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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 computationFri, 04 Aug 2017 19:52:24 +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/04/t15018691810jp6zc3ywa36jw0.htm/, Retrieved Sun, 12 May 2024 07:00:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306920, Retrieved Sun, 12 May 2024 07:00:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact97
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [] [2017-08-04 17:52:24] [bb1ebaef39f3ee233240b5c77a617fca] [Current]
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Dataseries X:
1755000,00
1690000,00
1787500,00
1430000,00
1852500,00
1820000,00
1950000,00
2015000,00
2242500,00
1950000,00
1852500,00
2307500,00
1950000,00
1462500,00
1722500,00
1300000,00
1820000,00
1495000,00
1982500,00
1787500,00
1885000,00
2112500,00
2080000,00
2470000,00
1787500,00
1495000,00
1657500,00
1202500,00
1722500,00
1332500,00
1885000,00
1787500,00
1592500,00
2275000,00
2047500,00
2340000,00
1755000,00
1625000,00
1462500,00
1202500,00
1592500,00
1430000,00
1950000,00
1885000,00
1625000,00
2177500,00
2015000,00
2600000,00
2080000,00
1267500,00
1267500,00
1267500,00
1495000,00
1495000,00
2015000,00
1852500,00
1657500,00
2080000,00
1917500,00
2762500,00
2177500,00
1267500,00
1332500,00
1105000,00
1527500,00
1755000,00
2210000,00
2177500,00
1755000,00
2047500,00
1820000,00
2600000,00
1982500,00
1592500,00
1430000,00
1072500,00
1592500,00
1917500,00
2242500,00
2112500,00
1560000,00
2242500,00
1755000,00
2697500,00
2242500,00
1625000,00
1495000,00
1007500,00
1592500,00
1527500,00
2307500,00
2307500,00
1755000,00
2275000,00
1690000,00
2632500,00
2242500,00
1657500,00
1267500,00
877500,00
1722500,00
1657500,00
2177500,00
2502500,00
1852500,00
2080000,00
1560000,00
2697500,00




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=306920&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=306920&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306920&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=306920&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=306920&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306920&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')