline
Winsorized Mean ( 10 / 36 ) & 1739110 & 30741.3 & 56.5724 \tabularnewline
Winsorized Mean ( 11 / 36 ) & 1742290 & 30246.3 & 57.6034 \tabularnewline
Winsorized Mean ( 12 / 36 ) & 1742290 & 29226.5 & 59.6133 \tabularnewline
Winsorized Mean ( 13 / 36 ) & 1742290 & 29226.5 & 59.6133 \tabularnewline
Winsorized Mean ( 14 / 36 ) & 1750380 & 26962.1 & 64.92 \tabularnewline
Winsorized Mean ( 15 / 36 ) & 1750380 & 26962.1 & 64.92 \tabularnewline
Winsorized Mean ( 16 / 36 ) & 1750380 & 26962.1 & 64.92 \tabularnewline
Winsorized Mean ( 17 / 36 ) & 1755290 & 26334.4 & 66.6539 \tabularnewline
Winsorized Mean ( 18 / 36 ) & 1755290 & 26334.4 & 66.6539 \tabularnewline
Winsorized Mean ( 19 / 36 ) & 1755290 & 24897.3 & 70.5011 \tabularnewline
Winsorized Mean ( 20 / 36 ) & 1749510 & 24118.2 & 72.5389 \tabularnewline
Winsorized Mean ( 21 / 36 ) & 1749510 & 24118.2 & 72.5389 \tabularnewline
Winsorized Mean ( 22 / 36 ) & 1749510 & 24118.2 & 72.5389 \tabularnewline
Winsorized Mean ( 23 / 36 ) & 1749510 & 24118.2 & 72.5389 \tabularnewline
Winsorized Mean ( 24 / 36 ) & 1742580 & 21500.2 & 81.0492 \tabularnewline
Winsorized Mean ( 25 / 36 ) & 1742580 & 21500.2 & 81.0492 \tabularnewline
Winsorized Mean ( 26 / 36 ) & 1742580 & 19692.5 & 88.4894 \tabularnewline
Winsorized Mean ( 27 / 36 ) & 1742580 & 19692.5 & 88.4894 \tabularnewline
Winsorized Mean ( 28 / 36 ) & 1750670 & 18775.1 & 93.2443 \tabularnewline
Winsorized Mean ( 29 / 36 ) & 1750670 & 18775.1 & 93.2443 \tabularnewline
Winsorized Mean ( 30 / 36 ) & 1742000 & 17732.9 & 98.2355 \tabularnewline
Winsorized Mean ( 31 / 36 ) & 1742000 & 17732.9 & 98.2355 \tabularnewline
Winsorized Mean ( 32 / 36 ) & 1732760 & 16664.8 & 103.977 \tabularnewline
Winsorized Mean ( 33 / 36 ) & 1742290 & 15598.2 & 111.698 \tabularnewline
Winsorized Mean ( 34 / 36 ) & 1742290 & 15598.2 & 111.698 \tabularnewline
Winsorized Mean ( 35 / 36 ) & 1732180 & 14456.4 & 119.821 \tabularnewline
Winsorized Mean ( 36 / 36 ) & 1742580 & 13324.4 & 130.781 \tabularnewline
Trimmed Mean ( 1 / 36 ) & 1751620 & 35187.7 & 49.7792 \tabularnewline
Trimmed Mean ( 2 / 36 ) & 1751100 & 34110 & 51.3369 \tabularnewline
Trimmed Mean ( 3 / 36 ) & 1749950 & 33030.4 & 52.9801 \tabularnewline
Trimmed Mean ( 4 / 36 ) & 1749070 & 32036 & 54.5971 \tabularnewline
Trimmed Mean ( 5 / 36 ) & 1747520 & 31191.8 & 56.025 \tabularnewline
Trimmed Mean ( 6 / 36 ) & 1745900 & 30233.7 & 57.7468 \tabularnewline
Trimmed Mean ( 7 / 36 ) & 1744540 & 29534 & 59.0691 \tabularnewline
Trimmed Mean ( 8 / 36 ) & 1743470 & 28820.5 & 60.4941 \tabularnewline
Trimmed Mean ( 9 / 36 ) & 1743730 & 28318.7 & 61.5753 \tabularnewline
Trimmed Mean ( 10 / 36 ) & 1744360 & 27815.9 & 62.7109 \tabularnewline
Trimmed Mean ( 11 / 36 ) & 1745020 & 27238.6 & 64.0643 \tabularnewline
Trimmed Mean ( 12 / 36 ) & 1745340 & 26660.3 & 65.466 \tabularnewline
Trimmed Mean ( 13 / 36 ) & 1745680 & 26161 & 66.7282 \tabularnewline
Trimmed Mean ( 14 / 36 ) & 1746030 & 25582 & 68.2524 \tabularnewline
Trimmed Mean ( 15 / 36 ) & 1745600 & 25256.8 & 69.1141 \tabularnewline
Trimmed Mean ( 16 / 36 ) & 1745150 & 24873.2 & 70.1618 \tabularnewline
Trimmed Mean ( 17 / 36 ) & 1744670 & 24420.8 & 71.4421 \tabularnewline
Trimmed Mean ( 18 / 36 ) & 1743730 & 23977.2 & 72.7246 \tabularnewline
Trimmed Mean ( 19 / 36 ) & 1742740 & 23450.3 & 74.3164 \tabularnewline
Trimmed Mean ( 20 / 36 ) & 1741690 & 23037.1 & 75.6038 \tabularnewline
Trimmed Mean ( 21 / 36 ) & 1741050 & 22657.6 & 76.8419 \tabularnewline
Trimmed Mean ( 22 / 36 ) & 1740380 & 22197.8 & 78.4032 \tabularnewline
Trimmed Mean ( 23 / 36 ) & 1739650 & 21639.9 & 80.3911 \tabularnewline
Trimmed Mean ( 24 / 36 ) & 1738880 & 20960.7 & 82.9592 \tabularnewline
Trimmed Mean ( 25 / 36 ) & 1738590 & 20559.7 & 84.563 \tabularnewline
Trimmed Mean ( 26 / 36 ) & 1738290 & 20061.6 & 86.6472 \tabularnewline
Trimmed Mean ( 27 / 36 ) & 1737960 & 19736.6 & 88.0576 \tabularnewline
Trimmed Mean ( 28 / 36 ) & 1737600 & 19321.7 & 89.9301 \tabularnewline
Trimmed Mean ( 29 / 36 ) & 1736590 & 18943.9 & 91.6705 \tabularnewline
Trimmed Mean ( 30 / 36 ) & 1735500 & 18454.1 & 94.0443 \tabularnewline
Trimmed Mean ( 31 / 36 ) & 1734990 & 18025.8 & 96.2504 \tabularnewline
Trimmed Mean ( 32 / 36 ) & 1734440 & 17461.8 & 99.3277 \tabularnewline
Trimmed Mean ( 33 / 36 ) & 1734570 & 16948.1 & 102.346 \tabularnewline
Trimmed Mean ( 34 / 36 ) & 1733940 & 16490.1 & 105.15 \tabularnewline
Trimmed Mean ( 35 / 36 ) & 1733240 & 15861.8 & 109.272 \tabularnewline
Trimmed Mean ( 36 / 36 ) & 1733330 & 15297.2 & 113.311 \tabularnewline
Median & 1716000 &  &  \tabularnewline
Midrange & 1747200 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 1738290 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 1738290 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 1738290 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 1738290 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 1738290 &  &  \tabularnewline
Midmean - Closest Observation & 1738290 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 1738290 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 1738290 &  &  \tabularnewline
Number of observations & 108 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=307328&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]1751530[/C][C]36527[/C][C]47.9518[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]1709450[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]1665540[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]1791820[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 36 )[/C][C]1752110[/C][C]36146.1[/C][C]48.4731[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 36 )[/C][C]1753270[/C][C]35920[/C][C]48.8103[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 36 )[/C][C]1752400[/C][C]35393.3[/C][C]49.5122[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 36 )[/C][C]1754710[/C][C]34560.9[/C][C]50.7716[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 36 )[/C][C]1754710[/C][C]34560.9[/C][C]50.7716[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 36 )[/C][C]1752980[/C][C]33015.9[/C][C]53.095[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 36 )[/C][C]1750960[/C][C]32650.5[/C][C]53.6273[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 36 )[/C][C]1741710[/C][C]31126.6[/C][C]55.9558[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 36 )[/C][C]1739110[/C][C]30741.3[/C][C]56.5724[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 36 )[/C][C]1739110[/C][C]30741.3[/C][C]56.5724[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 36 )[/C][C]1742290[/C][C]30246.3[/C][C]57.6034[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 36 )[/C][C]1742290[/C][C]29226.5[/C][C]59.6133[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 36 )[/C][C]1742290[/C][C]29226.5[/C][C]59.6133[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 36 )[/C][C]1750380[/C][C]26962.1[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 36 )[/C][C]1750380[/C][C]26962.1[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 36 )[/C][C]1750380[/C][C]26962.1[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 36 )[/C][C]1755290[/C][C]26334.4[/C][C]66.6539[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 36 )[/C][C]1755290[/C][C]26334.4[/C][C]66.6539[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 36 )[/C][C]1755290[/C][C]24897.3[/C][C]70.5011[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 36 )[/C][C]1749510[/C][C]24118.2[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 21 / 36 )[/C][C]1749510[/C][C]24118.2[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 22 / 36 )[/C][C]1749510[/C][C]24118.2[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 23 / 36 )[/C][C]1749510[/C][C]24118.2[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 24 / 36 )[/C][C]1742580[/C][C]21500.2[/C][C]81.0492[/C][/ROW]
[ROW][C]Winsorized Mean ( 25 / 36 )[/C][C]1742580[/C][C]21500.2[/C][C]81.0492[/C][/ROW]
[ROW][C]Winsorized Mean ( 26 / 36 )[/C][C]1742580[/C][C]19692.5[/C][C]88.4894[/C][/ROW]
[ROW][C]Winsorized Mean ( 27 / 36 )[/C][C]1742580[/C][C]19692.5[/C][C]88.4894[/C][/ROW]
[ROW][C]Winsorized Mean ( 28 / 36 )[/C][C]1750670[/C][C]18775.1[/C][C]93.2443[/C][/ROW]
[ROW][C]Winsorized Mean ( 29 / 36 )[/C][C]1750670[/C][C]18775.1[/C][C]93.2443[/C][/ROW]
[ROW][C]Winsorized Mean ( 30 / 36 )[/C][C]1742000[/C][C]17732.9[/C][C]98.2355[/C][/ROW]
[ROW][C]Winsorized Mean ( 31 / 36 )[/C][C]1742000[/C][C]17732.9[/C][C]98.2355[/C][/ROW]
[ROW][C]Winsorized Mean ( 32 / 36 )[/C][C]1732760[/C][C]16664.8[/C][C]103.977[/C][/ROW]
[ROW][C]Winsorized Mean ( 33 / 36 )[/C][C]1742290[/C][C]15598.2[/C][C]111.698[/C][/ROW]
[ROW][C]Winsorized Mean ( 34 / 36 )[/C][C]1742290[/C][C]15598.2[/C][C]111.698[/C][/ROW]
[ROW][C]Winsorized Mean ( 35 / 36 )[/C][C]1732180[/C][C]14456.4[/C][C]119.821[/C][/ROW]
[ROW][C]Winsorized Mean ( 36 / 36 )[/C][C]1742580[/C][C]13324.4[/C][C]130.781[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 36 )[/C][C]1751620[/C][C]35187.7[/C][C]49.7792[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 36 )[/C][C]1751100[/C][C]34110[/C][C]51.3369[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 36 )[/C][C]1749950[/C][C]33030.4[/C][C]52.9801[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 36 )[/C][C]1749070[/C][C]32036[/C][C]54.5971[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 36 )[/C][C]1747520[/C][C]31191.8[/C][C]56.025[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 36 )[/C][C]1745900[/C][C]30233.7[/C][C]57.7468[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 36 )[/C][C]1744540[/C][C]29534[/C][C]59.0691[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 36 )[/C][C]1743470[/C][C]28820.5[/C][C]60.4941[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 36 )[/C][C]1743730[/C][C]28318.7[/C][C]61.5753[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 36 )[/C][C]1744360[/C][C]27815.9[/C][C]62.7109[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 36 )[/C][C]1745020[/C][C]27238.6[/C][C]64.0643[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 36 )[/C][C]1745340[/C][C]26660.3[/C][C]65.466[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 36 )[/C][C]1745680[/C][C]26161[/C][C]66.7282[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 36 )[/C][C]1746030[/C][C]25582[/C][C]68.2524[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 36 )[/C][C]1745600[/C][C]25256.8[/C][C]69.1141[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 36 )[/C][C]1745150[/C][C]24873.2[/C][C]70.1618[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 36 )[/C][C]1744670[/C][C]24420.8[/C][C]71.4421[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 36 )[/C][C]1743730[/C][C]23977.2[/C][C]72.7246[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 36 )[/C][C]1742740[/C][C]23450.3[/C][C]74.3164[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 36 )[/C][C]1741690[/C][C]23037.1[/C][C]75.6038[/C][/ROW]
[ROW][C]Trimmed Mean ( 21 / 36 )[/C][C]1741050[/C][C]22657.6[/C][C]76.8419[/C][/ROW]
[ROW][C]Trimmed Mean ( 22 / 36 )[/C][C]1740380[/C][C]22197.8[/C][C]78.4032[/C][/ROW]
[ROW][C]Trimmed Mean ( 23 / 36 )[/C][C]1739650[/C][C]21639.9[/C][C]80.3911[/C][/ROW]
[ROW][C]Trimmed Mean ( 24 / 36 )[/C][C]1738880[/C][C]20960.7[/C][C]82.9592[/C][/ROW]
[ROW][C]Trimmed Mean ( 25 / 36 )[/C][C]1738590[/C][C]20559.7[/C][C]84.563[/C][/ROW]
[ROW][C]Trimmed Mean ( 26 / 36 )[/C][C]1738290[/C][C]20061.6[/C][C]86.6472[/C][/ROW]
[ROW][C]Trimmed Mean ( 27 / 36 )[/C][C]1737960[/C][C]19736.6[/C][C]88.0576[/C][/ROW]
[ROW][C]Trimmed Mean ( 28 / 36 )[/C][C]1737600[/C][C]19321.7[/C][C]89.9301[/C][/ROW]
[ROW][C]Trimmed Mean ( 29 / 36 )[/C][C]1736590[/C][C]18943.9[/C][C]91.6705[/C][/ROW]
[ROW][C]Trimmed Mean ( 30 / 36 )[/C][C]1735500[/C][C]18454.1[/C][C]94.0443[/C][/ROW]
[ROW][C]Trimmed Mean ( 31 / 36 )[/C][C]1734990[/C][C]18025.8[/C][C]96.2504[/C][/ROW]
[ROW][C]Trimmed Mean ( 32 / 36 )[/C][C]1734440[/C][C]17461.8[/C][C]99.3277[/C][/ROW]
[ROW][C]Trimmed Mean ( 33 / 36 )[/C][C]1734570[/C][C]16948.1[/C][C]102.346[/C][/ROW]
[ROW][C]Trimmed Mean ( 34 / 36 )[/C][C]1733940[/C][C]16490.1[/C][C]105.15[/C][/ROW]
[ROW][C]Trimmed Mean ( 35 / 36 )[/C][C]1733240[/C][C]15861.8[/C][C]109.272[/C][/ROW]
[ROW][C]Trimmed Mean ( 36 / 36 )[/C][C]1733330[/C][C]15297.2[/C][C]113.311[/C][/ROW]
[ROW][C]Median[/C][C]1716000[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]1747200[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]1738290[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]1738290[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]1738290[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]1738290[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]1738290[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]1738290[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]1738290[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]1738290[/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=307328&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307328&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 Mean17515303652747.9518
Geometric Mean1709450
Harmonic Mean1665540
Quadratic Mean1791820
Winsorized Mean ( 1 / 36 )175211036146.148.4731
Winsorized Mean ( 2 / 36 )17532703592048.8103
Winsorized Mean ( 3 / 36 )175240035393.349.5122
Winsorized Mean ( 4 / 36 )175471034560.950.7716
Winsorized Mean ( 5 / 36 )175471034560.950.7716
Winsorized Mean ( 6 / 36 )175298033015.953.095
Winsorized Mean ( 7 / 36 )175096032650.553.6273
Winsorized Mean ( 8 / 36 )174171031126.655.9558
Winsorized Mean ( 9 / 36 )173911030741.356.5724
Winsorized Mean ( 10 / 36 )173911030741.356.5724
Winsorized Mean ( 11 / 36 )174229030246.357.6034
Winsorized Mean ( 12 / 36 )174229029226.559.6133
Winsorized Mean ( 13 / 36 )174229029226.559.6133
Winsorized Mean ( 14 / 36 )175038026962.164.92
Winsorized Mean ( 15 / 36 )175038026962.164.92
Winsorized Mean ( 16 / 36 )175038026962.164.92
Winsorized Mean ( 17 / 36 )175529026334.466.6539
Winsorized Mean ( 18 / 36 )175529026334.466.6539
Winsorized Mean ( 19 / 36 )175529024897.370.5011
Winsorized Mean ( 20 / 36 )174951024118.272.5389
Winsorized Mean ( 21 / 36 )174951024118.272.5389
Winsorized Mean ( 22 / 36 )174951024118.272.5389
Winsorized Mean ( 23 / 36 )174951024118.272.5389
Winsorized Mean ( 24 / 36 )174258021500.281.0492
Winsorized Mean ( 25 / 36 )174258021500.281.0492
Winsorized Mean ( 26 / 36 )174258019692.588.4894
Winsorized Mean ( 27 / 36 )174258019692.588.4894
Winsorized Mean ( 28 / 36 )175067018775.193.2443
Winsorized Mean ( 29 / 36 )175067018775.193.2443
Winsorized Mean ( 30 / 36 )174200017732.998.2355
Winsorized Mean ( 31 / 36 )174200017732.998.2355
Winsorized Mean ( 32 / 36 )173276016664.8103.977
Winsorized Mean ( 33 / 36 )174229015598.2111.698
Winsorized Mean ( 34 / 36 )174229015598.2111.698
Winsorized Mean ( 35 / 36 )173218014456.4119.821
Winsorized Mean ( 36 / 36 )174258013324.4130.781
Trimmed Mean ( 1 / 36 )175162035187.749.7792
Trimmed Mean ( 2 / 36 )17511003411051.3369
Trimmed Mean ( 3 / 36 )174995033030.452.9801
Trimmed Mean ( 4 / 36 )17490703203654.5971
Trimmed Mean ( 5 / 36 )174752031191.856.025
Trimmed Mean ( 6 / 36 )174590030233.757.7468
Trimmed Mean ( 7 / 36 )17445402953459.0691
Trimmed Mean ( 8 / 36 )174347028820.560.4941
Trimmed Mean ( 9 / 36 )174373028318.761.5753
Trimmed Mean ( 10 / 36 )174436027815.962.7109
Trimmed Mean ( 11 / 36 )174502027238.664.0643
Trimmed Mean ( 12 / 36 )174534026660.365.466
Trimmed Mean ( 13 / 36 )17456802616166.7282
Trimmed Mean ( 14 / 36 )17460302558268.2524
Trimmed Mean ( 15 / 36 )174560025256.869.1141
Trimmed Mean ( 16 / 36 )174515024873.270.1618
Trimmed Mean ( 17 / 36 )174467024420.871.4421
Trimmed Mean ( 18 / 36 )174373023977.272.7246
Trimmed Mean ( 19 / 36 )174274023450.374.3164
Trimmed Mean ( 20 / 36 )174169023037.175.6038
Trimmed Mean ( 21 / 36 )174105022657.676.8419
Trimmed Mean ( 22 / 36 )174038022197.878.4032
Trimmed Mean ( 23 / 36 )173965021639.980.3911
Trimmed Mean ( 24 / 36 )173888020960.782.9592
Trimmed Mean ( 25 / 36 )173859020559.784.563
Trimmed Mean ( 26 / 36 )173829020061.686.6472
Trimmed Mean ( 27 / 36 )173796019736.688.0576
Trimmed Mean ( 28 / 36 )173760019321.789.9301
Trimmed Mean ( 29 / 36 )173659018943.991.6705
Trimmed Mean ( 30 / 36 )173550018454.194.0443
Trimmed Mean ( 31 / 36 )173499018025.896.2504
Trimmed Mean ( 32 / 36 )173444017461.899.3277
Trimmed Mean ( 33 / 36 )173457016948.1102.346
Trimmed Mean ( 34 / 36 )173394016490.1105.15
Trimmed Mean ( 35 / 36 )173324015861.8109.272
Trimmed Mean ( 36 / 36 )173333015297.2113.311
Median1716000
Midrange1747200
Midmean - Weighted Average at Xnp1738290
Midmean - Weighted Average at X(n+1)p1738290
Midmean - Empirical Distribution Function1738290
Midmean - Empirical Distribution Function - Averaging1738290
Midmean - Empirical Distribution Function - Interpolation1738290
Midmean - Closest Observation1738290
Midmean - True Basic - Statistics Graphics Toolkit1738290
Midmean - MS Excel (old versions)1738290
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')