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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 computationSun, 19 Oct 2008 11:39:42 -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/t1224438026dysxbufyzvldvzv.htm/, Retrieved Wed, 08 May 2024 19:36:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=16997, Retrieved Wed, 08 May 2024 19:36:43 +0000
QR Codes:

Original text written by user:Hypothecair krediet: ingediende aanvragen - Aantal aanvragen - Nieuwbouw
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact198
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [blog 1e tijdreeks...] [2008-10-13 19:23:31] [7173087adebe3e3a714c80ea2417b3eb]
-   PD  [Univariate Data Series] [tijdreeksen opnie...] [2008-10-19 17:18:46] [7173087adebe3e3a714c80ea2417b3eb]
- RMP       [Central Tendency] [tijdreeks 2 centr...] [2008-10-19 17:39:42] [95d95b0e883740fcbc85e18ec42dcafb] [Current]
-    D        [Central Tendency] [assumtion 4 centr...] [2008-10-25 14:06:52] [7173087adebe3e3a714c80ea2417b3eb]
- RMP         [Mean Plot] [mean plot aanvrag...] [2008-12-05 14:45:50] [7173087adebe3e3a714c80ea2417b3eb]
- RMP         [Standard Deviation-Mean Plot] [mean plot: aanvra...] [2008-12-16 14:37:33] [7d3039e6253bb5fb3b26df1537d500b4]
- RMP         [Spectral Analysis] [Spectral Analysis...] [2008-12-16 14:45:45] [7d3039e6253bb5fb3b26df1537d500b4]
- RMP         [(Partial) Autocorrelation Function] [ACF aanvragen hyp...] [2008-12-16 14:51:47] [7d3039e6253bb5fb3b26df1537d500b4]
-   P           [(Partial) Autocorrelation Function] [ACF met ingevulde...] [2008-12-16 15:15:28] [7d3039e6253bb5fb3b26df1537d500b4]
-  MPD            [(Partial) Autocorrelation Function] [] [2009-12-18 09:05:22] [ebd107afac1bd6180acb277edd05815b]
-                   [(Partial) Autocorrelation Function] [] [2009-12-18 10:51:45] [ebd107afac1bd6180acb277edd05815b]
-   PD              [(Partial) Autocorrelation Function] [] [2009-12-18 10:54:28] [ebd107afac1bd6180acb277edd05815b]
-    D                [(Partial) Autocorrelation Function] [] [2010-12-25 05:30:33] [6e5489189f7de5cfbcc25dd35ae15009]
-    D              [(Partial) Autocorrelation Function] [] [2010-12-24 15:46:54] [6e5489189f7de5cfbcc25dd35ae15009]
- RMP           [ARIMA Backward Selection] [Arima backward aa...] [2008-12-16 15:38:56] [7d3039e6253bb5fb3b26df1537d500b4]
- RMP             [(Partial) Autocorrelation Function] [acf hypothecair k...] [2008-12-17 15:13:05] [7173087adebe3e3a714c80ea2417b3eb]
- RMP               [ARIMA Backward Selection] [Arima backward se...] [2008-12-17 19:36:16] [7d3039e6253bb5fb3b26df1537d500b4]
-   P                 [ARIMA Backward Selection] [Arima aanvragen h...] [2008-12-18 11:08:22] [7d3039e6253bb5fb3b26df1537d500b4]
- RMPD                  [Cross Correlation Function] [cross correlation] [2008-12-18 13:15:14] [7173087adebe3e3a714c80ea2417b3eb]
- RMPD                  [Cross Correlation Function] [cross correlation] [2008-12-18 13:27:32] [7173087adebe3e3a714c80ea2417b3eb]
- RMP                   [ARIMA Forecasting] [Arima Forecast hy...] [2008-12-22 12:55:27] [7d3039e6253bb5fb3b26df1537d500b4]
- RMP                   [ARIMA Forecasting] [forecast aantal a...] [2008-12-22 13:06:40] [7173087adebe3e3a714c80ea2417b3eb]
- RMP                   [ARIMA Forecasting] [Arima forecasting...] [2008-12-22 13:16:21] [c993f605b206b366f754f7f8c1fcc291]
-   P             [ARIMA Backward Selection] [backward arima] [2008-12-17 15:31:43] [7173087adebe3e3a714c80ea2417b3eb]
-   P             [ARIMA Backward Selection] [Arima backward se...] [2008-12-17 15:40:14] [c993f605b206b366f754f7f8c1fcc291]
-   P             [ARIMA Backward Selection] [arima backward op...] [2008-12-22 12:34:56] [7173087adebe3e3a714c80ea2417b3eb]
- RMP           [ARIMA Forecasting] [Arima forecasting...] [2008-12-16 15:51:17] [7d3039e6253bb5fb3b26df1537d500b4]
-   P             [ARIMA Forecasting] [Arima forecast aa...] [2008-12-18 11:33:58] [7d3039e6253bb5fb3b26df1537d500b4]
-   P           [(Partial) Autocorrelation Function] [autocorrelatie aa...] [2008-12-22 12:17:52] [7173087adebe3e3a714c80ea2417b3eb]
-   P           [(Partial) Autocorrelation Function] [autocorrelatie op...] [2008-12-22 12:26:09] [7173087adebe3e3a714c80ea2417b3eb]
- RMP         [Variance Reduction Matrix] [VRM aanvragen hyp...] [2008-12-16 15:01:05] [7d3039e6253bb5fb3b26df1537d500b4]
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Post a new message
Dataseries X:
2400
4700
3700
2900
2800
3000
3100
3700
3000
2000
1900
1900
1800
3400
3800
2800
3100
2100
2000
2500
2400
2500
3300
3100
3700
5600
3700
2900
4000
2900
2400
3300
3800
4400
4000
3100
2700
5200
4600
3700
3200
2400
2200
3200
3100
2300
2500
2900
2700
5000
3500
3000
3800
2800
2400
2700
2800
2700
2600
3100




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=16997&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=16997&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=16997&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 Mean3113.33333333333106.84131182913829.1397894693789
Geometric Mean3013.36404536319
Harmonic Mean2920.20137951797
Quadratic Mean3219.67907303404
Winsorized Mean ( 1 / 20 )3108.33333333333104.05050019743829.8733146638911
Winsorized Mean ( 2 / 20 )3101.66666666667101.86097944605730.4499984541111
Winsorized Mean ( 3 / 20 )3091.6666666666796.38352259331132.0767137730783
Winsorized Mean ( 4 / 20 )308594.534990383347832.633419514722
Winsorized Mean ( 5 / 20 )3076.6666666666788.565678864292734.7388142463287
Winsorized Mean ( 6 / 20 )3046.6666666666777.403727945181539.3607226363097
Winsorized Mean ( 7 / 20 )3058.3333333333375.32572082931440.6014479471552
Winsorized Mean ( 8 / 20 )304567.749330265416144.9450937458842
Winsorized Mean ( 9 / 20 )304567.749330265416144.9450937458842
Winsorized Mean ( 10 / 20 )304567.749330265416144.9450937458842
Winsorized Mean ( 11 / 20 )3026.6666666666764.390522281611347.0048472883877
Winsorized Mean ( 12 / 20 )3026.6666666666764.390522281611347.0048472883877
Winsorized Mean ( 13 / 20 )3048.3333333333360.947864479056550.0154248124777
Winsorized Mean ( 14 / 20 )3048.3333333333360.947864479056550.0154248124777
Winsorized Mean ( 15 / 20 )3048.3333333333360.947864479056550.0154248124777
Winsorized Mean ( 16 / 20 )3021.6666666666747.484262434070963.6351184955662
Winsorized Mean ( 17 / 20 )3021.6666666666738.553775392130178.3753766248136
Winsorized Mean ( 18 / 20 )2991.6666666666733.72300482777288.7129329650639
Winsorized Mean ( 19 / 20 )2991.6666666666733.72300482777288.7129329650639
Winsorized Mean ( 20 / 20 )2958.3333333333328.7490276589853102.902030928644
Trimmed Mean ( 1 / 20 )3093.1034482758699.11471623824631.2073077103994
Trimmed Mean ( 2 / 20 )3076.7857142857192.931725791718133.1080229929391
Trimmed Mean ( 3 / 20 )3062.9629629629686.685060791779335.3343809762136
Trimmed Mean ( 4 / 20 )3051.9230769230881.672301862496437.3679081809314
Trimmed Mean ( 5 / 20 )304276.082125337810939.9831101785507
Trimmed Mean ( 6 / 20 )3033.3333333333371.2311523742142.5843641753518
Trimmed Mean ( 7 / 20 )3030.4347826087068.867271713140444.003990679806
Trimmed Mean ( 8 / 20 )302566.402975684106345.5551873818215
Trimmed Mean ( 9 / 20 )3021.4285714285765.335346352439246.2449308086628
Trimmed Mean ( 10 / 20 )3017.563.839492238196647.2669799556231
Trimmed Mean ( 11 / 20 )3013.1578947368461.76298622169348.7858194537642
Trimmed Mean ( 12 / 20 )3011.1111111111159.86463271783250.2986650783241
Trimmed Mean ( 13 / 20 )3008.8235294117657.171544574813952.6279909313008
Trimmed Mean ( 14 / 20 )3003.12554.344401846894755.2609817743647
Trimmed Mean ( 15 / 20 )2996.6666666666750.16829912950959.7322755338147
Trimmed Mean ( 16 / 20 )2989.2857142857143.746288040348668.3323282544247
Trimmed Mean ( 17 / 20 )2984.6153846153839.881481220971574.8371247316146
Trimmed Mean ( 18 / 20 )2979.1666666666737.58042904681979.274418686256
Trimmed Mean ( 19 / 20 )2977.2727272727335.99652351048182.7100074374083
Trimmed Mean ( 20 / 20 )297533.146405695434289.7533212902717
Median3000
Midrange3700
Midmean - Weighted Average at Xnp3047.22222222222
Midmean - Weighted Average at X(n+1)p3047.22222222222
Midmean - Empirical Distribution Function3047.22222222222
Midmean - Empirical Distribution Function - Averaging3047.22222222222
Midmean - Empirical Distribution Function - Interpolation3047.22222222222
Midmean - Closest Observation3047.22222222222
Midmean - True Basic - Statistics Graphics Toolkit3047.22222222222
Midmean - MS Excel (old versions)3047.22222222222
Number of observations60

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 3113.33333333333 & 106.841311829138 & 29.1397894693789 \tabularnewline
Geometric Mean & 3013.36404536319 &  &  \tabularnewline
Harmonic Mean & 2920.20137951797 &  &  \tabularnewline
Quadratic Mean & 3219.67907303404 &  &  \tabularnewline
Winsorized Mean ( 1 / 20 ) & 3108.33333333333 & 104.050500197438 & 29.8733146638911 \tabularnewline
Winsorized Mean ( 2 / 20 ) & 3101.66666666667 & 101.860979446057 & 30.4499984541111 \tabularnewline
Winsorized Mean ( 3 / 20 ) & 3091.66666666667 & 96.383522593311 & 32.0767137730783 \tabularnewline
Winsorized Mean ( 4 / 20 ) & 3085 & 94.5349903833478 & 32.633419514722 \tabularnewline
Winsorized Mean ( 5 / 20 ) & 3076.66666666667 & 88.5656788642927 & 34.7388142463287 \tabularnewline
Winsorized Mean ( 6 / 20 ) & 3046.66666666667 & 77.4037279451815 & 39.3607226363097 \tabularnewline
Winsorized Mean ( 7 / 20 ) & 3058.33333333333 & 75.325720829314 & 40.6014479471552 \tabularnewline
Winsorized Mean ( 8 / 20 ) & 3045 & 67.7493302654161 & 44.9450937458842 \tabularnewline
Winsorized Mean ( 9 / 20 ) & 3045 & 67.7493302654161 & 44.9450937458842 \tabularnewline
Winsorized Mean ( 10 / 20 ) & 3045 & 67.7493302654161 & 44.9450937458842 \tabularnewline
Winsorized Mean ( 11 / 20 ) & 3026.66666666667 & 64.3905222816113 & 47.0048472883877 \tabularnewline
Winsorized Mean ( 12 / 20 ) & 3026.66666666667 & 64.3905222816113 & 47.0048472883877 \tabularnewline
Winsorized Mean ( 13 / 20 ) & 3048.33333333333 & 60.9478644790565 & 50.0154248124777 \tabularnewline
Winsorized Mean ( 14 / 20 ) & 3048.33333333333 & 60.9478644790565 & 50.0154248124777 \tabularnewline
Winsorized Mean ( 15 / 20 ) & 3048.33333333333 & 60.9478644790565 & 50.0154248124777 \tabularnewline
Winsorized Mean ( 16 / 20 ) & 3021.66666666667 & 47.4842624340709 & 63.6351184955662 \tabularnewline
Winsorized Mean ( 17 / 20 ) & 3021.66666666667 & 38.5537753921301 & 78.3753766248136 \tabularnewline
Winsorized Mean ( 18 / 20 ) & 2991.66666666667 & 33.723004827772 & 88.7129329650639 \tabularnewline
Winsorized Mean ( 19 / 20 ) & 2991.66666666667 & 33.723004827772 & 88.7129329650639 \tabularnewline
Winsorized Mean ( 20 / 20 ) & 2958.33333333333 & 28.7490276589853 & 102.902030928644 \tabularnewline
Trimmed Mean ( 1 / 20 ) & 3093.10344827586 & 99.114716238246 & 31.2073077103994 \tabularnewline
Trimmed Mean ( 2 / 20 ) & 3076.78571428571 & 92.9317257917181 & 33.1080229929391 \tabularnewline
Trimmed Mean ( 3 / 20 ) & 3062.96296296296 & 86.6850607917793 & 35.3343809762136 \tabularnewline
Trimmed Mean ( 4 / 20 ) & 3051.92307692308 & 81.6723018624964 & 37.3679081809314 \tabularnewline
Trimmed Mean ( 5 / 20 ) & 3042 & 76.0821253378109 & 39.9831101785507 \tabularnewline
Trimmed Mean ( 6 / 20 ) & 3033.33333333333 & 71.23115237421 & 42.5843641753518 \tabularnewline
Trimmed Mean ( 7 / 20 ) & 3030.43478260870 & 68.8672717131404 & 44.003990679806 \tabularnewline
Trimmed Mean ( 8 / 20 ) & 3025 & 66.4029756841063 & 45.5551873818215 \tabularnewline
Trimmed Mean ( 9 / 20 ) & 3021.42857142857 & 65.3353463524392 & 46.2449308086628 \tabularnewline
Trimmed Mean ( 10 / 20 ) & 3017.5 & 63.8394922381966 & 47.2669799556231 \tabularnewline
Trimmed Mean ( 11 / 20 ) & 3013.15789473684 & 61.762986221693 & 48.7858194537642 \tabularnewline
Trimmed Mean ( 12 / 20 ) & 3011.11111111111 & 59.864632717832 & 50.2986650783241 \tabularnewline
Trimmed Mean ( 13 / 20 ) & 3008.82352941176 & 57.1715445748139 & 52.6279909313008 \tabularnewline
Trimmed Mean ( 14 / 20 ) & 3003.125 & 54.3444018468947 & 55.2609817743647 \tabularnewline
Trimmed Mean ( 15 / 20 ) & 2996.66666666667 & 50.168299129509 & 59.7322755338147 \tabularnewline
Trimmed Mean ( 16 / 20 ) & 2989.28571428571 & 43.7462880403486 & 68.3323282544247 \tabularnewline
Trimmed Mean ( 17 / 20 ) & 2984.61538461538 & 39.8814812209715 & 74.8371247316146 \tabularnewline
Trimmed Mean ( 18 / 20 ) & 2979.16666666667 & 37.580429046819 & 79.274418686256 \tabularnewline
Trimmed Mean ( 19 / 20 ) & 2977.27272727273 & 35.996523510481 & 82.7100074374083 \tabularnewline
Trimmed Mean ( 20 / 20 ) & 2975 & 33.1464056954342 & 89.7533212902717 \tabularnewline
Median & 3000 &  &  \tabularnewline
Midrange & 3700 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 3047.22222222222 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 3047.22222222222 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 3047.22222222222 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 3047.22222222222 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 3047.22222222222 &  &  \tabularnewline
Midmean - Closest Observation & 3047.22222222222 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 3047.22222222222 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 3047.22222222222 &  &  \tabularnewline
Number of observations & 60 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=16997&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]3113.33333333333[/C][C]106.841311829138[/C][C]29.1397894693789[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]3013.36404536319[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]2920.20137951797[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]3219.67907303404[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 20 )[/C][C]3108.33333333333[/C][C]104.050500197438[/C][C]29.8733146638911[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 20 )[/C][C]3101.66666666667[/C][C]101.860979446057[/C][C]30.4499984541111[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 20 )[/C][C]3091.66666666667[/C][C]96.383522593311[/C][C]32.0767137730783[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 20 )[/C][C]3085[/C][C]94.5349903833478[/C][C]32.633419514722[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 20 )[/C][C]3076.66666666667[/C][C]88.5656788642927[/C][C]34.7388142463287[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 20 )[/C][C]3046.66666666667[/C][C]77.4037279451815[/C][C]39.3607226363097[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 20 )[/C][C]3058.33333333333[/C][C]75.325720829314[/C][C]40.6014479471552[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 20 )[/C][C]3045[/C][C]67.7493302654161[/C][C]44.9450937458842[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 20 )[/C][C]3045[/C][C]67.7493302654161[/C][C]44.9450937458842[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 20 )[/C][C]3045[/C][C]67.7493302654161[/C][C]44.9450937458842[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 20 )[/C][C]3026.66666666667[/C][C]64.3905222816113[/C][C]47.0048472883877[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 20 )[/C][C]3026.66666666667[/C][C]64.3905222816113[/C][C]47.0048472883877[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 20 )[/C][C]3048.33333333333[/C][C]60.9478644790565[/C][C]50.0154248124777[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 20 )[/C][C]3048.33333333333[/C][C]60.9478644790565[/C][C]50.0154248124777[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 20 )[/C][C]3048.33333333333[/C][C]60.9478644790565[/C][C]50.0154248124777[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 20 )[/C][C]3021.66666666667[/C][C]47.4842624340709[/C][C]63.6351184955662[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 20 )[/C][C]3021.66666666667[/C][C]38.5537753921301[/C][C]78.3753766248136[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 20 )[/C][C]2991.66666666667[/C][C]33.723004827772[/C][C]88.7129329650639[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 20 )[/C][C]2991.66666666667[/C][C]33.723004827772[/C][C]88.7129329650639[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 20 )[/C][C]2958.33333333333[/C][C]28.7490276589853[/C][C]102.902030928644[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 20 )[/C][C]3093.10344827586[/C][C]99.114716238246[/C][C]31.2073077103994[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 20 )[/C][C]3076.78571428571[/C][C]92.9317257917181[/C][C]33.1080229929391[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 20 )[/C][C]3062.96296296296[/C][C]86.6850607917793[/C][C]35.3343809762136[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 20 )[/C][C]3051.92307692308[/C][C]81.6723018624964[/C][C]37.3679081809314[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 20 )[/C][C]3042[/C][C]76.0821253378109[/C][C]39.9831101785507[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 20 )[/C][C]3033.33333333333[/C][C]71.23115237421[/C][C]42.5843641753518[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 20 )[/C][C]3030.43478260870[/C][C]68.8672717131404[/C][C]44.003990679806[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 20 )[/C][C]3025[/C][C]66.4029756841063[/C][C]45.5551873818215[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 20 )[/C][C]3021.42857142857[/C][C]65.3353463524392[/C][C]46.2449308086628[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 20 )[/C][C]3017.5[/C][C]63.8394922381966[/C][C]47.2669799556231[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 20 )[/C][C]3013.15789473684[/C][C]61.762986221693[/C][C]48.7858194537642[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 20 )[/C][C]3011.11111111111[/C][C]59.864632717832[/C][C]50.2986650783241[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 20 )[/C][C]3008.82352941176[/C][C]57.1715445748139[/C][C]52.6279909313008[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 20 )[/C][C]3003.125[/C][C]54.3444018468947[/C][C]55.2609817743647[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 20 )[/C][C]2996.66666666667[/C][C]50.168299129509[/C][C]59.7322755338147[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 20 )[/C][C]2989.28571428571[/C][C]43.7462880403486[/C][C]68.3323282544247[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 20 )[/C][C]2984.61538461538[/C][C]39.8814812209715[/C][C]74.8371247316146[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 20 )[/C][C]2979.16666666667[/C][C]37.580429046819[/C][C]79.274418686256[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 20 )[/C][C]2977.27272727273[/C][C]35.996523510481[/C][C]82.7100074374083[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 20 )[/C][C]2975[/C][C]33.1464056954342[/C][C]89.7533212902717[/C][/ROW]
[ROW][C]Median[/C][C]3000[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]3700[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]3047.22222222222[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]3047.22222222222[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]3047.22222222222[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]3047.22222222222[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]3047.22222222222[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]3047.22222222222[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]3047.22222222222[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]3047.22222222222[/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=16997&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=16997&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 Mean3113.33333333333106.84131182913829.1397894693789
Geometric Mean3013.36404536319
Harmonic Mean2920.20137951797
Quadratic Mean3219.67907303404
Winsorized Mean ( 1 / 20 )3108.33333333333104.05050019743829.8733146638911
Winsorized Mean ( 2 / 20 )3101.66666666667101.86097944605730.4499984541111
Winsorized Mean ( 3 / 20 )3091.6666666666796.38352259331132.0767137730783
Winsorized Mean ( 4 / 20 )308594.534990383347832.633419514722
Winsorized Mean ( 5 / 20 )3076.6666666666788.565678864292734.7388142463287
Winsorized Mean ( 6 / 20 )3046.6666666666777.403727945181539.3607226363097
Winsorized Mean ( 7 / 20 )3058.3333333333375.32572082931440.6014479471552
Winsorized Mean ( 8 / 20 )304567.749330265416144.9450937458842
Winsorized Mean ( 9 / 20 )304567.749330265416144.9450937458842
Winsorized Mean ( 10 / 20 )304567.749330265416144.9450937458842
Winsorized Mean ( 11 / 20 )3026.6666666666764.390522281611347.0048472883877
Winsorized Mean ( 12 / 20 )3026.6666666666764.390522281611347.0048472883877
Winsorized Mean ( 13 / 20 )3048.3333333333360.947864479056550.0154248124777
Winsorized Mean ( 14 / 20 )3048.3333333333360.947864479056550.0154248124777
Winsorized Mean ( 15 / 20 )3048.3333333333360.947864479056550.0154248124777
Winsorized Mean ( 16 / 20 )3021.6666666666747.484262434070963.6351184955662
Winsorized Mean ( 17 / 20 )3021.6666666666738.553775392130178.3753766248136
Winsorized Mean ( 18 / 20 )2991.6666666666733.72300482777288.7129329650639
Winsorized Mean ( 19 / 20 )2991.6666666666733.72300482777288.7129329650639
Winsorized Mean ( 20 / 20 )2958.3333333333328.7490276589853102.902030928644
Trimmed Mean ( 1 / 20 )3093.1034482758699.11471623824631.2073077103994
Trimmed Mean ( 2 / 20 )3076.7857142857192.931725791718133.1080229929391
Trimmed Mean ( 3 / 20 )3062.9629629629686.685060791779335.3343809762136
Trimmed Mean ( 4 / 20 )3051.9230769230881.672301862496437.3679081809314
Trimmed Mean ( 5 / 20 )304276.082125337810939.9831101785507
Trimmed Mean ( 6 / 20 )3033.3333333333371.2311523742142.5843641753518
Trimmed Mean ( 7 / 20 )3030.4347826087068.867271713140444.003990679806
Trimmed Mean ( 8 / 20 )302566.402975684106345.5551873818215
Trimmed Mean ( 9 / 20 )3021.4285714285765.335346352439246.2449308086628
Trimmed Mean ( 10 / 20 )3017.563.839492238196647.2669799556231
Trimmed Mean ( 11 / 20 )3013.1578947368461.76298622169348.7858194537642
Trimmed Mean ( 12 / 20 )3011.1111111111159.86463271783250.2986650783241
Trimmed Mean ( 13 / 20 )3008.8235294117657.171544574813952.6279909313008
Trimmed Mean ( 14 / 20 )3003.12554.344401846894755.2609817743647
Trimmed Mean ( 15 / 20 )2996.6666666666750.16829912950959.7322755338147
Trimmed Mean ( 16 / 20 )2989.2857142857143.746288040348668.3323282544247
Trimmed Mean ( 17 / 20 )2984.6153846153839.881481220971574.8371247316146
Trimmed Mean ( 18 / 20 )2979.1666666666737.58042904681979.274418686256
Trimmed Mean ( 19 / 20 )2977.2727272727335.99652351048182.7100074374083
Trimmed Mean ( 20 / 20 )297533.146405695434289.7533212902717
Median3000
Midrange3700
Midmean - Weighted Average at Xnp3047.22222222222
Midmean - Weighted Average at X(n+1)p3047.22222222222
Midmean - Empirical Distribution Function3047.22222222222
Midmean - Empirical Distribution Function - Averaging3047.22222222222
Midmean - Empirical Distribution Function - Interpolation3047.22222222222
Midmean - Closest Observation3047.22222222222
Midmean - True Basic - Statistics Graphics Toolkit3047.22222222222
Midmean - MS Excel (old versions)3047.22222222222
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')