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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_centraltendency.wasp
Title produced by softwareCentral Tendency
Date of computationMon, 20 Oct 2008 08:01:17 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Oct/20/t122451141055b78f054vp6i30.htm/, Retrieved Sun, 19 May 2024 16:11:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=17305, Retrieved Sun, 19 May 2024 16:11:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Werkloosheid jong...] [2008-10-13 15:38:30] [7d3039e6253bb5fb3b26df1537d500b4]
F RMPD    [Central Tendency] [Central tendency ...] [2008-10-20 14:01:17] [35348cd8592af0baf5f138bd59921307] [Current]
Feedback Forum
2008-10-24 13:41:55 [Annelies Michiels] [reply
Bij de oplossing van deze vraag neemt de student wel de oorspronkelijke tijdreeks op waardoor ik kon controleren of de gegevens die ze hier heeft opgenomen klopte.

De student heeft in haar oplossing wel een toekomstvoorspelling gemaakt maar niet geantwoord op de vraag of deze toekomstvoorspelling nutig kan zijn.

Ik ben het eens met de student haar voorspelling dat als we deze curve bezien de werkloosheid onder de 25 zal blijven dalen.
Deze voorspelling kan nuttig zijn voor de overheid maar ik denk dat het belangrijk is dat men deze curve dan ook gaat vergelijken met een grafiek over hoe het gaat met onze economie.

Als het met onze economie nu binnekort nog slechter zou gaan, lijkt het mij raar dat de werkloosheid voor mensen onder de 25 jaar nog gaat dalen.
Het is dus belangrijk dat men alle factoren gaat onderzoeken die de werkloosheid kunnen beinvloeden, vooraleer men een toekomst voorspelling doet.
2008-10-27 23:58:38 [Jeroen Michel] [reply
Ook hier haalt de vorige studente de belangrijkste redenen naar boven. Er is controle van de data mogelijk, maar er wordt geen specifieke reden opgegeven. Het is dus moeilijk te zeggen op de student de vraagstelling kan verantwoorden.

zie ook:
http://www.freestatistics.org/blog/index.php?v=date/2008/Oct/13/t1223912368wocw6uasyr3a0b9.htm

Post a new message
Dataseries X:
21.1
21.0
20.4
19.5
18.6
18.8
23.7
24.8
25.0
23.6
22.3
21.8
20.8
19.7
18.3
17.4
17.0
18.1
23.9
25.6
25.3
23.6
21.9
21.4
20.6
20.5
20.2
20.6
19.7
19.3
22.8
23.5
23.8
22.6
22.0
21.7
20.7
20.2
19.1
19.5
18.7
18.6
22.2
23.2
23.5
21.3
20.0
18.7
18.9
18.3
18.4
19.9
19.2
18.5
20.9
20.5
19.4
18.1
17.0
17.0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=17305&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]2 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=17305&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=17305&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean20.71166666666670.28786268370602271.9498144046289
Geometric Mean20.5956336093196
Harmonic Mean20.4817732630759
Quadratic Mean20.8293582874429
Winsorized Mean ( 1 / 20 )20.70666666666670.28646359907846472.2837621718039
Winsorized Mean ( 2 / 20 )20.69666666666670.28381945668411672.9219444941067
Winsorized Mean ( 3 / 20 )20.70666666666670.27704928956996874.7400099773121
Winsorized Mean ( 4 / 20 )20.69333333333330.25429493382880881.3753267584328
Winsorized Mean ( 5 / 20 )20.6850.25253321088298581.9100185978498
Winsorized Mean ( 6 / 20 )20.6950.24707812300982183.7589331985388
Winsorized Mean ( 7 / 20 )20.68333333333330.24469705317856884.5262869522161
Winsorized Mean ( 8 / 20 )20.69666666666670.24252632994302185.3378133068239
Winsorized Mean ( 9 / 20 )20.69666666666670.237119801738887.2835862500641
Winsorized Mean ( 10 / 20 )20.71333333333330.234538453675288.3152975930255
Winsorized Mean ( 11 / 20 )20.65833333333330.22369889552001392.3488392077695
Winsorized Mean ( 12 / 20 )20.59833333333330.205592593497289100.190055404914
Winsorized Mean ( 13 / 20 )20.5550.197861804766132103.885638889707
Winsorized Mean ( 14 / 20 )20.50833333333330.182175982259575112.574298098812
Winsorized Mean ( 15 / 20 )20.50833333333330.174088289612017117.804209456244
Winsorized Mean ( 16 / 20 )20.50833333333330.157169376171143130.485555347637
Winsorized Mean ( 17 / 20 )20.50833333333330.148367608558281138.226487119508
Winsorized Mean ( 18 / 20 )20.50833333333330.139172121751064147.359493232534
Winsorized Mean ( 19 / 20 )20.50833333333330.129586296676201158.260046466007
Winsorized Mean ( 20 / 20 )20.44166666666670.109500449310707186.68112135927
Trimmed Mean ( 1 / 20 )20.69137931034480.27807875649539874.4083423383953
Trimmed Mean ( 2 / 20 )20.6750.26772550878081877.2246174604386
Trimmed Mean ( 3 / 20 )20.66296296296300.2566692027468280.5042550560498
Trimmed Mean ( 4 / 20 )20.64615384615380.24621511303908783.8541289822297
Trimmed Mean ( 5 / 20 )20.6320.24217450277830385.1947656062183
Trimmed Mean ( 6 / 20 )20.618750.23748191887126986.822399355703
Trimmed Mean ( 7 / 20 )20.60217391304350.23297050735852188.432540868096
Trimmed Mean ( 8 / 20 )20.58636363636360.22764979291554790.4299686492606
Trimmed Mean ( 9 / 20 )20.56666666666670.22109080929409293.0236165507411
Trimmed Mean ( 10 / 20 )20.5450.21375609153113496.1142199636802
Trimmed Mean ( 11 / 20 )20.51842105263160.204391866369012100.387659338593
Trimmed Mean ( 12 / 20 )20.49722222222220.194874241544738105.181793446604
Trimmed Mean ( 13 / 20 )20.48235294117650.187176724915266109.427883998124
Trimmed Mean ( 14 / 20 )20.4718750.178605393515263114.620698720672
Trimmed Mean ( 15 / 20 )20.46666666666670.171225020551423119.530817404808
Trimmed Mean ( 16 / 20 )20.46071428571430.162840143455119125.649080451428
Trimmed Mean ( 17 / 20 )20.45384615384620.156030644843177131.088647197504
Trimmed Mean ( 18 / 20 )20.44583333333330.148419104822555137.757422521701
Trimmed Mean ( 19 / 20 )20.43636363636360.139657195415793146.332335942446
Trimmed Mean ( 20 / 20 )20.4250.129141904813842158.159352144005
Median20.5
Midrange21.3
Midmean - Weighted Average at Xnp20.4129032258065
Midmean - Weighted Average at X(n+1)p20.4666666666667
Midmean - Empirical Distribution Function20.4129032258065
Midmean - Empirical Distribution Function - Averaging20.4666666666667
Midmean - Empirical Distribution Function - Interpolation20.4666666666667
Midmean - Closest Observation20.4129032258065
Midmean - True Basic - Statistics Graphics Toolkit20.4666666666667
Midmean - MS Excel (old versions)20.471875
Number of observations60

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 20.7116666666667 & 0.287862683706022 & 71.9498144046289 \tabularnewline
Geometric Mean & 20.5956336093196 &  &  \tabularnewline
Harmonic Mean & 20.4817732630759 &  &  \tabularnewline
Quadratic Mean & 20.8293582874429 &  &  \tabularnewline
Winsorized Mean ( 1 / 20 ) & 20.7066666666667 & 0.286463599078464 & 72.2837621718039 \tabularnewline
Winsorized Mean ( 2 / 20 ) & 20.6966666666667 & 0.283819456684116 & 72.9219444941067 \tabularnewline
Winsorized Mean ( 3 / 20 ) & 20.7066666666667 & 0.277049289569968 & 74.7400099773121 \tabularnewline
Winsorized Mean ( 4 / 20 ) & 20.6933333333333 & 0.254294933828808 & 81.3753267584328 \tabularnewline
Winsorized Mean ( 5 / 20 ) & 20.685 & 0.252533210882985 & 81.9100185978498 \tabularnewline
Winsorized Mean ( 6 / 20 ) & 20.695 & 0.247078123009821 & 83.7589331985388 \tabularnewline
Winsorized Mean ( 7 / 20 ) & 20.6833333333333 & 0.244697053178568 & 84.5262869522161 \tabularnewline
Winsorized Mean ( 8 / 20 ) & 20.6966666666667 & 0.242526329943021 & 85.3378133068239 \tabularnewline
Winsorized Mean ( 9 / 20 ) & 20.6966666666667 & 0.2371198017388 & 87.2835862500641 \tabularnewline
Winsorized Mean ( 10 / 20 ) & 20.7133333333333 & 0.2345384536752 & 88.3152975930255 \tabularnewline
Winsorized Mean ( 11 / 20 ) & 20.6583333333333 & 0.223698895520013 & 92.3488392077695 \tabularnewline
Winsorized Mean ( 12 / 20 ) & 20.5983333333333 & 0.205592593497289 & 100.190055404914 \tabularnewline
Winsorized Mean ( 13 / 20 ) & 20.555 & 0.197861804766132 & 103.885638889707 \tabularnewline
Winsorized Mean ( 14 / 20 ) & 20.5083333333333 & 0.182175982259575 & 112.574298098812 \tabularnewline
Winsorized Mean ( 15 / 20 ) & 20.5083333333333 & 0.174088289612017 & 117.804209456244 \tabularnewline
Winsorized Mean ( 16 / 20 ) & 20.5083333333333 & 0.157169376171143 & 130.485555347637 \tabularnewline
Winsorized Mean ( 17 / 20 ) & 20.5083333333333 & 0.148367608558281 & 138.226487119508 \tabularnewline
Winsorized Mean ( 18 / 20 ) & 20.5083333333333 & 0.139172121751064 & 147.359493232534 \tabularnewline
Winsorized Mean ( 19 / 20 ) & 20.5083333333333 & 0.129586296676201 & 158.260046466007 \tabularnewline
Winsorized Mean ( 20 / 20 ) & 20.4416666666667 & 0.109500449310707 & 186.68112135927 \tabularnewline
Trimmed Mean ( 1 / 20 ) & 20.6913793103448 & 0.278078756495398 & 74.4083423383953 \tabularnewline
Trimmed Mean ( 2 / 20 ) & 20.675 & 0.267725508780818 & 77.2246174604386 \tabularnewline
Trimmed Mean ( 3 / 20 ) & 20.6629629629630 & 0.25666920274682 & 80.5042550560498 \tabularnewline
Trimmed Mean ( 4 / 20 ) & 20.6461538461538 & 0.246215113039087 & 83.8541289822297 \tabularnewline
Trimmed Mean ( 5 / 20 ) & 20.632 & 0.242174502778303 & 85.1947656062183 \tabularnewline
Trimmed Mean ( 6 / 20 ) & 20.61875 & 0.237481918871269 & 86.822399355703 \tabularnewline
Trimmed Mean ( 7 / 20 ) & 20.6021739130435 & 0.232970507358521 & 88.432540868096 \tabularnewline
Trimmed Mean ( 8 / 20 ) & 20.5863636363636 & 0.227649792915547 & 90.4299686492606 \tabularnewline
Trimmed Mean ( 9 / 20 ) & 20.5666666666667 & 0.221090809294092 & 93.0236165507411 \tabularnewline
Trimmed Mean ( 10 / 20 ) & 20.545 & 0.213756091531134 & 96.1142199636802 \tabularnewline
Trimmed Mean ( 11 / 20 ) & 20.5184210526316 & 0.204391866369012 & 100.387659338593 \tabularnewline
Trimmed Mean ( 12 / 20 ) & 20.4972222222222 & 0.194874241544738 & 105.181793446604 \tabularnewline
Trimmed Mean ( 13 / 20 ) & 20.4823529411765 & 0.187176724915266 & 109.427883998124 \tabularnewline
Trimmed Mean ( 14 / 20 ) & 20.471875 & 0.178605393515263 & 114.620698720672 \tabularnewline
Trimmed Mean ( 15 / 20 ) & 20.4666666666667 & 0.171225020551423 & 119.530817404808 \tabularnewline
Trimmed Mean ( 16 / 20 ) & 20.4607142857143 & 0.162840143455119 & 125.649080451428 \tabularnewline
Trimmed Mean ( 17 / 20 ) & 20.4538461538462 & 0.156030644843177 & 131.088647197504 \tabularnewline
Trimmed Mean ( 18 / 20 ) & 20.4458333333333 & 0.148419104822555 & 137.757422521701 \tabularnewline
Trimmed Mean ( 19 / 20 ) & 20.4363636363636 & 0.139657195415793 & 146.332335942446 \tabularnewline
Trimmed Mean ( 20 / 20 ) & 20.425 & 0.129141904813842 & 158.159352144005 \tabularnewline
Median & 20.5 &  &  \tabularnewline
Midrange & 21.3 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 20.4129032258065 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 20.4666666666667 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 20.4129032258065 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 20.4666666666667 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 20.4666666666667 &  &  \tabularnewline
Midmean - Closest Observation & 20.4129032258065 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 20.4666666666667 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 20.471875 &  &  \tabularnewline
Number of observations & 60 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=17305&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]20.7116666666667[/C][C]0.287862683706022[/C][C]71.9498144046289[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]20.5956336093196[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]20.4817732630759[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]20.8293582874429[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 20 )[/C][C]20.7066666666667[/C][C]0.286463599078464[/C][C]72.2837621718039[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 20 )[/C][C]20.6966666666667[/C][C]0.283819456684116[/C][C]72.9219444941067[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 20 )[/C][C]20.7066666666667[/C][C]0.277049289569968[/C][C]74.7400099773121[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 20 )[/C][C]20.6933333333333[/C][C]0.254294933828808[/C][C]81.3753267584328[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 20 )[/C][C]20.685[/C][C]0.252533210882985[/C][C]81.9100185978498[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 20 )[/C][C]20.695[/C][C]0.247078123009821[/C][C]83.7589331985388[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 20 )[/C][C]20.6833333333333[/C][C]0.244697053178568[/C][C]84.5262869522161[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 20 )[/C][C]20.6966666666667[/C][C]0.242526329943021[/C][C]85.3378133068239[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 20 )[/C][C]20.6966666666667[/C][C]0.2371198017388[/C][C]87.2835862500641[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 20 )[/C][C]20.7133333333333[/C][C]0.2345384536752[/C][C]88.3152975930255[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 20 )[/C][C]20.6583333333333[/C][C]0.223698895520013[/C][C]92.3488392077695[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 20 )[/C][C]20.5983333333333[/C][C]0.205592593497289[/C][C]100.190055404914[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 20 )[/C][C]20.555[/C][C]0.197861804766132[/C][C]103.885638889707[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 20 )[/C][C]20.5083333333333[/C][C]0.182175982259575[/C][C]112.574298098812[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 20 )[/C][C]20.5083333333333[/C][C]0.174088289612017[/C][C]117.804209456244[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 20 )[/C][C]20.5083333333333[/C][C]0.157169376171143[/C][C]130.485555347637[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 20 )[/C][C]20.5083333333333[/C][C]0.148367608558281[/C][C]138.226487119508[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 20 )[/C][C]20.5083333333333[/C][C]0.139172121751064[/C][C]147.359493232534[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 20 )[/C][C]20.5083333333333[/C][C]0.129586296676201[/C][C]158.260046466007[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 20 )[/C][C]20.4416666666667[/C][C]0.109500449310707[/C][C]186.68112135927[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 20 )[/C][C]20.6913793103448[/C][C]0.278078756495398[/C][C]74.4083423383953[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 20 )[/C][C]20.675[/C][C]0.267725508780818[/C][C]77.2246174604386[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 20 )[/C][C]20.6629629629630[/C][C]0.25666920274682[/C][C]80.5042550560498[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 20 )[/C][C]20.6461538461538[/C][C]0.246215113039087[/C][C]83.8541289822297[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 20 )[/C][C]20.632[/C][C]0.242174502778303[/C][C]85.1947656062183[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 20 )[/C][C]20.61875[/C][C]0.237481918871269[/C][C]86.822399355703[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 20 )[/C][C]20.6021739130435[/C][C]0.232970507358521[/C][C]88.432540868096[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 20 )[/C][C]20.5863636363636[/C][C]0.227649792915547[/C][C]90.4299686492606[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 20 )[/C][C]20.5666666666667[/C][C]0.221090809294092[/C][C]93.0236165507411[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 20 )[/C][C]20.545[/C][C]0.213756091531134[/C][C]96.1142199636802[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 20 )[/C][C]20.5184210526316[/C][C]0.204391866369012[/C][C]100.387659338593[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 20 )[/C][C]20.4972222222222[/C][C]0.194874241544738[/C][C]105.181793446604[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 20 )[/C][C]20.4823529411765[/C][C]0.187176724915266[/C][C]109.427883998124[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 20 )[/C][C]20.471875[/C][C]0.178605393515263[/C][C]114.620698720672[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 20 )[/C][C]20.4666666666667[/C][C]0.171225020551423[/C][C]119.530817404808[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 20 )[/C][C]20.4607142857143[/C][C]0.162840143455119[/C][C]125.649080451428[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 20 )[/C][C]20.4538461538462[/C][C]0.156030644843177[/C][C]131.088647197504[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 20 )[/C][C]20.4458333333333[/C][C]0.148419104822555[/C][C]137.757422521701[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 20 )[/C][C]20.4363636363636[/C][C]0.139657195415793[/C][C]146.332335942446[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 20 )[/C][C]20.425[/C][C]0.129141904813842[/C][C]158.159352144005[/C][/ROW]
[ROW][C]Median[/C][C]20.5[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]21.3[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]20.4129032258065[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]20.4666666666667[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]20.4129032258065[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]20.4666666666667[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]20.4666666666667[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]20.4129032258065[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]20.4666666666667[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]20.471875[/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=17305&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=17305&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 Mean20.71166666666670.28786268370602271.9498144046289
Geometric Mean20.5956336093196
Harmonic Mean20.4817732630759
Quadratic Mean20.8293582874429
Winsorized Mean ( 1 / 20 )20.70666666666670.28646359907846472.2837621718039
Winsorized Mean ( 2 / 20 )20.69666666666670.28381945668411672.9219444941067
Winsorized Mean ( 3 / 20 )20.70666666666670.27704928956996874.7400099773121
Winsorized Mean ( 4 / 20 )20.69333333333330.25429493382880881.3753267584328
Winsorized Mean ( 5 / 20 )20.6850.25253321088298581.9100185978498
Winsorized Mean ( 6 / 20 )20.6950.24707812300982183.7589331985388
Winsorized Mean ( 7 / 20 )20.68333333333330.24469705317856884.5262869522161
Winsorized Mean ( 8 / 20 )20.69666666666670.24252632994302185.3378133068239
Winsorized Mean ( 9 / 20 )20.69666666666670.237119801738887.2835862500641
Winsorized Mean ( 10 / 20 )20.71333333333330.234538453675288.3152975930255
Winsorized Mean ( 11 / 20 )20.65833333333330.22369889552001392.3488392077695
Winsorized Mean ( 12 / 20 )20.59833333333330.205592593497289100.190055404914
Winsorized Mean ( 13 / 20 )20.5550.197861804766132103.885638889707
Winsorized Mean ( 14 / 20 )20.50833333333330.182175982259575112.574298098812
Winsorized Mean ( 15 / 20 )20.50833333333330.174088289612017117.804209456244
Winsorized Mean ( 16 / 20 )20.50833333333330.157169376171143130.485555347637
Winsorized Mean ( 17 / 20 )20.50833333333330.148367608558281138.226487119508
Winsorized Mean ( 18 / 20 )20.50833333333330.139172121751064147.359493232534
Winsorized Mean ( 19 / 20 )20.50833333333330.129586296676201158.260046466007
Winsorized Mean ( 20 / 20 )20.44166666666670.109500449310707186.68112135927
Trimmed Mean ( 1 / 20 )20.69137931034480.27807875649539874.4083423383953
Trimmed Mean ( 2 / 20 )20.6750.26772550878081877.2246174604386
Trimmed Mean ( 3 / 20 )20.66296296296300.2566692027468280.5042550560498
Trimmed Mean ( 4 / 20 )20.64615384615380.24621511303908783.8541289822297
Trimmed Mean ( 5 / 20 )20.6320.24217450277830385.1947656062183
Trimmed Mean ( 6 / 20 )20.618750.23748191887126986.822399355703
Trimmed Mean ( 7 / 20 )20.60217391304350.23297050735852188.432540868096
Trimmed Mean ( 8 / 20 )20.58636363636360.22764979291554790.4299686492606
Trimmed Mean ( 9 / 20 )20.56666666666670.22109080929409293.0236165507411
Trimmed Mean ( 10 / 20 )20.5450.21375609153113496.1142199636802
Trimmed Mean ( 11 / 20 )20.51842105263160.204391866369012100.387659338593
Trimmed Mean ( 12 / 20 )20.49722222222220.194874241544738105.181793446604
Trimmed Mean ( 13 / 20 )20.48235294117650.187176724915266109.427883998124
Trimmed Mean ( 14 / 20 )20.4718750.178605393515263114.620698720672
Trimmed Mean ( 15 / 20 )20.46666666666670.171225020551423119.530817404808
Trimmed Mean ( 16 / 20 )20.46071428571430.162840143455119125.649080451428
Trimmed Mean ( 17 / 20 )20.45384615384620.156030644843177131.088647197504
Trimmed Mean ( 18 / 20 )20.44583333333330.148419104822555137.757422521701
Trimmed Mean ( 19 / 20 )20.43636363636360.139657195415793146.332335942446
Trimmed Mean ( 20 / 20 )20.4250.129141904813842158.159352144005
Median20.5
Midrange21.3
Midmean - Weighted Average at Xnp20.4129032258065
Midmean - Weighted Average at X(n+1)p20.4666666666667
Midmean - Empirical Distribution Function20.4129032258065
Midmean - Empirical Distribution Function - Averaging20.4666666666667
Midmean - Empirical Distribution Function - Interpolation20.4666666666667
Midmean - Closest Observation20.4129032258065
Midmean - True Basic - Statistics Graphics Toolkit20.4666666666667
Midmean - MS Excel (old versions)20.471875
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')