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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 18:00:44 +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/t1502899304vpnj7jv2zc624ma.htm/, Retrieved Mon, 20 May 2024 01:29:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=307434, Retrieved Mon, 20 May 2024 01:29:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact73
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [Centrummaten: bou...] [2017-08-16 16:00:44] [de0d54ff4aa383cef5d270d23e3500df] [Current]
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Dataseries X:
336960.00
324480.00
343200.00
274560.00
355680.00
349440.00
374400.00
386880.00
430560.00
374400.00
355680.00
443040.00
374400.00
280800.00
330720.00
249600.00
349440.00
287040.00
380640.00
343200.00
361920.00
405600.00
399360.00
474240.00
343200.00
287040.00
318240.00
230880.00
330720.00
255840.00
361920.00
343200.00
305760.00
436800.00
393120.00
449280.00
336960.00
312000.00
280800.00
230880.00
305760.00
274560.00
374400.00
361920.00
312000.00
418080.00
386880.00
499200.00
399360.00
243360.00
243360.00
243360.00
287040.00
287040.00
386880.00
355680.00
318240.00
399360.00
368160.00
530400.00
418080.00
243360.00
255840.00
212160.00
293280.00
336960.00
424320.00
418080.00
336960.00
393120.00
349440.00
499200.00
380640.00
305760.00
274560.00
205920.00
305760.00
368160.00
430560.00
405600.00
299520.00
430560.00
336960.00
517920.00
430560.00
312000.00
287040.00
193440.00
305760.00
293280.00
443040.00
443040.00
336960.00
436800.00
324480.00
505440.00
430560.00
318240.00
243360.00
168480.00
330720.00
318240.00
418080.00
480480.00
355680.00
399360.00
299520.00
517920.00




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 time2 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=307434&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]2 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=307434&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307434&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 time2 seconds
R ServerBig Analytics Cloud Computing Center







Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean3503077305.3947.9518
Geometric Mean341890
Harmonic Mean333108
Quadratic Mean358365
Winsorized Mean ( 1 / 36 )3504227229.2248.4731
Winsorized Mean ( 2 / 36 )350653718448.8103
Winsorized Mean ( 3 / 36 )3504807078.6649.5122
Winsorized Mean ( 4 / 36 )3509426912.1850.7716
Winsorized Mean ( 5 / 36 )3509426912.1850.7716
Winsorized Mean ( 6 / 36 )3505966603.1853.095
Winsorized Mean ( 7 / 36 )3501916530.0953.6273
Winsorized Mean ( 8 / 36 )3483426225.3155.9558
Winsorized Mean ( 9 / 36 )3478226148.2756.5724
Winsorized Mean ( 10 / 36 )3478226148.2756.5724
Winsorized Mean ( 11 / 36 )3484586049.2657.6034
Winsorized Mean ( 12 / 36 )3484585845.359.6133
Winsorized Mean ( 13 / 36 )3484585845.359.6133
Winsorized Mean ( 14 / 36 )3500765392.4164.92
Winsorized Mean ( 15 / 36 )3500765392.4164.92
Winsorized Mean ( 16 / 36 )3500765392.4164.92
Winsorized Mean ( 17 / 36 )3510585266.8866.6539
Winsorized Mean ( 18 / 36 )3510585266.8866.6539
Winsorized Mean ( 19 / 36 )3510584979.4670.5011
Winsorized Mean ( 20 / 36 )3499024823.6572.5389
Winsorized Mean ( 21 / 36 )3499024823.6572.5389
Winsorized Mean ( 22 / 36 )3499024823.6572.5389
Winsorized Mean ( 23 / 36 )3499024823.6572.5389
Winsorized Mean ( 24 / 36 )3485164300.0581.0492
Winsorized Mean ( 25 / 36 )3485164300.0581.0492
Winsorized Mean ( 26 / 36 )3485163938.588.4894
Winsorized Mean ( 27 / 36 )3485163938.588.4894
Winsorized Mean ( 28 / 36 )3501333755.0193.2443
Winsorized Mean ( 29 / 36 )3501333755.0193.2443
Winsorized Mean ( 30 / 36 )3484003546.5898.2355
Winsorized Mean ( 31 / 36 )3484003546.5898.2355
Winsorized Mean ( 32 / 36 )3465513332.96103.977
Winsorized Mean ( 33 / 36 )3484583119.63111.698
Winsorized Mean ( 34 / 36 )3484583119.63111.698
Winsorized Mean ( 35 / 36 )3464362891.29119.821
Winsorized Mean ( 36 / 36 )3485162664.88130.781
Trimmed Mean ( 1 / 36 )3503237037.5449.7792
Trimmed Mean ( 2 / 36 )350220682251.3369
Trimmed Mean ( 3 / 36 )3499916606.0752.9801
Trimmed Mean ( 4 / 36 )3498146407.254.5971
Trimmed Mean ( 5 / 36 )3495046238.3656.025
Trimmed Mean ( 6 / 36 )3491806046.7457.7468
Trimmed Mean ( 7 / 36 )3489095906.859.0691
Trimmed Mean ( 8 / 36 )3486945764.0960.4941
Trimmed Mean ( 9 / 36 )3487475663.7561.5753
Trimmed Mean ( 10 / 36 )3488735563.1962.7109
Trimmed Mean ( 11 / 36 )3490055447.7264.0643
Trimmed Mean ( 12 / 36 )3490695332.0665.466
Trimmed Mean ( 13 / 36 )3491365232.266.7282
Trimmed Mean ( 14 / 36 )3492065116.3968.2524
Trimmed Mean ( 15 / 36 )3491205051.3669.1141
Trimmed Mean ( 16 / 36 )3490294974.6470.1618
Trimmed Mean ( 17 / 36 )3489344884.1571.4421
Trimmed Mean ( 18 / 36 )3487474795.4472.7246
Trimmed Mean ( 19 / 36 )3485494690.0674.3164
Trimmed Mean ( 20 / 36 )3483394607.4275.6038
Trimmed Mean ( 21 / 36 )3482114531.5376.8419
Trimmed Mean ( 22 / 36 )3480754439.5578.4032
Trimmed Mean ( 23 / 36 )3479304327.9780.3911
Trimmed Mean ( 24 / 36 )3477764192.1382.9592
Trimmed Mean ( 25 / 36 )3477194111.9584.563
Trimmed Mean ( 26 / 36 )3476574012.3386.6472
Trimmed Mean ( 27 / 36 )3475913947.3188.0576
Trimmed Mean ( 28 / 36 )3475203864.3389.9301
Trimmed Mean ( 29 / 36 )3473183788.7791.6705
Trimmed Mean ( 30 / 36 )3471003690.8294.0443
Trimmed Mean ( 31 / 36 )3469983605.1696.2504
Trimmed Mean ( 32 / 36 )3468873492.3599.3277
Trimmed Mean ( 33 / 36 )3469143389.62102.346
Trimmed Mean ( 34 / 36 )3467883298.02105.15
Trimmed Mean ( 35 / 36 )3466483172.35109.272
Trimmed Mean ( 36 / 36 )3466673059.43113.311
Median343200
Midrange349440
Midmean - Weighted Average at Xnp347657
Midmean - Weighted Average at X(n+1)p347657
Midmean - Empirical Distribution Function347657
Midmean - Empirical Distribution Function - Averaging347657
Midmean - Empirical Distribution Function - Interpolation347657
Midmean - Closest Observation347657
Midmean - True Basic - Statistics Graphics Toolkit347657
Midmean - MS Excel (old versions)347657
Number of observations108

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 350307 & 7305.39 & 47.9518 \tabularnewline
Geometric Mean & 341890 &  &  \tabularnewline
Harmonic Mean & 333108 &  &  \tabularnewline
Quadratic Mean & 358365 &  &  \tabularnewline
Winsorized Mean ( 1 / 36 ) & 350422 & 7229.22 & 48.4731 \tabularnewline
Winsorized Mean ( 2 / 36 ) & 350653 & 7184 & 48.8103 \tabularnewline
Winsorized Mean ( 3 / 36 ) & 350480 & 7078.66 & 49.5122 \tabularnewline
Winsorized Mean ( 4 / 36 ) & 350942 & 6912.18 & 50.7716 \tabularnewline
Winsorized Mean ( 5 / 36 ) & 350942 & 6912.18 & 50.7716 \tabularnewline
Winsorized Mean ( 6 / 36 ) & 350596 & 6603.18 & 53.095 \tabularnewline
Winsorized Mean ( 7 / 36 ) & 350191 & 6530.09 & 53.6273 \tabularnewline
Winsorized Mean ( 8 / 36 ) & 348342 & 6225.31 & 55.9558 \tabularnewline
Winsorized Mean ( 9 / 36 ) & 347822 & 6148.27 & 56.5724 \tabularnewline
Winsorized Mean ( 10 / 36 ) & 347822 & 6148.27 & 56.5724 \tabularnewline
Winsorized Mean ( 11 / 36 ) & 348458 & 6049.26 & 57.6034 \tabularnewline
Winsorized Mean ( 12 / 36 ) & 348458 & 5845.3 & 59.6133 \tabularnewline
Winsorized Mean ( 13 / 36 ) & 348458 & 5845.3 & 59.6133 \tabularnewline
Winsorized Mean ( 14 / 36 ) & 350076 & 5392.41 & 64.92 \tabularnewline
Winsorized Mean ( 15 / 36 ) & 350076 & 5392.41 & 64.92 \tabularnewline
Winsorized Mean ( 16 / 36 ) & 350076 & 5392.41 & 64.92 \tabularnewline
Winsorized Mean ( 17 / 36 ) & 351058 & 5266.88 & 66.6539 \tabularnewline
Winsorized Mean ( 18 / 36 ) & 351058 & 5266.88 & 66.6539 \tabularnewline
Winsorized Mean ( 19 / 36 ) & 351058 & 4979.46 & 70.5011 \tabularnewline
Winsorized Mean ( 20 / 36 ) & 349902 & 4823.65 & 72.5389 \tabularnewline
Winsorized Mean ( 21 / 36 ) & 349902 & 4823.65 & 72.5389 \tabularnewline
Winsorized Mean ( 22 / 36 ) & 349902 & 4823.65 & 72.5389 \tabularnewline
Winsorized Mean ( 23 / 36 ) & 349902 & 4823.65 & 72.5389 \tabularnewline
Winsorized Mean ( 24 / 36 ) & 348516 & 4300.05 & 81.0492 \tabularnewline
Winsorized Mean ( 25 / 36 ) & 348516 & 4300.05 & 81.0492 \tabularnewline
Winsorized Mean ( 26 / 36 ) & 348516 & 3938.5 & 88.4894 \tabularnewline
Winsorized Mean ( 27 / 36 ) & 348516 & 3938.5 & 88.4894 \tabularnewline
Winsorized Mean ( 28 / 36 ) & 350133 & 3755.01 & 93.2443 \tabularnewline
Winsorized Mean ( 29 / 36 ) & 350133 & 3755.01 & 93.2443 \tabularnewline
Winsorized Mean ( 30 / 36 ) & 348400 & 3546.58 & 98.2355 \tabularnewline
Winsorized Mean ( 31 / 36 ) & 348400 & 3546.58 & 98.2355 \tabularnewline
Winsorized Mean ( 32 / 36 ) & 346551 & 3332.96 & 103.977 \tabularnewline
Winsorized Mean ( 33 / 36 ) & 348458 & 3119.63 & 111.698 \tabularnewline
Winsorized Mean ( 34 / 36 ) & 348458 & 3119.63 & 111.698 \tabularnewline
Winsorized Mean ( 35 / 36 ) & 346436 & 2891.29 & 119.821 \tabularnewline
Winsorized Mean ( 36 / 36 ) & 348516 & 2664.88 & 130.781 \tabularnewline
Trimmed Mean ( 1 / 36 ) & 350323 & 7037.54 & 49.7792 \tabularnewline
Trimmed Mean ( 2 / 36 ) & 350220 & 6822 & 51.3369 \tabularnewline
Trimmed Mean ( 3 / 36 ) & 349991 & 6606.07 & 52.9801 \tabularnewline
Trimmed Mean ( 4 / 36 ) & 349814 & 6407.2 & 54.5971 \tabularnewline
Trimmed Mean ( 5 / 36 ) & 349504 & 6238.36 & 56.025 \tabularnewline
Trimmed Mean ( 6 / 36 ) & 349180 & 6046.74 & 57.7468 \tabularnewline
Trimmed Mean ( 7 / 36 ) & 348909 & 5906.8 & 59.0691 \tabularnewline
Trimmed Mean ( 8 / 36 ) & 348694 & 5764.09 & 60.4941 \tabularnewline
Trimmed Mean ( 9 / 36 ) & 348747 & 5663.75 & 61.5753 \tabularnewline
Trimmed Mean ( 10 / 36 ) & 348873 & 5563.19 & 62.7109 \tabularnewline
Trimmed Mean ( 11 / 36 ) & 349005 & 5447.72 & 64.0643 \tabularnewline
Trimmed Mean ( 12 / 36 ) & 349069 & 5332.06 & 65.466 \tabularnewline
Trimmed Mean ( 13 / 36 ) & 349136 & 5232.2 & 66.7282 \tabularnewline
Trimmed Mean ( 14 / 36 ) & 349206 & 5116.39 & 68.2524 \tabularnewline
Trimmed Mean ( 15 / 36 ) & 349120 & 5051.36 & 69.1141 \tabularnewline
Trimmed Mean ( 16 / 36 ) & 349029 & 4974.64 & 70.1618 \tabularnewline
Trimmed Mean ( 17 / 36 ) & 348934 & 4884.15 & 71.4421 \tabularnewline
Trimmed Mean ( 18 / 36 ) & 348747 & 4795.44 & 72.7246 \tabularnewline
Trimmed Mean ( 19 / 36 ) & 348549 & 4690.06 & 74.3164 \tabularnewline
Trimmed Mean ( 20 / 36 ) & 348339 & 4607.42 & 75.6038 \tabularnewline
Trimmed Mean ( 21 / 36 ) & 348211 & 4531.53 & 76.8419 \tabularnewline
Trimmed Mean ( 22 / 36 ) & 348075 & 4439.55 & 78.4032 \tabularnewline
Trimmed Mean ( 23 / 36 ) & 347930 & 4327.97 & 80.3911 \tabularnewline
Trimmed Mean ( 24 / 36 ) & 347776 & 4192.13 & 82.9592 \tabularnewline
Trimmed Mean ( 25 / 36 ) & 347719 & 4111.95 & 84.563 \tabularnewline
Trimmed Mean ( 26 / 36 ) & 347657 & 4012.33 & 86.6472 \tabularnewline
Trimmed Mean ( 27 / 36 ) & 347591 & 3947.31 & 88.0576 \tabularnewline
Trimmed Mean ( 28 / 36 ) & 347520 & 3864.33 & 89.9301 \tabularnewline
Trimmed Mean ( 29 / 36 ) & 347318 & 3788.77 & 91.6705 \tabularnewline
Trimmed Mean ( 30 / 36 ) & 347100 & 3690.82 & 94.0443 \tabularnewline
Trimmed Mean ( 31 / 36 ) & 346998 & 3605.16 & 96.2504 \tabularnewline
Trimmed Mean ( 32 / 36 ) & 346887 & 3492.35 & 99.3277 \tabularnewline
Trimmed Mean ( 33 / 36 ) & 346914 & 3389.62 & 102.346 \tabularnewline
Trimmed Mean ( 34 / 36 ) & 346788 & 3298.02 & 105.15 \tabularnewline
Trimmed Mean ( 35 / 36 ) & 346648 & 3172.35 & 109.272 \tabularnewline
Trimmed Mean ( 36 / 36 ) & 346667 & 3059.43 & 113.311 \tabularnewline
Median & 343200 &  &  \tabularnewline
Midrange & 349440 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 347657 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 347657 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 347657 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 347657 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 347657 &  &  \tabularnewline
Midmean - Closest Observation & 347657 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 347657 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 347657 &  &  \tabularnewline
Number of observations & 108 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=307434&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]350307[/C][C]7305.39[/C][C]47.9518[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]341890[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]333108[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]358365[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 36 )[/C][C]350422[/C][C]7229.22[/C][C]48.4731[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 36 )[/C][C]350653[/C][C]7184[/C][C]48.8103[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 36 )[/C][C]350480[/C][C]7078.66[/C][C]49.5122[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 36 )[/C][C]350942[/C][C]6912.18[/C][C]50.7716[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 36 )[/C][C]350942[/C][C]6912.18[/C][C]50.7716[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 36 )[/C][C]350596[/C][C]6603.18[/C][C]53.095[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 36 )[/C][C]350191[/C][C]6530.09[/C][C]53.6273[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 36 )[/C][C]348342[/C][C]6225.31[/C][C]55.9558[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 36 )[/C][C]347822[/C][C]6148.27[/C][C]56.5724[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 36 )[/C][C]347822[/C][C]6148.27[/C][C]56.5724[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 36 )[/C][C]348458[/C][C]6049.26[/C][C]57.6034[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 36 )[/C][C]348458[/C][C]5845.3[/C][C]59.6133[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 36 )[/C][C]348458[/C][C]5845.3[/C][C]59.6133[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 36 )[/C][C]350076[/C][C]5392.41[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 36 )[/C][C]350076[/C][C]5392.41[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 36 )[/C][C]350076[/C][C]5392.41[/C][C]64.92[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 36 )[/C][C]351058[/C][C]5266.88[/C][C]66.6539[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 36 )[/C][C]351058[/C][C]5266.88[/C][C]66.6539[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 36 )[/C][C]351058[/C][C]4979.46[/C][C]70.5011[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 36 )[/C][C]349902[/C][C]4823.65[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 21 / 36 )[/C][C]349902[/C][C]4823.65[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 22 / 36 )[/C][C]349902[/C][C]4823.65[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 23 / 36 )[/C][C]349902[/C][C]4823.65[/C][C]72.5389[/C][/ROW]
[ROW][C]Winsorized Mean ( 24 / 36 )[/C][C]348516[/C][C]4300.05[/C][C]81.0492[/C][/ROW]
[ROW][C]Winsorized Mean ( 25 / 36 )[/C][C]348516[/C][C]4300.05[/C][C]81.0492[/C][/ROW]
[ROW][C]Winsorized Mean ( 26 / 36 )[/C][C]348516[/C][C]3938.5[/C][C]88.4894[/C][/ROW]
[ROW][C]Winsorized Mean ( 27 / 36 )[/C][C]348516[/C][C]3938.5[/C][C]88.4894[/C][/ROW]
[ROW][C]Winsorized Mean ( 28 / 36 )[/C][C]350133[/C][C]3755.01[/C][C]93.2443[/C][/ROW]
[ROW][C]Winsorized Mean ( 29 / 36 )[/C][C]350133[/C][C]3755.01[/C][C]93.2443[/C][/ROW]
[ROW][C]Winsorized Mean ( 30 / 36 )[/C][C]348400[/C][C]3546.58[/C][C]98.2355[/C][/ROW]
[ROW][C]Winsorized Mean ( 31 / 36 )[/C][C]348400[/C][C]3546.58[/C][C]98.2355[/C][/ROW]
[ROW][C]Winsorized Mean ( 32 / 36 )[/C][C]346551[/C][C]3332.96[/C][C]103.977[/C][/ROW]
[ROW][C]Winsorized Mean ( 33 / 36 )[/C][C]348458[/C][C]3119.63[/C][C]111.698[/C][/ROW]
[ROW][C]Winsorized Mean ( 34 / 36 )[/C][C]348458[/C][C]3119.63[/C][C]111.698[/C][/ROW]
[ROW][C]Winsorized Mean ( 35 / 36 )[/C][C]346436[/C][C]2891.29[/C][C]119.821[/C][/ROW]
[ROW][C]Winsorized Mean ( 36 / 36 )[/C][C]348516[/C][C]2664.88[/C][C]130.781[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 36 )[/C][C]350323[/C][C]7037.54[/C][C]49.7792[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 36 )[/C][C]350220[/C][C]6822[/C][C]51.3369[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 36 )[/C][C]349991[/C][C]6606.07[/C][C]52.9801[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 36 )[/C][C]349814[/C][C]6407.2[/C][C]54.5971[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 36 )[/C][C]349504[/C][C]6238.36[/C][C]56.025[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 36 )[/C][C]349180[/C][C]6046.74[/C][C]57.7468[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 36 )[/C][C]348909[/C][C]5906.8[/C][C]59.0691[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 36 )[/C][C]348694[/C][C]5764.09[/C][C]60.4941[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 36 )[/C][C]348747[/C][C]5663.75[/C][C]61.5753[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 36 )[/C][C]348873[/C][C]5563.19[/C][C]62.7109[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 36 )[/C][C]349005[/C][C]5447.72[/C][C]64.0643[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 36 )[/C][C]349069[/C][C]5332.06[/C][C]65.466[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 36 )[/C][C]349136[/C][C]5232.2[/C][C]66.7282[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 36 )[/C][C]349206[/C][C]5116.39[/C][C]68.2524[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 36 )[/C][C]349120[/C][C]5051.36[/C][C]69.1141[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 36 )[/C][C]349029[/C][C]4974.64[/C][C]70.1618[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 36 )[/C][C]348934[/C][C]4884.15[/C][C]71.4421[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 36 )[/C][C]348747[/C][C]4795.44[/C][C]72.7246[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 36 )[/C][C]348549[/C][C]4690.06[/C][C]74.3164[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 36 )[/C][C]348339[/C][C]4607.42[/C][C]75.6038[/C][/ROW]
[ROW][C]Trimmed Mean ( 21 / 36 )[/C][C]348211[/C][C]4531.53[/C][C]76.8419[/C][/ROW]
[ROW][C]Trimmed Mean ( 22 / 36 )[/C][C]348075[/C][C]4439.55[/C][C]78.4032[/C][/ROW]
[ROW][C]Trimmed Mean ( 23 / 36 )[/C][C]347930[/C][C]4327.97[/C][C]80.3911[/C][/ROW]
[ROW][C]Trimmed Mean ( 24 / 36 )[/C][C]347776[/C][C]4192.13[/C][C]82.9592[/C][/ROW]
[ROW][C]Trimmed Mean ( 25 / 36 )[/C][C]347719[/C][C]4111.95[/C][C]84.563[/C][/ROW]
[ROW][C]Trimmed Mean ( 26 / 36 )[/C][C]347657[/C][C]4012.33[/C][C]86.6472[/C][/ROW]
[ROW][C]Trimmed Mean ( 27 / 36 )[/C][C]347591[/C][C]3947.31[/C][C]88.0576[/C][/ROW]
[ROW][C]Trimmed Mean ( 28 / 36 )[/C][C]347520[/C][C]3864.33[/C][C]89.9301[/C][/ROW]
[ROW][C]Trimmed Mean ( 29 / 36 )[/C][C]347318[/C][C]3788.77[/C][C]91.6705[/C][/ROW]
[ROW][C]Trimmed Mean ( 30 / 36 )[/C][C]347100[/C][C]3690.82[/C][C]94.0443[/C][/ROW]
[ROW][C]Trimmed Mean ( 31 / 36 )[/C][C]346998[/C][C]3605.16[/C][C]96.2504[/C][/ROW]
[ROW][C]Trimmed Mean ( 32 / 36 )[/C][C]346887[/C][C]3492.35[/C][C]99.3277[/C][/ROW]
[ROW][C]Trimmed Mean ( 33 / 36 )[/C][C]346914[/C][C]3389.62[/C][C]102.346[/C][/ROW]
[ROW][C]Trimmed Mean ( 34 / 36 )[/C][C]346788[/C][C]3298.02[/C][C]105.15[/C][/ROW]
[ROW][C]Trimmed Mean ( 35 / 36 )[/C][C]346648[/C][C]3172.35[/C][C]109.272[/C][/ROW]
[ROW][C]Trimmed Mean ( 36 / 36 )[/C][C]346667[/C][C]3059.43[/C][C]113.311[/C][/ROW]
[ROW][C]Median[/C][C]343200[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]349440[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]347657[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]347657[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]347657[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]347657[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]347657[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]347657[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]347657[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]347657[/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=307434&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307434&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 Mean3503077305.3947.9518
Geometric Mean341890
Harmonic Mean333108
Quadratic Mean358365
Winsorized Mean ( 1 / 36 )3504227229.2248.4731
Winsorized Mean ( 2 / 36 )350653718448.8103
Winsorized Mean ( 3 / 36 )3504807078.6649.5122
Winsorized Mean ( 4 / 36 )3509426912.1850.7716
Winsorized Mean ( 5 / 36 )3509426912.1850.7716
Winsorized Mean ( 6 / 36 )3505966603.1853.095
Winsorized Mean ( 7 / 36 )3501916530.0953.6273
Winsorized Mean ( 8 / 36 )3483426225.3155.9558
Winsorized Mean ( 9 / 36 )3478226148.2756.5724
Winsorized Mean ( 10 / 36 )3478226148.2756.5724
Winsorized Mean ( 11 / 36 )3484586049.2657.6034
Winsorized Mean ( 12 / 36 )3484585845.359.6133
Winsorized Mean ( 13 / 36 )3484585845.359.6133
Winsorized Mean ( 14 / 36 )3500765392.4164.92
Winsorized Mean ( 15 / 36 )3500765392.4164.92
Winsorized Mean ( 16 / 36 )3500765392.4164.92
Winsorized Mean ( 17 / 36 )3510585266.8866.6539
Winsorized Mean ( 18 / 36 )3510585266.8866.6539
Winsorized Mean ( 19 / 36 )3510584979.4670.5011
Winsorized Mean ( 20 / 36 )3499024823.6572.5389
Winsorized Mean ( 21 / 36 )3499024823.6572.5389
Winsorized Mean ( 22 / 36 )3499024823.6572.5389
Winsorized Mean ( 23 / 36 )3499024823.6572.5389
Winsorized Mean ( 24 / 36 )3485164300.0581.0492
Winsorized Mean ( 25 / 36 )3485164300.0581.0492
Winsorized Mean ( 26 / 36 )3485163938.588.4894
Winsorized Mean ( 27 / 36 )3485163938.588.4894
Winsorized Mean ( 28 / 36 )3501333755.0193.2443
Winsorized Mean ( 29 / 36 )3501333755.0193.2443
Winsorized Mean ( 30 / 36 )3484003546.5898.2355
Winsorized Mean ( 31 / 36 )3484003546.5898.2355
Winsorized Mean ( 32 / 36 )3465513332.96103.977
Winsorized Mean ( 33 / 36 )3484583119.63111.698
Winsorized Mean ( 34 / 36 )3484583119.63111.698
Winsorized Mean ( 35 / 36 )3464362891.29119.821
Winsorized Mean ( 36 / 36 )3485162664.88130.781
Trimmed Mean ( 1 / 36 )3503237037.5449.7792
Trimmed Mean ( 2 / 36 )350220682251.3369
Trimmed Mean ( 3 / 36 )3499916606.0752.9801
Trimmed Mean ( 4 / 36 )3498146407.254.5971
Trimmed Mean ( 5 / 36 )3495046238.3656.025
Trimmed Mean ( 6 / 36 )3491806046.7457.7468
Trimmed Mean ( 7 / 36 )3489095906.859.0691
Trimmed Mean ( 8 / 36 )3486945764.0960.4941
Trimmed Mean ( 9 / 36 )3487475663.7561.5753
Trimmed Mean ( 10 / 36 )3488735563.1962.7109
Trimmed Mean ( 11 / 36 )3490055447.7264.0643
Trimmed Mean ( 12 / 36 )3490695332.0665.466
Trimmed Mean ( 13 / 36 )3491365232.266.7282
Trimmed Mean ( 14 / 36 )3492065116.3968.2524
Trimmed Mean ( 15 / 36 )3491205051.3669.1141
Trimmed Mean ( 16 / 36 )3490294974.6470.1618
Trimmed Mean ( 17 / 36 )3489344884.1571.4421
Trimmed Mean ( 18 / 36 )3487474795.4472.7246
Trimmed Mean ( 19 / 36 )3485494690.0674.3164
Trimmed Mean ( 20 / 36 )3483394607.4275.6038
Trimmed Mean ( 21 / 36 )3482114531.5376.8419
Trimmed Mean ( 22 / 36 )3480754439.5578.4032
Trimmed Mean ( 23 / 36 )3479304327.9780.3911
Trimmed Mean ( 24 / 36 )3477764192.1382.9592
Trimmed Mean ( 25 / 36 )3477194111.9584.563
Trimmed Mean ( 26 / 36 )3476574012.3386.6472
Trimmed Mean ( 27 / 36 )3475913947.3188.0576
Trimmed Mean ( 28 / 36 )3475203864.3389.9301
Trimmed Mean ( 29 / 36 )3473183788.7791.6705
Trimmed Mean ( 30 / 36 )3471003690.8294.0443
Trimmed Mean ( 31 / 36 )3469983605.1696.2504
Trimmed Mean ( 32 / 36 )3468873492.3599.3277
Trimmed Mean ( 33 / 36 )3469143389.62102.346
Trimmed Mean ( 34 / 36 )3467883298.02105.15
Trimmed Mean ( 35 / 36 )3466483172.35109.272
Trimmed Mean ( 36 / 36 )3466673059.43113.311
Median343200
Midrange349440
Midmean - Weighted Average at Xnp347657
Midmean - Weighted Average at X(n+1)p347657
Midmean - Empirical Distribution Function347657
Midmean - Empirical Distribution Function - Averaging347657
Midmean - Empirical Distribution Function - Interpolation347657
Midmean - Closest Observation347657
Midmean - True Basic - Statistics Graphics Toolkit347657
Midmean - MS Excel (old versions)347657
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')