Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_centraltendency.wasp
Title produced by softwareCentral Tendency
Date of computationTue, 21 Oct 2008 00:41:45 -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/21/t122457142620h1ee40dieeb5s.htm/, Retrieved Sat, 18 May 2024 23:22:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=18336, Retrieved Sat, 18 May 2024 23:22:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact180
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Central Tendency] [Investigating Ass...] [2007-10-22 10:34:53] [b9964c45117f7aac638ab9056d451faa]
F    D    [Central Tendency] [Prediction] [2008-10-21 06:41:45] [35d0e2338b01fcbd55af9d60f940d770] [Current]
- RM        [Percentiles] [Percentielen pred...] [2008-10-22 18:46:58] [ed2ba3b6182103c15c0ab511ae4e6284]
Feedback Forum
2008-10-22 18:50:38 [Tom Ardies] [reply
De student gebruikt dit hier als blog om zijn schatting te staven zonder bijkomende uitleg. Hij had de percentielen berekening kunnen gebruiken om een betrouwbaarheidsinterval te nemen. voorbeeld: ik schat dat de volgende waarde tussen 3550 en 5638 zal liggen. Dit is het 80% betrouwbaarheidsinterval van zijn dataset. Link: http://www.freestatistics.org/blog/index.php?v=date/2008/Oct/22/t1224701250gm6ac52s0lxjdvb.htm
2008-10-24 13:32:58 [Gregory Van Overmeiren] [reply
Wat de student hierboven zegt klopt. Je maakt hier een voorspelling van één van je tijdreeksken, maar van welke? Ook geef je geen antwoord op de vraag, enkel deze link...
2008-10-27 10:26:17 [Evelien Blockx] [reply
Inderdaad, het is moeilijk om op deze berekening feedback te geven, omdat het niet duidelijk is wat je ermee wil doen om de schatting te maken.
2008-10-27 18:49:56 [Jeroen Aerts] [reply
Je geeft inderdaad geen antwoord hier, enkel een berekening. Maar als je de juiste oplossing wil dan verwijs ik naar de post van Tom Ardies, met zijn berekening van het betrouwbaarheidsinterval.

Post a new message
Dataseries X:
3421.3
3531.4
3219.2
3552.3
3787.7
3392.7
3550
3681.9
3519.1
4283.2
4046.2
3824.9
4793.1
3977.7
3983.4
4152.9
4286.1
4348.1
3949.3
4166.7
4217.9
4528.2
4232.2
4470.9
5121.2
4170.8
4398.6
4491.4
4251.8
4901.9
4745.2
4666.9
4210.4
5273.6
4095.3
4610.1
4718.1
4185.5
4314.7
4422.6
5059.2
5043.6
4436.6
4922.6
4454.8
5058.7
4768.9
5171.8
4989.3
5202.1
4838.4
4876.5
5845.3
5686.3
4753.8
6620.4
5597.2
5643.5
6357.3
5909.1
6165.8




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18336&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]1 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=18336&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18336&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean4572.060655737795.898171554695647.6762025971478
Geometric Mean4513.77933161587
Harmonic Mean4457.32463086084
Quadratic Mean4632.01103174385
Winsorized Mean ( 1 / 20 )4570.5918032786993.812617712382548.7204377698055
Winsorized Mean ( 2 / 20 )4565.2508196721391.706388113862849.7811647974174
Winsorized Mean ( 3 / 20 )4557.4360655737787.257929385336252.2294775692861
Winsorized Mean ( 4 / 20 )4554.0590163934486.034643148748652.9328518108659
Winsorized Mean ( 5 / 20 )4542.5508196721382.594442139023954.9982626194879
Winsorized Mean ( 6 / 20 )4538.5672131147581.588016160406655.6278657908739
Winsorized Mean ( 7 / 20 )4548.126229508277.502822895940858.6833622255894
Winsorized Mean ( 8 / 20 )4519.5622950819766.043451798690768.4331628949105
Winsorized Mean ( 9 / 20 )4514.5016393442663.073295055320771.5754842898989
Winsorized Mean ( 10 / 20 )4529.9278688524658.621144804609577.2746401311539
Winsorized Mean ( 11 / 20 )4525.9245901639456.142177810148580.6154083560651
Winsorized Mean ( 12 / 20 )4514.8491803278753.852621979941683.8371283390716
Winsorized Mean ( 13 / 20 )4528.126229508251.692864549801787.5967363957114
Winsorized Mean ( 14 / 20 )4535.9295081967249.385368640529591.8476389477465
Winsorized Mean ( 15 / 20 )4536.7409836065645.0621250809866100.677475273371
Winsorized Mean ( 16 / 20 )4522.8655737704941.6825667510727108.507367139383
Winsorized Mean ( 17 / 20 )4518.2393442622940.6043791736835111.274681111013
Winsorized Mean ( 18 / 20 )4515.0819672131138.816189056007116.319558334241
Winsorized Mean ( 19 / 20 )4510.9704918032835.9025983765413125.64468021208
Winsorized Mean ( 20 / 20 )4498.5770491803333.3349100222826134.950928206294
Trimmed Mean ( 1 / 20 )4560.2728813559389.848428924439250.7551766452259
Trimmed Mean ( 2 / 20 )4549.2298245614084.932217001221453.563064584738
Trimmed Mean ( 3 / 20 )4540.3454545454580.26799978834356.5648261638237
Trimmed Mean ( 4 / 20 )4533.7886792452876.708543531055759.1040902426959
Trimmed Mean ( 5 / 20 )4527.7274509803972.734107007536562.2504026963752
Trimmed Mean ( 6 / 20 )4524.0367346938868.979531969492665.5852048502549
Trimmed Mean ( 7 / 20 )4520.8936170212864.495820731317570.095915762586
Trimmed Mean ( 8 / 20 )4515.6260.007144911285775.2513722603512
Trimmed Mean ( 9 / 20 )4514.9209302325657.760318869097378.1664820872053
Trimmed Mean ( 10 / 20 )4514.9902439024455.63892388678181.1480511932608
Trimmed Mean ( 11 / 20 )4512.6538461538554.023748694671583.5309276973395
Trimmed Mean ( 12 / 20 )4510.6648648648752.480967122885685.9485850232718
Trimmed Mean ( 13 / 20 )4510.0571428571450.95517543843488.5102858355648
Trimmed Mean ( 14 / 20 )4507.4878787878849.364918563718691.3095374191646
Trimmed Mean ( 15 / 20 )4503.4903225806447.694715415847594.423255979514
Trimmed Mean ( 16 / 20 )4498.827586206946.503293992273596.7421272771425
Trimmed Mean ( 17 / 20 )4495.4333333333345.694902057823498.3793187179766
Trimmed Mean ( 18 / 20 )4492.1644.6266564585189100.660913375295
Trimmed Mean ( 19 / 20 )4488.7826086956543.3959418933752103.437842638021
Trimmed Mean ( 20 / 20 )4485.3904761904842.3402402903645105.936821459448
Median4454.8
Midrange4919.8
Midmean - Weighted Average at Xnp4487.29666666667
Midmean - Weighted Average at X(n+1)p4503.49032258064
Midmean - Empirical Distribution Function4503.49032258064
Midmean - Empirical Distribution Function - Averaging4503.49032258064
Midmean - Empirical Distribution Function - Interpolation4503.49032258064
Midmean - Closest Observation4490.734375
Midmean - True Basic - Statistics Graphics Toolkit4503.49032258064
Midmean - MS Excel (old versions)4503.49032258064
Number of observations61

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 4572.0606557377 & 95.8981715546956 & 47.6762025971478 \tabularnewline
Geometric Mean & 4513.77933161587 &  &  \tabularnewline
Harmonic Mean & 4457.32463086084 &  &  \tabularnewline
Quadratic Mean & 4632.01103174385 &  &  \tabularnewline
Winsorized Mean ( 1 / 20 ) & 4570.59180327869 & 93.8126177123825 & 48.7204377698055 \tabularnewline
Winsorized Mean ( 2 / 20 ) & 4565.25081967213 & 91.7063881138628 & 49.7811647974174 \tabularnewline
Winsorized Mean ( 3 / 20 ) & 4557.43606557377 & 87.2579293853362 & 52.2294775692861 \tabularnewline
Winsorized Mean ( 4 / 20 ) & 4554.05901639344 & 86.0346431487486 & 52.9328518108659 \tabularnewline
Winsorized Mean ( 5 / 20 ) & 4542.55081967213 & 82.5944421390239 & 54.9982626194879 \tabularnewline
Winsorized Mean ( 6 / 20 ) & 4538.56721311475 & 81.5880161604066 & 55.6278657908739 \tabularnewline
Winsorized Mean ( 7 / 20 ) & 4548.1262295082 & 77.5028228959408 & 58.6833622255894 \tabularnewline
Winsorized Mean ( 8 / 20 ) & 4519.56229508197 & 66.0434517986907 & 68.4331628949105 \tabularnewline
Winsorized Mean ( 9 / 20 ) & 4514.50163934426 & 63.0732950553207 & 71.5754842898989 \tabularnewline
Winsorized Mean ( 10 / 20 ) & 4529.92786885246 & 58.6211448046095 & 77.2746401311539 \tabularnewline
Winsorized Mean ( 11 / 20 ) & 4525.92459016394 & 56.1421778101485 & 80.6154083560651 \tabularnewline
Winsorized Mean ( 12 / 20 ) & 4514.84918032787 & 53.8526219799416 & 83.8371283390716 \tabularnewline
Winsorized Mean ( 13 / 20 ) & 4528.1262295082 & 51.6928645498017 & 87.5967363957114 \tabularnewline
Winsorized Mean ( 14 / 20 ) & 4535.92950819672 & 49.3853686405295 & 91.8476389477465 \tabularnewline
Winsorized Mean ( 15 / 20 ) & 4536.74098360656 & 45.0621250809866 & 100.677475273371 \tabularnewline
Winsorized Mean ( 16 / 20 ) & 4522.86557377049 & 41.6825667510727 & 108.507367139383 \tabularnewline
Winsorized Mean ( 17 / 20 ) & 4518.23934426229 & 40.6043791736835 & 111.274681111013 \tabularnewline
Winsorized Mean ( 18 / 20 ) & 4515.08196721311 & 38.816189056007 & 116.319558334241 \tabularnewline
Winsorized Mean ( 19 / 20 ) & 4510.97049180328 & 35.9025983765413 & 125.64468021208 \tabularnewline
Winsorized Mean ( 20 / 20 ) & 4498.57704918033 & 33.3349100222826 & 134.950928206294 \tabularnewline
Trimmed Mean ( 1 / 20 ) & 4560.27288135593 & 89.8484289244392 & 50.7551766452259 \tabularnewline
Trimmed Mean ( 2 / 20 ) & 4549.22982456140 & 84.9322170012214 & 53.563064584738 \tabularnewline
Trimmed Mean ( 3 / 20 ) & 4540.34545454545 & 80.267999788343 & 56.5648261638237 \tabularnewline
Trimmed Mean ( 4 / 20 ) & 4533.78867924528 & 76.7085435310557 & 59.1040902426959 \tabularnewline
Trimmed Mean ( 5 / 20 ) & 4527.72745098039 & 72.7341070075365 & 62.2504026963752 \tabularnewline
Trimmed Mean ( 6 / 20 ) & 4524.03673469388 & 68.9795319694926 & 65.5852048502549 \tabularnewline
Trimmed Mean ( 7 / 20 ) & 4520.89361702128 & 64.4958207313175 & 70.095915762586 \tabularnewline
Trimmed Mean ( 8 / 20 ) & 4515.62 & 60.0071449112857 & 75.2513722603512 \tabularnewline
Trimmed Mean ( 9 / 20 ) & 4514.92093023256 & 57.7603188690973 & 78.1664820872053 \tabularnewline
Trimmed Mean ( 10 / 20 ) & 4514.99024390244 & 55.638923886781 & 81.1480511932608 \tabularnewline
Trimmed Mean ( 11 / 20 ) & 4512.65384615385 & 54.0237486946715 & 83.5309276973395 \tabularnewline
Trimmed Mean ( 12 / 20 ) & 4510.66486486487 & 52.4809671228856 & 85.9485850232718 \tabularnewline
Trimmed Mean ( 13 / 20 ) & 4510.05714285714 & 50.955175438434 & 88.5102858355648 \tabularnewline
Trimmed Mean ( 14 / 20 ) & 4507.48787878788 & 49.3649185637186 & 91.3095374191646 \tabularnewline
Trimmed Mean ( 15 / 20 ) & 4503.49032258064 & 47.6947154158475 & 94.423255979514 \tabularnewline
Trimmed Mean ( 16 / 20 ) & 4498.8275862069 & 46.5032939922735 & 96.7421272771425 \tabularnewline
Trimmed Mean ( 17 / 20 ) & 4495.43333333333 & 45.6949020578234 & 98.3793187179766 \tabularnewline
Trimmed Mean ( 18 / 20 ) & 4492.16 & 44.6266564585189 & 100.660913375295 \tabularnewline
Trimmed Mean ( 19 / 20 ) & 4488.78260869565 & 43.3959418933752 & 103.437842638021 \tabularnewline
Trimmed Mean ( 20 / 20 ) & 4485.39047619048 & 42.3402402903645 & 105.936821459448 \tabularnewline
Median & 4454.8 &  &  \tabularnewline
Midrange & 4919.8 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 4487.29666666667 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 4503.49032258064 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 4503.49032258064 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 4503.49032258064 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 4503.49032258064 &  &  \tabularnewline
Midmean - Closest Observation & 4490.734375 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 4503.49032258064 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 4503.49032258064 &  &  \tabularnewline
Number of observations & 61 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18336&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]4572.0606557377[/C][C]95.8981715546956[/C][C]47.6762025971478[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]4513.77933161587[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]4457.32463086084[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]4632.01103174385[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 20 )[/C][C]4570.59180327869[/C][C]93.8126177123825[/C][C]48.7204377698055[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 20 )[/C][C]4565.25081967213[/C][C]91.7063881138628[/C][C]49.7811647974174[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 20 )[/C][C]4557.43606557377[/C][C]87.2579293853362[/C][C]52.2294775692861[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 20 )[/C][C]4554.05901639344[/C][C]86.0346431487486[/C][C]52.9328518108659[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 20 )[/C][C]4542.55081967213[/C][C]82.5944421390239[/C][C]54.9982626194879[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 20 )[/C][C]4538.56721311475[/C][C]81.5880161604066[/C][C]55.6278657908739[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 20 )[/C][C]4548.1262295082[/C][C]77.5028228959408[/C][C]58.6833622255894[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 20 )[/C][C]4519.56229508197[/C][C]66.0434517986907[/C][C]68.4331628949105[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 20 )[/C][C]4514.50163934426[/C][C]63.0732950553207[/C][C]71.5754842898989[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 20 )[/C][C]4529.92786885246[/C][C]58.6211448046095[/C][C]77.2746401311539[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 20 )[/C][C]4525.92459016394[/C][C]56.1421778101485[/C][C]80.6154083560651[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 20 )[/C][C]4514.84918032787[/C][C]53.8526219799416[/C][C]83.8371283390716[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 20 )[/C][C]4528.1262295082[/C][C]51.6928645498017[/C][C]87.5967363957114[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 20 )[/C][C]4535.92950819672[/C][C]49.3853686405295[/C][C]91.8476389477465[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 20 )[/C][C]4536.74098360656[/C][C]45.0621250809866[/C][C]100.677475273371[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 20 )[/C][C]4522.86557377049[/C][C]41.6825667510727[/C][C]108.507367139383[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 20 )[/C][C]4518.23934426229[/C][C]40.6043791736835[/C][C]111.274681111013[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 20 )[/C][C]4515.08196721311[/C][C]38.816189056007[/C][C]116.319558334241[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 20 )[/C][C]4510.97049180328[/C][C]35.9025983765413[/C][C]125.64468021208[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 20 )[/C][C]4498.57704918033[/C][C]33.3349100222826[/C][C]134.950928206294[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 20 )[/C][C]4560.27288135593[/C][C]89.8484289244392[/C][C]50.7551766452259[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 20 )[/C][C]4549.22982456140[/C][C]84.9322170012214[/C][C]53.563064584738[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 20 )[/C][C]4540.34545454545[/C][C]80.267999788343[/C][C]56.5648261638237[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 20 )[/C][C]4533.78867924528[/C][C]76.7085435310557[/C][C]59.1040902426959[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 20 )[/C][C]4527.72745098039[/C][C]72.7341070075365[/C][C]62.2504026963752[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 20 )[/C][C]4524.03673469388[/C][C]68.9795319694926[/C][C]65.5852048502549[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 20 )[/C][C]4520.89361702128[/C][C]64.4958207313175[/C][C]70.095915762586[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 20 )[/C][C]4515.62[/C][C]60.0071449112857[/C][C]75.2513722603512[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 20 )[/C][C]4514.92093023256[/C][C]57.7603188690973[/C][C]78.1664820872053[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 20 )[/C][C]4514.99024390244[/C][C]55.638923886781[/C][C]81.1480511932608[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 20 )[/C][C]4512.65384615385[/C][C]54.0237486946715[/C][C]83.5309276973395[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 20 )[/C][C]4510.66486486487[/C][C]52.4809671228856[/C][C]85.9485850232718[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 20 )[/C][C]4510.05714285714[/C][C]50.955175438434[/C][C]88.5102858355648[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 20 )[/C][C]4507.48787878788[/C][C]49.3649185637186[/C][C]91.3095374191646[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 20 )[/C][C]4503.49032258064[/C][C]47.6947154158475[/C][C]94.423255979514[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 20 )[/C][C]4498.8275862069[/C][C]46.5032939922735[/C][C]96.7421272771425[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 20 )[/C][C]4495.43333333333[/C][C]45.6949020578234[/C][C]98.3793187179766[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 20 )[/C][C]4492.16[/C][C]44.6266564585189[/C][C]100.660913375295[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 20 )[/C][C]4488.78260869565[/C][C]43.3959418933752[/C][C]103.437842638021[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 20 )[/C][C]4485.39047619048[/C][C]42.3402402903645[/C][C]105.936821459448[/C][/ROW]
[ROW][C]Median[/C][C]4454.8[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]4919.8[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]4487.29666666667[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]4503.49032258064[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]4503.49032258064[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]4503.49032258064[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]4503.49032258064[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]4490.734375[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]4503.49032258064[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]4503.49032258064[/C][C][/C][C][/C][/ROW]
[ROW][C]Number of observations[/C][C]61[/C][C][/C][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18336&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18336&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 Mean4572.060655737795.898171554695647.6762025971478
Geometric Mean4513.77933161587
Harmonic Mean4457.32463086084
Quadratic Mean4632.01103174385
Winsorized Mean ( 1 / 20 )4570.5918032786993.812617712382548.7204377698055
Winsorized Mean ( 2 / 20 )4565.2508196721391.706388113862849.7811647974174
Winsorized Mean ( 3 / 20 )4557.4360655737787.257929385336252.2294775692861
Winsorized Mean ( 4 / 20 )4554.0590163934486.034643148748652.9328518108659
Winsorized Mean ( 5 / 20 )4542.5508196721382.594442139023954.9982626194879
Winsorized Mean ( 6 / 20 )4538.5672131147581.588016160406655.6278657908739
Winsorized Mean ( 7 / 20 )4548.126229508277.502822895940858.6833622255894
Winsorized Mean ( 8 / 20 )4519.5622950819766.043451798690768.4331628949105
Winsorized Mean ( 9 / 20 )4514.5016393442663.073295055320771.5754842898989
Winsorized Mean ( 10 / 20 )4529.9278688524658.621144804609577.2746401311539
Winsorized Mean ( 11 / 20 )4525.9245901639456.142177810148580.6154083560651
Winsorized Mean ( 12 / 20 )4514.8491803278753.852621979941683.8371283390716
Winsorized Mean ( 13 / 20 )4528.126229508251.692864549801787.5967363957114
Winsorized Mean ( 14 / 20 )4535.9295081967249.385368640529591.8476389477465
Winsorized Mean ( 15 / 20 )4536.7409836065645.0621250809866100.677475273371
Winsorized Mean ( 16 / 20 )4522.8655737704941.6825667510727108.507367139383
Winsorized Mean ( 17 / 20 )4518.2393442622940.6043791736835111.274681111013
Winsorized Mean ( 18 / 20 )4515.0819672131138.816189056007116.319558334241
Winsorized Mean ( 19 / 20 )4510.9704918032835.9025983765413125.64468021208
Winsorized Mean ( 20 / 20 )4498.5770491803333.3349100222826134.950928206294
Trimmed Mean ( 1 / 20 )4560.2728813559389.848428924439250.7551766452259
Trimmed Mean ( 2 / 20 )4549.2298245614084.932217001221453.563064584738
Trimmed Mean ( 3 / 20 )4540.3454545454580.26799978834356.5648261638237
Trimmed Mean ( 4 / 20 )4533.7886792452876.708543531055759.1040902426959
Trimmed Mean ( 5 / 20 )4527.7274509803972.734107007536562.2504026963752
Trimmed Mean ( 6 / 20 )4524.0367346938868.979531969492665.5852048502549
Trimmed Mean ( 7 / 20 )4520.8936170212864.495820731317570.095915762586
Trimmed Mean ( 8 / 20 )4515.6260.007144911285775.2513722603512
Trimmed Mean ( 9 / 20 )4514.9209302325657.760318869097378.1664820872053
Trimmed Mean ( 10 / 20 )4514.9902439024455.63892388678181.1480511932608
Trimmed Mean ( 11 / 20 )4512.6538461538554.023748694671583.5309276973395
Trimmed Mean ( 12 / 20 )4510.6648648648752.480967122885685.9485850232718
Trimmed Mean ( 13 / 20 )4510.0571428571450.95517543843488.5102858355648
Trimmed Mean ( 14 / 20 )4507.4878787878849.364918563718691.3095374191646
Trimmed Mean ( 15 / 20 )4503.4903225806447.694715415847594.423255979514
Trimmed Mean ( 16 / 20 )4498.827586206946.503293992273596.7421272771425
Trimmed Mean ( 17 / 20 )4495.4333333333345.694902057823498.3793187179766
Trimmed Mean ( 18 / 20 )4492.1644.6266564585189100.660913375295
Trimmed Mean ( 19 / 20 )4488.7826086956543.3959418933752103.437842638021
Trimmed Mean ( 20 / 20 )4485.3904761904842.3402402903645105.936821459448
Median4454.8
Midrange4919.8
Midmean - Weighted Average at Xnp4487.29666666667
Midmean - Weighted Average at X(n+1)p4503.49032258064
Midmean - Empirical Distribution Function4503.49032258064
Midmean - Empirical Distribution Function - Averaging4503.49032258064
Midmean - Empirical Distribution Function - Interpolation4503.49032258064
Midmean - Closest Observation4490.734375
Midmean - True Basic - Statistics Graphics Toolkit4503.49032258064
Midmean - MS Excel (old versions)4503.49032258064
Number of observations61



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,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')