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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_centraltendency.wasp
Title produced by softwareCentral Tendency
Date of computationThu, 21 Dec 2017 21:17:29 +0100
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/Dec/21/t1513887466n8unj7s8c9hr7rt.htm/, Retrieved Wed, 22 May 2024 07:11:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310710, Retrieved Wed, 22 May 2024 07:11:54 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact63
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [Paper] [2017-12-21 20:17:29] [2fb711e06e7eb81d34c9e51edb934d8a] [Current]
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Dataseries X:
57000
40200
21450
21900
45000
32100
36000
21900
27900
24000
30300
28350
27750
35100
27300
40800
46000
103750
42300
26250
38850
21750
24000
16950
21150
31050
60375
32550
135000
31200
36150
110625
42000
92000
81250
31350
29100
31350
36000
19200
23550
35100
23250
29250
30750
22350
30000
30750
34800
60000
35550
45150
73750
25050
27000
26850
33900
26400
28050
30900
22500
48000
55000
53125
21900
78125
46000
45250
56550
41100
82500
54000
26400
33900
24150
29250
27600
22950
34800
51000
24300
24750
22950
25050
25950
31650
24150
72500
68750
16200
20100
24000
25950
24600
28500
30750
40200
30000
22050
78250
60625
39900
97000
27450
31650
91250
25200
21000
30450
28350
30750
30750
54875
37800
33450
30300
31500
31650
25200




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=310710&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=310710&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310710&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 Mean38685.31943.8719.9012
Geometric Mean34771.7
Harmonic Mean32093
Quadratic Mean44073
Winsorized Mean ( 1 / 39 )38486.81866.5120.6197
Winsorized Mean ( 2 / 39 )384091826.5221.0285
Winsorized Mean ( 3 / 39 )38261.61774.8421.5578
Winsorized Mean ( 4 / 39 )38123.71726.4422.0822
Winsorized Mean ( 5 / 39 )38098.51717.6222.181
Winsorized Mean ( 6 / 39 )37672.51606.1223.4555
Winsorized Mean ( 7 / 39 )37616.61587.3623.6976
Winsorized Mean ( 8 / 39 )374251540.3524.2964
Winsorized Mean ( 9 / 39 )37415.51538.2324.3238
Winsorized Mean ( 10 / 39 )37047.91457.7225.4149
Winsorized Mean ( 11 / 39 )36946.21432.0225.8001
Winsorized Mean ( 12 / 39 )36598.31351.3827.0822
Winsorized Mean ( 13 / 39 )35727.11180.3730.2677
Winsorized Mean ( 14 / 39 )35750.61170.1530.5521
Winsorized Mean ( 15 / 39 )35703.41161.7530.7324
Winsorized Mean ( 16 / 39 )35340.31088.1532.4774
Winsorized Mean ( 17 / 39 )35318.91073.3332.9058
Winsorized Mean ( 18 / 39 )35152.51028.234.1883
Winsorized Mean ( 19 / 39 )35132.61024.9434.2776
Winsorized Mean ( 20 / 39 )34985.51001.134.947
Winsorized Mean ( 21 / 39 )34857.6973.93635.7904
Winsorized Mean ( 22 / 39 )34464.7912.51537.7689
Winsorized Mean ( 23 / 39 )33913.9822.99341.2079
Winsorized Mean ( 24 / 39 )33571759.70544.1895
Winsorized Mean ( 25 / 39 )33602.5756.56644.4145
Winsorized Mean ( 26 / 39 )33504.2727.49646.0541
Winsorized Mean ( 27 / 39 )33481.5724.39546.2199
Winsorized Mean ( 28 / 39 )33481.5716.11146.7547
Winsorized Mean ( 29 / 39 )32823.5629.08252.1769
Winsorized Mean ( 30 / 39 )32937600.37554.8607
Winsorized Mean ( 31 / 39 )32702.5570.75257.2972
Winsorized Mean ( 32 / 39 )32702.5552.67859.171
Winsorized Mean ( 33 / 39 )32577.7528.04861.6946
Winsorized Mean ( 34 / 39 )32577.7528.04861.6946
Winsorized Mean ( 35 / 39 )32621.8504.27264.691
Winsorized Mean ( 36 / 39 )32349.6461.43570.1064
Winsorized Mean ( 37 / 39 )32116.4413.73577.6255
Winsorized Mean ( 38 / 39 )31637.4349.27790.5798
Winsorized Mean ( 39 / 39 )31637.4338.92593.3463
Trimmed Mean ( 1 / 39 )38054.31784.7321.3221
Trimmed Mean ( 2 / 39 )37606.71691.1822.237
Trimmed Mean ( 3 / 39 )37184.31608.9223.1113
Trimmed Mean ( 4 / 39 )36799.31537.8223.9295
Trimmed Mean ( 5 / 39 )36437.81473.6524.7263
Trimmed Mean ( 6 / 39 )36068.51402.5625.7161
Trimmed Mean ( 7 / 39 )35765.51351.0526.4724
Trimmed Mean ( 8 / 39 )354601296.4327.352
Trimmed Mean ( 9 / 39 )35170.51244.1728.2683
Trimmed Mean ( 10 / 39 )34870.71184.1529.4478
Trimmed Mean ( 11 / 39 )34603.61131.2830.5879
Trimmed Mean ( 12 / 39 )34336.81074.8631.9454
Trimmed Mean ( 13 / 39 )34095.71024.6933.2742
Trimmed Mean ( 14 / 39 )33931.6997.12534.0294
Trimmed Mean ( 15 / 39 )33757.9966.7934.9175
Trimmed Mean ( 16 / 39 )33580.5932.94635.994
Trimmed Mean ( 17 / 39 )33426.5905.55736.9126
Trimmed Mean ( 18 / 39 )33266.9875.82537.9835
Trimmed Mean ( 19 / 39 )33113848.06939.0451
Trimmed Mean ( 20 / 39 )32952.8815.8940.3888
Trimmed Mean ( 21 / 39 )32795.8781.80641.9488
Trimmed Mean ( 22 / 39 )32640745.80143.765
Trimmed Mean ( 23 / 39 )32504.8713.60345.5502
Trimmed Mean ( 24 / 39 )32402.1690.3146.9385
Trimmed Mean ( 25 / 39 )32318.1672.51548.0556
Trimmed Mean ( 26 / 39 )32226.9651.52149.464
Trimmed Mean ( 27 / 39 )32136.9630.82250.9445
Trimmed Mean ( 28 / 39 )32042.9606.18452.86
Trimmed Mean ( 29 / 39 )31942.6577.34355.3269
Trimmed Mean ( 30 / 39 )31881.4558.39857.0943
Trimmed Mean ( 31 / 39 )31807.9539.5258.9559
Trimmed Mean ( 32 / 39 )31745.5521.67260.8533
Trimmed Mean ( 33 / 39 )31678.3502.47563.0446
Trimmed Mean ( 34 / 39 )31614.7483.10765.4403
Trimmed Mean ( 35 / 39 )31545.9458.30468.8319
Trimmed Mean ( 36 / 39 )31468.1431.14972.9866
Trimmed Mean ( 37 / 39 )31403.3406.54277.245
Trimmed Mean ( 38 / 39 )31350386.20381.1749
Trimmed Mean ( 39 / 39 )31328375.38683.4555
Median30900
Midrange75600
Midmean - Weighted Average at Xnp31662.3
Midmean - Weighted Average at X(n+1)p31833.9
Midmean - Empirical Distribution Function31833.9
Midmean - Empirical Distribution Function - Averaging31833.9
Midmean - Empirical Distribution Function - Interpolation31881.4
Midmean - Closest Observation31662.3
Midmean - True Basic - Statistics Graphics Toolkit31833.9
Midmean - MS Excel (old versions)31833.9
Number of observations119

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 38685.3 & 1943.87 & 19.9012 \tabularnewline
Geometric Mean & 34771.7 &  &  \tabularnewline
Harmonic Mean & 32093 &  &  \tabularnewline
Quadratic Mean & 44073 &  &  \tabularnewline
Winsorized Mean ( 1 / 39 ) & 38486.8 & 1866.51 & 20.6197 \tabularnewline
Winsorized Mean ( 2 / 39 ) & 38409 & 1826.52 & 21.0285 \tabularnewline
Winsorized Mean ( 3 / 39 ) & 38261.6 & 1774.84 & 21.5578 \tabularnewline
Winsorized Mean ( 4 / 39 ) & 38123.7 & 1726.44 & 22.0822 \tabularnewline
Winsorized Mean ( 5 / 39 ) & 38098.5 & 1717.62 & 22.181 \tabularnewline
Winsorized Mean ( 6 / 39 ) & 37672.5 & 1606.12 & 23.4555 \tabularnewline
Winsorized Mean ( 7 / 39 ) & 37616.6 & 1587.36 & 23.6976 \tabularnewline
Winsorized Mean ( 8 / 39 ) & 37425 & 1540.35 & 24.2964 \tabularnewline
Winsorized Mean ( 9 / 39 ) & 37415.5 & 1538.23 & 24.3238 \tabularnewline
Winsorized Mean ( 10 / 39 ) & 37047.9 & 1457.72 & 25.4149 \tabularnewline
Winsorized Mean ( 11 / 39 ) & 36946.2 & 1432.02 & 25.8001 \tabularnewline
Winsorized Mean ( 12 / 39 ) & 36598.3 & 1351.38 & 27.0822 \tabularnewline
Winsorized Mean ( 13 / 39 ) & 35727.1 & 1180.37 & 30.2677 \tabularnewline
Winsorized Mean ( 14 / 39 ) & 35750.6 & 1170.15 & 30.5521 \tabularnewline
Winsorized Mean ( 15 / 39 ) & 35703.4 & 1161.75 & 30.7324 \tabularnewline
Winsorized Mean ( 16 / 39 ) & 35340.3 & 1088.15 & 32.4774 \tabularnewline
Winsorized Mean ( 17 / 39 ) & 35318.9 & 1073.33 & 32.9058 \tabularnewline
Winsorized Mean ( 18 / 39 ) & 35152.5 & 1028.2 & 34.1883 \tabularnewline
Winsorized Mean ( 19 / 39 ) & 35132.6 & 1024.94 & 34.2776 \tabularnewline
Winsorized Mean ( 20 / 39 ) & 34985.5 & 1001.1 & 34.947 \tabularnewline
Winsorized Mean ( 21 / 39 ) & 34857.6 & 973.936 & 35.7904 \tabularnewline
Winsorized Mean ( 22 / 39 ) & 34464.7 & 912.515 & 37.7689 \tabularnewline
Winsorized Mean ( 23 / 39 ) & 33913.9 & 822.993 & 41.2079 \tabularnewline
Winsorized Mean ( 24 / 39 ) & 33571 & 759.705 & 44.1895 \tabularnewline
Winsorized Mean ( 25 / 39 ) & 33602.5 & 756.566 & 44.4145 \tabularnewline
Winsorized Mean ( 26 / 39 ) & 33504.2 & 727.496 & 46.0541 \tabularnewline
Winsorized Mean ( 27 / 39 ) & 33481.5 & 724.395 & 46.2199 \tabularnewline
Winsorized Mean ( 28 / 39 ) & 33481.5 & 716.111 & 46.7547 \tabularnewline
Winsorized Mean ( 29 / 39 ) & 32823.5 & 629.082 & 52.1769 \tabularnewline
Winsorized Mean ( 30 / 39 ) & 32937 & 600.375 & 54.8607 \tabularnewline
Winsorized Mean ( 31 / 39 ) & 32702.5 & 570.752 & 57.2972 \tabularnewline
Winsorized Mean ( 32 / 39 ) & 32702.5 & 552.678 & 59.171 \tabularnewline
Winsorized Mean ( 33 / 39 ) & 32577.7 & 528.048 & 61.6946 \tabularnewline
Winsorized Mean ( 34 / 39 ) & 32577.7 & 528.048 & 61.6946 \tabularnewline
Winsorized Mean ( 35 / 39 ) & 32621.8 & 504.272 & 64.691 \tabularnewline
Winsorized Mean ( 36 / 39 ) & 32349.6 & 461.435 & 70.1064 \tabularnewline
Winsorized Mean ( 37 / 39 ) & 32116.4 & 413.735 & 77.6255 \tabularnewline
Winsorized Mean ( 38 / 39 ) & 31637.4 & 349.277 & 90.5798 \tabularnewline
Winsorized Mean ( 39 / 39 ) & 31637.4 & 338.925 & 93.3463 \tabularnewline
Trimmed Mean ( 1 / 39 ) & 38054.3 & 1784.73 & 21.3221 \tabularnewline
Trimmed Mean ( 2 / 39 ) & 37606.7 & 1691.18 & 22.237 \tabularnewline
Trimmed Mean ( 3 / 39 ) & 37184.3 & 1608.92 & 23.1113 \tabularnewline
Trimmed Mean ( 4 / 39 ) & 36799.3 & 1537.82 & 23.9295 \tabularnewline
Trimmed Mean ( 5 / 39 ) & 36437.8 & 1473.65 & 24.7263 \tabularnewline
Trimmed Mean ( 6 / 39 ) & 36068.5 & 1402.56 & 25.7161 \tabularnewline
Trimmed Mean ( 7 / 39 ) & 35765.5 & 1351.05 & 26.4724 \tabularnewline
Trimmed Mean ( 8 / 39 ) & 35460 & 1296.43 & 27.352 \tabularnewline
Trimmed Mean ( 9 / 39 ) & 35170.5 & 1244.17 & 28.2683 \tabularnewline
Trimmed Mean ( 10 / 39 ) & 34870.7 & 1184.15 & 29.4478 \tabularnewline
Trimmed Mean ( 11 / 39 ) & 34603.6 & 1131.28 & 30.5879 \tabularnewline
Trimmed Mean ( 12 / 39 ) & 34336.8 & 1074.86 & 31.9454 \tabularnewline
Trimmed Mean ( 13 / 39 ) & 34095.7 & 1024.69 & 33.2742 \tabularnewline
Trimmed Mean ( 14 / 39 ) & 33931.6 & 997.125 & 34.0294 \tabularnewline
Trimmed Mean ( 15 / 39 ) & 33757.9 & 966.79 & 34.9175 \tabularnewline
Trimmed Mean ( 16 / 39 ) & 33580.5 & 932.946 & 35.994 \tabularnewline
Trimmed Mean ( 17 / 39 ) & 33426.5 & 905.557 & 36.9126 \tabularnewline
Trimmed Mean ( 18 / 39 ) & 33266.9 & 875.825 & 37.9835 \tabularnewline
Trimmed Mean ( 19 / 39 ) & 33113 & 848.069 & 39.0451 \tabularnewline
Trimmed Mean ( 20 / 39 ) & 32952.8 & 815.89 & 40.3888 \tabularnewline
Trimmed Mean ( 21 / 39 ) & 32795.8 & 781.806 & 41.9488 \tabularnewline
Trimmed Mean ( 22 / 39 ) & 32640 & 745.801 & 43.765 \tabularnewline
Trimmed Mean ( 23 / 39 ) & 32504.8 & 713.603 & 45.5502 \tabularnewline
Trimmed Mean ( 24 / 39 ) & 32402.1 & 690.31 & 46.9385 \tabularnewline
Trimmed Mean ( 25 / 39 ) & 32318.1 & 672.515 & 48.0556 \tabularnewline
Trimmed Mean ( 26 / 39 ) & 32226.9 & 651.521 & 49.464 \tabularnewline
Trimmed Mean ( 27 / 39 ) & 32136.9 & 630.822 & 50.9445 \tabularnewline
Trimmed Mean ( 28 / 39 ) & 32042.9 & 606.184 & 52.86 \tabularnewline
Trimmed Mean ( 29 / 39 ) & 31942.6 & 577.343 & 55.3269 \tabularnewline
Trimmed Mean ( 30 / 39 ) & 31881.4 & 558.398 & 57.0943 \tabularnewline
Trimmed Mean ( 31 / 39 ) & 31807.9 & 539.52 & 58.9559 \tabularnewline
Trimmed Mean ( 32 / 39 ) & 31745.5 & 521.672 & 60.8533 \tabularnewline
Trimmed Mean ( 33 / 39 ) & 31678.3 & 502.475 & 63.0446 \tabularnewline
Trimmed Mean ( 34 / 39 ) & 31614.7 & 483.107 & 65.4403 \tabularnewline
Trimmed Mean ( 35 / 39 ) & 31545.9 & 458.304 & 68.8319 \tabularnewline
Trimmed Mean ( 36 / 39 ) & 31468.1 & 431.149 & 72.9866 \tabularnewline
Trimmed Mean ( 37 / 39 ) & 31403.3 & 406.542 & 77.245 \tabularnewline
Trimmed Mean ( 38 / 39 ) & 31350 & 386.203 & 81.1749 \tabularnewline
Trimmed Mean ( 39 / 39 ) & 31328 & 375.386 & 83.4555 \tabularnewline
Median & 30900 &  &  \tabularnewline
Midrange & 75600 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 31662.3 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 31833.9 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 31833.9 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 31833.9 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 31881.4 &  &  \tabularnewline
Midmean - Closest Observation & 31662.3 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 31833.9 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 31833.9 &  &  \tabularnewline
Number of observations & 119 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310710&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]38685.3[/C][C]1943.87[/C][C]19.9012[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]34771.7[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]32093[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]44073[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 39 )[/C][C]38486.8[/C][C]1866.51[/C][C]20.6197[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 39 )[/C][C]38409[/C][C]1826.52[/C][C]21.0285[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 39 )[/C][C]38261.6[/C][C]1774.84[/C][C]21.5578[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 39 )[/C][C]38123.7[/C][C]1726.44[/C][C]22.0822[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 39 )[/C][C]38098.5[/C][C]1717.62[/C][C]22.181[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 39 )[/C][C]37672.5[/C][C]1606.12[/C][C]23.4555[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 39 )[/C][C]37616.6[/C][C]1587.36[/C][C]23.6976[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 39 )[/C][C]37425[/C][C]1540.35[/C][C]24.2964[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 39 )[/C][C]37415.5[/C][C]1538.23[/C][C]24.3238[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 39 )[/C][C]37047.9[/C][C]1457.72[/C][C]25.4149[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 39 )[/C][C]36946.2[/C][C]1432.02[/C][C]25.8001[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 39 )[/C][C]36598.3[/C][C]1351.38[/C][C]27.0822[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 39 )[/C][C]35727.1[/C][C]1180.37[/C][C]30.2677[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 39 )[/C][C]35750.6[/C][C]1170.15[/C][C]30.5521[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 39 )[/C][C]35703.4[/C][C]1161.75[/C][C]30.7324[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 39 )[/C][C]35340.3[/C][C]1088.15[/C][C]32.4774[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 39 )[/C][C]35318.9[/C][C]1073.33[/C][C]32.9058[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 39 )[/C][C]35152.5[/C][C]1028.2[/C][C]34.1883[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 39 )[/C][C]35132.6[/C][C]1024.94[/C][C]34.2776[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 39 )[/C][C]34985.5[/C][C]1001.1[/C][C]34.947[/C][/ROW]
[ROW][C]Winsorized Mean ( 21 / 39 )[/C][C]34857.6[/C][C]973.936[/C][C]35.7904[/C][/ROW]
[ROW][C]Winsorized Mean ( 22 / 39 )[/C][C]34464.7[/C][C]912.515[/C][C]37.7689[/C][/ROW]
[ROW][C]Winsorized Mean ( 23 / 39 )[/C][C]33913.9[/C][C]822.993[/C][C]41.2079[/C][/ROW]
[ROW][C]Winsorized Mean ( 24 / 39 )[/C][C]33571[/C][C]759.705[/C][C]44.1895[/C][/ROW]
[ROW][C]Winsorized Mean ( 25 / 39 )[/C][C]33602.5[/C][C]756.566[/C][C]44.4145[/C][/ROW]
[ROW][C]Winsorized Mean ( 26 / 39 )[/C][C]33504.2[/C][C]727.496[/C][C]46.0541[/C][/ROW]
[ROW][C]Winsorized Mean ( 27 / 39 )[/C][C]33481.5[/C][C]724.395[/C][C]46.2199[/C][/ROW]
[ROW][C]Winsorized Mean ( 28 / 39 )[/C][C]33481.5[/C][C]716.111[/C][C]46.7547[/C][/ROW]
[ROW][C]Winsorized Mean ( 29 / 39 )[/C][C]32823.5[/C][C]629.082[/C][C]52.1769[/C][/ROW]
[ROW][C]Winsorized Mean ( 30 / 39 )[/C][C]32937[/C][C]600.375[/C][C]54.8607[/C][/ROW]
[ROW][C]Winsorized Mean ( 31 / 39 )[/C][C]32702.5[/C][C]570.752[/C][C]57.2972[/C][/ROW]
[ROW][C]Winsorized Mean ( 32 / 39 )[/C][C]32702.5[/C][C]552.678[/C][C]59.171[/C][/ROW]
[ROW][C]Winsorized Mean ( 33 / 39 )[/C][C]32577.7[/C][C]528.048[/C][C]61.6946[/C][/ROW]
[ROW][C]Winsorized Mean ( 34 / 39 )[/C][C]32577.7[/C][C]528.048[/C][C]61.6946[/C][/ROW]
[ROW][C]Winsorized Mean ( 35 / 39 )[/C][C]32621.8[/C][C]504.272[/C][C]64.691[/C][/ROW]
[ROW][C]Winsorized Mean ( 36 / 39 )[/C][C]32349.6[/C][C]461.435[/C][C]70.1064[/C][/ROW]
[ROW][C]Winsorized Mean ( 37 / 39 )[/C][C]32116.4[/C][C]413.735[/C][C]77.6255[/C][/ROW]
[ROW][C]Winsorized Mean ( 38 / 39 )[/C][C]31637.4[/C][C]349.277[/C][C]90.5798[/C][/ROW]
[ROW][C]Winsorized Mean ( 39 / 39 )[/C][C]31637.4[/C][C]338.925[/C][C]93.3463[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 39 )[/C][C]38054.3[/C][C]1784.73[/C][C]21.3221[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 39 )[/C][C]37606.7[/C][C]1691.18[/C][C]22.237[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 39 )[/C][C]37184.3[/C][C]1608.92[/C][C]23.1113[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 39 )[/C][C]36799.3[/C][C]1537.82[/C][C]23.9295[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 39 )[/C][C]36437.8[/C][C]1473.65[/C][C]24.7263[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 39 )[/C][C]36068.5[/C][C]1402.56[/C][C]25.7161[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 39 )[/C][C]35765.5[/C][C]1351.05[/C][C]26.4724[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 39 )[/C][C]35460[/C][C]1296.43[/C][C]27.352[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 39 )[/C][C]35170.5[/C][C]1244.17[/C][C]28.2683[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 39 )[/C][C]34870.7[/C][C]1184.15[/C][C]29.4478[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 39 )[/C][C]34603.6[/C][C]1131.28[/C][C]30.5879[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 39 )[/C][C]34336.8[/C][C]1074.86[/C][C]31.9454[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 39 )[/C][C]34095.7[/C][C]1024.69[/C][C]33.2742[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 39 )[/C][C]33931.6[/C][C]997.125[/C][C]34.0294[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 39 )[/C][C]33757.9[/C][C]966.79[/C][C]34.9175[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 39 )[/C][C]33580.5[/C][C]932.946[/C][C]35.994[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 39 )[/C][C]33426.5[/C][C]905.557[/C][C]36.9126[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 39 )[/C][C]33266.9[/C][C]875.825[/C][C]37.9835[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 39 )[/C][C]33113[/C][C]848.069[/C][C]39.0451[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 39 )[/C][C]32952.8[/C][C]815.89[/C][C]40.3888[/C][/ROW]
[ROW][C]Trimmed Mean ( 21 / 39 )[/C][C]32795.8[/C][C]781.806[/C][C]41.9488[/C][/ROW]
[ROW][C]Trimmed Mean ( 22 / 39 )[/C][C]32640[/C][C]745.801[/C][C]43.765[/C][/ROW]
[ROW][C]Trimmed Mean ( 23 / 39 )[/C][C]32504.8[/C][C]713.603[/C][C]45.5502[/C][/ROW]
[ROW][C]Trimmed Mean ( 24 / 39 )[/C][C]32402.1[/C][C]690.31[/C][C]46.9385[/C][/ROW]
[ROW][C]Trimmed Mean ( 25 / 39 )[/C][C]32318.1[/C][C]672.515[/C][C]48.0556[/C][/ROW]
[ROW][C]Trimmed Mean ( 26 / 39 )[/C][C]32226.9[/C][C]651.521[/C][C]49.464[/C][/ROW]
[ROW][C]Trimmed Mean ( 27 / 39 )[/C][C]32136.9[/C][C]630.822[/C][C]50.9445[/C][/ROW]
[ROW][C]Trimmed Mean ( 28 / 39 )[/C][C]32042.9[/C][C]606.184[/C][C]52.86[/C][/ROW]
[ROW][C]Trimmed Mean ( 29 / 39 )[/C][C]31942.6[/C][C]577.343[/C][C]55.3269[/C][/ROW]
[ROW][C]Trimmed Mean ( 30 / 39 )[/C][C]31881.4[/C][C]558.398[/C][C]57.0943[/C][/ROW]
[ROW][C]Trimmed Mean ( 31 / 39 )[/C][C]31807.9[/C][C]539.52[/C][C]58.9559[/C][/ROW]
[ROW][C]Trimmed Mean ( 32 / 39 )[/C][C]31745.5[/C][C]521.672[/C][C]60.8533[/C][/ROW]
[ROW][C]Trimmed Mean ( 33 / 39 )[/C][C]31678.3[/C][C]502.475[/C][C]63.0446[/C][/ROW]
[ROW][C]Trimmed Mean ( 34 / 39 )[/C][C]31614.7[/C][C]483.107[/C][C]65.4403[/C][/ROW]
[ROW][C]Trimmed Mean ( 35 / 39 )[/C][C]31545.9[/C][C]458.304[/C][C]68.8319[/C][/ROW]
[ROW][C]Trimmed Mean ( 36 / 39 )[/C][C]31468.1[/C][C]431.149[/C][C]72.9866[/C][/ROW]
[ROW][C]Trimmed Mean ( 37 / 39 )[/C][C]31403.3[/C][C]406.542[/C][C]77.245[/C][/ROW]
[ROW][C]Trimmed Mean ( 38 / 39 )[/C][C]31350[/C][C]386.203[/C][C]81.1749[/C][/ROW]
[ROW][C]Trimmed Mean ( 39 / 39 )[/C][C]31328[/C][C]375.386[/C][C]83.4555[/C][/ROW]
[ROW][C]Median[/C][C]30900[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]75600[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]31662.3[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]31833.9[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]31833.9[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]31833.9[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]31881.4[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]31662.3[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]31833.9[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]31833.9[/C][C][/C][C][/C][/ROW]
[ROW][C]Number of observations[/C][C]119[/C][C][/C][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310710&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310710&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 Mean38685.31943.8719.9012
Geometric Mean34771.7
Harmonic Mean32093
Quadratic Mean44073
Winsorized Mean ( 1 / 39 )38486.81866.5120.6197
Winsorized Mean ( 2 / 39 )384091826.5221.0285
Winsorized Mean ( 3 / 39 )38261.61774.8421.5578
Winsorized Mean ( 4 / 39 )38123.71726.4422.0822
Winsorized Mean ( 5 / 39 )38098.51717.6222.181
Winsorized Mean ( 6 / 39 )37672.51606.1223.4555
Winsorized Mean ( 7 / 39 )37616.61587.3623.6976
Winsorized Mean ( 8 / 39 )374251540.3524.2964
Winsorized Mean ( 9 / 39 )37415.51538.2324.3238
Winsorized Mean ( 10 / 39 )37047.91457.7225.4149
Winsorized Mean ( 11 / 39 )36946.21432.0225.8001
Winsorized Mean ( 12 / 39 )36598.31351.3827.0822
Winsorized Mean ( 13 / 39 )35727.11180.3730.2677
Winsorized Mean ( 14 / 39 )35750.61170.1530.5521
Winsorized Mean ( 15 / 39 )35703.41161.7530.7324
Winsorized Mean ( 16 / 39 )35340.31088.1532.4774
Winsorized Mean ( 17 / 39 )35318.91073.3332.9058
Winsorized Mean ( 18 / 39 )35152.51028.234.1883
Winsorized Mean ( 19 / 39 )35132.61024.9434.2776
Winsorized Mean ( 20 / 39 )34985.51001.134.947
Winsorized Mean ( 21 / 39 )34857.6973.93635.7904
Winsorized Mean ( 22 / 39 )34464.7912.51537.7689
Winsorized Mean ( 23 / 39 )33913.9822.99341.2079
Winsorized Mean ( 24 / 39 )33571759.70544.1895
Winsorized Mean ( 25 / 39 )33602.5756.56644.4145
Winsorized Mean ( 26 / 39 )33504.2727.49646.0541
Winsorized Mean ( 27 / 39 )33481.5724.39546.2199
Winsorized Mean ( 28 / 39 )33481.5716.11146.7547
Winsorized Mean ( 29 / 39 )32823.5629.08252.1769
Winsorized Mean ( 30 / 39 )32937600.37554.8607
Winsorized Mean ( 31 / 39 )32702.5570.75257.2972
Winsorized Mean ( 32 / 39 )32702.5552.67859.171
Winsorized Mean ( 33 / 39 )32577.7528.04861.6946
Winsorized Mean ( 34 / 39 )32577.7528.04861.6946
Winsorized Mean ( 35 / 39 )32621.8504.27264.691
Winsorized Mean ( 36 / 39 )32349.6461.43570.1064
Winsorized Mean ( 37 / 39 )32116.4413.73577.6255
Winsorized Mean ( 38 / 39 )31637.4349.27790.5798
Winsorized Mean ( 39 / 39 )31637.4338.92593.3463
Trimmed Mean ( 1 / 39 )38054.31784.7321.3221
Trimmed Mean ( 2 / 39 )37606.71691.1822.237
Trimmed Mean ( 3 / 39 )37184.31608.9223.1113
Trimmed Mean ( 4 / 39 )36799.31537.8223.9295
Trimmed Mean ( 5 / 39 )36437.81473.6524.7263
Trimmed Mean ( 6 / 39 )36068.51402.5625.7161
Trimmed Mean ( 7 / 39 )35765.51351.0526.4724
Trimmed Mean ( 8 / 39 )354601296.4327.352
Trimmed Mean ( 9 / 39 )35170.51244.1728.2683
Trimmed Mean ( 10 / 39 )34870.71184.1529.4478
Trimmed Mean ( 11 / 39 )34603.61131.2830.5879
Trimmed Mean ( 12 / 39 )34336.81074.8631.9454
Trimmed Mean ( 13 / 39 )34095.71024.6933.2742
Trimmed Mean ( 14 / 39 )33931.6997.12534.0294
Trimmed Mean ( 15 / 39 )33757.9966.7934.9175
Trimmed Mean ( 16 / 39 )33580.5932.94635.994
Trimmed Mean ( 17 / 39 )33426.5905.55736.9126
Trimmed Mean ( 18 / 39 )33266.9875.82537.9835
Trimmed Mean ( 19 / 39 )33113848.06939.0451
Trimmed Mean ( 20 / 39 )32952.8815.8940.3888
Trimmed Mean ( 21 / 39 )32795.8781.80641.9488
Trimmed Mean ( 22 / 39 )32640745.80143.765
Trimmed Mean ( 23 / 39 )32504.8713.60345.5502
Trimmed Mean ( 24 / 39 )32402.1690.3146.9385
Trimmed Mean ( 25 / 39 )32318.1672.51548.0556
Trimmed Mean ( 26 / 39 )32226.9651.52149.464
Trimmed Mean ( 27 / 39 )32136.9630.82250.9445
Trimmed Mean ( 28 / 39 )32042.9606.18452.86
Trimmed Mean ( 29 / 39 )31942.6577.34355.3269
Trimmed Mean ( 30 / 39 )31881.4558.39857.0943
Trimmed Mean ( 31 / 39 )31807.9539.5258.9559
Trimmed Mean ( 32 / 39 )31745.5521.67260.8533
Trimmed Mean ( 33 / 39 )31678.3502.47563.0446
Trimmed Mean ( 34 / 39 )31614.7483.10765.4403
Trimmed Mean ( 35 / 39 )31545.9458.30468.8319
Trimmed Mean ( 36 / 39 )31468.1431.14972.9866
Trimmed Mean ( 37 / 39 )31403.3406.54277.245
Trimmed Mean ( 38 / 39 )31350386.20381.1749
Trimmed Mean ( 39 / 39 )31328375.38683.4555
Median30900
Midrange75600
Midmean - Weighted Average at Xnp31662.3
Midmean - Weighted Average at X(n+1)p31833.9
Midmean - Empirical Distribution Function31833.9
Midmean - Empirical Distribution Function - Averaging31833.9
Midmean - Empirical Distribution Function - Interpolation31881.4
Midmean - Closest Observation31662.3
Midmean - True Basic - Statistics Graphics Toolkit31833.9
Midmean - MS Excel (old versions)31833.9
Number of observations119



Parameters (Session):
par1 = 0 ; par2 = no ; par3 = 512 ;
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')