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of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_centraltendency.wasp
Title produced by softwareCentral Tendency
Date of computationWed, 16 Aug 2017 20:20:13 +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/16/t1502907662q0sqe41m23mn5kw.htm/, Retrieved Sun, 12 May 2024 08:16:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=307483, Retrieved Sun, 12 May 2024 08:16:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [Central Tendency ...] [2017-08-16 18:20:13] [41db9c2917eeaa94887144dd7479aea5] [Current]
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Dataseries X:
1263600
1216800
1287000
1029600
1333800
1310400
1404000
1450800
1614600
1404000
1333800
1661400
1404000
1053000
1240200
936000
1310400
1076400
1427400
1287000
1357200
1521000
1497600
1778400
1287000
1076400
1193400
865800
1240200
959400
1357200
1287000
1146600
1638000
1474200
1684800
1263600
1170000
1053000
865800
1146600
1029600
1404000
1357200
1170000
1567800
1450800
1872000
1497600
912600
912600
912600
1076400
1076400
1450800
1333800
1193400
1497600
1380600
1989000
1567800
912600
959400
795600
1099800
1263600
1591200
1567800
1263600
1474200
1310400
1872000
1427400
1146600
1029600
772200
1146600
1380600
1614600
1521000
1123200
1614600
1263600
1942200
1614600
1170000
1076400
725400
1146600
1099800
1661400
1661400
1263600
1638000
1216800
1895400
1614600
1193400
912600
631800
1240200
1193400
1567800
1801800
1333800
1497600
1123200
1942200




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=307483&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]3 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=307483&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307483&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center







Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean131365027395.247.9518
Geometric Mean1282090
Harmonic Mean1249150
Quadratic Mean1343870
Winsorized Mean ( 1 / 36 )131408027109.648.4731
Winsorized Mean ( 2 / 36 )13149502694048.8103
Winsorized Mean ( 3 / 36 )13143002654549.5122
Winsorized Mean ( 4 / 36 )131603025920.750.7716
Winsorized Mean ( 5 / 36 )131603025920.750.7716
Winsorized Mean ( 6 / 36 )131473024761.953.095
Winsorized Mean ( 7 / 36 )131322024487.853.6273
Winsorized Mean ( 8 / 36 )130628023344.955.9558
Winsorized Mean ( 9 / 36 )13043302305656.5724
Winsorized Mean ( 10 / 36 )13043302305656.5724
Winsorized Mean ( 11 / 36 )130672022684.757.6034
Winsorized Mean ( 12 / 36 )130672021919.959.6133
Winsorized Mean ( 13 / 36 )130672021919.959.6133
Winsorized Mean ( 14 / 36 )131278020221.664.92
Winsorized Mean ( 15 / 36 )131278020221.664.92
Winsorized Mean ( 16 / 36 )131278020221.664.92
Winsorized Mean ( 17 / 36 )131647019750.866.6539
Winsorized Mean ( 18 / 36 )131647019750.866.6539
Winsorized Mean ( 19 / 36 )13164701867370.5011
Winsorized Mean ( 20 / 36 )131213018088.772.5389
Winsorized Mean ( 21 / 36 )131213018088.772.5389
Winsorized Mean ( 22 / 36 )131213018088.772.5389
Winsorized Mean ( 23 / 36 )131213018088.772.5389
Winsorized Mean ( 24 / 36 )130693016125.281.0492
Winsorized Mean ( 25 / 36 )130693016125.281.0492
Winsorized Mean ( 26 / 36 )130693014769.488.4894
Winsorized Mean ( 27 / 36 )130693014769.488.4894
Winsorized Mean ( 28 / 36 )131300014081.393.2443
Winsorized Mean ( 29 / 36 )131300014081.393.2443
Winsorized Mean ( 30 / 36 )130650013299.798.2355
Winsorized Mean ( 31 / 36 )130650013299.798.2355
Winsorized Mean ( 32 / 36 )129957012498.6103.977
Winsorized Mean ( 33 / 36 )130672011698.6111.698
Winsorized Mean ( 34 / 36 )130672011698.6111.698
Winsorized Mean ( 35 / 36 )129913010842.3119.821
Winsorized Mean ( 36 / 36 )13069309993.31130.781
Trimmed Mean ( 1 / 36 )131371026390.849.7792
Trimmed Mean ( 2 / 36 )131332025582.551.3369
Trimmed Mean ( 3 / 36 )131246024772.852.9801
Trimmed Mean ( 4 / 36 )13118002402754.5971
Trimmed Mean ( 5 / 36 )131064023393.856.025
Trimmed Mean ( 6 / 36 )130942022675.357.7468
Trimmed Mean ( 7 / 36 )130841022150.559.0691
Trimmed Mean ( 8 / 36 )130760021615.460.4941
Trimmed Mean ( 9 / 36 )13078002123961.5753
Trimmed Mean ( 10 / 36 )13082702086262.7109
Trimmed Mean ( 11 / 36 )13087702042964.0643
Trimmed Mean ( 12 / 36 )130901019995.265.466
Trimmed Mean ( 13 / 36 )130926019620.866.7282
Trimmed Mean ( 14 / 36 )130952019186.568.2524
Trimmed Mean ( 15 / 36 )130920018942.669.1141
Trimmed Mean ( 16 / 36 )130886018654.970.1618
Trimmed Mean ( 17 / 36 )130850018315.671.4421
Trimmed Mean ( 18 / 36 )130780017982.972.7246
Trimmed Mean ( 19 / 36 )130706017587.774.3164
Trimmed Mean ( 20 / 36 )130627017277.875.6038
Trimmed Mean ( 21 / 36 )130579016993.276.8419
Trimmed Mean ( 22 / 36 )130528016648.378.4032
Trimmed Mean ( 23 / 36 )130474016229.980.3911
Trimmed Mean ( 24 / 36 )130416015720.582.9592
Trimmed Mean ( 25 / 36 )130394015419.884.563
Trimmed Mean ( 26 / 36 )130371015046.286.6472
Trimmed Mean ( 27 / 36 )130347014802.488.0576
Trimmed Mean ( 28 / 36 )130320014491.389.9301
Trimmed Mean ( 29 / 36 )130244014207.991.6705
Trimmed Mean ( 30 / 36 )130162013840.694.0443
Trimmed Mean ( 31 / 36 )130124013519.496.2504
Trimmed Mean ( 32 / 36 )130083013096.399.3277
Trimmed Mean ( 33 / 36 )130093012711.1102.346
Trimmed Mean ( 34 / 36 )130046012367.6105.15
Trimmed Mean ( 35 / 36 )129993011896.3109.272
Trimmed Mean ( 36 / 36 )130000011472.9113.311
Median1287000
Midrange1310400
Midmean - Weighted Average at Xnp1303710
Midmean - Weighted Average at X(n+1)p1303710
Midmean - Empirical Distribution Function1303710
Midmean - Empirical Distribution Function - Averaging1303710
Midmean - Empirical Distribution Function - Interpolation1303710
Midmean - Closest Observation1303710
Midmean - True Basic - Statistics Graphics Toolkit1303710
Midmean - MS Excel (old versions)1303710
Number of observations108

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 1313650 & 27395.2 & 47.9518 \tabularnewline
Geometric Mean & 1282090 &  &  \tabularnewline
Harmonic Mean & 1249150 &  &  \tabularnewline
Quadratic Mean & 1343870 &  &  \tabularnewline
Winsorized Mean ( 1 / 36 ) & 1314080 & 27109.6 & 48.4731 \tabularnewline
Winsorized Mean ( 2 / 36 ) & 1314950 & 26940 & 48.8103 \tabularnewline
Winsorized Mean ( 3 / 36 ) & 1314300 & 26545 & 49.5122 \tabularnewline
Winsorized Mean ( 4 / 36 ) & 1316030 & 25920.7 & 50.7716 \tabularnewline
Winsorized Mean ( 5 / 36 ) & 1316030 & 25920.7 & 50.7716 \tabularnewline
Winsorized Mean ( 6 / 36 ) & 1314730 & 24761.9 & 53.095 \tabularnewline
Winsorized Mean ( 7 / 36 ) & 1313220 & 24487.8 & 53.6273 \tabularnewline
Winsorized Mean ( 8 / 36 ) & 1306280 & 23344.9 & 55.9558 \tabularnewline
Winsorized Mean ( 9 / 36 ) & 1304330 & 23056 & 56.5724 \tabularnewline
Winsorized Mean ( 10 / 36 ) & 1304330 & 23056 & 56.5724 \tabularnewline
Winsorized Mean ( 11 / 36 ) & 1306720 & 22684.7 & 57.6034 \tabularnewline
Winsorized Mean ( 12 / 36 ) & 1306720 & 21919.9 & 59.6133 \tabularnewline
Winsorized Mean ( 13 / 36 ) & 1306720 & 21919.9 & 59.6133 \tabularnewline
Winsorized Mean ( 14 / 36 ) & 1312780 & 20221.6 & 64.92 \tabularnewline
Winsorized Mean ( 15 / 36 ) & 1312780 & 20221.6 & 64.92 \tabularnewline
Winsorized Mean ( 16 / 36 ) & 1312780 & 20221.6 & 64.92 \tabularnewline
Winsorized Mean ( 17 / 36 ) & 1316470 & 19750.8 & 66.6539 \tabularnewline
Winsorized Mean ( 18 / 36 ) & 1316470 & 19750.8 & 66.6539 \tabularnewline
Winsorized Mean ( 19 / 36 ) & 1316470 & 18673 & 70.5011 \tabularnewline
Winsorized Mean ( 20 / 36 ) & 1312130 & 18088.7 & 72.5389 \tabularnewline
Winsorized Mean ( 21 / 36 ) & 1312130 & 18088.7 & 72.5389 \tabularnewline
Winsorized Mean ( 22 / 36 ) & 1312130 & 18088.7 & 72.5389 \tabularnewline
Winsorized Mean ( 23 / 36 ) & 1312130 & 18088.7 & 72.5389 \tabularnewline
Winsorized Mean ( 24 / 36 ) & 1306930 & 16125.2 & 81.0492 \tabularnewline
Winsorized Mean ( 25 / 36 ) & 1306930 & 16125.2 & 81.0492 \tabularnewline
Winsorized Mean ( 26 / 36 ) & 1306930 & 14769.4 & 88.4894 \tabularnewline
Winsorized Mean ( 27 / 36 ) & 1306930 & 14769.4 & 88.4894 \tabularnewline
Winsorized Mean ( 28 / 36 ) & 1313000 & 14081.3 & 93.2443 \tabularnewline
Winsorized Mean ( 29 / 36 ) & 1313000 & 14081.3 & 93.2443 \tabularnewline
Winsorized Mean ( 30 / 36 ) & 1306500 & 13299.7 & 98.2355 \tabularnewline
Winsorized Mean ( 31 / 36 ) & 1306500 & 13299.7 & 98.2355 \tabularnewline
Winsorized Mean ( 32 / 36 ) & 1299570 & 12498.6 & 103.977 \tabularnewline
Winsorized Mean ( 33 / 36 ) & 1306720 & 11698.6 & 111.698 \tabularnewline
Winsorized Mean ( 34 / 36 ) & 1306720 & 11698.6 & 111.698 \tabularnewline
Winsorized Mean ( 35 / 36 ) & 1299130 & 10842.3 & 119.821 \tabularnewline
Winsorized Mean ( 36 / 36 ) & 1306930 & 9993.31 & 130.781 \tabularnewline
Trimmed Mean ( 1 / 36 ) & 1313710 & 26390.8 & 49.7792 \tabularnewline
Trimmed Mean ( 2 / 36 ) & 1313320 & 25582.5 & 51.3369 \tabularnewline
Trimmed Mean ( 3 / 36 ) & 1312460 & 24772.8 & 52.9801 \tabularnewline
Trimmed Mean ( 4 / 36 ) & 1311800 & 24027 & 54.5971 \tabularnewline
Trimmed Mean ( 5 / 36 ) & 1310640 & 23393.8 & 56.025 \tabularnewline
Trimmed Mean ( 6 / 36 ) & 1309420 & 22675.3 & 57.7468 \tabularnewline
Trimmed Mean ( 7 / 36 ) & 1308410 & 22150.5 & 59.0691 \tabularnewline
Trimmed Mean ( 8 / 36 ) & 1307600 & 21615.4 & 60.4941 \tabularnewline
Trimmed Mean ( 9 / 36 ) & 1307800 & 21239 & 61.5753 \tabularnewline
Trimmed Mean ( 10 / 36 ) & 1308270 & 20862 & 62.7109 \tabularnewline
Trimmed Mean ( 11 / 36 ) & 1308770 & 20429 & 64.0643 \tabularnewline
Trimmed Mean ( 12 / 36 ) & 1309010 & 19995.2 & 65.466 \tabularnewline
Trimmed Mean ( 13 / 36 ) & 1309260 & 19620.8 & 66.7282 \tabularnewline
Trimmed Mean ( 14 / 36 ) & 1309520 & 19186.5 & 68.2524 \tabularnewline
Trimmed Mean ( 15 / 36 ) & 1309200 & 18942.6 & 69.1141 \tabularnewline
Trimmed Mean ( 16 / 36 ) & 1308860 & 18654.9 & 70.1618 \tabularnewline
Trimmed Mean ( 17 / 36 ) & 1308500 & 18315.6 & 71.4421 \tabularnewline
Trimmed Mean ( 18 / 36 ) & 1307800 & 17982.9 & 72.7246 \tabularnewline
Trimmed Mean ( 19 / 36 ) & 1307060 & 17587.7 & 74.3164 \tabularnewline
Trimmed Mean ( 20 / 36 ) & 1306270 & 17277.8 & 75.6038 \tabularnewline
Trimmed Mean ( 21 / 36 ) & 1305790 & 16993.2 & 76.8419 \tabularnewline
Trimmed Mean ( 22 / 36 ) & 1305280 & 16648.3 & 78.4032 \tabularnewline
Trimmed Mean ( 23 / 36 ) & 1304740 & 16229.9 & 80.3911 \tabularnewline
Trimmed Mean ( 24 / 36 ) & 1304160 & 15720.5 & 82.9592 \tabularnewline
Trimmed Mean ( 25 / 36 ) & 1303940 & 15419.8 & 84.563 \tabularnewline
Trimmed Mean ( 26 / 36 ) & 1303710 & 15046.2 & 86.6472 \tabularnewline
Trimmed Mean ( 27 / 36 ) & 1303470 & 14802.4 & 88.0576 \tabularnewline
Trimmed Mean ( 28 / 36 ) & 1303200 & 14491.3 & 89.9301 \tabularnewline
Trimmed Mean ( 29 / 36 ) & 1302440 & 14207.9 & 91.6705 \tabularnewline
Trimmed Mean ( 30 / 36 ) & 1301620 & 13840.6 & 94.0443 \tabularnewline
Trimmed Mean ( 31 / 36 ) & 1301240 & 13519.4 & 96.2504 \tabularnewline
Trimmed Mean ( 32 / 36 ) & 1300830 & 13096.3 & 99.3277 \tabularnewline
Trimmed Mean ( 33 / 36 ) & 1300930 & 12711.1 & 102.346 \tabularnewline
Trimmed Mean ( 34 / 36 ) & 1300460 & 12367.6 & 105.15 \tabularnewline
Trimmed Mean ( 35 / 36 ) & 1299930 & 11896.3 & 109.272 \tabularnewline
Trimmed Mean ( 36 / 36 ) & 1300000 & 11472.9 & 113.311 \tabularnewline
Median & 1287000 &  &  \tabularnewline
Midrange & 1310400 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 1303710 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 1303710 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 1303710 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 1303710 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 1303710 &  &  \tabularnewline
Midmean - Closest Observation & 1303710 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 1303710 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 1303710 &  &  \tabularnewline
Number of observations & 108 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=307483&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]1313650[/C][C]27395.2[/C][C]47.9518[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]1282090[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]1249150[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]1343870[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 36 )[/C][C]1314080[/C][C]27109.6[/C][C]48.4731[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 36 )[/C][C]1314950[/C][C]26940[/C][C]48.8103[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 36 )[/C][C]1314300[/C][C]26545[/C][C]49.5122[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 36 )[/C][C]1316030[/C][C]25920.7[/C][C]50.7716[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 36 )[/C][C]1316030[/C][C]25920.7[/C][C]50.7716[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 36 )[/C][C]1314730[/C][C]24761.9[/C][C]53.095[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 36 )[/C][C]1313220[/C][C]24487.8[/C][C]53.6273[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 36 )[/C][C]1306280[/C][C]23344.9[/C][C]55.9558[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 36 )[/C][C]1304330[/C][C]23056[/C][C]56.5724[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 36 )[/C][C]1304330[/C][C]23056[/C][C]56.5724[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 36 )[/C][C]1306720[/C][C]22684.7[/C][C]57.6034[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 36 )[/C][C]1306720[/C][C]21919.9[/C][C]59.6133[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 36 )[/C][C]1306720[/C][C]21919.9[/C][C]59.6133[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 36 )[/C][C]1312780[/C][C]20221.6[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 36 )[/C][C]1312780[/C][C]20221.6[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 36 )[/C][C]1312780[/C][C]20221.6[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 36 )[/C][C]1316470[/C][C]19750.8[/C][C]66.6539[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 36 )[/C][C]1316470[/C][C]19750.8[/C][C]66.6539[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 36 )[/C][C]1316470[/C][C]18673[/C][C]70.5011[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 36 )[/C][C]1312130[/C][C]18088.7[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 21 / 36 )[/C][C]1312130[/C][C]18088.7[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 22 / 36 )[/C][C]1312130[/C][C]18088.7[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 23 / 36 )[/C][C]1312130[/C][C]18088.7[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 24 / 36 )[/C][C]1306930[/C][C]16125.2[/C][C]81.0492[/C][/ROW]
[ROW][C]Winsorized Mean ( 25 / 36 )[/C][C]1306930[/C][C]16125.2[/C][C]81.0492[/C][/ROW]
[ROW][C]Winsorized Mean ( 26 / 36 )[/C][C]1306930[/C][C]14769.4[/C][C]88.4894[/C][/ROW]
[ROW][C]Winsorized Mean ( 27 / 36 )[/C][C]1306930[/C][C]14769.4[/C][C]88.4894[/C][/ROW]
[ROW][C]Winsorized Mean ( 28 / 36 )[/C][C]1313000[/C][C]14081.3[/C][C]93.2443[/C][/ROW]
[ROW][C]Winsorized Mean ( 29 / 36 )[/C][C]1313000[/C][C]14081.3[/C][C]93.2443[/C][/ROW]
[ROW][C]Winsorized Mean ( 30 / 36 )[/C][C]1306500[/C][C]13299.7[/C][C]98.2355[/C][/ROW]
[ROW][C]Winsorized Mean ( 31 / 36 )[/C][C]1306500[/C][C]13299.7[/C][C]98.2355[/C][/ROW]
[ROW][C]Winsorized Mean ( 32 / 36 )[/C][C]1299570[/C][C]12498.6[/C][C]103.977[/C][/ROW]
[ROW][C]Winsorized Mean ( 33 / 36 )[/C][C]1306720[/C][C]11698.6[/C][C]111.698[/C][/ROW]
[ROW][C]Winsorized Mean ( 34 / 36 )[/C][C]1306720[/C][C]11698.6[/C][C]111.698[/C][/ROW]
[ROW][C]Winsorized Mean ( 35 / 36 )[/C][C]1299130[/C][C]10842.3[/C][C]119.821[/C][/ROW]
[ROW][C]Winsorized Mean ( 36 / 36 )[/C][C]1306930[/C][C]9993.31[/C][C]130.781[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 36 )[/C][C]1313710[/C][C]26390.8[/C][C]49.7792[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 36 )[/C][C]1313320[/C][C]25582.5[/C][C]51.3369[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 36 )[/C][C]1312460[/C][C]24772.8[/C][C]52.9801[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 36 )[/C][C]1311800[/C][C]24027[/C][C]54.5971[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 36 )[/C][C]1310640[/C][C]23393.8[/C][C]56.025[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 36 )[/C][C]1309420[/C][C]22675.3[/C][C]57.7468[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 36 )[/C][C]1308410[/C][C]22150.5[/C][C]59.0691[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 36 )[/C][C]1307600[/C][C]21615.4[/C][C]60.4941[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 36 )[/C][C]1307800[/C][C]21239[/C][C]61.5753[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 36 )[/C][C]1308270[/C][C]20862[/C][C]62.7109[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 36 )[/C][C]1308770[/C][C]20429[/C][C]64.0643[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 36 )[/C][C]1309010[/C][C]19995.2[/C][C]65.466[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 36 )[/C][C]1309260[/C][C]19620.8[/C][C]66.7282[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 36 )[/C][C]1309520[/C][C]19186.5[/C][C]68.2524[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 36 )[/C][C]1309200[/C][C]18942.6[/C][C]69.1141[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 36 )[/C][C]1308860[/C][C]18654.9[/C][C]70.1618[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 36 )[/C][C]1308500[/C][C]18315.6[/C][C]71.4421[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 36 )[/C][C]1307800[/C][C]17982.9[/C][C]72.7246[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 36 )[/C][C]1307060[/C][C]17587.7[/C][C]74.3164[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 36 )[/C][C]1306270[/C][C]17277.8[/C][C]75.6038[/C][/ROW]
[ROW][C]Trimmed Mean ( 21 / 36 )[/C][C]1305790[/C][C]16993.2[/C][C]76.8419[/C][/ROW]
[ROW][C]Trimmed Mean ( 22 / 36 )[/C][C]1305280[/C][C]16648.3[/C][C]78.4032[/C][/ROW]
[ROW][C]Trimmed Mean ( 23 / 36 )[/C][C]1304740[/C][C]16229.9[/C][C]80.3911[/C][/ROW]
[ROW][C]Trimmed Mean ( 24 / 36 )[/C][C]1304160[/C][C]15720.5[/C][C]82.9592[/C][/ROW]
[ROW][C]Trimmed Mean ( 25 / 36 )[/C][C]1303940[/C][C]15419.8[/C][C]84.563[/C][/ROW]
[ROW][C]Trimmed Mean ( 26 / 36 )[/C][C]1303710[/C][C]15046.2[/C][C]86.6472[/C][/ROW]
[ROW][C]Trimmed Mean ( 27 / 36 )[/C][C]1303470[/C][C]14802.4[/C][C]88.0576[/C][/ROW]
[ROW][C]Trimmed Mean ( 28 / 36 )[/C][C]1303200[/C][C]14491.3[/C][C]89.9301[/C][/ROW]
[ROW][C]Trimmed Mean ( 29 / 36 )[/C][C]1302440[/C][C]14207.9[/C][C]91.6705[/C][/ROW]
[ROW][C]Trimmed Mean ( 30 / 36 )[/C][C]1301620[/C][C]13840.6[/C][C]94.0443[/C][/ROW]
[ROW][C]Trimmed Mean ( 31 / 36 )[/C][C]1301240[/C][C]13519.4[/C][C]96.2504[/C][/ROW]
[ROW][C]Trimmed Mean ( 32 / 36 )[/C][C]1300830[/C][C]13096.3[/C][C]99.3277[/C][/ROW]
[ROW][C]Trimmed Mean ( 33 / 36 )[/C][C]1300930[/C][C]12711.1[/C][C]102.346[/C][/ROW]
[ROW][C]Trimmed Mean ( 34 / 36 )[/C][C]1300460[/C][C]12367.6[/C][C]105.15[/C][/ROW]
[ROW][C]Trimmed Mean ( 35 / 36 )[/C][C]1299930[/C][C]11896.3[/C][C]109.272[/C][/ROW]
[ROW][C]Trimmed Mean ( 36 / 36 )[/C][C]1300000[/C][C]11472.9[/C][C]113.311[/C][/ROW]
[ROW][C]Median[/C][C]1287000[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]1310400[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]1303710[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]1303710[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]1303710[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]1303710[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]1303710[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]1303710[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]1303710[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]1303710[/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=307483&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307483&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 Mean131365027395.247.9518
Geometric Mean1282090
Harmonic Mean1249150
Quadratic Mean1343870
Winsorized Mean ( 1 / 36 )131408027109.648.4731
Winsorized Mean ( 2 / 36 )13149502694048.8103
Winsorized Mean ( 3 / 36 )13143002654549.5122
Winsorized Mean ( 4 / 36 )131603025920.750.7716
Winsorized Mean ( 5 / 36 )131603025920.750.7716
Winsorized Mean ( 6 / 36 )131473024761.953.095
Winsorized Mean ( 7 / 36 )131322024487.853.6273
Winsorized Mean ( 8 / 36 )130628023344.955.9558
Winsorized Mean ( 9 / 36 )13043302305656.5724
Winsorized Mean ( 10 / 36 )13043302305656.5724
Winsorized Mean ( 11 / 36 )130672022684.757.6034
Winsorized Mean ( 12 / 36 )130672021919.959.6133
Winsorized Mean ( 13 / 36 )130672021919.959.6133
Winsorized Mean ( 14 / 36 )131278020221.664.92
Winsorized Mean ( 15 / 36 )131278020221.664.92
Winsorized Mean ( 16 / 36 )131278020221.664.92
Winsorized Mean ( 17 / 36 )131647019750.866.6539
Winsorized Mean ( 18 / 36 )131647019750.866.6539
Winsorized Mean ( 19 / 36 )13164701867370.5011
Winsorized Mean ( 20 / 36 )131213018088.772.5389
Winsorized Mean ( 21 / 36 )131213018088.772.5389
Winsorized Mean ( 22 / 36 )131213018088.772.5389
Winsorized Mean ( 23 / 36 )131213018088.772.5389
Winsorized Mean ( 24 / 36 )130693016125.281.0492
Winsorized Mean ( 25 / 36 )130693016125.281.0492
Winsorized Mean ( 26 / 36 )130693014769.488.4894
Winsorized Mean ( 27 / 36 )130693014769.488.4894
Winsorized Mean ( 28 / 36 )131300014081.393.2443
Winsorized Mean ( 29 / 36 )131300014081.393.2443
Winsorized Mean ( 30 / 36 )130650013299.798.2355
Winsorized Mean ( 31 / 36 )130650013299.798.2355
Winsorized Mean ( 32 / 36 )129957012498.6103.977
Winsorized Mean ( 33 / 36 )130672011698.6111.698
Winsorized Mean ( 34 / 36 )130672011698.6111.698
Winsorized Mean ( 35 / 36 )129913010842.3119.821
Winsorized Mean ( 36 / 36 )13069309993.31130.781
Trimmed Mean ( 1 / 36 )131371026390.849.7792
Trimmed Mean ( 2 / 36 )131332025582.551.3369
Trimmed Mean ( 3 / 36 )131246024772.852.9801
Trimmed Mean ( 4 / 36 )13118002402754.5971
Trimmed Mean ( 5 / 36 )131064023393.856.025
Trimmed Mean ( 6 / 36 )130942022675.357.7468
Trimmed Mean ( 7 / 36 )130841022150.559.0691
Trimmed Mean ( 8 / 36 )130760021615.460.4941
Trimmed Mean ( 9 / 36 )13078002123961.5753
Trimmed Mean ( 10 / 36 )13082702086262.7109
Trimmed Mean ( 11 / 36 )13087702042964.0643
Trimmed Mean ( 12 / 36 )130901019995.265.466
Trimmed Mean ( 13 / 36 )130926019620.866.7282
Trimmed Mean ( 14 / 36 )130952019186.568.2524
Trimmed Mean ( 15 / 36 )130920018942.669.1141
Trimmed Mean ( 16 / 36 )130886018654.970.1618
Trimmed Mean ( 17 / 36 )130850018315.671.4421
Trimmed Mean ( 18 / 36 )130780017982.972.7246
Trimmed Mean ( 19 / 36 )130706017587.774.3164
Trimmed Mean ( 20 / 36 )130627017277.875.6038
Trimmed Mean ( 21 / 36 )130579016993.276.8419
Trimmed Mean ( 22 / 36 )130528016648.378.4032
Trimmed Mean ( 23 / 36 )130474016229.980.3911
Trimmed Mean ( 24 / 36 )130416015720.582.9592
Trimmed Mean ( 25 / 36 )130394015419.884.563
Trimmed Mean ( 26 / 36 )130371015046.286.6472
Trimmed Mean ( 27 / 36 )130347014802.488.0576
Trimmed Mean ( 28 / 36 )130320014491.389.9301
Trimmed Mean ( 29 / 36 )130244014207.991.6705
Trimmed Mean ( 30 / 36 )130162013840.694.0443
Trimmed Mean ( 31 / 36 )130124013519.496.2504
Trimmed Mean ( 32 / 36 )130083013096.399.3277
Trimmed Mean ( 33 / 36 )130093012711.1102.346
Trimmed Mean ( 34 / 36 )130046012367.6105.15
Trimmed Mean ( 35 / 36 )129993011896.3109.272
Trimmed Mean ( 36 / 36 )130000011472.9113.311
Median1287000
Midrange1310400
Midmean - Weighted Average at Xnp1303710
Midmean - Weighted Average at X(n+1)p1303710
Midmean - Empirical Distribution Function1303710
Midmean - Empirical Distribution Function - Averaging1303710
Midmean - Empirical Distribution Function - Interpolation1303710
Midmean - Closest Observation1303710
Midmean - True Basic - Statistics Graphics Toolkit1303710
Midmean - MS Excel (old versions)1303710
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')