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Author's title

Author*Unverified author*
R Software Modulerwasp_centraltendency.wasp
Title produced by softwareCentral Tendency
Date of computationSun, 06 Aug 2017 16:56:20 +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/06/t1502031688bfekp906rat6kor.htm/, Retrieved Sat, 11 May 2024 04:13:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306954, Retrieved Sat, 11 May 2024 04:13:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact127
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [] [2017-08-06 14:56:20] [eec775fda337aa2da775a098928b5865] [Current]
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Dataseries X:
1053000
1014000
1072500
858000
1111500
1092000
1170000
1209000
1345500
1170000
1111500
1384500
1170000
877500
1033500
780000
1092000
897000
1189500
1072500
1131000
1267500
1248000
1482000
1072500
897000
994500
721500
1033500
799500
1131000
1072500
955500
1365000
1228500
1404000
1053000
975000
877500
721500
955500
858000
1170000
1131000
975000
1306500
1209000
1560000
1248000
760500
760500
760500
897000
897000
1209000
1111500
994500
1248000
1150500
1657500
1306500
760500
799500
663000
916500
1053000
1326000
1306500
1053000
1228500
1092000
1560000
1189500
955500
858000
643500
955500
1150500
1345500
1267500
936000
1345500
1053000
1618500
1345500
975000
897000
604500
955500
916500
1384500
1384500
1053000
1365000
1014000
1579500
1345500
994500
760500
526500
1033500
994500
1306500
1501500
1111500
1248000
936000
1618500




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=306954&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=306954&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306954&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 Mean109471022829.447.9518
Geometric Mean1068410
Harmonic Mean1040960
Quadratic Mean1119890
Winsorized Mean ( 1 / 36 )109507022591.348.4731
Winsorized Mean ( 2 / 36 )10957902245048.8103
Winsorized Mean ( 3 / 36 )109525022120.849.5122
Winsorized Mean ( 4 / 36 )109669021600.650.7716
Winsorized Mean ( 5 / 36 )109669021600.650.7716
Winsorized Mean ( 6 / 36 )109561020634.953.095
Winsorized Mean ( 7 / 36 )109435020406.553.6273
Winsorized Mean ( 8 / 36 )108857019454.155.9558
Winsorized Mean ( 9 / 36 )108694019213.356.5724
Winsorized Mean ( 10 / 36 )108694019213.356.5724
Winsorized Mean ( 11 / 36 )108893018903.957.6034
Winsorized Mean ( 12 / 36 )108893018266.659.6133
Winsorized Mean ( 13 / 36 )108893018266.659.6133
Winsorized Mean ( 14 / 36 )109399016851.364.92
Winsorized Mean ( 15 / 36 )109399016851.364.92
Winsorized Mean ( 16 / 36 )109399016851.364.92
Winsorized Mean ( 17 / 36 )10970601645966.6539
Winsorized Mean ( 18 / 36 )10970601645966.6539
Winsorized Mean ( 19 / 36 )109706015560.870.5011
Winsorized Mean ( 20 / 36 )109344015073.972.5389
Winsorized Mean ( 21 / 36 )109344015073.972.5389
Winsorized Mean ( 22 / 36 )109344015073.972.5389
Winsorized Mean ( 23 / 36 )109344015073.972.5389
Winsorized Mean ( 24 / 36 )108911013437.681.0492
Winsorized Mean ( 25 / 36 )108911013437.681.0492
Winsorized Mean ( 26 / 36 )108911012307.888.4894
Winsorized Mean ( 27 / 36 )108911012307.888.4894
Winsorized Mean ( 28 / 36 )109417011734.493.2443
Winsorized Mean ( 29 / 36 )109417011734.493.2443
Winsorized Mean ( 30 / 36 )108875011083.198.2355
Winsorized Mean ( 31 / 36 )108875011083.198.2355
Winsorized Mean ( 32 / 36 )108297010415.5103.977
Winsorized Mean ( 33 / 36 )10889309748.86111.698
Winsorized Mean ( 34 / 36 )10889309748.86111.698
Winsorized Mean ( 35 / 36 )10826109035.27119.821
Winsorized Mean ( 36 / 36 )10891108327.76130.781
Trimmed Mean ( 1 / 36 )109476021992.349.7792
Trimmed Mean ( 2 / 36 )109444021318.751.3369
Trimmed Mean ( 3 / 36 )10937202064452.9801
Trimmed Mean ( 4 / 36 )109317020022.554.5971
Trimmed Mean ( 5 / 36 )109220019494.956.025
Trimmed Mean ( 6 / 36 )109119018896.157.7468
Trimmed Mean ( 7 / 36 )109034018458.759.0691
Trimmed Mean ( 8 / 36 )108967018012.860.4941
Trimmed Mean ( 9 / 36 )108983017699.261.5753
Trimmed Mean ( 10 / 36 )10902301738562.7109
Trimmed Mean ( 11 / 36 )109064017024.164.0643
Trimmed Mean ( 12 / 36 )109084016662.765.466
Trimmed Mean ( 13 / 36 )109105016350.666.7282
Trimmed Mean ( 14 / 36 )109127015988.768.2524
Trimmed Mean ( 15 / 36 )109100015785.569.1141
Trimmed Mean ( 16 / 36 )109072015545.770.1618
Trimmed Mean ( 17 / 36 )10904201526371.4421
Trimmed Mean ( 18 / 36 )108983014985.872.7246
Trimmed Mean ( 19 / 36 )108921014656.474.3164
Trimmed Mean ( 20 / 36 )108856014398.275.6038
Trimmed Mean ( 21 / 36 )10881601416176.8419
Trimmed Mean ( 22 / 36 )108773013873.678.4032
Trimmed Mean ( 23 / 36 )108728013524.980.3911
Trimmed Mean ( 24 / 36 )108680013100.482.9592
Trimmed Mean ( 25 / 36 )108662012849.884.563
Trimmed Mean ( 26 / 36 )108643012538.586.6472
Trimmed Mean ( 27 / 36 )108622012335.488.0576
Trimmed Mean ( 28 / 36 )10860001207689.9301
Trimmed Mean ( 29 / 36 )108537011839.991.6705
Trimmed Mean ( 30 / 36 )108469011533.894.0443
Trimmed Mean ( 31 / 36 )108437011266.196.2504
Trimmed Mean ( 32 / 36 )108402010913.699.3277
Trimmed Mean ( 33 / 36 )108411010592.6102.346
Trimmed Mean ( 34 / 36 )108371010306.3105.15
Trimmed Mean ( 35 / 36 )10832809913.6109.272
Trimmed Mean ( 36 / 36 )10833309560.73113.311
Median1072500
Midrange1092000
Midmean - Weighted Average at Xnp1086430
Midmean - Weighted Average at X(n+1)p1086430
Midmean - Empirical Distribution Function1086430
Midmean - Empirical Distribution Function - Averaging1086430
Midmean - Empirical Distribution Function - Interpolation1086430
Midmean - Closest Observation1086430
Midmean - True Basic - Statistics Graphics Toolkit1086430
Midmean - MS Excel (old versions)1086430
Number of observations108

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 1094710 & 22829.4 & 47.9518 \tabularnewline
Geometric Mean & 1068410 &  &  \tabularnewline
Harmonic Mean & 1040960 &  &  \tabularnewline
Quadratic Mean & 1119890 &  &  \tabularnewline
Winsorized Mean ( 1 / 36 ) & 1095070 & 22591.3 & 48.4731 \tabularnewline
Winsorized Mean ( 2 / 36 ) & 1095790 & 22450 & 48.8103 \tabularnewline
Winsorized Mean ( 3 / 36 ) & 1095250 & 22120.8 & 49.5122 \tabularnewline
Winsorized Mean ( 4 / 36 ) & 1096690 & 21600.6 & 50.7716 \tabularnewline
Winsorized Mean ( 5 / 36 ) & 1096690 & 21600.6 & 50.7716 \tabularnewline
Winsorized Mean ( 6 / 36 ) & 1095610 & 20634.9 & 53.095 \tabularnewline
Winsorized Mean ( 7 / 36 ) & 1094350 & 20406.5 & 53.6273 \tabularnewline
Winsorized Mean ( 8 / 36 ) & 1088570 & 19454.1 & 55.9558 \tabularnewline
Winsorized Mean ( 9 / 36 ) & 1086940 & 19213.3 & 56.5724 \tabularnewline
Winsorized Mean ( 10 / 36 ) & 1086940 & 19213.3 & 56.5724 \tabularnewline
Winsorized Mean ( 11 / 36 ) & 1088930 & 18903.9 & 57.6034 \tabularnewline
Winsorized Mean ( 12 / 36 ) & 1088930 & 18266.6 & 59.6133 \tabularnewline
Winsorized Mean ( 13 / 36 ) & 1088930 & 18266.6 & 59.6133 \tabularnewline
Winsorized Mean ( 14 / 36 ) & 1093990 & 16851.3 & 64.92 \tabularnewline
Winsorized Mean ( 15 / 36 ) & 1093990 & 16851.3 & 64.92 \tabularnewline
Winsorized Mean ( 16 / 36 ) & 1093990 & 16851.3 & 64.92 \tabularnewline
Winsorized Mean ( 17 / 36 ) & 1097060 & 16459 & 66.6539 \tabularnewline
Winsorized Mean ( 18 / 36 ) & 1097060 & 16459 & 66.6539 \tabularnewline
Winsorized Mean ( 19 / 36 ) & 1097060 & 15560.8 & 70.5011 \tabularnewline
Winsorized Mean ( 20 / 36 ) & 1093440 & 15073.9 & 72.5389 \tabularnewline
Winsorized Mean ( 21 / 36 ) & 1093440 & 15073.9 & 72.5389 \tabularnewline
Winsorized Mean ( 22 / 36 ) & 1093440 & 15073.9 & 72.5389 \tabularnewline
Winsorized Mean ( 23 / 36 ) & 1093440 & 15073.9 & 72.5389 \tabularnewline
Winsorized Mean ( 24 / 36 ) & 1089110 & 13437.6 & 81.0492 \tabularnewline
Winsorized Mean ( 25 / 36 ) & 1089110 & 13437.6 & 81.0492 \tabularnewline
Winsorized Mean ( 26 / 36 ) & 1089110 & 12307.8 & 88.4894 \tabularnewline
Winsorized Mean ( 27 / 36 ) & 1089110 & 12307.8 & 88.4894 \tabularnewline
Winsorized Mean ( 28 / 36 ) & 1094170 & 11734.4 & 93.2443 \tabularnewline
Winsorized Mean ( 29 / 36 ) & 1094170 & 11734.4 & 93.2443 \tabularnewline
Winsorized Mean ( 30 / 36 ) & 1088750 & 11083.1 & 98.2355 \tabularnewline
Winsorized Mean ( 31 / 36 ) & 1088750 & 11083.1 & 98.2355 \tabularnewline
Winsorized Mean ( 32 / 36 ) & 1082970 & 10415.5 & 103.977 \tabularnewline
Winsorized Mean ( 33 / 36 ) & 1088930 & 9748.86 & 111.698 \tabularnewline
Winsorized Mean ( 34 / 36 ) & 1088930 & 9748.86 & 111.698 \tabularnewline
Winsorized Mean ( 35 / 36 ) & 1082610 & 9035.27 & 119.821 \tabularnewline
Winsorized Mean ( 36 / 36 ) & 1089110 & 8327.76 & 130.781 \tabularnewline
Trimmed Mean ( 1 / 36 ) & 1094760 & 21992.3 & 49.7792 \tabularnewline
Trimmed Mean ( 2 / 36 ) & 1094440 & 21318.7 & 51.3369 \tabularnewline
Trimmed Mean ( 3 / 36 ) & 1093720 & 20644 & 52.9801 \tabularnewline
Trimmed Mean ( 4 / 36 ) & 1093170 & 20022.5 & 54.5971 \tabularnewline
Trimmed Mean ( 5 / 36 ) & 1092200 & 19494.9 & 56.025 \tabularnewline
Trimmed Mean ( 6 / 36 ) & 1091190 & 18896.1 & 57.7468 \tabularnewline
Trimmed Mean ( 7 / 36 ) & 1090340 & 18458.7 & 59.0691 \tabularnewline
Trimmed Mean ( 8 / 36 ) & 1089670 & 18012.8 & 60.4941 \tabularnewline
Trimmed Mean ( 9 / 36 ) & 1089830 & 17699.2 & 61.5753 \tabularnewline
Trimmed Mean ( 10 / 36 ) & 1090230 & 17385 & 62.7109 \tabularnewline
Trimmed Mean ( 11 / 36 ) & 1090640 & 17024.1 & 64.0643 \tabularnewline
Trimmed Mean ( 12 / 36 ) & 1090840 & 16662.7 & 65.466 \tabularnewline
Trimmed Mean ( 13 / 36 ) & 1091050 & 16350.6 & 66.7282 \tabularnewline
Trimmed Mean ( 14 / 36 ) & 1091270 & 15988.7 & 68.2524 \tabularnewline
Trimmed Mean ( 15 / 36 ) & 1091000 & 15785.5 & 69.1141 \tabularnewline
Trimmed Mean ( 16 / 36 ) & 1090720 & 15545.7 & 70.1618 \tabularnewline
Trimmed Mean ( 17 / 36 ) & 1090420 & 15263 & 71.4421 \tabularnewline
Trimmed Mean ( 18 / 36 ) & 1089830 & 14985.8 & 72.7246 \tabularnewline
Trimmed Mean ( 19 / 36 ) & 1089210 & 14656.4 & 74.3164 \tabularnewline
Trimmed Mean ( 20 / 36 ) & 1088560 & 14398.2 & 75.6038 \tabularnewline
Trimmed Mean ( 21 / 36 ) & 1088160 & 14161 & 76.8419 \tabularnewline
Trimmed Mean ( 22 / 36 ) & 1087730 & 13873.6 & 78.4032 \tabularnewline
Trimmed Mean ( 23 / 36 ) & 1087280 & 13524.9 & 80.3911 \tabularnewline
Trimmed Mean ( 24 / 36 ) & 1086800 & 13100.4 & 82.9592 \tabularnewline
Trimmed Mean ( 25 / 36 ) & 1086620 & 12849.8 & 84.563 \tabularnewline
Trimmed Mean ( 26 / 36 ) & 1086430 & 12538.5 & 86.6472 \tabularnewline
Trimmed Mean ( 27 / 36 ) & 1086220 & 12335.4 & 88.0576 \tabularnewline
Trimmed Mean ( 28 / 36 ) & 1086000 & 12076 & 89.9301 \tabularnewline
Trimmed Mean ( 29 / 36 ) & 1085370 & 11839.9 & 91.6705 \tabularnewline
Trimmed Mean ( 30 / 36 ) & 1084690 & 11533.8 & 94.0443 \tabularnewline
Trimmed Mean ( 31 / 36 ) & 1084370 & 11266.1 & 96.2504 \tabularnewline
Trimmed Mean ( 32 / 36 ) & 1084020 & 10913.6 & 99.3277 \tabularnewline
Trimmed Mean ( 33 / 36 ) & 1084110 & 10592.6 & 102.346 \tabularnewline
Trimmed Mean ( 34 / 36 ) & 1083710 & 10306.3 & 105.15 \tabularnewline
Trimmed Mean ( 35 / 36 ) & 1083280 & 9913.6 & 109.272 \tabularnewline
Trimmed Mean ( 36 / 36 ) & 1083330 & 9560.73 & 113.311 \tabularnewline
Median & 1072500 &  &  \tabularnewline
Midrange & 1092000 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 1086430 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 1086430 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 1086430 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 1086430 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 1086430 &  &  \tabularnewline
Midmean - Closest Observation & 1086430 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 1086430 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 1086430 &  &  \tabularnewline
Number of observations & 108 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306954&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]1094710[/C][C]22829.4[/C][C]47.9518[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]1068410[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]1040960[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]1119890[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 36 )[/C][C]1095070[/C][C]22591.3[/C][C]48.4731[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 36 )[/C][C]1095790[/C][C]22450[/C][C]48.8103[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 36 )[/C][C]1095250[/C][C]22120.8[/C][C]49.5122[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 36 )[/C][C]1096690[/C][C]21600.6[/C][C]50.7716[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 36 )[/C][C]1096690[/C][C]21600.6[/C][C]50.7716[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 36 )[/C][C]1095610[/C][C]20634.9[/C][C]53.095[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 36 )[/C][C]1094350[/C][C]20406.5[/C][C]53.6273[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 36 )[/C][C]1088570[/C][C]19454.1[/C][C]55.9558[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 36 )[/C][C]1086940[/C][C]19213.3[/C][C]56.5724[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 36 )[/C][C]1086940[/C][C]19213.3[/C][C]56.5724[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 36 )[/C][C]1088930[/C][C]18903.9[/C][C]57.6034[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 36 )[/C][C]1088930[/C][C]18266.6[/C][C]59.6133[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 36 )[/C][C]1088930[/C][C]18266.6[/C][C]59.6133[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 36 )[/C][C]1093990[/C][C]16851.3[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 36 )[/C][C]1093990[/C][C]16851.3[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 36 )[/C][C]1093990[/C][C]16851.3[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 36 )[/C][C]1097060[/C][C]16459[/C][C]66.6539[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 36 )[/C][C]1097060[/C][C]16459[/C][C]66.6539[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 36 )[/C][C]1097060[/C][C]15560.8[/C][C]70.5011[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 36 )[/C][C]1093440[/C][C]15073.9[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 21 / 36 )[/C][C]1093440[/C][C]15073.9[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 22 / 36 )[/C][C]1093440[/C][C]15073.9[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 23 / 36 )[/C][C]1093440[/C][C]15073.9[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 24 / 36 )[/C][C]1089110[/C][C]13437.6[/C][C]81.0492[/C][/ROW]
[ROW][C]Winsorized Mean ( 25 / 36 )[/C][C]1089110[/C][C]13437.6[/C][C]81.0492[/C][/ROW]
[ROW][C]Winsorized Mean ( 26 / 36 )[/C][C]1089110[/C][C]12307.8[/C][C]88.4894[/C][/ROW]
[ROW][C]Winsorized Mean ( 27 / 36 )[/C][C]1089110[/C][C]12307.8[/C][C]88.4894[/C][/ROW]
[ROW][C]Winsorized Mean ( 28 / 36 )[/C][C]1094170[/C][C]11734.4[/C][C]93.2443[/C][/ROW]
[ROW][C]Winsorized Mean ( 29 / 36 )[/C][C]1094170[/C][C]11734.4[/C][C]93.2443[/C][/ROW]
[ROW][C]Winsorized Mean ( 30 / 36 )[/C][C]1088750[/C][C]11083.1[/C][C]98.2355[/C][/ROW]
[ROW][C]Winsorized Mean ( 31 / 36 )[/C][C]1088750[/C][C]11083.1[/C][C]98.2355[/C][/ROW]
[ROW][C]Winsorized Mean ( 32 / 36 )[/C][C]1082970[/C][C]10415.5[/C][C]103.977[/C][/ROW]
[ROW][C]Winsorized Mean ( 33 / 36 )[/C][C]1088930[/C][C]9748.86[/C][C]111.698[/C][/ROW]
[ROW][C]Winsorized Mean ( 34 / 36 )[/C][C]1088930[/C][C]9748.86[/C][C]111.698[/C][/ROW]
[ROW][C]Winsorized Mean ( 35 / 36 )[/C][C]1082610[/C][C]9035.27[/C][C]119.821[/C][/ROW]
[ROW][C]Winsorized Mean ( 36 / 36 )[/C][C]1089110[/C][C]8327.76[/C][C]130.781[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 36 )[/C][C]1094760[/C][C]21992.3[/C][C]49.7792[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 36 )[/C][C]1094440[/C][C]21318.7[/C][C]51.3369[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 36 )[/C][C]1093720[/C][C]20644[/C][C]52.9801[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 36 )[/C][C]1093170[/C][C]20022.5[/C][C]54.5971[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 36 )[/C][C]1092200[/C][C]19494.9[/C][C]56.025[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 36 )[/C][C]1091190[/C][C]18896.1[/C][C]57.7468[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 36 )[/C][C]1090340[/C][C]18458.7[/C][C]59.0691[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 36 )[/C][C]1089670[/C][C]18012.8[/C][C]60.4941[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 36 )[/C][C]1089830[/C][C]17699.2[/C][C]61.5753[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 36 )[/C][C]1090230[/C][C]17385[/C][C]62.7109[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 36 )[/C][C]1090640[/C][C]17024.1[/C][C]64.0643[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 36 )[/C][C]1090840[/C][C]16662.7[/C][C]65.466[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 36 )[/C][C]1091050[/C][C]16350.6[/C][C]66.7282[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 36 )[/C][C]1091270[/C][C]15988.7[/C][C]68.2524[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 36 )[/C][C]1091000[/C][C]15785.5[/C][C]69.1141[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 36 )[/C][C]1090720[/C][C]15545.7[/C][C]70.1618[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 36 )[/C][C]1090420[/C][C]15263[/C][C]71.4421[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 36 )[/C][C]1089830[/C][C]14985.8[/C][C]72.7246[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 36 )[/C][C]1089210[/C][C]14656.4[/C][C]74.3164[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 36 )[/C][C]1088560[/C][C]14398.2[/C][C]75.6038[/C][/ROW]
[ROW][C]Trimmed Mean ( 21 / 36 )[/C][C]1088160[/C][C]14161[/C][C]76.8419[/C][/ROW]
[ROW][C]Trimmed Mean ( 22 / 36 )[/C][C]1087730[/C][C]13873.6[/C][C]78.4032[/C][/ROW]
[ROW][C]Trimmed Mean ( 23 / 36 )[/C][C]1087280[/C][C]13524.9[/C][C]80.3911[/C][/ROW]
[ROW][C]Trimmed Mean ( 24 / 36 )[/C][C]1086800[/C][C]13100.4[/C][C]82.9592[/C][/ROW]
[ROW][C]Trimmed Mean ( 25 / 36 )[/C][C]1086620[/C][C]12849.8[/C][C]84.563[/C][/ROW]
[ROW][C]Trimmed Mean ( 26 / 36 )[/C][C]1086430[/C][C]12538.5[/C][C]86.6472[/C][/ROW]
[ROW][C]Trimmed Mean ( 27 / 36 )[/C][C]1086220[/C][C]12335.4[/C][C]88.0576[/C][/ROW]
[ROW][C]Trimmed Mean ( 28 / 36 )[/C][C]1086000[/C][C]12076[/C][C]89.9301[/C][/ROW]
[ROW][C]Trimmed Mean ( 29 / 36 )[/C][C]1085370[/C][C]11839.9[/C][C]91.6705[/C][/ROW]
[ROW][C]Trimmed Mean ( 30 / 36 )[/C][C]1084690[/C][C]11533.8[/C][C]94.0443[/C][/ROW]
[ROW][C]Trimmed Mean ( 31 / 36 )[/C][C]1084370[/C][C]11266.1[/C][C]96.2504[/C][/ROW]
[ROW][C]Trimmed Mean ( 32 / 36 )[/C][C]1084020[/C][C]10913.6[/C][C]99.3277[/C][/ROW]
[ROW][C]Trimmed Mean ( 33 / 36 )[/C][C]1084110[/C][C]10592.6[/C][C]102.346[/C][/ROW]
[ROW][C]Trimmed Mean ( 34 / 36 )[/C][C]1083710[/C][C]10306.3[/C][C]105.15[/C][/ROW]
[ROW][C]Trimmed Mean ( 35 / 36 )[/C][C]1083280[/C][C]9913.6[/C][C]109.272[/C][/ROW]
[ROW][C]Trimmed Mean ( 36 / 36 )[/C][C]1083330[/C][C]9560.73[/C][C]113.311[/C][/ROW]
[ROW][C]Median[/C][C]1072500[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]1092000[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]1086430[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]1086430[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]1086430[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]1086430[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]1086430[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]1086430[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]1086430[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]1086430[/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=306954&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306954&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 Mean109471022829.447.9518
Geometric Mean1068410
Harmonic Mean1040960
Quadratic Mean1119890
Winsorized Mean ( 1 / 36 )109507022591.348.4731
Winsorized Mean ( 2 / 36 )10957902245048.8103
Winsorized Mean ( 3 / 36 )109525022120.849.5122
Winsorized Mean ( 4 / 36 )109669021600.650.7716
Winsorized Mean ( 5 / 36 )109669021600.650.7716
Winsorized Mean ( 6 / 36 )109561020634.953.095
Winsorized Mean ( 7 / 36 )109435020406.553.6273
Winsorized Mean ( 8 / 36 )108857019454.155.9558
Winsorized Mean ( 9 / 36 )108694019213.356.5724
Winsorized Mean ( 10 / 36 )108694019213.356.5724
Winsorized Mean ( 11 / 36 )108893018903.957.6034
Winsorized Mean ( 12 / 36 )108893018266.659.6133
Winsorized Mean ( 13 / 36 )108893018266.659.6133
Winsorized Mean ( 14 / 36 )109399016851.364.92
Winsorized Mean ( 15 / 36 )109399016851.364.92
Winsorized Mean ( 16 / 36 )109399016851.364.92
Winsorized Mean ( 17 / 36 )10970601645966.6539
Winsorized Mean ( 18 / 36 )10970601645966.6539
Winsorized Mean ( 19 / 36 )109706015560.870.5011
Winsorized Mean ( 20 / 36 )109344015073.972.5389
Winsorized Mean ( 21 / 36 )109344015073.972.5389
Winsorized Mean ( 22 / 36 )109344015073.972.5389
Winsorized Mean ( 23 / 36 )109344015073.972.5389
Winsorized Mean ( 24 / 36 )108911013437.681.0492
Winsorized Mean ( 25 / 36 )108911013437.681.0492
Winsorized Mean ( 26 / 36 )108911012307.888.4894
Winsorized Mean ( 27 / 36 )108911012307.888.4894
Winsorized Mean ( 28 / 36 )109417011734.493.2443
Winsorized Mean ( 29 / 36 )109417011734.493.2443
Winsorized Mean ( 30 / 36 )108875011083.198.2355
Winsorized Mean ( 31 / 36 )108875011083.198.2355
Winsorized Mean ( 32 / 36 )108297010415.5103.977
Winsorized Mean ( 33 / 36 )10889309748.86111.698
Winsorized Mean ( 34 / 36 )10889309748.86111.698
Winsorized Mean ( 35 / 36 )10826109035.27119.821
Winsorized Mean ( 36 / 36 )10891108327.76130.781
Trimmed Mean ( 1 / 36 )109476021992.349.7792
Trimmed Mean ( 2 / 36 )109444021318.751.3369
Trimmed Mean ( 3 / 36 )10937202064452.9801
Trimmed Mean ( 4 / 36 )109317020022.554.5971
Trimmed Mean ( 5 / 36 )109220019494.956.025
Trimmed Mean ( 6 / 36 )109119018896.157.7468
Trimmed Mean ( 7 / 36 )109034018458.759.0691
Trimmed Mean ( 8 / 36 )108967018012.860.4941
Trimmed Mean ( 9 / 36 )108983017699.261.5753
Trimmed Mean ( 10 / 36 )10902301738562.7109
Trimmed Mean ( 11 / 36 )109064017024.164.0643
Trimmed Mean ( 12 / 36 )109084016662.765.466
Trimmed Mean ( 13 / 36 )109105016350.666.7282
Trimmed Mean ( 14 / 36 )109127015988.768.2524
Trimmed Mean ( 15 / 36 )109100015785.569.1141
Trimmed Mean ( 16 / 36 )109072015545.770.1618
Trimmed Mean ( 17 / 36 )10904201526371.4421
Trimmed Mean ( 18 / 36 )108983014985.872.7246
Trimmed Mean ( 19 / 36 )108921014656.474.3164
Trimmed Mean ( 20 / 36 )108856014398.275.6038
Trimmed Mean ( 21 / 36 )10881601416176.8419
Trimmed Mean ( 22 / 36 )108773013873.678.4032
Trimmed Mean ( 23 / 36 )108728013524.980.3911
Trimmed Mean ( 24 / 36 )108680013100.482.9592
Trimmed Mean ( 25 / 36 )108662012849.884.563
Trimmed Mean ( 26 / 36 )108643012538.586.6472
Trimmed Mean ( 27 / 36 )108622012335.488.0576
Trimmed Mean ( 28 / 36 )10860001207689.9301
Trimmed Mean ( 29 / 36 )108537011839.991.6705
Trimmed Mean ( 30 / 36 )108469011533.894.0443
Trimmed Mean ( 31 / 36 )108437011266.196.2504
Trimmed Mean ( 32 / 36 )108402010913.699.3277
Trimmed Mean ( 33 / 36 )108411010592.6102.346
Trimmed Mean ( 34 / 36 )108371010306.3105.15
Trimmed Mean ( 35 / 36 )10832809913.6109.272
Trimmed Mean ( 36 / 36 )10833309560.73113.311
Median1072500
Midrange1092000
Midmean - Weighted Average at Xnp1086430
Midmean - Weighted Average at X(n+1)p1086430
Midmean - Empirical Distribution Function1086430
Midmean - Empirical Distribution Function - Averaging1086430
Midmean - Empirical Distribution Function - Interpolation1086430
Midmean - Closest Observation1086430
Midmean - True Basic - Statistics Graphics Toolkit1086430
Midmean - MS Excel (old versions)1086430
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')