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Author's title

Author*Unverified author*
R Software Modulerwasp_centraltendency.wasp
Title produced by softwareCentral Tendency
Date of computationSun, 19 Oct 2008 11:54:54 -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/19/t1224438926rdsxr2nmsar5cmh.htm/, Retrieved Sun, 19 May 2024 15:55:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=17011, Retrieved Sun, 19 May 2024 15:55:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact145
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Central Tendency] [Central Tendency ...] [2008-10-19 17:54:54] [7f3f68cb5622d7511b280fd3e248ef1b] [Current]
Feedback Forum
2008-10-23 10:03:14 [Ellen Smolders] [reply
Ik denk dat de student de juiste voorspelling heeft gemaakt. Zowel de midrange, de mediaan en het gemiddelde liggen zeer dicht bij elkaar, dit betekent dat er geen extreme waarden voorkomen in de dataset. Doorheen de jaren blijft de curve vrij stabiel zodat we kunnen veronderstellen dat dit in de toekomst hetzelfde zal blijven.
2008-10-24 10:25:00 [Stijn Van de Velde] [reply
In de gegevens zitten niet al te veel extreme waarden, wat leid tot een vrij stabiele grafiek.
De central tendency is hier dus vrij robuust.
Daardoor is zijn conclusie dat in de toekomst de waarde ongeveer gelijk zullen blijven correct.
2008-10-26 13:46:00 [Natascha Meeus] [reply
De waarden van de midrange, de mediaan en het gemiddelde liggen zeer dicht bij elkaar. Dit betekent dat er geen extreme waarden aanwezig zijn en we dus te maken hebben met een stabiele grafiek. Daardoor is de voorspelling dat de waarden in de toekomst ongeveer gelijk zullen blijven correct.
2008-10-27 16:08:46 [Bonifer Spillemaeckers] [reply
Ik ga akkoord met bovenstaande opmerkingen. De student heeft inderdaad een goede voorspelling gemaakt. Als we kijken naar de central tendency-parameters (midrange, mean en median), kunnen we stellen dat geen outliers voorkomen in de dataset. De midrange, mean en median liggen immers vrij dicht bij elkaar.
2008-10-27 19:24:25 [Jan Helsen] [reply
Ik ga ook akkoord met eerder opmerkingen. De waarden liggen allemaal immers dicht bij elkaar wat een stabiele grafiek als gevolg heeft. Toch zou ik oppassen met het lichtjes dalende verloop als voorspelling.
Wanneer we de grafiek in zijn geheel bekijken zien we dat er een geleidelijke stijging is. Het kan dus best zijn dat er nu een periode van daling is maar dat deze in de toekomst terug zal toenemen. Onvoorspelbare gebeurtenissen die deze grafiek serieus kunnen beïnvloeden zijn nooit uitgesloten maar dit zal eerder hoogst uitzonderlijk zijn.

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Dataseries X:
578
565
547
555
562
561
555
544
537
543
594
611
613
611
594
595
591
589
584
573
567
569
621
629
628
612
595
597
593
590
580
574
573
573
620
626
620
588
566
557
561
549
532
526
511
499
555
565
542
527
510
514
517
508
493
490
469
478
528
534
518




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=17011&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=17011&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=17011&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 Mean562.3934426229515.11903544366619109.863166374205
Geometric Mean560.96896182976
Harmonic Mean559.517889263267
Quadratic Mean563.789549156159
Winsorized Mean ( 1 / 20 )562.5245901639345.07256666245154110.895455416661
Winsorized Mean ( 2 / 20 )562.8524590163934.95564126664769113.578128183831
Winsorized Mean ( 3 / 20 )562.7540983606564.86931149056912115.571595584016
Winsorized Mean ( 4 / 20 )563.0819672131154.76515672607164118.166515727032
Winsorized Mean ( 5 / 20 )563.8196721311484.60787566458611122.360001261404
Winsorized Mean ( 6 / 20 )563.3278688524594.43395500662853127.04862092879
Winsorized Mean ( 7 / 20 )563.3278688524594.38973724343269128.328379949217
Winsorized Mean ( 8 / 20 )563.5901639344264.28852688791904131.418125305937
Winsorized Mean ( 9 / 20 )564.0327868852464.20460112356207134.146562375317
Winsorized Mean ( 10 / 20 )561.9016393442623.78035654186539148.637207396050
Winsorized Mean ( 11 / 20 )562.9836065573773.45584891101748162.907471088376
Winsorized Mean ( 12 / 20 )563.1803278688523.42096622952691164.626099786649
Winsorized Mean ( 13 / 20 )563.1803278688523.35067958172824168.079434076585
Winsorized Mean ( 14 / 20 )564.0983606557383.19337642013491176.646372503717
Winsorized Mean ( 15 / 20 )564.3442622950823.07340835753837183.621633262913
Winsorized Mean ( 16 / 20 )564.6065573770492.86435400631813197.114796610912
Winsorized Mean ( 17 / 20 )565.721311475412.60329038826843217.310106480936
Winsorized Mean ( 18 / 20 )565.721311475412.51293170692791225.124029401902
Winsorized Mean ( 19 / 20 )565.721311475412.41818375003421233.944716346476
Winsorized Mean ( 20 / 20 )565.3934426229512.07275834389811272.773449103406
Trimmed Mean ( 1 / 20 )562.8474576271194.9171281621033114.466704765808
Trimmed Mean ( 2 / 20 )563.192982456144.72577840961136119.174648838953
Trimmed Mean ( 3 / 20 )563.3818181818184.56833567591882123.323209621304
Trimmed Mean ( 4 / 20 )563.6226415094344.41441976909155127.677627183475
Trimmed Mean ( 5 / 20 )563.784313725494.26341607721359132.237694729987
Trimmed Mean ( 6 / 20 )563.7755102040824.12797669407651136.574295831921
Trimmed Mean ( 7 / 20 )563.8723404255324.00988798905995140.620471684977
Trimmed Mean ( 8 / 20 )563.9777777777783.86978569198709145.738762470896
Trimmed Mean ( 9 / 20 )564.0465116279073.71625883400797151.778047983699
Trimmed Mean ( 10 / 20 )564.0487804878053.53759447335922159.444160356853
Trimmed Mean ( 11 / 20 )564.3846153846153.41794411153085165.124003485193
Trimmed Mean ( 12 / 20 )564.5945945945953.34467876861956168.8038324911
Trimmed Mean ( 13 / 20 )564.83.24899467321118173.838389042902
Trimmed Mean ( 14 / 20 )565.0303030303033.13112576826225180.455958926201
Trimmed Mean ( 15 / 20 )565.161290322583.00933914589896187.802458587217
Trimmed Mean ( 16 / 20 )565.2758620689652.86730188123579197.145569417803
Trimmed Mean ( 17 / 20 )565.370370370372.72539400855116207.445370686393
Trimmed Mean ( 18 / 20 )565.322.60174300549971217.285104180157
Trimmed Mean ( 19 / 20 )565.2608695652172.43591165239589232.053107923370
Trimmed Mean ( 20 / 20 )565.1904761904762.20023705175152256.877083194536
Median565
Midrange549
Midmean - Weighted Average at Xnp564.233333333333
Midmean - Weighted Average at X(n+1)p565.16129032258
Midmean - Empirical Distribution Function565.16129032258
Midmean - Empirical Distribution Function - Averaging565.16129032258
Midmean - Empirical Distribution Function - Interpolation565.16129032258
Midmean - Closest Observation564.125
Midmean - True Basic - Statistics Graphics Toolkit565.16129032258
Midmean - MS Excel (old versions)565.16129032258
Number of observations61

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 562.393442622951 & 5.11903544366619 & 109.863166374205 \tabularnewline
Geometric Mean & 560.96896182976 &  &  \tabularnewline
Harmonic Mean & 559.517889263267 &  &  \tabularnewline
Quadratic Mean & 563.789549156159 &  &  \tabularnewline
Winsorized Mean ( 1 / 20 ) & 562.524590163934 & 5.07256666245154 & 110.895455416661 \tabularnewline
Winsorized Mean ( 2 / 20 ) & 562.852459016393 & 4.95564126664769 & 113.578128183831 \tabularnewline
Winsorized Mean ( 3 / 20 ) & 562.754098360656 & 4.86931149056912 & 115.571595584016 \tabularnewline
Winsorized Mean ( 4 / 20 ) & 563.081967213115 & 4.76515672607164 & 118.166515727032 \tabularnewline
Winsorized Mean ( 5 / 20 ) & 563.819672131148 & 4.60787566458611 & 122.360001261404 \tabularnewline
Winsorized Mean ( 6 / 20 ) & 563.327868852459 & 4.43395500662853 & 127.04862092879 \tabularnewline
Winsorized Mean ( 7 / 20 ) & 563.327868852459 & 4.38973724343269 & 128.328379949217 \tabularnewline
Winsorized Mean ( 8 / 20 ) & 563.590163934426 & 4.28852688791904 & 131.418125305937 \tabularnewline
Winsorized Mean ( 9 / 20 ) & 564.032786885246 & 4.20460112356207 & 134.146562375317 \tabularnewline
Winsorized Mean ( 10 / 20 ) & 561.901639344262 & 3.78035654186539 & 148.637207396050 \tabularnewline
Winsorized Mean ( 11 / 20 ) & 562.983606557377 & 3.45584891101748 & 162.907471088376 \tabularnewline
Winsorized Mean ( 12 / 20 ) & 563.180327868852 & 3.42096622952691 & 164.626099786649 \tabularnewline
Winsorized Mean ( 13 / 20 ) & 563.180327868852 & 3.35067958172824 & 168.079434076585 \tabularnewline
Winsorized Mean ( 14 / 20 ) & 564.098360655738 & 3.19337642013491 & 176.646372503717 \tabularnewline
Winsorized Mean ( 15 / 20 ) & 564.344262295082 & 3.07340835753837 & 183.621633262913 \tabularnewline
Winsorized Mean ( 16 / 20 ) & 564.606557377049 & 2.86435400631813 & 197.114796610912 \tabularnewline
Winsorized Mean ( 17 / 20 ) & 565.72131147541 & 2.60329038826843 & 217.310106480936 \tabularnewline
Winsorized Mean ( 18 / 20 ) & 565.72131147541 & 2.51293170692791 & 225.124029401902 \tabularnewline
Winsorized Mean ( 19 / 20 ) & 565.72131147541 & 2.41818375003421 & 233.944716346476 \tabularnewline
Winsorized Mean ( 20 / 20 ) & 565.393442622951 & 2.07275834389811 & 272.773449103406 \tabularnewline
Trimmed Mean ( 1 / 20 ) & 562.847457627119 & 4.9171281621033 & 114.466704765808 \tabularnewline
Trimmed Mean ( 2 / 20 ) & 563.19298245614 & 4.72577840961136 & 119.174648838953 \tabularnewline
Trimmed Mean ( 3 / 20 ) & 563.381818181818 & 4.56833567591882 & 123.323209621304 \tabularnewline
Trimmed Mean ( 4 / 20 ) & 563.622641509434 & 4.41441976909155 & 127.677627183475 \tabularnewline
Trimmed Mean ( 5 / 20 ) & 563.78431372549 & 4.26341607721359 & 132.237694729987 \tabularnewline
Trimmed Mean ( 6 / 20 ) & 563.775510204082 & 4.12797669407651 & 136.574295831921 \tabularnewline
Trimmed Mean ( 7 / 20 ) & 563.872340425532 & 4.00988798905995 & 140.620471684977 \tabularnewline
Trimmed Mean ( 8 / 20 ) & 563.977777777778 & 3.86978569198709 & 145.738762470896 \tabularnewline
Trimmed Mean ( 9 / 20 ) & 564.046511627907 & 3.71625883400797 & 151.778047983699 \tabularnewline
Trimmed Mean ( 10 / 20 ) & 564.048780487805 & 3.53759447335922 & 159.444160356853 \tabularnewline
Trimmed Mean ( 11 / 20 ) & 564.384615384615 & 3.41794411153085 & 165.124003485193 \tabularnewline
Trimmed Mean ( 12 / 20 ) & 564.594594594595 & 3.34467876861956 & 168.8038324911 \tabularnewline
Trimmed Mean ( 13 / 20 ) & 564.8 & 3.24899467321118 & 173.838389042902 \tabularnewline
Trimmed Mean ( 14 / 20 ) & 565.030303030303 & 3.13112576826225 & 180.455958926201 \tabularnewline
Trimmed Mean ( 15 / 20 ) & 565.16129032258 & 3.00933914589896 & 187.802458587217 \tabularnewline
Trimmed Mean ( 16 / 20 ) & 565.275862068965 & 2.86730188123579 & 197.145569417803 \tabularnewline
Trimmed Mean ( 17 / 20 ) & 565.37037037037 & 2.72539400855116 & 207.445370686393 \tabularnewline
Trimmed Mean ( 18 / 20 ) & 565.32 & 2.60174300549971 & 217.285104180157 \tabularnewline
Trimmed Mean ( 19 / 20 ) & 565.260869565217 & 2.43591165239589 & 232.053107923370 \tabularnewline
Trimmed Mean ( 20 / 20 ) & 565.190476190476 & 2.20023705175152 & 256.877083194536 \tabularnewline
Median & 565 &  &  \tabularnewline
Midrange & 549 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 564.233333333333 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 565.16129032258 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 565.16129032258 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 565.16129032258 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 565.16129032258 &  &  \tabularnewline
Midmean - Closest Observation & 564.125 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 565.16129032258 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 565.16129032258 &  &  \tabularnewline
Number of observations & 61 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=17011&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]562.393442622951[/C][C]5.11903544366619[/C][C]109.863166374205[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]560.96896182976[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]559.517889263267[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]563.789549156159[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 20 )[/C][C]562.524590163934[/C][C]5.07256666245154[/C][C]110.895455416661[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 20 )[/C][C]562.852459016393[/C][C]4.95564126664769[/C][C]113.578128183831[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 20 )[/C][C]562.754098360656[/C][C]4.86931149056912[/C][C]115.571595584016[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 20 )[/C][C]563.081967213115[/C][C]4.76515672607164[/C][C]118.166515727032[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 20 )[/C][C]563.819672131148[/C][C]4.60787566458611[/C][C]122.360001261404[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 20 )[/C][C]563.327868852459[/C][C]4.43395500662853[/C][C]127.04862092879[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 20 )[/C][C]563.327868852459[/C][C]4.38973724343269[/C][C]128.328379949217[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 20 )[/C][C]563.590163934426[/C][C]4.28852688791904[/C][C]131.418125305937[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 20 )[/C][C]564.032786885246[/C][C]4.20460112356207[/C][C]134.146562375317[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 20 )[/C][C]561.901639344262[/C][C]3.78035654186539[/C][C]148.637207396050[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 20 )[/C][C]562.983606557377[/C][C]3.45584891101748[/C][C]162.907471088376[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 20 )[/C][C]563.180327868852[/C][C]3.42096622952691[/C][C]164.626099786649[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 20 )[/C][C]563.180327868852[/C][C]3.35067958172824[/C][C]168.079434076585[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 20 )[/C][C]564.098360655738[/C][C]3.19337642013491[/C][C]176.646372503717[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 20 )[/C][C]564.344262295082[/C][C]3.07340835753837[/C][C]183.621633262913[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 20 )[/C][C]564.606557377049[/C][C]2.86435400631813[/C][C]197.114796610912[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 20 )[/C][C]565.72131147541[/C][C]2.60329038826843[/C][C]217.310106480936[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 20 )[/C][C]565.72131147541[/C][C]2.51293170692791[/C][C]225.124029401902[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 20 )[/C][C]565.72131147541[/C][C]2.41818375003421[/C][C]233.944716346476[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 20 )[/C][C]565.393442622951[/C][C]2.07275834389811[/C][C]272.773449103406[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 20 )[/C][C]562.847457627119[/C][C]4.9171281621033[/C][C]114.466704765808[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 20 )[/C][C]563.19298245614[/C][C]4.72577840961136[/C][C]119.174648838953[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 20 )[/C][C]563.381818181818[/C][C]4.56833567591882[/C][C]123.323209621304[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 20 )[/C][C]563.622641509434[/C][C]4.41441976909155[/C][C]127.677627183475[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 20 )[/C][C]563.78431372549[/C][C]4.26341607721359[/C][C]132.237694729987[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 20 )[/C][C]563.775510204082[/C][C]4.12797669407651[/C][C]136.574295831921[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 20 )[/C][C]563.872340425532[/C][C]4.00988798905995[/C][C]140.620471684977[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 20 )[/C][C]563.977777777778[/C][C]3.86978569198709[/C][C]145.738762470896[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 20 )[/C][C]564.046511627907[/C][C]3.71625883400797[/C][C]151.778047983699[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 20 )[/C][C]564.048780487805[/C][C]3.53759447335922[/C][C]159.444160356853[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 20 )[/C][C]564.384615384615[/C][C]3.41794411153085[/C][C]165.124003485193[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 20 )[/C][C]564.594594594595[/C][C]3.34467876861956[/C][C]168.8038324911[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 20 )[/C][C]564.8[/C][C]3.24899467321118[/C][C]173.838389042902[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 20 )[/C][C]565.030303030303[/C][C]3.13112576826225[/C][C]180.455958926201[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 20 )[/C][C]565.16129032258[/C][C]3.00933914589896[/C][C]187.802458587217[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 20 )[/C][C]565.275862068965[/C][C]2.86730188123579[/C][C]197.145569417803[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 20 )[/C][C]565.37037037037[/C][C]2.72539400855116[/C][C]207.445370686393[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 20 )[/C][C]565.32[/C][C]2.60174300549971[/C][C]217.285104180157[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 20 )[/C][C]565.260869565217[/C][C]2.43591165239589[/C][C]232.053107923370[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 20 )[/C][C]565.190476190476[/C][C]2.20023705175152[/C][C]256.877083194536[/C][/ROW]
[ROW][C]Median[/C][C]565[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]549[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]564.233333333333[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]565.16129032258[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]565.16129032258[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]565.16129032258[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]565.16129032258[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]564.125[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]565.16129032258[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]565.16129032258[/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=17011&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=17011&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 Mean562.3934426229515.11903544366619109.863166374205
Geometric Mean560.96896182976
Harmonic Mean559.517889263267
Quadratic Mean563.789549156159
Winsorized Mean ( 1 / 20 )562.5245901639345.07256666245154110.895455416661
Winsorized Mean ( 2 / 20 )562.8524590163934.95564126664769113.578128183831
Winsorized Mean ( 3 / 20 )562.7540983606564.86931149056912115.571595584016
Winsorized Mean ( 4 / 20 )563.0819672131154.76515672607164118.166515727032
Winsorized Mean ( 5 / 20 )563.8196721311484.60787566458611122.360001261404
Winsorized Mean ( 6 / 20 )563.3278688524594.43395500662853127.04862092879
Winsorized Mean ( 7 / 20 )563.3278688524594.38973724343269128.328379949217
Winsorized Mean ( 8 / 20 )563.5901639344264.28852688791904131.418125305937
Winsorized Mean ( 9 / 20 )564.0327868852464.20460112356207134.146562375317
Winsorized Mean ( 10 / 20 )561.9016393442623.78035654186539148.637207396050
Winsorized Mean ( 11 / 20 )562.9836065573773.45584891101748162.907471088376
Winsorized Mean ( 12 / 20 )563.1803278688523.42096622952691164.626099786649
Winsorized Mean ( 13 / 20 )563.1803278688523.35067958172824168.079434076585
Winsorized Mean ( 14 / 20 )564.0983606557383.19337642013491176.646372503717
Winsorized Mean ( 15 / 20 )564.3442622950823.07340835753837183.621633262913
Winsorized Mean ( 16 / 20 )564.6065573770492.86435400631813197.114796610912
Winsorized Mean ( 17 / 20 )565.721311475412.60329038826843217.310106480936
Winsorized Mean ( 18 / 20 )565.721311475412.51293170692791225.124029401902
Winsorized Mean ( 19 / 20 )565.721311475412.41818375003421233.944716346476
Winsorized Mean ( 20 / 20 )565.3934426229512.07275834389811272.773449103406
Trimmed Mean ( 1 / 20 )562.8474576271194.9171281621033114.466704765808
Trimmed Mean ( 2 / 20 )563.192982456144.72577840961136119.174648838953
Trimmed Mean ( 3 / 20 )563.3818181818184.56833567591882123.323209621304
Trimmed Mean ( 4 / 20 )563.6226415094344.41441976909155127.677627183475
Trimmed Mean ( 5 / 20 )563.784313725494.26341607721359132.237694729987
Trimmed Mean ( 6 / 20 )563.7755102040824.12797669407651136.574295831921
Trimmed Mean ( 7 / 20 )563.8723404255324.00988798905995140.620471684977
Trimmed Mean ( 8 / 20 )563.9777777777783.86978569198709145.738762470896
Trimmed Mean ( 9 / 20 )564.0465116279073.71625883400797151.778047983699
Trimmed Mean ( 10 / 20 )564.0487804878053.53759447335922159.444160356853
Trimmed Mean ( 11 / 20 )564.3846153846153.41794411153085165.124003485193
Trimmed Mean ( 12 / 20 )564.5945945945953.34467876861956168.8038324911
Trimmed Mean ( 13 / 20 )564.83.24899467321118173.838389042902
Trimmed Mean ( 14 / 20 )565.0303030303033.13112576826225180.455958926201
Trimmed Mean ( 15 / 20 )565.161290322583.00933914589896187.802458587217
Trimmed Mean ( 16 / 20 )565.2758620689652.86730188123579197.145569417803
Trimmed Mean ( 17 / 20 )565.370370370372.72539400855116207.445370686393
Trimmed Mean ( 18 / 20 )565.322.60174300549971217.285104180157
Trimmed Mean ( 19 / 20 )565.2608695652172.43591165239589232.053107923370
Trimmed Mean ( 20 / 20 )565.1904761904762.20023705175152256.877083194536
Median565
Midrange549
Midmean - Weighted Average at Xnp564.233333333333
Midmean - Weighted Average at X(n+1)p565.16129032258
Midmean - Empirical Distribution Function565.16129032258
Midmean - Empirical Distribution Function - Averaging565.16129032258
Midmean - Empirical Distribution Function - Interpolation565.16129032258
Midmean - Closest Observation564.125
Midmean - True Basic - Statistics Graphics Toolkit565.16129032258
Midmean - MS Excel (old versions)565.16129032258
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')