Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_centraltendency.wasp
Title produced by softwareCentral Tendency
Date of computationMon, 20 Oct 2008 12:01:54 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Oct/20/t1224525799wpkqd7ntv82b6ys.htm/, Retrieved Sun, 19 May 2024 15:57:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=17814, Retrieved Sun, 19 May 2024 15:57:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact165
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Harrell-Davis Quantiles] [Q7 95% confidence...] [2007-10-20 15:02:46] [b731da8b544846036771bbf9bf2f34ce]
-    D  [Harrell-Davis Quantiles] [Harrell - Davis Q...] [2008-10-18 17:45:28] [b943bd7078334192ff8343563ee31113]
F RMPD      [Central Tendency] [Central Tendency ...] [2008-10-20 18:01:54] [620b6ad5c4696049e39cb73ce029682c] [Current]
Feedback Forum
2008-10-23 19:40:05 [Ciska Tanghe] [reply
Aangezien de waarde van de mediaan en de mid-range dicht bij elkaar liggen, kunnen we concluderen dat er weinig of geen extreme waarden zijn.
2008-10-27 11:38:25 [Michael Van Spaandonck] [reply
We zien een plots stijgende tendens in de grafiek van winsorized mean. Deze stijging komt ook terug in de grafiek van de trimmed mean, zij het minder plots. Beide vertonen aanvankelijk echter een licht dalend verloop en de trimmed mean begint sneller met stijgen dan de winsorized mean.

Verder sluit ik me aan bij de opmerking in de eerste post.

2008-10-27 20:21:55 [Joren Nuyts] [reply
Bij de winsorized mean merken we naar het einde toe plots een kleine stijging die ook te merken is in de trimmed mean.
Bij de trimmed mean is er een lichte stijging over het algemeen. We kunnen dus als voorspelling stellen dat het naar de toekomst toe een lichte, stijgende tendens zal zijn.


Post a new message
Dataseries X:
1045,9
1401,9
1027,6
1703,8
1481,3
1422,7
1304,7
1246,1
1417,8
1459,1
1156,4
1304,5
1336,9
1372,3
975,5
1180,8
1361,3
1428,1
1355,9
1781,2
1697
1852
1844,1
1967,2
1747,1
1863,9
1559,3
1675
2237,5
1965,2
1871,5
1752,2
1360,7
1444,3
1621,6
1368
1553,9
1695,3
1397,1
1848,4
1809,2
1551,1
1546,6
1467,9
1662,4
1972,3
1673,5
1762
2019,8
1754,3
1400,4
1453,6
1740,9
1694,6
1541,2
1482,3
1632,1
1837,3
1797
2066,2
1983,8
1601,7
1660,3
1954
1991,9
1881,4
2345,5
1773,1
1719,2
2240,9
1816,4
2171,3
1823,3
2022,5
1991
1920
2168,4
2013,5
1790,8
1855,7
2074
2535,8
1837,2
1805,1
1785,7
2250
1959,7
1890,8
2405,7
2090,3
1666,5
1803,5
1793,8
1488,8
1545
1369,9
1451,6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=17814&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=17814&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=17814&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean1705.4422680412431.237447576411054.5960825982819
Geometric Mean1677.09101893079
Harmonic Mean1647.58025843055
Quadratic Mean1732.68809374515
Winsorized Mean ( 1 / 32 )1704.6381443299030.765408700611755.4076222720877
Winsorized Mean ( 2 / 32 )1703.7742268041230.395755826222156.0530304475697
Winsorized Mean ( 3 / 32 )1704.2381443299029.059325629147558.6468580200115
Winsorized Mean ( 4 / 32 )1704.8690721649528.792029839943259.2132295514568
Winsorized Mean ( 5 / 32 )1708.0597938144328.150727013700760.6755126779899
Winsorized Mean ( 6 / 32 )1707.5773195876326.787124915197863.7461961667572
Winsorized Mean ( 7 / 32 )1707.3824742268026.747205510098863.8340507602618
Winsorized Mean ( 8 / 32 )1703.5969072164925.243823691455967.4856918681895
Winsorized Mean ( 9 / 32 )1703.8474226804124.742755113601468.8624777175193
Winsorized Mean ( 10 / 32 )1703.5381443299024.54597553656169.401932784969
Winsorized Mean ( 11 / 32 )1698.6505154639223.803025727223471.3627979455226
Winsorized Mean ( 12 / 32 )1699.1453608247423.634080284694671.8938642992216
Winsorized Mean ( 13 / 32 )1698.5556701030923.478276680339972.3458409332666
Winsorized Mean ( 14 / 32 )1695.7845360824723.000710822172173.727483867489
Winsorized Mean ( 15 / 32 )1699.4804123711322.431639309119875.7626488617884
Winsorized Mean ( 16 / 32 )1698.8371134020622.195495102253776.5397259928465
Winsorized Mean ( 17 / 32 )1697.0845360824721.891866317610877.5212360362929
Winsorized Mean ( 18 / 32 )1699.0886597938121.358368958384379.5514237582654
Winsorized Mean ( 19 / 32 )1699.6567010309321.175952293975780.263530888028
Winsorized Mean ( 20 / 32 )1699.6360824742320.87703815314981.411743850162
Winsorized Mean ( 21 / 32 )1701.9092783505220.246870281376484.0578941188742
Winsorized Mean ( 22 / 32 )1695.8536082474219.053238683816389.0060548964765
Winsorized Mean ( 23 / 32 )1689.4041237113418.164898254681693.0037757451207
Winsorized Mean ( 24 / 32 )1688.4391752577317.714852522869295.312065007486
Winsorized Mean ( 25 / 32 )1688.1556701030917.122863699562198.590732235185
Winsorized Mean ( 26 / 32 )1689.7103092783516.4185098799011102.914961323429
Winsorized Mean ( 27 / 32 )1687.7061855670116.1317058843416104.620441115604
Winsorized Mean ( 28 / 32 )1688.5144329896915.7678829903851107.085677514179
Winsorized Mean ( 29 / 32 )1703.1041237113413.6437798880611124.826414504211
Winsorized Mean ( 30 / 32 )1702.9494845360813.3517350028111127.545183017604
Winsorized Mean ( 31 / 32 )1701.2876288659813.051027370138130.356605699770
Winsorized Mean ( 32 / 32 )1702.7391752577312.8646553341225132.357931948736
Trimmed Mean ( 1 / 32 )1704.3852631578929.675418695164357.4342448430435
Trimmed Mean ( 2 / 32 )1704.1215053763428.427180777228759.946904996693
Trimmed Mean ( 3 / 32 )1704.3065934065927.222947782410662.605512343075
Trimmed Mean ( 4 / 32 )1704.3314606741626.43128338846964.4816006708814
Trimmed Mean ( 5 / 32 )1704.1816091954025.616019202811666.5279642282729
Trimmed Mean ( 6 / 32 )1703.2964705882424.867177102570168.4957710946691
Trimmed Mean ( 7 / 32 )1703.2964705882424.354989708219269.9362426752912
Trimmed Mean ( 8 / 32 )1701.6209876543223.771398528746671.5827041306198
Trimmed Mean ( 9 / 32 )1701.3177215189923.412305716017372.6676706752144
Trimmed Mean ( 10 / 32 )1700.9636363636423.087841034920673.6735684289801
Trimmed Mean ( 11 / 32 )1700.6306666666722.741057046223274.7823930615882
Trimmed Mean ( 12 / 32 )1700.869863013722.460283963621875.7278877581664
Trimmed Mean ( 13 / 32 )1701.066197183122.152494801494276.788922079709
Trimmed Mean ( 14 / 32 )1701.066197183121.808924366371577.998628846
Trimmed Mean ( 15 / 32 )1701.9119402985121.472994675345179.2582481405172
Trimmed Mean ( 16 / 32 )1702.1538461538521.159034608462980.445723429794
Trimmed Mean ( 17 / 32 )1702.4730158730220.813372213798981.7970773013087
Trimmed Mean ( 18 / 32 )1702.9770491803320.438493281414983.3220446210132
Trimmed Mean ( 19 / 32 )1703.3322033898320.065293687307684.8894728347424
Trimmed Mean ( 20 / 32 )1703.6614035087719.633361165157586.7738024670062
Trimmed Mean ( 21 / 32 )1704.0163636363619.147837525684488.9926270447322
Trimmed Mean ( 22 / 32 )1704.218.653646086937591.3601551169875
Trimmed Mean ( 23 / 32 )1704.9215686274518.242061999401493.4610116270517
Trimmed Mean ( 24 / 32 )1706.2571428571417.866306934265695.5013898023171
Trimmed Mean ( 25 / 32 )1706.2571428571417.457467339288897.7379541771866
Trimmed Mean ( 26 / 32 )1709.4822222222217.0326621135877100.364946525798
Trimmed Mean ( 27 / 32 )1711.1976744186016.6120213208063103.009600178839
Trimmed Mean ( 28 / 32 )1711.1976744186016.0967657139111106.306925554602
Trimmed Mean ( 29 / 32 )1715.4538461538515.4796204215781110.820149295299
Trimmed Mean ( 30 / 32 )1716.5702702702715.1823163813588113.063792582923
Trimmed Mean ( 31 / 32 )1716.5702702702714.8174899900031115.847574145715
Trimmed Mean ( 32 / 32 )1719.3969696969714.3548181680327119.778387268322
Median1740.9
Midrange1755.65
Midmean - Weighted Average at Xnp1702.60833333333
Midmean - Weighted Average at X(n+1)p1706.25714285714
Midmean - Empirical Distribution Function1706.25714285714
Midmean - Empirical Distribution Function - Averaging1706.25714285714
Midmean - Empirical Distribution Function - Interpolation1706.25714285714
Midmean - Closest Observation1701.204
Midmean - True Basic - Statistics Graphics Toolkit1706.25714285714
Midmean - MS Excel (old versions)1706.25714285714
Number of observations97

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 1705.44226804124 & 31.2374475764110 & 54.5960825982819 \tabularnewline
Geometric Mean & 1677.09101893079 &  &  \tabularnewline
Harmonic Mean & 1647.58025843055 &  &  \tabularnewline
Quadratic Mean & 1732.68809374515 &  &  \tabularnewline
Winsorized Mean ( 1 / 32 ) & 1704.63814432990 & 30.7654087006117 & 55.4076222720877 \tabularnewline
Winsorized Mean ( 2 / 32 ) & 1703.77422680412 & 30.3957558262221 & 56.0530304475697 \tabularnewline
Winsorized Mean ( 3 / 32 ) & 1704.23814432990 & 29.0593256291475 & 58.6468580200115 \tabularnewline
Winsorized Mean ( 4 / 32 ) & 1704.86907216495 & 28.7920298399432 & 59.2132295514568 \tabularnewline
Winsorized Mean ( 5 / 32 ) & 1708.05979381443 & 28.1507270137007 & 60.6755126779899 \tabularnewline
Winsorized Mean ( 6 / 32 ) & 1707.57731958763 & 26.7871249151978 & 63.7461961667572 \tabularnewline
Winsorized Mean ( 7 / 32 ) & 1707.38247422680 & 26.7472055100988 & 63.8340507602618 \tabularnewline
Winsorized Mean ( 8 / 32 ) & 1703.59690721649 & 25.2438236914559 & 67.4856918681895 \tabularnewline
Winsorized Mean ( 9 / 32 ) & 1703.84742268041 & 24.7427551136014 & 68.8624777175193 \tabularnewline
Winsorized Mean ( 10 / 32 ) & 1703.53814432990 & 24.545975536561 & 69.401932784969 \tabularnewline
Winsorized Mean ( 11 / 32 ) & 1698.65051546392 & 23.8030257272234 & 71.3627979455226 \tabularnewline
Winsorized Mean ( 12 / 32 ) & 1699.14536082474 & 23.6340802846946 & 71.8938642992216 \tabularnewline
Winsorized Mean ( 13 / 32 ) & 1698.55567010309 & 23.4782766803399 & 72.3458409332666 \tabularnewline
Winsorized Mean ( 14 / 32 ) & 1695.78453608247 & 23.0007108221721 & 73.727483867489 \tabularnewline
Winsorized Mean ( 15 / 32 ) & 1699.48041237113 & 22.4316393091198 & 75.7626488617884 \tabularnewline
Winsorized Mean ( 16 / 32 ) & 1698.83711340206 & 22.1954951022537 & 76.5397259928465 \tabularnewline
Winsorized Mean ( 17 / 32 ) & 1697.08453608247 & 21.8918663176108 & 77.5212360362929 \tabularnewline
Winsorized Mean ( 18 / 32 ) & 1699.08865979381 & 21.3583689583843 & 79.5514237582654 \tabularnewline
Winsorized Mean ( 19 / 32 ) & 1699.65670103093 & 21.1759522939757 & 80.263530888028 \tabularnewline
Winsorized Mean ( 20 / 32 ) & 1699.63608247423 & 20.877038153149 & 81.411743850162 \tabularnewline
Winsorized Mean ( 21 / 32 ) & 1701.90927835052 & 20.2468702813764 & 84.0578941188742 \tabularnewline
Winsorized Mean ( 22 / 32 ) & 1695.85360824742 & 19.0532386838163 & 89.0060548964765 \tabularnewline
Winsorized Mean ( 23 / 32 ) & 1689.40412371134 & 18.1648982546816 & 93.0037757451207 \tabularnewline
Winsorized Mean ( 24 / 32 ) & 1688.43917525773 & 17.7148525228692 & 95.312065007486 \tabularnewline
Winsorized Mean ( 25 / 32 ) & 1688.15567010309 & 17.1228636995621 & 98.590732235185 \tabularnewline
Winsorized Mean ( 26 / 32 ) & 1689.71030927835 & 16.4185098799011 & 102.914961323429 \tabularnewline
Winsorized Mean ( 27 / 32 ) & 1687.70618556701 & 16.1317058843416 & 104.620441115604 \tabularnewline
Winsorized Mean ( 28 / 32 ) & 1688.51443298969 & 15.7678829903851 & 107.085677514179 \tabularnewline
Winsorized Mean ( 29 / 32 ) & 1703.10412371134 & 13.6437798880611 & 124.826414504211 \tabularnewline
Winsorized Mean ( 30 / 32 ) & 1702.94948453608 & 13.3517350028111 & 127.545183017604 \tabularnewline
Winsorized Mean ( 31 / 32 ) & 1701.28762886598 & 13.051027370138 & 130.356605699770 \tabularnewline
Winsorized Mean ( 32 / 32 ) & 1702.73917525773 & 12.8646553341225 & 132.357931948736 \tabularnewline
Trimmed Mean ( 1 / 32 ) & 1704.38526315789 & 29.6754186951643 & 57.4342448430435 \tabularnewline
Trimmed Mean ( 2 / 32 ) & 1704.12150537634 & 28.4271807772287 & 59.946904996693 \tabularnewline
Trimmed Mean ( 3 / 32 ) & 1704.30659340659 & 27.2229477824106 & 62.605512343075 \tabularnewline
Trimmed Mean ( 4 / 32 ) & 1704.33146067416 & 26.431283388469 & 64.4816006708814 \tabularnewline
Trimmed Mean ( 5 / 32 ) & 1704.18160919540 & 25.6160192028116 & 66.5279642282729 \tabularnewline
Trimmed Mean ( 6 / 32 ) & 1703.29647058824 & 24.8671771025701 & 68.4957710946691 \tabularnewline
Trimmed Mean ( 7 / 32 ) & 1703.29647058824 & 24.3549897082192 & 69.9362426752912 \tabularnewline
Trimmed Mean ( 8 / 32 ) & 1701.62098765432 & 23.7713985287466 & 71.5827041306198 \tabularnewline
Trimmed Mean ( 9 / 32 ) & 1701.31772151899 & 23.4123057160173 & 72.6676706752144 \tabularnewline
Trimmed Mean ( 10 / 32 ) & 1700.96363636364 & 23.0878410349206 & 73.6735684289801 \tabularnewline
Trimmed Mean ( 11 / 32 ) & 1700.63066666667 & 22.7410570462232 & 74.7823930615882 \tabularnewline
Trimmed Mean ( 12 / 32 ) & 1700.8698630137 & 22.4602839636218 & 75.7278877581664 \tabularnewline
Trimmed Mean ( 13 / 32 ) & 1701.0661971831 & 22.1524948014942 & 76.788922079709 \tabularnewline
Trimmed Mean ( 14 / 32 ) & 1701.0661971831 & 21.8089243663715 & 77.998628846 \tabularnewline
Trimmed Mean ( 15 / 32 ) & 1701.91194029851 & 21.4729946753451 & 79.2582481405172 \tabularnewline
Trimmed Mean ( 16 / 32 ) & 1702.15384615385 & 21.1590346084629 & 80.445723429794 \tabularnewline
Trimmed Mean ( 17 / 32 ) & 1702.47301587302 & 20.8133722137989 & 81.7970773013087 \tabularnewline
Trimmed Mean ( 18 / 32 ) & 1702.97704918033 & 20.4384932814149 & 83.3220446210132 \tabularnewline
Trimmed Mean ( 19 / 32 ) & 1703.33220338983 & 20.0652936873076 & 84.8894728347424 \tabularnewline
Trimmed Mean ( 20 / 32 ) & 1703.66140350877 & 19.6333611651575 & 86.7738024670062 \tabularnewline
Trimmed Mean ( 21 / 32 ) & 1704.01636363636 & 19.1478375256844 & 88.9926270447322 \tabularnewline
Trimmed Mean ( 22 / 32 ) & 1704.2 & 18.6536460869375 & 91.3601551169875 \tabularnewline
Trimmed Mean ( 23 / 32 ) & 1704.92156862745 & 18.2420619994014 & 93.4610116270517 \tabularnewline
Trimmed Mean ( 24 / 32 ) & 1706.25714285714 & 17.8663069342656 & 95.5013898023171 \tabularnewline
Trimmed Mean ( 25 / 32 ) & 1706.25714285714 & 17.4574673392888 & 97.7379541771866 \tabularnewline
Trimmed Mean ( 26 / 32 ) & 1709.48222222222 & 17.0326621135877 & 100.364946525798 \tabularnewline
Trimmed Mean ( 27 / 32 ) & 1711.19767441860 & 16.6120213208063 & 103.009600178839 \tabularnewline
Trimmed Mean ( 28 / 32 ) & 1711.19767441860 & 16.0967657139111 & 106.306925554602 \tabularnewline
Trimmed Mean ( 29 / 32 ) & 1715.45384615385 & 15.4796204215781 & 110.820149295299 \tabularnewline
Trimmed Mean ( 30 / 32 ) & 1716.57027027027 & 15.1823163813588 & 113.063792582923 \tabularnewline
Trimmed Mean ( 31 / 32 ) & 1716.57027027027 & 14.8174899900031 & 115.847574145715 \tabularnewline
Trimmed Mean ( 32 / 32 ) & 1719.39696969697 & 14.3548181680327 & 119.778387268322 \tabularnewline
Median & 1740.9 &  &  \tabularnewline
Midrange & 1755.65 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 1702.60833333333 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 1706.25714285714 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 1706.25714285714 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 1706.25714285714 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 1706.25714285714 &  &  \tabularnewline
Midmean - Closest Observation & 1701.204 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 1706.25714285714 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 1706.25714285714 &  &  \tabularnewline
Number of observations & 97 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=17814&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]1705.44226804124[/C][C]31.2374475764110[/C][C]54.5960825982819[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]1677.09101893079[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]1647.58025843055[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]1732.68809374515[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 32 )[/C][C]1704.63814432990[/C][C]30.7654087006117[/C][C]55.4076222720877[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 32 )[/C][C]1703.77422680412[/C][C]30.3957558262221[/C][C]56.0530304475697[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 32 )[/C][C]1704.23814432990[/C][C]29.0593256291475[/C][C]58.6468580200115[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 32 )[/C][C]1704.86907216495[/C][C]28.7920298399432[/C][C]59.2132295514568[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 32 )[/C][C]1708.05979381443[/C][C]28.1507270137007[/C][C]60.6755126779899[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 32 )[/C][C]1707.57731958763[/C][C]26.7871249151978[/C][C]63.7461961667572[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 32 )[/C][C]1707.38247422680[/C][C]26.7472055100988[/C][C]63.8340507602618[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 32 )[/C][C]1703.59690721649[/C][C]25.2438236914559[/C][C]67.4856918681895[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 32 )[/C][C]1703.84742268041[/C][C]24.7427551136014[/C][C]68.8624777175193[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 32 )[/C][C]1703.53814432990[/C][C]24.545975536561[/C][C]69.401932784969[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 32 )[/C][C]1698.65051546392[/C][C]23.8030257272234[/C][C]71.3627979455226[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 32 )[/C][C]1699.14536082474[/C][C]23.6340802846946[/C][C]71.8938642992216[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 32 )[/C][C]1698.55567010309[/C][C]23.4782766803399[/C][C]72.3458409332666[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 32 )[/C][C]1695.78453608247[/C][C]23.0007108221721[/C][C]73.727483867489[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 32 )[/C][C]1699.48041237113[/C][C]22.4316393091198[/C][C]75.7626488617884[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 32 )[/C][C]1698.83711340206[/C][C]22.1954951022537[/C][C]76.5397259928465[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 32 )[/C][C]1697.08453608247[/C][C]21.8918663176108[/C][C]77.5212360362929[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 32 )[/C][C]1699.08865979381[/C][C]21.3583689583843[/C][C]79.5514237582654[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 32 )[/C][C]1699.65670103093[/C][C]21.1759522939757[/C][C]80.263530888028[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 32 )[/C][C]1699.63608247423[/C][C]20.877038153149[/C][C]81.411743850162[/C][/ROW]
[ROW][C]Winsorized Mean ( 21 / 32 )[/C][C]1701.90927835052[/C][C]20.2468702813764[/C][C]84.0578941188742[/C][/ROW]
[ROW][C]Winsorized Mean ( 22 / 32 )[/C][C]1695.85360824742[/C][C]19.0532386838163[/C][C]89.0060548964765[/C][/ROW]
[ROW][C]Winsorized Mean ( 23 / 32 )[/C][C]1689.40412371134[/C][C]18.1648982546816[/C][C]93.0037757451207[/C][/ROW]
[ROW][C]Winsorized Mean ( 24 / 32 )[/C][C]1688.43917525773[/C][C]17.7148525228692[/C][C]95.312065007486[/C][/ROW]
[ROW][C]Winsorized Mean ( 25 / 32 )[/C][C]1688.15567010309[/C][C]17.1228636995621[/C][C]98.590732235185[/C][/ROW]
[ROW][C]Winsorized Mean ( 26 / 32 )[/C][C]1689.71030927835[/C][C]16.4185098799011[/C][C]102.914961323429[/C][/ROW]
[ROW][C]Winsorized Mean ( 27 / 32 )[/C][C]1687.70618556701[/C][C]16.1317058843416[/C][C]104.620441115604[/C][/ROW]
[ROW][C]Winsorized Mean ( 28 / 32 )[/C][C]1688.51443298969[/C][C]15.7678829903851[/C][C]107.085677514179[/C][/ROW]
[ROW][C]Winsorized Mean ( 29 / 32 )[/C][C]1703.10412371134[/C][C]13.6437798880611[/C][C]124.826414504211[/C][/ROW]
[ROW][C]Winsorized Mean ( 30 / 32 )[/C][C]1702.94948453608[/C][C]13.3517350028111[/C][C]127.545183017604[/C][/ROW]
[ROW][C]Winsorized Mean ( 31 / 32 )[/C][C]1701.28762886598[/C][C]13.051027370138[/C][C]130.356605699770[/C][/ROW]
[ROW][C]Winsorized Mean ( 32 / 32 )[/C][C]1702.73917525773[/C][C]12.8646553341225[/C][C]132.357931948736[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 32 )[/C][C]1704.38526315789[/C][C]29.6754186951643[/C][C]57.4342448430435[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 32 )[/C][C]1704.12150537634[/C][C]28.4271807772287[/C][C]59.946904996693[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 32 )[/C][C]1704.30659340659[/C][C]27.2229477824106[/C][C]62.605512343075[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 32 )[/C][C]1704.33146067416[/C][C]26.431283388469[/C][C]64.4816006708814[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 32 )[/C][C]1704.18160919540[/C][C]25.6160192028116[/C][C]66.5279642282729[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 32 )[/C][C]1703.29647058824[/C][C]24.8671771025701[/C][C]68.4957710946691[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 32 )[/C][C]1703.29647058824[/C][C]24.3549897082192[/C][C]69.9362426752912[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 32 )[/C][C]1701.62098765432[/C][C]23.7713985287466[/C][C]71.5827041306198[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 32 )[/C][C]1701.31772151899[/C][C]23.4123057160173[/C][C]72.6676706752144[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 32 )[/C][C]1700.96363636364[/C][C]23.0878410349206[/C][C]73.6735684289801[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 32 )[/C][C]1700.63066666667[/C][C]22.7410570462232[/C][C]74.7823930615882[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 32 )[/C][C]1700.8698630137[/C][C]22.4602839636218[/C][C]75.7278877581664[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 32 )[/C][C]1701.0661971831[/C][C]22.1524948014942[/C][C]76.788922079709[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 32 )[/C][C]1701.0661971831[/C][C]21.8089243663715[/C][C]77.998628846[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 32 )[/C][C]1701.91194029851[/C][C]21.4729946753451[/C][C]79.2582481405172[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 32 )[/C][C]1702.15384615385[/C][C]21.1590346084629[/C][C]80.445723429794[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 32 )[/C][C]1702.47301587302[/C][C]20.8133722137989[/C][C]81.7970773013087[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 32 )[/C][C]1702.97704918033[/C][C]20.4384932814149[/C][C]83.3220446210132[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 32 )[/C][C]1703.33220338983[/C][C]20.0652936873076[/C][C]84.8894728347424[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 32 )[/C][C]1703.66140350877[/C][C]19.6333611651575[/C][C]86.7738024670062[/C][/ROW]
[ROW][C]Trimmed Mean ( 21 / 32 )[/C][C]1704.01636363636[/C][C]19.1478375256844[/C][C]88.9926270447322[/C][/ROW]
[ROW][C]Trimmed Mean ( 22 / 32 )[/C][C]1704.2[/C][C]18.6536460869375[/C][C]91.3601551169875[/C][/ROW]
[ROW][C]Trimmed Mean ( 23 / 32 )[/C][C]1704.92156862745[/C][C]18.2420619994014[/C][C]93.4610116270517[/C][/ROW]
[ROW][C]Trimmed Mean ( 24 / 32 )[/C][C]1706.25714285714[/C][C]17.8663069342656[/C][C]95.5013898023171[/C][/ROW]
[ROW][C]Trimmed Mean ( 25 / 32 )[/C][C]1706.25714285714[/C][C]17.4574673392888[/C][C]97.7379541771866[/C][/ROW]
[ROW][C]Trimmed Mean ( 26 / 32 )[/C][C]1709.48222222222[/C][C]17.0326621135877[/C][C]100.364946525798[/C][/ROW]
[ROW][C]Trimmed Mean ( 27 / 32 )[/C][C]1711.19767441860[/C][C]16.6120213208063[/C][C]103.009600178839[/C][/ROW]
[ROW][C]Trimmed Mean ( 28 / 32 )[/C][C]1711.19767441860[/C][C]16.0967657139111[/C][C]106.306925554602[/C][/ROW]
[ROW][C]Trimmed Mean ( 29 / 32 )[/C][C]1715.45384615385[/C][C]15.4796204215781[/C][C]110.820149295299[/C][/ROW]
[ROW][C]Trimmed Mean ( 30 / 32 )[/C][C]1716.57027027027[/C][C]15.1823163813588[/C][C]113.063792582923[/C][/ROW]
[ROW][C]Trimmed Mean ( 31 / 32 )[/C][C]1716.57027027027[/C][C]14.8174899900031[/C][C]115.847574145715[/C][/ROW]
[ROW][C]Trimmed Mean ( 32 / 32 )[/C][C]1719.39696969697[/C][C]14.3548181680327[/C][C]119.778387268322[/C][/ROW]
[ROW][C]Median[/C][C]1740.9[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]1755.65[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]1702.60833333333[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]1706.25714285714[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]1706.25714285714[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]1706.25714285714[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]1706.25714285714[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]1701.204[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]1706.25714285714[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]1706.25714285714[/C][C][/C][C][/C][/ROW]
[ROW][C]Number of observations[/C][C]97[/C][C][/C][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=17814&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=17814&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 Mean1705.4422680412431.237447576411054.5960825982819
Geometric Mean1677.09101893079
Harmonic Mean1647.58025843055
Quadratic Mean1732.68809374515
Winsorized Mean ( 1 / 32 )1704.6381443299030.765408700611755.4076222720877
Winsorized Mean ( 2 / 32 )1703.7742268041230.395755826222156.0530304475697
Winsorized Mean ( 3 / 32 )1704.2381443299029.059325629147558.6468580200115
Winsorized Mean ( 4 / 32 )1704.8690721649528.792029839943259.2132295514568
Winsorized Mean ( 5 / 32 )1708.0597938144328.150727013700760.6755126779899
Winsorized Mean ( 6 / 32 )1707.5773195876326.787124915197863.7461961667572
Winsorized Mean ( 7 / 32 )1707.3824742268026.747205510098863.8340507602618
Winsorized Mean ( 8 / 32 )1703.5969072164925.243823691455967.4856918681895
Winsorized Mean ( 9 / 32 )1703.8474226804124.742755113601468.8624777175193
Winsorized Mean ( 10 / 32 )1703.5381443299024.54597553656169.401932784969
Winsorized Mean ( 11 / 32 )1698.6505154639223.803025727223471.3627979455226
Winsorized Mean ( 12 / 32 )1699.1453608247423.634080284694671.8938642992216
Winsorized Mean ( 13 / 32 )1698.5556701030923.478276680339972.3458409332666
Winsorized Mean ( 14 / 32 )1695.7845360824723.000710822172173.727483867489
Winsorized Mean ( 15 / 32 )1699.4804123711322.431639309119875.7626488617884
Winsorized Mean ( 16 / 32 )1698.8371134020622.195495102253776.5397259928465
Winsorized Mean ( 17 / 32 )1697.0845360824721.891866317610877.5212360362929
Winsorized Mean ( 18 / 32 )1699.0886597938121.358368958384379.5514237582654
Winsorized Mean ( 19 / 32 )1699.6567010309321.175952293975780.263530888028
Winsorized Mean ( 20 / 32 )1699.6360824742320.87703815314981.411743850162
Winsorized Mean ( 21 / 32 )1701.9092783505220.246870281376484.0578941188742
Winsorized Mean ( 22 / 32 )1695.8536082474219.053238683816389.0060548964765
Winsorized Mean ( 23 / 32 )1689.4041237113418.164898254681693.0037757451207
Winsorized Mean ( 24 / 32 )1688.4391752577317.714852522869295.312065007486
Winsorized Mean ( 25 / 32 )1688.1556701030917.122863699562198.590732235185
Winsorized Mean ( 26 / 32 )1689.7103092783516.4185098799011102.914961323429
Winsorized Mean ( 27 / 32 )1687.7061855670116.1317058843416104.620441115604
Winsorized Mean ( 28 / 32 )1688.5144329896915.7678829903851107.085677514179
Winsorized Mean ( 29 / 32 )1703.1041237113413.6437798880611124.826414504211
Winsorized Mean ( 30 / 32 )1702.9494845360813.3517350028111127.545183017604
Winsorized Mean ( 31 / 32 )1701.2876288659813.051027370138130.356605699770
Winsorized Mean ( 32 / 32 )1702.7391752577312.8646553341225132.357931948736
Trimmed Mean ( 1 / 32 )1704.3852631578929.675418695164357.4342448430435
Trimmed Mean ( 2 / 32 )1704.1215053763428.427180777228759.946904996693
Trimmed Mean ( 3 / 32 )1704.3065934065927.222947782410662.605512343075
Trimmed Mean ( 4 / 32 )1704.3314606741626.43128338846964.4816006708814
Trimmed Mean ( 5 / 32 )1704.1816091954025.616019202811666.5279642282729
Trimmed Mean ( 6 / 32 )1703.2964705882424.867177102570168.4957710946691
Trimmed Mean ( 7 / 32 )1703.2964705882424.354989708219269.9362426752912
Trimmed Mean ( 8 / 32 )1701.6209876543223.771398528746671.5827041306198
Trimmed Mean ( 9 / 32 )1701.3177215189923.412305716017372.6676706752144
Trimmed Mean ( 10 / 32 )1700.9636363636423.087841034920673.6735684289801
Trimmed Mean ( 11 / 32 )1700.6306666666722.741057046223274.7823930615882
Trimmed Mean ( 12 / 32 )1700.869863013722.460283963621875.7278877581664
Trimmed Mean ( 13 / 32 )1701.066197183122.152494801494276.788922079709
Trimmed Mean ( 14 / 32 )1701.066197183121.808924366371577.998628846
Trimmed Mean ( 15 / 32 )1701.9119402985121.472994675345179.2582481405172
Trimmed Mean ( 16 / 32 )1702.1538461538521.159034608462980.445723429794
Trimmed Mean ( 17 / 32 )1702.4730158730220.813372213798981.7970773013087
Trimmed Mean ( 18 / 32 )1702.9770491803320.438493281414983.3220446210132
Trimmed Mean ( 19 / 32 )1703.3322033898320.065293687307684.8894728347424
Trimmed Mean ( 20 / 32 )1703.6614035087719.633361165157586.7738024670062
Trimmed Mean ( 21 / 32 )1704.0163636363619.147837525684488.9926270447322
Trimmed Mean ( 22 / 32 )1704.218.653646086937591.3601551169875
Trimmed Mean ( 23 / 32 )1704.9215686274518.242061999401493.4610116270517
Trimmed Mean ( 24 / 32 )1706.2571428571417.866306934265695.5013898023171
Trimmed Mean ( 25 / 32 )1706.2571428571417.457467339288897.7379541771866
Trimmed Mean ( 26 / 32 )1709.4822222222217.0326621135877100.364946525798
Trimmed Mean ( 27 / 32 )1711.1976744186016.6120213208063103.009600178839
Trimmed Mean ( 28 / 32 )1711.1976744186016.0967657139111106.306925554602
Trimmed Mean ( 29 / 32 )1715.4538461538515.4796204215781110.820149295299
Trimmed Mean ( 30 / 32 )1716.5702702702715.1823163813588113.063792582923
Trimmed Mean ( 31 / 32 )1716.5702702702714.8174899900031115.847574145715
Trimmed Mean ( 32 / 32 )1719.3969696969714.3548181680327119.778387268322
Median1740.9
Midrange1755.65
Midmean - Weighted Average at Xnp1702.60833333333
Midmean - Weighted Average at X(n+1)p1706.25714285714
Midmean - Empirical Distribution Function1706.25714285714
Midmean - Empirical Distribution Function - Averaging1706.25714285714
Midmean - Empirical Distribution Function - Interpolation1706.25714285714
Midmean - Closest Observation1701.204
Midmean - True Basic - Statistics Graphics Toolkit1706.25714285714
Midmean - MS Excel (old versions)1706.25714285714
Number of observations97



Parameters (Session):
par1 = grey ; par2 = grey ; par3 = TRUE ; par4 = Uitvoer naar Amerika ; par5 = Invoer vanuit Amerika ;
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,hyperlink('arithmetic_mean.htm', 'Arithmetic Mean', 'click to view the definition of the Arithmetic Mean'),header=TRUE)
a<-table.element(a,arm)
a<-table.element(a,hyperlink('arithmetic_mean_standard_error.htm', armse, 'click to view the definition of the Standard Error of the Arithmetic Mean'))
a<-table.element(a,armose)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('geometric_mean.htm', 'Geometric Mean', 'click to view the definition of the Geometric Mean'),header=TRUE)
a<-table.element(a,geo)
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('harmonic_mean.htm', 'Harmonic Mean', 'click to view the definition of the Harmonic Mean'),header=TRUE)
a<-table.element(a,har)
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('quadratic_mean.htm', 'Quadratic Mean', 'click to view the definition of the Quadratic Mean'),header=TRUE)
a<-table.element(a,qua)
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,hyperlink('winsorized_mean.htm', mylabel, 'click to view the definition of the Winsorized Mean'),header=TRUE)
a<-table.element(a,win[j,1])
a<-table.element(a,win[j,2])
a<-table.element(a,win[j,1]/win[j,2])
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,hyperlink('arithmetic_mean.htm', mylabel, 'click to view the definition of the Trimmed Mean'),header=TRUE)
a<-table.element(a,tri[j,1])
a<-table.element(a,tri[j,2])
a<-table.element(a,tri[j,1]/tri[j,2])
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,hyperlink('median_1.htm', 'Median', 'click to view the definition of the Median'),header=TRUE)
a<-table.element(a,median(x))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('midrange.htm', 'Midrange', 'click to view the definition of the Midrange'),header=TRUE)
a<-table.element(a,midr)
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('method_1.htm','Weighted Average at Xnp',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[1])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('method_2.htm','Weighted Average at X(n+1)p',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[2])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('method_3.htm','Empirical Distribution Function',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[3])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('method_4.htm','Empirical Distribution Function - Averaging',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[4])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('method_5.htm','Empirical Distribution Function - Interpolation',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[5])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('method_6.htm','Closest Observation',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[6])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('method_7.htm','True Basic - Statistics Graphics Toolkit',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[7])
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- hyperlink('midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
mylabel <- paste(mymid,hyperlink('method_8.htm','MS Excel (old versions)',''),sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,midm[8])
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,length(x))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')