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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 19:05:55 +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/t1502903173pqnvkhpvi0iemc8.htm/, Retrieved Sat, 11 May 2024 09:53:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=307468, Retrieved Sat, 11 May 2024 09:53:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact64
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [] [2017-08-16 17:05:55] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
43200
41600
44000
35200
45600
44800
48000
49600
55200
48000
45600
56800
48000
36000
42400
32000
44800
36800
48800
44000
46400
52000
51200
60800
44000
36800
40800
29600
42400
32800
46400
44000
39200
56000
50400
57600
43200
40000
36000
29600
39200
35200
48000
46400
40000
53600
49600
64000
51200
31200
31200
31200
36800
36800
49600
45600
40800
51200
47200
68000
53600
31200
32800
27200
37600
43200
54400
53600
43200
50400
44800
64000
48800
39200
35200
26400
39200
47200
55200
52000
38400
55200
43200
66400
55200
40000
36800
24800
39200
37600
56800
56800
43200
56000
41600
64800
55200
40800
31200
21600
42400
40800
53600
61600
45600
51200
38400
66400




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=307468&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=307468&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307468&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 Mean44911.1936.58947.9518
Geometric Mean43832
Harmonic Mean42706.1
Quadratic Mean45944.2
Winsorized Mean ( 1 / 36 )44925.9926.82348.4731
Winsorized Mean ( 2 / 36 )44955.6921.02648.8103
Winsorized Mean ( 3 / 36 )44933.3907.5249.5122
Winsorized Mean ( 4 / 36 )44992.6886.17750.7716
Winsorized Mean ( 5 / 36 )44992.6886.17750.7716
Winsorized Mean ( 6 / 36 )44948.1846.56153.095
Winsorized Mean ( 7 / 36 )44896.3837.19153.6273
Winsorized Mean ( 8 / 36 )44659.3798.11755.9558
Winsorized Mean ( 9 / 36 )44592.6788.2456.5724
Winsorized Mean ( 10 / 36 )44592.6788.2456.5724
Winsorized Mean ( 11 / 36 )44674.1775.54657.6034
Winsorized Mean ( 12 / 36 )44674.1749.39759.6133
Winsorized Mean ( 13 / 36 )44674.1749.39759.6133
Winsorized Mean ( 14 / 36 )44881.5691.33564.92
Winsorized Mean ( 15 / 36 )44881.5691.33564.92
Winsorized Mean ( 16 / 36 )44881.5691.33564.92
Winsorized Mean ( 17 / 36 )45007.4675.2466.6539
Winsorized Mean ( 18 / 36 )45007.4675.2466.6539
Winsorized Mean ( 19 / 36 )45007.4638.39370.5011
Winsorized Mean ( 20 / 36 )44859.3618.41672.5389
Winsorized Mean ( 21 / 36 )44859.3618.41672.5389
Winsorized Mean ( 22 / 36 )44859.3618.41672.5389
Winsorized Mean ( 23 / 36 )44859.3618.41672.5389
Winsorized Mean ( 24 / 36 )44681.5551.28881.0492
Winsorized Mean ( 25 / 36 )44681.5551.28881.0492
Winsorized Mean ( 26 / 36 )44681.5504.93688.4894
Winsorized Mean ( 27 / 36 )44681.5504.93688.4894
Winsorized Mean ( 28 / 36 )44888.9481.41293.2443
Winsorized Mean ( 29 / 36 )44888.9481.41293.2443
Winsorized Mean ( 30 / 36 )44666.7454.6998.2355
Winsorized Mean ( 31 / 36 )44666.7454.6998.2355
Winsorized Mean ( 32 / 36 )44429.6427.303103.977
Winsorized Mean ( 33 / 36 )44674.1399.953111.698
Winsorized Mean ( 34 / 36 )44674.1399.953111.698
Winsorized Mean ( 35 / 36 )44414.8370.678119.821
Winsorized Mean ( 36 / 36 )44681.5341.652130.781
Trimmed Mean ( 1 / 36 )44913.2902.24849.7792
Trimmed Mean ( 2 / 36 )44900874.61551.3369
Trimmed Mean ( 3 / 36 )44870.6846.93352.9801
Trimmed Mean ( 4 / 36 )44848821.43654.5971
Trimmed Mean ( 5 / 36 )44808.2799.78956.025
Trimmed Mean ( 6 / 36 )44766.7775.22357.7468
Trimmed Mean ( 7 / 36 )44731.9757.28259.0691
Trimmed Mean ( 8 / 36 )44704.3738.98760.4941
Trimmed Mean ( 9 / 36 )44711.1726.12161.5753
Trimmed Mean ( 10 / 36 )44727.3713.22962.7109
Trimmed Mean ( 11 / 36 )44744.2698.42664.0643
Trimmed Mean ( 12 / 36 )44752.4683.59765.466
Trimmed Mean ( 13 / 36 )44761670.79566.7282
Trimmed Mean ( 14 / 36 )44770655.94768.2524
Trimmed Mean ( 15 / 36 )44759647.6169.1141
Trimmed Mean ( 16 / 36 )44747.4637.77470.1618
Trimmed Mean ( 17 / 36 )44735.1626.17471.4421
Trimmed Mean ( 18 / 36 )44711.1614.872.7246
Trimmed Mean ( 19 / 36 )44685.7601.2974.3164
Trimmed Mean ( 20 / 36 )44658.8590.69575.6038
Trimmed Mean ( 21 / 36 )44642.4580.96576.8419
Trimmed Mean ( 22 / 36 )44625569.17378.4032
Trimmed Mean ( 23 / 36 )44606.5554.86880.3911
Trimmed Mean ( 24 / 36 )44586.7537.45382.9592
Trimmed Mean ( 25 / 36 )44579.3527.17384.563
Trimmed Mean ( 26 / 36 )44571.4514.40186.6472
Trimmed Mean ( 27 / 36 )44563506.06688.0576
Trimmed Mean ( 28 / 36 )44553.8495.42889.9301
Trimmed Mean ( 29 / 36 )44528485.7491.6705
Trimmed Mean ( 30 / 36 )44500473.18194.0443
Trimmed Mean ( 31 / 36 )44487462.296.2504
Trimmed Mean ( 32 / 36 )44472.7447.73899.3277
Trimmed Mean ( 33 / 36 )44476.2434.566102.346
Trimmed Mean ( 34 / 36 )44460422.823105.15
Trimmed Mean ( 35 / 36 )44442.1406.712109.272
Trimmed Mean ( 36 / 36 )44444.4392.235113.311
Median44000
Midrange44800
Midmean - Weighted Average at Xnp44571.4
Midmean - Weighted Average at X(n+1)p44571.4
Midmean - Empirical Distribution Function44571.4
Midmean - Empirical Distribution Function - Averaging44571.4
Midmean - Empirical Distribution Function - Interpolation44571.4
Midmean - Closest Observation44571.4
Midmean - True Basic - Statistics Graphics Toolkit44571.4
Midmean - MS Excel (old versions)44571.4
Number of observations108

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 44911.1 & 936.589 & 47.9518 \tabularnewline
Geometric Mean & 43832 &  &  \tabularnewline
Harmonic Mean & 42706.1 &  &  \tabularnewline
Quadratic Mean & 45944.2 &  &  \tabularnewline
Winsorized Mean ( 1 / 36 ) & 44925.9 & 926.823 & 48.4731 \tabularnewline
Winsorized Mean ( 2 / 36 ) & 44955.6 & 921.026 & 48.8103 \tabularnewline
Winsorized Mean ( 3 / 36 ) & 44933.3 & 907.52 & 49.5122 \tabularnewline
Winsorized Mean ( 4 / 36 ) & 44992.6 & 886.177 & 50.7716 \tabularnewline
Winsorized Mean ( 5 / 36 ) & 44992.6 & 886.177 & 50.7716 \tabularnewline
Winsorized Mean ( 6 / 36 ) & 44948.1 & 846.561 & 53.095 \tabularnewline
Winsorized Mean ( 7 / 36 ) & 44896.3 & 837.191 & 53.6273 \tabularnewline
Winsorized Mean ( 8 / 36 ) & 44659.3 & 798.117 & 55.9558 \tabularnewline
Winsorized Mean ( 9 / 36 ) & 44592.6 & 788.24 & 56.5724 \tabularnewline
Winsorized Mean ( 10 / 36 ) & 44592.6 & 788.24 & 56.5724 \tabularnewline
Winsorized Mean ( 11 / 36 ) & 44674.1 & 775.546 & 57.6034 \tabularnewline
Winsorized Mean ( 12 / 36 ) & 44674.1 & 749.397 & 59.6133 \tabularnewline
Winsorized Mean ( 13 / 36 ) & 44674.1 & 749.397 & 59.6133 \tabularnewline
Winsorized Mean ( 14 / 36 ) & 44881.5 & 691.335 & 64.92 \tabularnewline
Winsorized Mean ( 15 / 36 ) & 44881.5 & 691.335 & 64.92 \tabularnewline
Winsorized Mean ( 16 / 36 ) & 44881.5 & 691.335 & 64.92 \tabularnewline
Winsorized Mean ( 17 / 36 ) & 45007.4 & 675.24 & 66.6539 \tabularnewline
Winsorized Mean ( 18 / 36 ) & 45007.4 & 675.24 & 66.6539 \tabularnewline
Winsorized Mean ( 19 / 36 ) & 45007.4 & 638.393 & 70.5011 \tabularnewline
Winsorized Mean ( 20 / 36 ) & 44859.3 & 618.416 & 72.5389 \tabularnewline
Winsorized Mean ( 21 / 36 ) & 44859.3 & 618.416 & 72.5389 \tabularnewline
Winsorized Mean ( 22 / 36 ) & 44859.3 & 618.416 & 72.5389 \tabularnewline
Winsorized Mean ( 23 / 36 ) & 44859.3 & 618.416 & 72.5389 \tabularnewline
Winsorized Mean ( 24 / 36 ) & 44681.5 & 551.288 & 81.0492 \tabularnewline
Winsorized Mean ( 25 / 36 ) & 44681.5 & 551.288 & 81.0492 \tabularnewline
Winsorized Mean ( 26 / 36 ) & 44681.5 & 504.936 & 88.4894 \tabularnewline
Winsorized Mean ( 27 / 36 ) & 44681.5 & 504.936 & 88.4894 \tabularnewline
Winsorized Mean ( 28 / 36 ) & 44888.9 & 481.412 & 93.2443 \tabularnewline
Winsorized Mean ( 29 / 36 ) & 44888.9 & 481.412 & 93.2443 \tabularnewline
Winsorized Mean ( 30 / 36 ) & 44666.7 & 454.69 & 98.2355 \tabularnewline
Winsorized Mean ( 31 / 36 ) & 44666.7 & 454.69 & 98.2355 \tabularnewline
Winsorized Mean ( 32 / 36 ) & 44429.6 & 427.303 & 103.977 \tabularnewline
Winsorized Mean ( 33 / 36 ) & 44674.1 & 399.953 & 111.698 \tabularnewline
Winsorized Mean ( 34 / 36 ) & 44674.1 & 399.953 & 111.698 \tabularnewline
Winsorized Mean ( 35 / 36 ) & 44414.8 & 370.678 & 119.821 \tabularnewline
Winsorized Mean ( 36 / 36 ) & 44681.5 & 341.652 & 130.781 \tabularnewline
Trimmed Mean ( 1 / 36 ) & 44913.2 & 902.248 & 49.7792 \tabularnewline
Trimmed Mean ( 2 / 36 ) & 44900 & 874.615 & 51.3369 \tabularnewline
Trimmed Mean ( 3 / 36 ) & 44870.6 & 846.933 & 52.9801 \tabularnewline
Trimmed Mean ( 4 / 36 ) & 44848 & 821.436 & 54.5971 \tabularnewline
Trimmed Mean ( 5 / 36 ) & 44808.2 & 799.789 & 56.025 \tabularnewline
Trimmed Mean ( 6 / 36 ) & 44766.7 & 775.223 & 57.7468 \tabularnewline
Trimmed Mean ( 7 / 36 ) & 44731.9 & 757.282 & 59.0691 \tabularnewline
Trimmed Mean ( 8 / 36 ) & 44704.3 & 738.987 & 60.4941 \tabularnewline
Trimmed Mean ( 9 / 36 ) & 44711.1 & 726.121 & 61.5753 \tabularnewline
Trimmed Mean ( 10 / 36 ) & 44727.3 & 713.229 & 62.7109 \tabularnewline
Trimmed Mean ( 11 / 36 ) & 44744.2 & 698.426 & 64.0643 \tabularnewline
Trimmed Mean ( 12 / 36 ) & 44752.4 & 683.597 & 65.466 \tabularnewline
Trimmed Mean ( 13 / 36 ) & 44761 & 670.795 & 66.7282 \tabularnewline
Trimmed Mean ( 14 / 36 ) & 44770 & 655.947 & 68.2524 \tabularnewline
Trimmed Mean ( 15 / 36 ) & 44759 & 647.61 & 69.1141 \tabularnewline
Trimmed Mean ( 16 / 36 ) & 44747.4 & 637.774 & 70.1618 \tabularnewline
Trimmed Mean ( 17 / 36 ) & 44735.1 & 626.174 & 71.4421 \tabularnewline
Trimmed Mean ( 18 / 36 ) & 44711.1 & 614.8 & 72.7246 \tabularnewline
Trimmed Mean ( 19 / 36 ) & 44685.7 & 601.29 & 74.3164 \tabularnewline
Trimmed Mean ( 20 / 36 ) & 44658.8 & 590.695 & 75.6038 \tabularnewline
Trimmed Mean ( 21 / 36 ) & 44642.4 & 580.965 & 76.8419 \tabularnewline
Trimmed Mean ( 22 / 36 ) & 44625 & 569.173 & 78.4032 \tabularnewline
Trimmed Mean ( 23 / 36 ) & 44606.5 & 554.868 & 80.3911 \tabularnewline
Trimmed Mean ( 24 / 36 ) & 44586.7 & 537.453 & 82.9592 \tabularnewline
Trimmed Mean ( 25 / 36 ) & 44579.3 & 527.173 & 84.563 \tabularnewline
Trimmed Mean ( 26 / 36 ) & 44571.4 & 514.401 & 86.6472 \tabularnewline
Trimmed Mean ( 27 / 36 ) & 44563 & 506.066 & 88.0576 \tabularnewline
Trimmed Mean ( 28 / 36 ) & 44553.8 & 495.428 & 89.9301 \tabularnewline
Trimmed Mean ( 29 / 36 ) & 44528 & 485.74 & 91.6705 \tabularnewline
Trimmed Mean ( 30 / 36 ) & 44500 & 473.181 & 94.0443 \tabularnewline
Trimmed Mean ( 31 / 36 ) & 44487 & 462.2 & 96.2504 \tabularnewline
Trimmed Mean ( 32 / 36 ) & 44472.7 & 447.738 & 99.3277 \tabularnewline
Trimmed Mean ( 33 / 36 ) & 44476.2 & 434.566 & 102.346 \tabularnewline
Trimmed Mean ( 34 / 36 ) & 44460 & 422.823 & 105.15 \tabularnewline
Trimmed Mean ( 35 / 36 ) & 44442.1 & 406.712 & 109.272 \tabularnewline
Trimmed Mean ( 36 / 36 ) & 44444.4 & 392.235 & 113.311 \tabularnewline
Median & 44000 &  &  \tabularnewline
Midrange & 44800 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 44571.4 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 44571.4 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 44571.4 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 44571.4 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 44571.4 &  &  \tabularnewline
Midmean - Closest Observation & 44571.4 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 44571.4 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 44571.4 &  &  \tabularnewline
Number of observations & 108 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=307468&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]44911.1[/C][C]936.589[/C][C]47.9518[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]43832[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]42706.1[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]45944.2[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 36 )[/C][C]44925.9[/C][C]926.823[/C][C]48.4731[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 36 )[/C][C]44955.6[/C][C]921.026[/C][C]48.8103[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 36 )[/C][C]44933.3[/C][C]907.52[/C][C]49.5122[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 36 )[/C][C]44992.6[/C][C]886.177[/C][C]50.7716[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 36 )[/C][C]44992.6[/C][C]886.177[/C][C]50.7716[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 36 )[/C][C]44948.1[/C][C]846.561[/C][C]53.095[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 36 )[/C][C]44896.3[/C][C]837.191[/C][C]53.6273[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 36 )[/C][C]44659.3[/C][C]798.117[/C][C]55.9558[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 36 )[/C][C]44592.6[/C][C]788.24[/C][C]56.5724[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 36 )[/C][C]44592.6[/C][C]788.24[/C][C]56.5724[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 36 )[/C][C]44674.1[/C][C]775.546[/C][C]57.6034[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 36 )[/C][C]44674.1[/C][C]749.397[/C][C]59.6133[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 36 )[/C][C]44674.1[/C][C]749.397[/C][C]59.6133[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 36 )[/C][C]44881.5[/C][C]691.335[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 36 )[/C][C]44881.5[/C][C]691.335[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 36 )[/C][C]44881.5[/C][C]691.335[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 36 )[/C][C]45007.4[/C][C]675.24[/C][C]66.6539[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 36 )[/C][C]45007.4[/C][C]675.24[/C][C]66.6539[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 36 )[/C][C]45007.4[/C][C]638.393[/C][C]70.5011[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 36 )[/C][C]44859.3[/C][C]618.416[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 21 / 36 )[/C][C]44859.3[/C][C]618.416[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 22 / 36 )[/C][C]44859.3[/C][C]618.416[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 23 / 36 )[/C][C]44859.3[/C][C]618.416[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 24 / 36 )[/C][C]44681.5[/C][C]551.288[/C][C]81.0492[/C][/ROW]
[ROW][C]Winsorized Mean ( 25 / 36 )[/C][C]44681.5[/C][C]551.288[/C][C]81.0492[/C][/ROW]
[ROW][C]Winsorized Mean ( 26 / 36 )[/C][C]44681.5[/C][C]504.936[/C][C]88.4894[/C][/ROW]
[ROW][C]Winsorized Mean ( 27 / 36 )[/C][C]44681.5[/C][C]504.936[/C][C]88.4894[/C][/ROW]
[ROW][C]Winsorized Mean ( 28 / 36 )[/C][C]44888.9[/C][C]481.412[/C][C]93.2443[/C][/ROW]
[ROW][C]Winsorized Mean ( 29 / 36 )[/C][C]44888.9[/C][C]481.412[/C][C]93.2443[/C][/ROW]
[ROW][C]Winsorized Mean ( 30 / 36 )[/C][C]44666.7[/C][C]454.69[/C][C]98.2355[/C][/ROW]
[ROW][C]Winsorized Mean ( 31 / 36 )[/C][C]44666.7[/C][C]454.69[/C][C]98.2355[/C][/ROW]
[ROW][C]Winsorized Mean ( 32 / 36 )[/C][C]44429.6[/C][C]427.303[/C][C]103.977[/C][/ROW]
[ROW][C]Winsorized Mean ( 33 / 36 )[/C][C]44674.1[/C][C]399.953[/C][C]111.698[/C][/ROW]
[ROW][C]Winsorized Mean ( 34 / 36 )[/C][C]44674.1[/C][C]399.953[/C][C]111.698[/C][/ROW]
[ROW][C]Winsorized Mean ( 35 / 36 )[/C][C]44414.8[/C][C]370.678[/C][C]119.821[/C][/ROW]
[ROW][C]Winsorized Mean ( 36 / 36 )[/C][C]44681.5[/C][C]341.652[/C][C]130.781[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 36 )[/C][C]44913.2[/C][C]902.248[/C][C]49.7792[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 36 )[/C][C]44900[/C][C]874.615[/C][C]51.3369[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 36 )[/C][C]44870.6[/C][C]846.933[/C][C]52.9801[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 36 )[/C][C]44848[/C][C]821.436[/C][C]54.5971[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 36 )[/C][C]44808.2[/C][C]799.789[/C][C]56.025[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 36 )[/C][C]44766.7[/C][C]775.223[/C][C]57.7468[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 36 )[/C][C]44731.9[/C][C]757.282[/C][C]59.0691[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 36 )[/C][C]44704.3[/C][C]738.987[/C][C]60.4941[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 36 )[/C][C]44711.1[/C][C]726.121[/C][C]61.5753[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 36 )[/C][C]44727.3[/C][C]713.229[/C][C]62.7109[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 36 )[/C][C]44744.2[/C][C]698.426[/C][C]64.0643[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 36 )[/C][C]44752.4[/C][C]683.597[/C][C]65.466[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 36 )[/C][C]44761[/C][C]670.795[/C][C]66.7282[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 36 )[/C][C]44770[/C][C]655.947[/C][C]68.2524[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 36 )[/C][C]44759[/C][C]647.61[/C][C]69.1141[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 36 )[/C][C]44747.4[/C][C]637.774[/C][C]70.1618[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 36 )[/C][C]44735.1[/C][C]626.174[/C][C]71.4421[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 36 )[/C][C]44711.1[/C][C]614.8[/C][C]72.7246[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 36 )[/C][C]44685.7[/C][C]601.29[/C][C]74.3164[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 36 )[/C][C]44658.8[/C][C]590.695[/C][C]75.6038[/C][/ROW]
[ROW][C]Trimmed Mean ( 21 / 36 )[/C][C]44642.4[/C][C]580.965[/C][C]76.8419[/C][/ROW]
[ROW][C]Trimmed Mean ( 22 / 36 )[/C][C]44625[/C][C]569.173[/C][C]78.4032[/C][/ROW]
[ROW][C]Trimmed Mean ( 23 / 36 )[/C][C]44606.5[/C][C]554.868[/C][C]80.3911[/C][/ROW]
[ROW][C]Trimmed Mean ( 24 / 36 )[/C][C]44586.7[/C][C]537.453[/C][C]82.9592[/C][/ROW]
[ROW][C]Trimmed Mean ( 25 / 36 )[/C][C]44579.3[/C][C]527.173[/C][C]84.563[/C][/ROW]
[ROW][C]Trimmed Mean ( 26 / 36 )[/C][C]44571.4[/C][C]514.401[/C][C]86.6472[/C][/ROW]
[ROW][C]Trimmed Mean ( 27 / 36 )[/C][C]44563[/C][C]506.066[/C][C]88.0576[/C][/ROW]
[ROW][C]Trimmed Mean ( 28 / 36 )[/C][C]44553.8[/C][C]495.428[/C][C]89.9301[/C][/ROW]
[ROW][C]Trimmed Mean ( 29 / 36 )[/C][C]44528[/C][C]485.74[/C][C]91.6705[/C][/ROW]
[ROW][C]Trimmed Mean ( 30 / 36 )[/C][C]44500[/C][C]473.181[/C][C]94.0443[/C][/ROW]
[ROW][C]Trimmed Mean ( 31 / 36 )[/C][C]44487[/C][C]462.2[/C][C]96.2504[/C][/ROW]
[ROW][C]Trimmed Mean ( 32 / 36 )[/C][C]44472.7[/C][C]447.738[/C][C]99.3277[/C][/ROW]
[ROW][C]Trimmed Mean ( 33 / 36 )[/C][C]44476.2[/C][C]434.566[/C][C]102.346[/C][/ROW]
[ROW][C]Trimmed Mean ( 34 / 36 )[/C][C]44460[/C][C]422.823[/C][C]105.15[/C][/ROW]
[ROW][C]Trimmed Mean ( 35 / 36 )[/C][C]44442.1[/C][C]406.712[/C][C]109.272[/C][/ROW]
[ROW][C]Trimmed Mean ( 36 / 36 )[/C][C]44444.4[/C][C]392.235[/C][C]113.311[/C][/ROW]
[ROW][C]Median[/C][C]44000[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]44800[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]44571.4[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]44571.4[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]44571.4[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]44571.4[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]44571.4[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]44571.4[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]44571.4[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]44571.4[/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=307468&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307468&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 Mean44911.1936.58947.9518
Geometric Mean43832
Harmonic Mean42706.1
Quadratic Mean45944.2
Winsorized Mean ( 1 / 36 )44925.9926.82348.4731
Winsorized Mean ( 2 / 36 )44955.6921.02648.8103
Winsorized Mean ( 3 / 36 )44933.3907.5249.5122
Winsorized Mean ( 4 / 36 )44992.6886.17750.7716
Winsorized Mean ( 5 / 36 )44992.6886.17750.7716
Winsorized Mean ( 6 / 36 )44948.1846.56153.095
Winsorized Mean ( 7 / 36 )44896.3837.19153.6273
Winsorized Mean ( 8 / 36 )44659.3798.11755.9558
Winsorized Mean ( 9 / 36 )44592.6788.2456.5724
Winsorized Mean ( 10 / 36 )44592.6788.2456.5724
Winsorized Mean ( 11 / 36 )44674.1775.54657.6034
Winsorized Mean ( 12 / 36 )44674.1749.39759.6133
Winsorized Mean ( 13 / 36 )44674.1749.39759.6133
Winsorized Mean ( 14 / 36 )44881.5691.33564.92
Winsorized Mean ( 15 / 36 )44881.5691.33564.92
Winsorized Mean ( 16 / 36 )44881.5691.33564.92
Winsorized Mean ( 17 / 36 )45007.4675.2466.6539
Winsorized Mean ( 18 / 36 )45007.4675.2466.6539
Winsorized Mean ( 19 / 36 )45007.4638.39370.5011
Winsorized Mean ( 20 / 36 )44859.3618.41672.5389
Winsorized Mean ( 21 / 36 )44859.3618.41672.5389
Winsorized Mean ( 22 / 36 )44859.3618.41672.5389
Winsorized Mean ( 23 / 36 )44859.3618.41672.5389
Winsorized Mean ( 24 / 36 )44681.5551.28881.0492
Winsorized Mean ( 25 / 36 )44681.5551.28881.0492
Winsorized Mean ( 26 / 36 )44681.5504.93688.4894
Winsorized Mean ( 27 / 36 )44681.5504.93688.4894
Winsorized Mean ( 28 / 36 )44888.9481.41293.2443
Winsorized Mean ( 29 / 36 )44888.9481.41293.2443
Winsorized Mean ( 30 / 36 )44666.7454.6998.2355
Winsorized Mean ( 31 / 36 )44666.7454.6998.2355
Winsorized Mean ( 32 / 36 )44429.6427.303103.977
Winsorized Mean ( 33 / 36 )44674.1399.953111.698
Winsorized Mean ( 34 / 36 )44674.1399.953111.698
Winsorized Mean ( 35 / 36 )44414.8370.678119.821
Winsorized Mean ( 36 / 36 )44681.5341.652130.781
Trimmed Mean ( 1 / 36 )44913.2902.24849.7792
Trimmed Mean ( 2 / 36 )44900874.61551.3369
Trimmed Mean ( 3 / 36 )44870.6846.93352.9801
Trimmed Mean ( 4 / 36 )44848821.43654.5971
Trimmed Mean ( 5 / 36 )44808.2799.78956.025
Trimmed Mean ( 6 / 36 )44766.7775.22357.7468
Trimmed Mean ( 7 / 36 )44731.9757.28259.0691
Trimmed Mean ( 8 / 36 )44704.3738.98760.4941
Trimmed Mean ( 9 / 36 )44711.1726.12161.5753
Trimmed Mean ( 10 / 36 )44727.3713.22962.7109
Trimmed Mean ( 11 / 36 )44744.2698.42664.0643
Trimmed Mean ( 12 / 36 )44752.4683.59765.466
Trimmed Mean ( 13 / 36 )44761670.79566.7282
Trimmed Mean ( 14 / 36 )44770655.94768.2524
Trimmed Mean ( 15 / 36 )44759647.6169.1141
Trimmed Mean ( 16 / 36 )44747.4637.77470.1618
Trimmed Mean ( 17 / 36 )44735.1626.17471.4421
Trimmed Mean ( 18 / 36 )44711.1614.872.7246
Trimmed Mean ( 19 / 36 )44685.7601.2974.3164
Trimmed Mean ( 20 / 36 )44658.8590.69575.6038
Trimmed Mean ( 21 / 36 )44642.4580.96576.8419
Trimmed Mean ( 22 / 36 )44625569.17378.4032
Trimmed Mean ( 23 / 36 )44606.5554.86880.3911
Trimmed Mean ( 24 / 36 )44586.7537.45382.9592
Trimmed Mean ( 25 / 36 )44579.3527.17384.563
Trimmed Mean ( 26 / 36 )44571.4514.40186.6472
Trimmed Mean ( 27 / 36 )44563506.06688.0576
Trimmed Mean ( 28 / 36 )44553.8495.42889.9301
Trimmed Mean ( 29 / 36 )44528485.7491.6705
Trimmed Mean ( 30 / 36 )44500473.18194.0443
Trimmed Mean ( 31 / 36 )44487462.296.2504
Trimmed Mean ( 32 / 36 )44472.7447.73899.3277
Trimmed Mean ( 33 / 36 )44476.2434.566102.346
Trimmed Mean ( 34 / 36 )44460422.823105.15
Trimmed Mean ( 35 / 36 )44442.1406.712109.272
Trimmed Mean ( 36 / 36 )44444.4392.235113.311
Median44000
Midrange44800
Midmean - Weighted Average at Xnp44571.4
Midmean - Weighted Average at X(n+1)p44571.4
Midmean - Empirical Distribution Function44571.4
Midmean - Empirical Distribution Function - Averaging44571.4
Midmean - Empirical Distribution Function - Interpolation44571.4
Midmean - Closest Observation44571.4
Midmean - True Basic - Statistics Graphics Toolkit44571.4
Midmean - MS Excel (old versions)44571.4
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')