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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 computationMon, 31 Jul 2017 02:59:56 +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/Jul/31/t1501462835k045e8yl4pr4eln.htm/, Retrieved Wed, 15 May 2024 17:06:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306831, Retrieved Wed, 15 May 2024 17:06:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [Reeks B stap 9 en 10] [2017-07-31 00:59:56] [5e513ceaaef205c0c6f269c0b513af8d] [Current]
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Dataseries X:
1755000
1690000
1787500
1430000
1852500
1820000
1950000
2015000
2242500
1950000
1852500
2307500
1950000
1462500
1722500
1300000
1820000
1495000
1982500
1787500
1885000
2112500
2080000
2470000
1787500
1495000
1657500
1202500
1722500
1332500
1885000
1787500
1592500
2275000
2047500
2340000
1755000
1625000
1462500
1202500
1592500
1430000
1950000
1885000
1625000
2177500
2015000
2600000
2080000
1267500
1267500
1267500
1495000
1495000
2015000
1852500
1657500
2080000
1917500
2762500
2177500
1267500
1332500
1105000
1527500
1755000
2210000
2177500
1755000
2047500
1820000
2600000
1982500
1592500
1430000
1072500
1592500
1917500
2242500
2112500
1560000
2242500
1755000
2697500
2242500
1625000
1495000
1007500
1592500
1527500
2307500
2307500
1755000
2275000
1690000
2632500
2242500
1657500
1267500
877500
1722500
1657500
2177500
2502500
1852500
2080000
1560000
2697500




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=306831&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=306831&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306831&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 Mean182451038048.947.9518
Geometric Mean1780680
Harmonic Mean1734940
Quadratic Mean1866480
Winsorized Mean ( 1 / 36 )182512037652.248.4731
Winsorized Mean ( 2 / 36 )182632037416.748.8103
Winsorized Mean ( 3 / 36 )18254203686849.5122
Winsorized Mean ( 4 / 36 )182782036000.950.7716
Winsorized Mean ( 5 / 36 )182782036000.950.7716
Winsorized Mean ( 6 / 36 )182602034391.553.095
Winsorized Mean ( 7 / 36 )182391034010.953.6273
Winsorized Mean ( 8 / 36 )181428032423.555.9558
Winsorized Mean ( 9 / 36 )181157032022.256.5724
Winsorized Mean ( 10 / 36 )181157032022.256.5724
Winsorized Mean ( 11 / 36 )181488031506.657.6034
Winsorized Mean ( 12 / 36 )181488030444.359.6133
Winsorized Mean ( 13 / 36 )181488030444.359.6133
Winsorized Mean ( 14 / 36 )182331028085.564.92
Winsorized Mean ( 15 / 36 )182331028085.564.92
Winsorized Mean ( 16 / 36 )182331028085.564.92
Winsorized Mean ( 17 / 36 )182843027431.666.6539
Winsorized Mean ( 18 / 36 )182843027431.666.6539
Winsorized Mean ( 19 / 36 )182843025934.770.5011
Winsorized Mean ( 20 / 36 )182241025123.272.5389
Winsorized Mean ( 21 / 36 )182241025123.272.5389
Winsorized Mean ( 22 / 36 )182241025123.272.5389
Winsorized Mean ( 23 / 36 )182241025123.272.5389
Winsorized Mean ( 24 / 36 )181519022396.181.0492
Winsorized Mean ( 25 / 36 )181519022396.181.0492
Winsorized Mean ( 26 / 36 )18151902051388.4894
Winsorized Mean ( 27 / 36 )18151902051388.4894
Winsorized Mean ( 28 / 36 )182361019557.493.2443
Winsorized Mean ( 29 / 36 )182361019557.493.2443
Winsorized Mean ( 30 / 36 )181458018471.898.2355
Winsorized Mean ( 31 / 36 )181458018471.898.2355
Winsorized Mean ( 32 / 36 )180495017359.2103.977
Winsorized Mean ( 33 / 36 )181488016248.1111.698
Winsorized Mean ( 34 / 36 )181488016248.1111.698
Winsorized Mean ( 35 / 36 )180435015058.8119.821
Winsorized Mean ( 36 / 36 )181519013879.6130.781
Trimmed Mean ( 1 / 36 )182460036653.849.7792
Trimmed Mean ( 2 / 36 )182406035531.251.3369
Trimmed Mean ( 3 / 36 )182287034406.652.9801
Trimmed Mean ( 4 / 36 )182195033370.854.5971
Trimmed Mean ( 5 / 36 )182033032491.456.025
Trimmed Mean ( 6 / 36 )181865031493.457.7468
Trimmed Mean ( 7 / 36 )181723030764.659.0691
Trimmed Mean ( 8 / 36 )181611030021.360.4941
Trimmed Mean ( 9 / 36 )181639029498.761.5753
Trimmed Mean ( 10 / 36 )181705028974.962.7109
Trimmed Mean ( 11 / 36 )181773028373.664.0643
Trimmed Mean ( 12 / 36 )181807027771.165.466
Trimmed Mean ( 13 / 36 )181841027251.166.7282
Trimmed Mean ( 14 / 36 )181878026647.968.2524
Trimmed Mean ( 15 / 36 )181833026309.269.1141
Trimmed Mean ( 16 / 36 )181786025909.670.1618
Trimmed Mean ( 17 / 36 )181736025438.371.4421
Trimmed Mean ( 18 / 36 )181639024976.372.7246
Trimmed Mean ( 19 / 36 )181536024427.474.3164
Trimmed Mean ( 20 / 36 )18142602399775.6038
Trimmed Mean ( 21 / 36 )181360023601.776.8419
Trimmed Mean ( 22 / 36 )181289023122.778.4032
Trimmed Mean ( 23 / 36 )181214022541.580.3911
Trimmed Mean ( 24 / 36 )18113302183482.9592
Trimmed Mean ( 25 / 36 )181103021416.484.563
Trimmed Mean ( 26 / 36 )181071020897.586.6472
Trimmed Mean ( 27 / 36 )181037020558.988.0576
Trimmed Mean ( 28 / 36 )181000020126.789.9301
Trimmed Mean ( 29 / 36 )180895019733.291.6705
Trimmed Mean ( 30 / 36 )18078101922394.0443
Trimmed Mean ( 31 / 36 )180728018776.996.2504
Trimmed Mean ( 32 / 36 )180670018189.399.3277
Trimmed Mean ( 33 / 36 )180685017654.3102.346
Trimmed Mean ( 34 / 36 )180619017177.2105.15
Trimmed Mean ( 35 / 36 )180546016522.7109.272
Trimmed Mean ( 36 / 36 )180556015934.5113.311
Median1787500
Midrange1820000
Midmean - Weighted Average at Xnp1810710
Midmean - Weighted Average at X(n+1)p1810710
Midmean - Empirical Distribution Function1810710
Midmean - Empirical Distribution Function - Averaging1810710
Midmean - Empirical Distribution Function - Interpolation1810710
Midmean - Closest Observation1810710
Midmean - True Basic - Statistics Graphics Toolkit1810710
Midmean - MS Excel (old versions)1810710
Number of observations108

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 1824510 & 38048.9 & 47.9518 \tabularnewline
Geometric Mean & 1780680 &  &  \tabularnewline
Harmonic Mean & 1734940 &  &  \tabularnewline
Quadratic Mean & 1866480 &  &  \tabularnewline
Winsorized Mean ( 1 / 36 ) & 1825120 & 37652.2 & 48.4731 \tabularnewline
Winsorized Mean ( 2 / 36 ) & 1826320 & 37416.7 & 48.8103 \tabularnewline
Winsorized Mean ( 3 / 36 ) & 1825420 & 36868 & 49.5122 \tabularnewline
Winsorized Mean ( 4 / 36 ) & 1827820 & 36000.9 & 50.7716 \tabularnewline
Winsorized Mean ( 5 / 36 ) & 1827820 & 36000.9 & 50.7716 \tabularnewline
Winsorized Mean ( 6 / 36 ) & 1826020 & 34391.5 & 53.095 \tabularnewline
Winsorized Mean ( 7 / 36 ) & 1823910 & 34010.9 & 53.6273 \tabularnewline
Winsorized Mean ( 8 / 36 ) & 1814280 & 32423.5 & 55.9558 \tabularnewline
Winsorized Mean ( 9 / 36 ) & 1811570 & 32022.2 & 56.5724 \tabularnewline
Winsorized Mean ( 10 / 36 ) & 1811570 & 32022.2 & 56.5724 \tabularnewline
Winsorized Mean ( 11 / 36 ) & 1814880 & 31506.6 & 57.6034 \tabularnewline
Winsorized Mean ( 12 / 36 ) & 1814880 & 30444.3 & 59.6133 \tabularnewline
Winsorized Mean ( 13 / 36 ) & 1814880 & 30444.3 & 59.6133 \tabularnewline
Winsorized Mean ( 14 / 36 ) & 1823310 & 28085.5 & 64.92 \tabularnewline
Winsorized Mean ( 15 / 36 ) & 1823310 & 28085.5 & 64.92 \tabularnewline
Winsorized Mean ( 16 / 36 ) & 1823310 & 28085.5 & 64.92 \tabularnewline
Winsorized Mean ( 17 / 36 ) & 1828430 & 27431.6 & 66.6539 \tabularnewline
Winsorized Mean ( 18 / 36 ) & 1828430 & 27431.6 & 66.6539 \tabularnewline
Winsorized Mean ( 19 / 36 ) & 1828430 & 25934.7 & 70.5011 \tabularnewline
Winsorized Mean ( 20 / 36 ) & 1822410 & 25123.2 & 72.5389 \tabularnewline
Winsorized Mean ( 21 / 36 ) & 1822410 & 25123.2 & 72.5389 \tabularnewline
Winsorized Mean ( 22 / 36 ) & 1822410 & 25123.2 & 72.5389 \tabularnewline
Winsorized Mean ( 23 / 36 ) & 1822410 & 25123.2 & 72.5389 \tabularnewline
Winsorized Mean ( 24 / 36 ) & 1815190 & 22396.1 & 81.0492 \tabularnewline
Winsorized Mean ( 25 / 36 ) & 1815190 & 22396.1 & 81.0492 \tabularnewline
Winsorized Mean ( 26 / 36 ) & 1815190 & 20513 & 88.4894 \tabularnewline
Winsorized Mean ( 27 / 36 ) & 1815190 & 20513 & 88.4894 \tabularnewline
Winsorized Mean ( 28 / 36 ) & 1823610 & 19557.4 & 93.2443 \tabularnewline
Winsorized Mean ( 29 / 36 ) & 1823610 & 19557.4 & 93.2443 \tabularnewline
Winsorized Mean ( 30 / 36 ) & 1814580 & 18471.8 & 98.2355 \tabularnewline
Winsorized Mean ( 31 / 36 ) & 1814580 & 18471.8 & 98.2355 \tabularnewline
Winsorized Mean ( 32 / 36 ) & 1804950 & 17359.2 & 103.977 \tabularnewline
Winsorized Mean ( 33 / 36 ) & 1814880 & 16248.1 & 111.698 \tabularnewline
Winsorized Mean ( 34 / 36 ) & 1814880 & 16248.1 & 111.698 \tabularnewline
Winsorized Mean ( 35 / 36 ) & 1804350 & 15058.8 & 119.821 \tabularnewline
Winsorized Mean ( 36 / 36 ) & 1815190 & 13879.6 & 130.781 \tabularnewline
Trimmed Mean ( 1 / 36 ) & 1824600 & 36653.8 & 49.7792 \tabularnewline
Trimmed Mean ( 2 / 36 ) & 1824060 & 35531.2 & 51.3369 \tabularnewline
Trimmed Mean ( 3 / 36 ) & 1822870 & 34406.6 & 52.9801 \tabularnewline
Trimmed Mean ( 4 / 36 ) & 1821950 & 33370.8 & 54.5971 \tabularnewline
Trimmed Mean ( 5 / 36 ) & 1820330 & 32491.4 & 56.025 \tabularnewline
Trimmed Mean ( 6 / 36 ) & 1818650 & 31493.4 & 57.7468 \tabularnewline
Trimmed Mean ( 7 / 36 ) & 1817230 & 30764.6 & 59.0691 \tabularnewline
Trimmed Mean ( 8 / 36 ) & 1816110 & 30021.3 & 60.4941 \tabularnewline
Trimmed Mean ( 9 / 36 ) & 1816390 & 29498.7 & 61.5753 \tabularnewline
Trimmed Mean ( 10 / 36 ) & 1817050 & 28974.9 & 62.7109 \tabularnewline
Trimmed Mean ( 11 / 36 ) & 1817730 & 28373.6 & 64.0643 \tabularnewline
Trimmed Mean ( 12 / 36 ) & 1818070 & 27771.1 & 65.466 \tabularnewline
Trimmed Mean ( 13 / 36 ) & 1818410 & 27251.1 & 66.7282 \tabularnewline
Trimmed Mean ( 14 / 36 ) & 1818780 & 26647.9 & 68.2524 \tabularnewline
Trimmed Mean ( 15 / 36 ) & 1818330 & 26309.2 & 69.1141 \tabularnewline
Trimmed Mean ( 16 / 36 ) & 1817860 & 25909.6 & 70.1618 \tabularnewline
Trimmed Mean ( 17 / 36 ) & 1817360 & 25438.3 & 71.4421 \tabularnewline
Trimmed Mean ( 18 / 36 ) & 1816390 & 24976.3 & 72.7246 \tabularnewline
Trimmed Mean ( 19 / 36 ) & 1815360 & 24427.4 & 74.3164 \tabularnewline
Trimmed Mean ( 20 / 36 ) & 1814260 & 23997 & 75.6038 \tabularnewline
Trimmed Mean ( 21 / 36 ) & 1813600 & 23601.7 & 76.8419 \tabularnewline
Trimmed Mean ( 22 / 36 ) & 1812890 & 23122.7 & 78.4032 \tabularnewline
Trimmed Mean ( 23 / 36 ) & 1812140 & 22541.5 & 80.3911 \tabularnewline
Trimmed Mean ( 24 / 36 ) & 1811330 & 21834 & 82.9592 \tabularnewline
Trimmed Mean ( 25 / 36 ) & 1811030 & 21416.4 & 84.563 \tabularnewline
Trimmed Mean ( 26 / 36 ) & 1810710 & 20897.5 & 86.6472 \tabularnewline
Trimmed Mean ( 27 / 36 ) & 1810370 & 20558.9 & 88.0576 \tabularnewline
Trimmed Mean ( 28 / 36 ) & 1810000 & 20126.7 & 89.9301 \tabularnewline
Trimmed Mean ( 29 / 36 ) & 1808950 & 19733.2 & 91.6705 \tabularnewline
Trimmed Mean ( 30 / 36 ) & 1807810 & 19223 & 94.0443 \tabularnewline
Trimmed Mean ( 31 / 36 ) & 1807280 & 18776.9 & 96.2504 \tabularnewline
Trimmed Mean ( 32 / 36 ) & 1806700 & 18189.3 & 99.3277 \tabularnewline
Trimmed Mean ( 33 / 36 ) & 1806850 & 17654.3 & 102.346 \tabularnewline
Trimmed Mean ( 34 / 36 ) & 1806190 & 17177.2 & 105.15 \tabularnewline
Trimmed Mean ( 35 / 36 ) & 1805460 & 16522.7 & 109.272 \tabularnewline
Trimmed Mean ( 36 / 36 ) & 1805560 & 15934.5 & 113.311 \tabularnewline
Median & 1787500 &  &  \tabularnewline
Midrange & 1820000 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 1810710 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 1810710 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 1810710 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 1810710 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 1810710 &  &  \tabularnewline
Midmean - Closest Observation & 1810710 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 1810710 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 1810710 &  &  \tabularnewline
Number of observations & 108 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306831&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]1824510[/C][C]38048.9[/C][C]47.9518[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]1780680[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]1734940[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]1866480[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 36 )[/C][C]1825120[/C][C]37652.2[/C][C]48.4731[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 36 )[/C][C]1826320[/C][C]37416.7[/C][C]48.8103[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 36 )[/C][C]1825420[/C][C]36868[/C][C]49.5122[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 36 )[/C][C]1827820[/C][C]36000.9[/C][C]50.7716[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 36 )[/C][C]1827820[/C][C]36000.9[/C][C]50.7716[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 36 )[/C][C]1826020[/C][C]34391.5[/C][C]53.095[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 36 )[/C][C]1823910[/C][C]34010.9[/C][C]53.6273[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 36 )[/C][C]1814280[/C][C]32423.5[/C][C]55.9558[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 36 )[/C][C]1811570[/C][C]32022.2[/C][C]56.5724[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 36 )[/C][C]1811570[/C][C]32022.2[/C][C]56.5724[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 36 )[/C][C]1814880[/C][C]31506.6[/C][C]57.6034[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 36 )[/C][C]1814880[/C][C]30444.3[/C][C]59.6133[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 36 )[/C][C]1814880[/C][C]30444.3[/C][C]59.6133[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 36 )[/C][C]1823310[/C][C]28085.5[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 36 )[/C][C]1823310[/C][C]28085.5[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 36 )[/C][C]1823310[/C][C]28085.5[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 36 )[/C][C]1828430[/C][C]27431.6[/C][C]66.6539[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 36 )[/C][C]1828430[/C][C]27431.6[/C][C]66.6539[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 36 )[/C][C]1828430[/C][C]25934.7[/C][C]70.5011[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 36 )[/C][C]1822410[/C][C]25123.2[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 21 / 36 )[/C][C]1822410[/C][C]25123.2[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 22 / 36 )[/C][C]1822410[/C][C]25123.2[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 23 / 36 )[/C][C]1822410[/C][C]25123.2[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 24 / 36 )[/C][C]1815190[/C][C]22396.1[/C][C]81.0492[/C][/ROW]
[ROW][C]Winsorized Mean ( 25 / 36 )[/C][C]1815190[/C][C]22396.1[/C][C]81.0492[/C][/ROW]
[ROW][C]Winsorized Mean ( 26 / 36 )[/C][C]1815190[/C][C]20513[/C][C]88.4894[/C][/ROW]
[ROW][C]Winsorized Mean ( 27 / 36 )[/C][C]1815190[/C][C]20513[/C][C]88.4894[/C][/ROW]
[ROW][C]Winsorized Mean ( 28 / 36 )[/C][C]1823610[/C][C]19557.4[/C][C]93.2443[/C][/ROW]
[ROW][C]Winsorized Mean ( 29 / 36 )[/C][C]1823610[/C][C]19557.4[/C][C]93.2443[/C][/ROW]
[ROW][C]Winsorized Mean ( 30 / 36 )[/C][C]1814580[/C][C]18471.8[/C][C]98.2355[/C][/ROW]
[ROW][C]Winsorized Mean ( 31 / 36 )[/C][C]1814580[/C][C]18471.8[/C][C]98.2355[/C][/ROW]
[ROW][C]Winsorized Mean ( 32 / 36 )[/C][C]1804950[/C][C]17359.2[/C][C]103.977[/C][/ROW]
[ROW][C]Winsorized Mean ( 33 / 36 )[/C][C]1814880[/C][C]16248.1[/C][C]111.698[/C][/ROW]
[ROW][C]Winsorized Mean ( 34 / 36 )[/C][C]1814880[/C][C]16248.1[/C][C]111.698[/C][/ROW]
[ROW][C]Winsorized Mean ( 35 / 36 )[/C][C]1804350[/C][C]15058.8[/C][C]119.821[/C][/ROW]
[ROW][C]Winsorized Mean ( 36 / 36 )[/C][C]1815190[/C][C]13879.6[/C][C]130.781[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 36 )[/C][C]1824600[/C][C]36653.8[/C][C]49.7792[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 36 )[/C][C]1824060[/C][C]35531.2[/C][C]51.3369[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 36 )[/C][C]1822870[/C][C]34406.6[/C][C]52.9801[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 36 )[/C][C]1821950[/C][C]33370.8[/C][C]54.5971[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 36 )[/C][C]1820330[/C][C]32491.4[/C][C]56.025[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 36 )[/C][C]1818650[/C][C]31493.4[/C][C]57.7468[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 36 )[/C][C]1817230[/C][C]30764.6[/C][C]59.0691[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 36 )[/C][C]1816110[/C][C]30021.3[/C][C]60.4941[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 36 )[/C][C]1816390[/C][C]29498.7[/C][C]61.5753[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 36 )[/C][C]1817050[/C][C]28974.9[/C][C]62.7109[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 36 )[/C][C]1817730[/C][C]28373.6[/C][C]64.0643[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 36 )[/C][C]1818070[/C][C]27771.1[/C][C]65.466[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 36 )[/C][C]1818410[/C][C]27251.1[/C][C]66.7282[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 36 )[/C][C]1818780[/C][C]26647.9[/C][C]68.2524[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 36 )[/C][C]1818330[/C][C]26309.2[/C][C]69.1141[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 36 )[/C][C]1817860[/C][C]25909.6[/C][C]70.1618[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 36 )[/C][C]1817360[/C][C]25438.3[/C][C]71.4421[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 36 )[/C][C]1816390[/C][C]24976.3[/C][C]72.7246[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 36 )[/C][C]1815360[/C][C]24427.4[/C][C]74.3164[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 36 )[/C][C]1814260[/C][C]23997[/C][C]75.6038[/C][/ROW]
[ROW][C]Trimmed Mean ( 21 / 36 )[/C][C]1813600[/C][C]23601.7[/C][C]76.8419[/C][/ROW]
[ROW][C]Trimmed Mean ( 22 / 36 )[/C][C]1812890[/C][C]23122.7[/C][C]78.4032[/C][/ROW]
[ROW][C]Trimmed Mean ( 23 / 36 )[/C][C]1812140[/C][C]22541.5[/C][C]80.3911[/C][/ROW]
[ROW][C]Trimmed Mean ( 24 / 36 )[/C][C]1811330[/C][C]21834[/C][C]82.9592[/C][/ROW]
[ROW][C]Trimmed Mean ( 25 / 36 )[/C][C]1811030[/C][C]21416.4[/C][C]84.563[/C][/ROW]
[ROW][C]Trimmed Mean ( 26 / 36 )[/C][C]1810710[/C][C]20897.5[/C][C]86.6472[/C][/ROW]
[ROW][C]Trimmed Mean ( 27 / 36 )[/C][C]1810370[/C][C]20558.9[/C][C]88.0576[/C][/ROW]
[ROW][C]Trimmed Mean ( 28 / 36 )[/C][C]1810000[/C][C]20126.7[/C][C]89.9301[/C][/ROW]
[ROW][C]Trimmed Mean ( 29 / 36 )[/C][C]1808950[/C][C]19733.2[/C][C]91.6705[/C][/ROW]
[ROW][C]Trimmed Mean ( 30 / 36 )[/C][C]1807810[/C][C]19223[/C][C]94.0443[/C][/ROW]
[ROW][C]Trimmed Mean ( 31 / 36 )[/C][C]1807280[/C][C]18776.9[/C][C]96.2504[/C][/ROW]
[ROW][C]Trimmed Mean ( 32 / 36 )[/C][C]1806700[/C][C]18189.3[/C][C]99.3277[/C][/ROW]
[ROW][C]Trimmed Mean ( 33 / 36 )[/C][C]1806850[/C][C]17654.3[/C][C]102.346[/C][/ROW]
[ROW][C]Trimmed Mean ( 34 / 36 )[/C][C]1806190[/C][C]17177.2[/C][C]105.15[/C][/ROW]
[ROW][C]Trimmed Mean ( 35 / 36 )[/C][C]1805460[/C][C]16522.7[/C][C]109.272[/C][/ROW]
[ROW][C]Trimmed Mean ( 36 / 36 )[/C][C]1805560[/C][C]15934.5[/C][C]113.311[/C][/ROW]
[ROW][C]Median[/C][C]1787500[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]1820000[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]1810710[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]1810710[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]1810710[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]1810710[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]1810710[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]1810710[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]1810710[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]1810710[/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=306831&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306831&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 Mean182451038048.947.9518
Geometric Mean1780680
Harmonic Mean1734940
Quadratic Mean1866480
Winsorized Mean ( 1 / 36 )182512037652.248.4731
Winsorized Mean ( 2 / 36 )182632037416.748.8103
Winsorized Mean ( 3 / 36 )18254203686849.5122
Winsorized Mean ( 4 / 36 )182782036000.950.7716
Winsorized Mean ( 5 / 36 )182782036000.950.7716
Winsorized Mean ( 6 / 36 )182602034391.553.095
Winsorized Mean ( 7 / 36 )182391034010.953.6273
Winsorized Mean ( 8 / 36 )181428032423.555.9558
Winsorized Mean ( 9 / 36 )181157032022.256.5724
Winsorized Mean ( 10 / 36 )181157032022.256.5724
Winsorized Mean ( 11 / 36 )181488031506.657.6034
Winsorized Mean ( 12 / 36 )181488030444.359.6133
Winsorized Mean ( 13 / 36 )181488030444.359.6133
Winsorized Mean ( 14 / 36 )182331028085.564.92
Winsorized Mean ( 15 / 36 )182331028085.564.92
Winsorized Mean ( 16 / 36 )182331028085.564.92
Winsorized Mean ( 17 / 36 )182843027431.666.6539
Winsorized Mean ( 18 / 36 )182843027431.666.6539
Winsorized Mean ( 19 / 36 )182843025934.770.5011
Winsorized Mean ( 20 / 36 )182241025123.272.5389
Winsorized Mean ( 21 / 36 )182241025123.272.5389
Winsorized Mean ( 22 / 36 )182241025123.272.5389
Winsorized Mean ( 23 / 36 )182241025123.272.5389
Winsorized Mean ( 24 / 36 )181519022396.181.0492
Winsorized Mean ( 25 / 36 )181519022396.181.0492
Winsorized Mean ( 26 / 36 )18151902051388.4894
Winsorized Mean ( 27 / 36 )18151902051388.4894
Winsorized Mean ( 28 / 36 )182361019557.493.2443
Winsorized Mean ( 29 / 36 )182361019557.493.2443
Winsorized Mean ( 30 / 36 )181458018471.898.2355
Winsorized Mean ( 31 / 36 )181458018471.898.2355
Winsorized Mean ( 32 / 36 )180495017359.2103.977
Winsorized Mean ( 33 / 36 )181488016248.1111.698
Winsorized Mean ( 34 / 36 )181488016248.1111.698
Winsorized Mean ( 35 / 36 )180435015058.8119.821
Winsorized Mean ( 36 / 36 )181519013879.6130.781
Trimmed Mean ( 1 / 36 )182460036653.849.7792
Trimmed Mean ( 2 / 36 )182406035531.251.3369
Trimmed Mean ( 3 / 36 )182287034406.652.9801
Trimmed Mean ( 4 / 36 )182195033370.854.5971
Trimmed Mean ( 5 / 36 )182033032491.456.025
Trimmed Mean ( 6 / 36 )181865031493.457.7468
Trimmed Mean ( 7 / 36 )181723030764.659.0691
Trimmed Mean ( 8 / 36 )181611030021.360.4941
Trimmed Mean ( 9 / 36 )181639029498.761.5753
Trimmed Mean ( 10 / 36 )181705028974.962.7109
Trimmed Mean ( 11 / 36 )181773028373.664.0643
Trimmed Mean ( 12 / 36 )181807027771.165.466
Trimmed Mean ( 13 / 36 )181841027251.166.7282
Trimmed Mean ( 14 / 36 )181878026647.968.2524
Trimmed Mean ( 15 / 36 )181833026309.269.1141
Trimmed Mean ( 16 / 36 )181786025909.670.1618
Trimmed Mean ( 17 / 36 )181736025438.371.4421
Trimmed Mean ( 18 / 36 )181639024976.372.7246
Trimmed Mean ( 19 / 36 )181536024427.474.3164
Trimmed Mean ( 20 / 36 )18142602399775.6038
Trimmed Mean ( 21 / 36 )181360023601.776.8419
Trimmed Mean ( 22 / 36 )181289023122.778.4032
Trimmed Mean ( 23 / 36 )181214022541.580.3911
Trimmed Mean ( 24 / 36 )18113302183482.9592
Trimmed Mean ( 25 / 36 )181103021416.484.563
Trimmed Mean ( 26 / 36 )181071020897.586.6472
Trimmed Mean ( 27 / 36 )181037020558.988.0576
Trimmed Mean ( 28 / 36 )181000020126.789.9301
Trimmed Mean ( 29 / 36 )180895019733.291.6705
Trimmed Mean ( 30 / 36 )18078101922394.0443
Trimmed Mean ( 31 / 36 )180728018776.996.2504
Trimmed Mean ( 32 / 36 )180670018189.399.3277
Trimmed Mean ( 33 / 36 )180685017654.3102.346
Trimmed Mean ( 34 / 36 )180619017177.2105.15
Trimmed Mean ( 35 / 36 )180546016522.7109.272
Trimmed Mean ( 36 / 36 )180556015934.5113.311
Median1787500
Midrange1820000
Midmean - Weighted Average at Xnp1810710
Midmean - Weighted Average at X(n+1)p1810710
Midmean - Empirical Distribution Function1810710
Midmean - Empirical Distribution Function - Averaging1810710
Midmean - Empirical Distribution Function - Interpolation1810710
Midmean - Closest Observation1810710
Midmean - True Basic - Statistics Graphics Toolkit1810710
Midmean - MS Excel (old versions)1810710
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')