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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 computationWed, 15 Oct 2008 08:31:31 -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/15/t1224081190v7vbv6g0bld4ieo.htm/, Retrieved Fri, 24 May 2024 02:55:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=16287, Retrieved Fri, 24 May 2024 02:55:48 +0000
QR Codes:

Original text written by user:Tijdreeks zijn de maandelijkse dieselprijzen in Belgie van 01/01/2000 tot 01/01/2005
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact212
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Totaal aantal ins...] [2008-10-11 17:28:43] [819b576fab25b35cfda70f80599828ec]
-   P   [Univariate Data Series] [Tijdreeks 3 Insch...] [2008-10-15 13:55:32] [819b576fab25b35cfda70f80599828ec]
F RMPD      [Central Tendency] [Central tendency ...] [2008-10-15 14:31:31] [e08fee3874f3333d6b7a377a061b860d] [Current]
Feedback Forum
2008-10-28 06:57:26 [An De Koninck] [reply
De student heeft gekozen om een voorspelling te maken van de prijzen van de diesel. Dit lijkt me echter een erg moeilijke tijdsreeks om te bespreken, maar wel interessant. De student vertelt dat de prijzen gedurende de geobserveerde periode nauwelijks gewijzigd zijn en dat daardoor de prijzen in de nabije toekomst ook niet sterk zullen wijzigen. Dit lijkt me echter een te enge zienswijze om zulke conclusies te kunnen trekken.
Als je de prijzen het afgelopen jaar (2008) zou bekijken, zouden deze wel erg grote schommelingen vertonen. Enkele jaren geleden was dit misschien anders, maar toch is het moeilijk om zulke prijzen te voorspellen. Brandstof is immers een product dat ingevoerd wordt uit het buitenland, dus het is de economische situatie van die landen die de prijzen bepalen. Deze situatie is echter erg moeilijk te beoordelen en kan snel wijzigen.
Verder doet de overheid nog een duit in het zakje door te pas en te onpas de accijnzen op diesel te verhogen en te verlagen...


2008-10-28 08:18:21 [Michael Van Spaandonck] [reply
Een correcte vaststelling van deze resultaten in het bijhorende document.

Om in te pikken op bovenstaande student: natuurlijk heeft ze helemaal gelijk in haar commentaar, maar de vraag was op basis van deze gegevens een voorspelling te doen, wat ook gebeurt is. In het document van de student vinden we de nodige nuancering terug, maar deze is niet zo uitgebreid als die van bovenstaande student.

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Dataseries X:
0.771
0.751
0.766
0.754
0.773
0.781
0.793
0.791
0.878
0.873
0.897
0.885
0.796
0.776
0.788
0.786
0.801
0.811
0.801
0.781
0.778
0.759
0.764
0.754
0.749
0.729
0.740
0.781
0.768
0.754
0.754
0.754
0.779
0.799
0.780
0.769
0.801
0.792
0.852
0.807
0.797
0.783
0.779
0.785
0.817
0.810
0.798
0.795
0.785
0.785
0.785
0.805
0.824
0.819
0.827
0.826
0.829
0.830
0.825
0.817




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=16287&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=16287&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=16287&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'George Udny Yule' @ 72.249.76.132







Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean0.793950.00448641546547057176.967560430056
Geometric Mean0.793220125070103
Harmonic Mean0.792507360002654
Quadratic Mean0.79469752107327
Winsorized Mean ( 1 / 20 )0.7939333333333330.00437041069491010181.661035714097
Winsorized Mean ( 2 / 20 )0.7940.00423144003215958187.642975905479
Winsorized Mean ( 3 / 20 )0.793850.00413096512710479192.170588609247
Winsorized Mean ( 4 / 20 )0.792650.00367452867956547215.714740344260
Winsorized Mean ( 5 / 20 )0.7908166666666670.00323115935418358244.747033489001
Winsorized Mean ( 6 / 20 )0.7907166666666670.00321077737270895246.269540014708
Winsorized Mean ( 7 / 20 )0.7904833333333330.00316437547204188249.807059976754
Winsorized Mean ( 8 / 20 )0.790350.00313850021759107251.824102343579
Winsorized Mean ( 9 / 20 )0.790950.00296728156517133266.557110482480
Winsorized Mean ( 10 / 20 )0.7916166666666670.00278850461784923283.885729146556
Winsorized Mean ( 11 / 20 )0.7910666666666670.00255171003553722310.014325942062
Winsorized Mean ( 12 / 20 )0.7910666666666670.0024124051653345327.916171808138
Winsorized Mean ( 13 / 20 )0.7912833333333330.00237763730070631332.802371959034
Winsorized Mean ( 14 / 20 )0.790350.00205514718292944384.57099645458
Winsorized Mean ( 15 / 20 )0.79060.00193411244772686408.766305666035
Winsorized Mean ( 16 / 20 )0.79060.00167891544222659470.899236563994
Winsorized Mean ( 17 / 20 )0.79060.00150389325261278525.702205676139
Winsorized Mean ( 18 / 20 )0.78970.00127299193814269620.349568868585
Winsorized Mean ( 19 / 20 )0.78970.00127299193814269620.349568868585
Winsorized Mean ( 20 / 20 )0.7900333333333330.00122612030328054644.335903434242
Trimmed Mean ( 1 / 20 )0.7932931034482760.00412999655191969192.080814953596
Trimmed Mean ( 2 / 20 )0.7926071428571430.0038271652534939207.100318475549
Trimmed Mean ( 3 / 20 )0.7918333333333330.0035410885580709223.612971081612
Trimmed Mean ( 4 / 20 )0.7910576923076920.00322607371740317245.20756858106
Trimmed Mean ( 5 / 20 )0.790580.00303060443893724260.865453057027
Trimmed Mean ( 6 / 20 )0.7905208333333330.00294828198310325268.129316620271
Trimmed Mean ( 7 / 20 )0.7904782608695650.00284846583513769277.510177976685
Trimmed Mean ( 8 / 20 )0.7904772727272730.00273291127835367289.243664435189
Trimmed Mean ( 9 / 20 )0.79050.00258855921162762305.382235974797
Trimmed Mean ( 10 / 20 )0.7904250.00244893684415639322.762508917328
Trimmed Mean ( 11 / 20 )0.7902368421052630.00231404709442774341.495574575023
Trimmed Mean ( 12 / 20 )0.7901111111111110.00220273198975610358.695980621136
Trimmed Mean ( 13 / 20 )0.7899705882352940.00208999198035018377.977808366008
Trimmed Mean ( 14 / 20 )0.789781250.00193895464384475407.323220533899
Trimmed Mean ( 15 / 20 )0.78970.00183400196793537430.588414738184
Trimmed Mean ( 16 / 20 )0.7895714285714290.00171934878424203459.227025841363
Trimmed Mean ( 17 / 20 )0.7894230769230770.00163911146373044481.616469893045
Trimmed Mean ( 18 / 20 )0.789250.00157683640352392500.52751080339
Trimmed Mean ( 19 / 20 )0.7891818181818180.00156342996724992504.775931582016
Trimmed Mean ( 20 / 20 )0.78910.00152677851425115516.839864220276
Median0.787
Midrange0.813
Midmean - Weighted Average at Xnp0.789096774193548
Midmean - Weighted Average at X(n+1)p0.7897
Midmean - Empirical Distribution Function0.789096774193548
Midmean - Empirical Distribution Function - Averaging0.7897
Midmean - Empirical Distribution Function - Interpolation0.7897
Midmean - Closest Observation0.789096774193548
Midmean - True Basic - Statistics Graphics Toolkit0.7897
Midmean - MS Excel (old versions)0.78978125
Number of observations60

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 0.79395 & 0.00448641546547057 & 176.967560430056 \tabularnewline
Geometric Mean & 0.793220125070103 &  &  \tabularnewline
Harmonic Mean & 0.792507360002654 &  &  \tabularnewline
Quadratic Mean & 0.79469752107327 &  &  \tabularnewline
Winsorized Mean ( 1 / 20 ) & 0.793933333333333 & 0.00437041069491010 & 181.661035714097 \tabularnewline
Winsorized Mean ( 2 / 20 ) & 0.794 & 0.00423144003215958 & 187.642975905479 \tabularnewline
Winsorized Mean ( 3 / 20 ) & 0.79385 & 0.00413096512710479 & 192.170588609247 \tabularnewline
Winsorized Mean ( 4 / 20 ) & 0.79265 & 0.00367452867956547 & 215.714740344260 \tabularnewline
Winsorized Mean ( 5 / 20 ) & 0.790816666666667 & 0.00323115935418358 & 244.747033489001 \tabularnewline
Winsorized Mean ( 6 / 20 ) & 0.790716666666667 & 0.00321077737270895 & 246.269540014708 \tabularnewline
Winsorized Mean ( 7 / 20 ) & 0.790483333333333 & 0.00316437547204188 & 249.807059976754 \tabularnewline
Winsorized Mean ( 8 / 20 ) & 0.79035 & 0.00313850021759107 & 251.824102343579 \tabularnewline
Winsorized Mean ( 9 / 20 ) & 0.79095 & 0.00296728156517133 & 266.557110482480 \tabularnewline
Winsorized Mean ( 10 / 20 ) & 0.791616666666667 & 0.00278850461784923 & 283.885729146556 \tabularnewline
Winsorized Mean ( 11 / 20 ) & 0.791066666666667 & 0.00255171003553722 & 310.014325942062 \tabularnewline
Winsorized Mean ( 12 / 20 ) & 0.791066666666667 & 0.0024124051653345 & 327.916171808138 \tabularnewline
Winsorized Mean ( 13 / 20 ) & 0.791283333333333 & 0.00237763730070631 & 332.802371959034 \tabularnewline
Winsorized Mean ( 14 / 20 ) & 0.79035 & 0.00205514718292944 & 384.57099645458 \tabularnewline
Winsorized Mean ( 15 / 20 ) & 0.7906 & 0.00193411244772686 & 408.766305666035 \tabularnewline
Winsorized Mean ( 16 / 20 ) & 0.7906 & 0.00167891544222659 & 470.899236563994 \tabularnewline
Winsorized Mean ( 17 / 20 ) & 0.7906 & 0.00150389325261278 & 525.702205676139 \tabularnewline
Winsorized Mean ( 18 / 20 ) & 0.7897 & 0.00127299193814269 & 620.349568868585 \tabularnewline
Winsorized Mean ( 19 / 20 ) & 0.7897 & 0.00127299193814269 & 620.349568868585 \tabularnewline
Winsorized Mean ( 20 / 20 ) & 0.790033333333333 & 0.00122612030328054 & 644.335903434242 \tabularnewline
Trimmed Mean ( 1 / 20 ) & 0.793293103448276 & 0.00412999655191969 & 192.080814953596 \tabularnewline
Trimmed Mean ( 2 / 20 ) & 0.792607142857143 & 0.0038271652534939 & 207.100318475549 \tabularnewline
Trimmed Mean ( 3 / 20 ) & 0.791833333333333 & 0.0035410885580709 & 223.612971081612 \tabularnewline
Trimmed Mean ( 4 / 20 ) & 0.791057692307692 & 0.00322607371740317 & 245.20756858106 \tabularnewline
Trimmed Mean ( 5 / 20 ) & 0.79058 & 0.00303060443893724 & 260.865453057027 \tabularnewline
Trimmed Mean ( 6 / 20 ) & 0.790520833333333 & 0.00294828198310325 & 268.129316620271 \tabularnewline
Trimmed Mean ( 7 / 20 ) & 0.790478260869565 & 0.00284846583513769 & 277.510177976685 \tabularnewline
Trimmed Mean ( 8 / 20 ) & 0.790477272727273 & 0.00273291127835367 & 289.243664435189 \tabularnewline
Trimmed Mean ( 9 / 20 ) & 0.7905 & 0.00258855921162762 & 305.382235974797 \tabularnewline
Trimmed Mean ( 10 / 20 ) & 0.790425 & 0.00244893684415639 & 322.762508917328 \tabularnewline
Trimmed Mean ( 11 / 20 ) & 0.790236842105263 & 0.00231404709442774 & 341.495574575023 \tabularnewline
Trimmed Mean ( 12 / 20 ) & 0.790111111111111 & 0.00220273198975610 & 358.695980621136 \tabularnewline
Trimmed Mean ( 13 / 20 ) & 0.789970588235294 & 0.00208999198035018 & 377.977808366008 \tabularnewline
Trimmed Mean ( 14 / 20 ) & 0.78978125 & 0.00193895464384475 & 407.323220533899 \tabularnewline
Trimmed Mean ( 15 / 20 ) & 0.7897 & 0.00183400196793537 & 430.588414738184 \tabularnewline
Trimmed Mean ( 16 / 20 ) & 0.789571428571429 & 0.00171934878424203 & 459.227025841363 \tabularnewline
Trimmed Mean ( 17 / 20 ) & 0.789423076923077 & 0.00163911146373044 & 481.616469893045 \tabularnewline
Trimmed Mean ( 18 / 20 ) & 0.78925 & 0.00157683640352392 & 500.52751080339 \tabularnewline
Trimmed Mean ( 19 / 20 ) & 0.789181818181818 & 0.00156342996724992 & 504.775931582016 \tabularnewline
Trimmed Mean ( 20 / 20 ) & 0.7891 & 0.00152677851425115 & 516.839864220276 \tabularnewline
Median & 0.787 &  &  \tabularnewline
Midrange & 0.813 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 0.789096774193548 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 0.7897 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 0.789096774193548 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 0.7897 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 0.7897 &  &  \tabularnewline
Midmean - Closest Observation & 0.789096774193548 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 0.7897 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 0.78978125 &  &  \tabularnewline
Number of observations & 60 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=16287&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]0.79395[/C][C]0.00448641546547057[/C][C]176.967560430056[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]0.793220125070103[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]0.792507360002654[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]0.79469752107327[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 20 )[/C][C]0.793933333333333[/C][C]0.00437041069491010[/C][C]181.661035714097[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 20 )[/C][C]0.794[/C][C]0.00423144003215958[/C][C]187.642975905479[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 20 )[/C][C]0.79385[/C][C]0.00413096512710479[/C][C]192.170588609247[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 20 )[/C][C]0.79265[/C][C]0.00367452867956547[/C][C]215.714740344260[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 20 )[/C][C]0.790816666666667[/C][C]0.00323115935418358[/C][C]244.747033489001[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 20 )[/C][C]0.790716666666667[/C][C]0.00321077737270895[/C][C]246.269540014708[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 20 )[/C][C]0.790483333333333[/C][C]0.00316437547204188[/C][C]249.807059976754[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 20 )[/C][C]0.79035[/C][C]0.00313850021759107[/C][C]251.824102343579[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 20 )[/C][C]0.79095[/C][C]0.00296728156517133[/C][C]266.557110482480[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 20 )[/C][C]0.791616666666667[/C][C]0.00278850461784923[/C][C]283.885729146556[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 20 )[/C][C]0.791066666666667[/C][C]0.00255171003553722[/C][C]310.014325942062[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 20 )[/C][C]0.791066666666667[/C][C]0.0024124051653345[/C][C]327.916171808138[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 20 )[/C][C]0.791283333333333[/C][C]0.00237763730070631[/C][C]332.802371959034[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 20 )[/C][C]0.79035[/C][C]0.00205514718292944[/C][C]384.57099645458[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 20 )[/C][C]0.7906[/C][C]0.00193411244772686[/C][C]408.766305666035[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 20 )[/C][C]0.7906[/C][C]0.00167891544222659[/C][C]470.899236563994[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 20 )[/C][C]0.7906[/C][C]0.00150389325261278[/C][C]525.702205676139[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 20 )[/C][C]0.7897[/C][C]0.00127299193814269[/C][C]620.349568868585[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 20 )[/C][C]0.7897[/C][C]0.00127299193814269[/C][C]620.349568868585[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 20 )[/C][C]0.790033333333333[/C][C]0.00122612030328054[/C][C]644.335903434242[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 20 )[/C][C]0.793293103448276[/C][C]0.00412999655191969[/C][C]192.080814953596[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 20 )[/C][C]0.792607142857143[/C][C]0.0038271652534939[/C][C]207.100318475549[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 20 )[/C][C]0.791833333333333[/C][C]0.0035410885580709[/C][C]223.612971081612[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 20 )[/C][C]0.791057692307692[/C][C]0.00322607371740317[/C][C]245.20756858106[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 20 )[/C][C]0.79058[/C][C]0.00303060443893724[/C][C]260.865453057027[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 20 )[/C][C]0.790520833333333[/C][C]0.00294828198310325[/C][C]268.129316620271[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 20 )[/C][C]0.790478260869565[/C][C]0.00284846583513769[/C][C]277.510177976685[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 20 )[/C][C]0.790477272727273[/C][C]0.00273291127835367[/C][C]289.243664435189[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 20 )[/C][C]0.7905[/C][C]0.00258855921162762[/C][C]305.382235974797[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 20 )[/C][C]0.790425[/C][C]0.00244893684415639[/C][C]322.762508917328[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 20 )[/C][C]0.790236842105263[/C][C]0.00231404709442774[/C][C]341.495574575023[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 20 )[/C][C]0.790111111111111[/C][C]0.00220273198975610[/C][C]358.695980621136[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 20 )[/C][C]0.789970588235294[/C][C]0.00208999198035018[/C][C]377.977808366008[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 20 )[/C][C]0.78978125[/C][C]0.00193895464384475[/C][C]407.323220533899[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 20 )[/C][C]0.7897[/C][C]0.00183400196793537[/C][C]430.588414738184[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 20 )[/C][C]0.789571428571429[/C][C]0.00171934878424203[/C][C]459.227025841363[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 20 )[/C][C]0.789423076923077[/C][C]0.00163911146373044[/C][C]481.616469893045[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 20 )[/C][C]0.78925[/C][C]0.00157683640352392[/C][C]500.52751080339[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 20 )[/C][C]0.789181818181818[/C][C]0.00156342996724992[/C][C]504.775931582016[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 20 )[/C][C]0.7891[/C][C]0.00152677851425115[/C][C]516.839864220276[/C][/ROW]
[ROW][C]Median[/C][C]0.787[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]0.813[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]0.789096774193548[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]0.7897[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]0.789096774193548[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]0.7897[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]0.7897[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]0.789096774193548[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]0.7897[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]0.78978125[/C][C][/C][C][/C][/ROW]
[ROW][C]Number of observations[/C][C]60[/C][C][/C][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=16287&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=16287&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 Mean0.793950.00448641546547057176.967560430056
Geometric Mean0.793220125070103
Harmonic Mean0.792507360002654
Quadratic Mean0.79469752107327
Winsorized Mean ( 1 / 20 )0.7939333333333330.00437041069491010181.661035714097
Winsorized Mean ( 2 / 20 )0.7940.00423144003215958187.642975905479
Winsorized Mean ( 3 / 20 )0.793850.00413096512710479192.170588609247
Winsorized Mean ( 4 / 20 )0.792650.00367452867956547215.714740344260
Winsorized Mean ( 5 / 20 )0.7908166666666670.00323115935418358244.747033489001
Winsorized Mean ( 6 / 20 )0.7907166666666670.00321077737270895246.269540014708
Winsorized Mean ( 7 / 20 )0.7904833333333330.00316437547204188249.807059976754
Winsorized Mean ( 8 / 20 )0.790350.00313850021759107251.824102343579
Winsorized Mean ( 9 / 20 )0.790950.00296728156517133266.557110482480
Winsorized Mean ( 10 / 20 )0.7916166666666670.00278850461784923283.885729146556
Winsorized Mean ( 11 / 20 )0.7910666666666670.00255171003553722310.014325942062
Winsorized Mean ( 12 / 20 )0.7910666666666670.0024124051653345327.916171808138
Winsorized Mean ( 13 / 20 )0.7912833333333330.00237763730070631332.802371959034
Winsorized Mean ( 14 / 20 )0.790350.00205514718292944384.57099645458
Winsorized Mean ( 15 / 20 )0.79060.00193411244772686408.766305666035
Winsorized Mean ( 16 / 20 )0.79060.00167891544222659470.899236563994
Winsorized Mean ( 17 / 20 )0.79060.00150389325261278525.702205676139
Winsorized Mean ( 18 / 20 )0.78970.00127299193814269620.349568868585
Winsorized Mean ( 19 / 20 )0.78970.00127299193814269620.349568868585
Winsorized Mean ( 20 / 20 )0.7900333333333330.00122612030328054644.335903434242
Trimmed Mean ( 1 / 20 )0.7932931034482760.00412999655191969192.080814953596
Trimmed Mean ( 2 / 20 )0.7926071428571430.0038271652534939207.100318475549
Trimmed Mean ( 3 / 20 )0.7918333333333330.0035410885580709223.612971081612
Trimmed Mean ( 4 / 20 )0.7910576923076920.00322607371740317245.20756858106
Trimmed Mean ( 5 / 20 )0.790580.00303060443893724260.865453057027
Trimmed Mean ( 6 / 20 )0.7905208333333330.00294828198310325268.129316620271
Trimmed Mean ( 7 / 20 )0.7904782608695650.00284846583513769277.510177976685
Trimmed Mean ( 8 / 20 )0.7904772727272730.00273291127835367289.243664435189
Trimmed Mean ( 9 / 20 )0.79050.00258855921162762305.382235974797
Trimmed Mean ( 10 / 20 )0.7904250.00244893684415639322.762508917328
Trimmed Mean ( 11 / 20 )0.7902368421052630.00231404709442774341.495574575023
Trimmed Mean ( 12 / 20 )0.7901111111111110.00220273198975610358.695980621136
Trimmed Mean ( 13 / 20 )0.7899705882352940.00208999198035018377.977808366008
Trimmed Mean ( 14 / 20 )0.789781250.00193895464384475407.323220533899
Trimmed Mean ( 15 / 20 )0.78970.00183400196793537430.588414738184
Trimmed Mean ( 16 / 20 )0.7895714285714290.00171934878424203459.227025841363
Trimmed Mean ( 17 / 20 )0.7894230769230770.00163911146373044481.616469893045
Trimmed Mean ( 18 / 20 )0.789250.00157683640352392500.52751080339
Trimmed Mean ( 19 / 20 )0.7891818181818180.00156342996724992504.775931582016
Trimmed Mean ( 20 / 20 )0.78910.00152677851425115516.839864220276
Median0.787
Midrange0.813
Midmean - Weighted Average at Xnp0.789096774193548
Midmean - Weighted Average at X(n+1)p0.7897
Midmean - Empirical Distribution Function0.789096774193548
Midmean - Empirical Distribution Function - Averaging0.7897
Midmean - Empirical Distribution Function - Interpolation0.7897
Midmean - Closest Observation0.789096774193548
Midmean - True Basic - Statistics Graphics Toolkit0.7897
Midmean - MS Excel (old versions)0.78978125
Number of observations60



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')