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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 06:56:07 -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/t1224507491gyvqfh92emmm2r8.htm/, Retrieved Sun, 19 May 2024 13:33:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=17227, Retrieved Sun, 19 May 2024 13:33:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Central Tendency] [Opdracht 2 Q9 cen...] [2008-10-20 12:56:07] [73ec5abea95a9c3c8c3a1ac44cab1f72] [Current]
Feedback Forum
2008-10-27 10:31:36 [Joris Deboel] [reply
De conclusie en voorspelling die men maakt zal waarschijnlijk correct zijn. We zien dat het gemiddelde wat blijft schommelen en daar zal hoogstwaarschijnlijk geen verandering in komen.
2008-10-27 15:54:08 [Bernard Femont] [reply
Deze grafiek kent een minimale dalende tendens waaruit ik niet concludeer dat deze trend zal aangehouden worden. Door de aanhoudende schommelingen van het gemiddelde zullen deze waarschijnlijk ook verder gezet worden in de toekomst!
2008-10-28 06:40:59 [An De Koninck] [reply
De student vertelt weinig over het verloop van de grafieken. Hij blijft vaag en zegt enkel dat de grafiek een minimale tendens laat uitschijnen. Hieruit concludeert hij onmiddellijk dat deze tendens zal aanhouden. Op zich kan dit zeker juist zijn, maar volgens mij is de woningindustrie een vrij onspelbare sector is. De economie, werkloosheid, de beurs, ... allemaal kunnen ze een effect hebben op de vastgoed en kunnen deze snel laten instorten. Dit kan je echter nooit voorspellen.



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Dataseries X:
2490
3266
3475
3127
2955
3870
2852
3142
3029
3180
2560
2733
2452
2553
2777
2520
2318
2873
2311
2395
2099
2268
2316
2181
2175
2627
2578
3090
2634
3225
2938
3174
3350
2588
2061
2691
2061
2918
2223
2651
2379
3146
2883
2768
3258
2839
2470
5072
1463
1600
2203
2013
2169
2640
2411
2528
2292
1988
1774
2279




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=17227&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=17227&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=17227&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 time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001







Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean2648.3573.183828695182936.1876393626604
Geometric Mean2592.95554790755
Harmonic Mean2539.30571453546
Quadratic Mean2707.35188268290
Winsorized Mean ( 1 / 20 )2630.663.453391409411141.4572009717646
Winsorized Mean ( 2 / 20 )2623.2333333333358.093570375909245.155312650936
Winsorized Mean ( 3 / 20 )2627.6833333333354.204178189001348.4775052611446
Winsorized Mean ( 4 / 20 )2623.7552.661672642793349.8227623303389
Winsorized Mean ( 5 / 20 )2627.0833333333351.759833759123850.7552505975786
Winsorized Mean ( 6 / 20 )2623.7833333333351.08976862903951.356336185128
Winsorized Mean ( 7 / 20 )2622.9666666666749.248757285458153.2595503164333
Winsorized Mean ( 8 / 20 )2631.547.481364170617855.4217437928714
Winsorized Mean ( 9 / 20 )2628.246.529911405108356.4840963722845
Winsorized Mean ( 10 / 20 )2628.5333333333346.239736785623856.8457676461204
Winsorized Mean ( 11 / 20 )2629.8166666666745.069221660181958.3506120095718
Winsorized Mean ( 12 / 20 )2626.4166666666743.063265059117260.9897243755466
Winsorized Mean ( 13 / 20 )2622.9539.170317836788566.9626938165037
Winsorized Mean ( 14 / 20 )2608.2535.836269423793172.7824084911104
Winsorized Mean ( 15 / 20 )2607.2534.640050829479575.2669218886124
Winsorized Mean ( 16 / 20 )2606.9833333333333.006517860577278.9838947672544
Winsorized Mean ( 17 / 20 )2598.4833333333331.232095360424383.1991354837498
Winsorized Mean ( 18 / 20 )2596.0833333333330.678912057134984.6211015729149
Winsorized Mean ( 19 / 20 )2608.7526.763809202930197.4730457917923
Winsorized Mean ( 20 / 20 )2609.7525.3262176856294103.045390843372
Trimmed Mean ( 1 / 20 )262759.373157308942444.2455836790127
Trimmed Mean ( 2 / 20 )2623.1428571428654.216996835177148.3822972548142
Trimmed Mean ( 3 / 20 )2623.0925925925951.554800574506150.8796962331713
Trimmed Mean ( 4 / 20 )2621.3269230769250.169409243046452.2495074713332
Trimmed Mean ( 5 / 20 )2620.649.028571428571453.4504662004662
Trimmed Mean ( 6 / 20 )2618.9791666666747.87022099532554.709986965016
Trimmed Mean ( 7 / 20 )2617.9347826087046.579124952358856.2040352901933
Trimmed Mean ( 8 / 20 )2616.9545454545545.435698524015057.5968815373524
Trimmed Mean ( 9 / 20 )2614.3571428571444.399488092660858.8825965155507
Trimmed Mean ( 10 / 20 )2612.0543.254701093253160.3876557687606
Trimmed Mean ( 11 / 20 )2609.4473684210541.759006785964762.4882526970947
Trimmed Mean ( 12 / 20 )2606.3611111111140.023805471358865.120272308345
Trimmed Mean ( 13 / 20 )2603.4117647058838.19959117398168.152869826501
Trimmed Mean ( 14 / 20 )2600.5937536.79925543204470.6697382723526
Trimmed Mean ( 15 / 20 )2599.535.788161851869872.6357506361893
Trimmed Mean ( 16 / 20 )2598.3928571428634.60121795290875.0954160249287
Trimmed Mean ( 17 / 20 )2597.1538461538533.289716236408478.0167012452147
Trimmed Mean ( 18 / 20 )2596.9583333333331.839069926621181.5651443122708
Trimmed Mean ( 19 / 20 )2597.0909090909129.628282169063687.655804486791
Trimmed Mean ( 20 / 20 )2595.2527.765453260064893.4704712252173
Median2583
Midrange3267.5
Midmean - Weighted Average at Xnp2589.16129032258
Midmean - Weighted Average at X(n+1)p2599.5
Midmean - Empirical Distribution Function2589.16129032258
Midmean - Empirical Distribution Function - Averaging2599.5
Midmean - Empirical Distribution Function - Interpolation2599.5
Midmean - Closest Observation2589.16129032258
Midmean - True Basic - Statistics Graphics Toolkit2599.5
Midmean - MS Excel (old versions)2600.59375
Number of observations60

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 2648.35 & 73.1838286951829 & 36.1876393626604 \tabularnewline
Geometric Mean & 2592.95554790755 &  &  \tabularnewline
Harmonic Mean & 2539.30571453546 &  &  \tabularnewline
Quadratic Mean & 2707.35188268290 &  &  \tabularnewline
Winsorized Mean ( 1 / 20 ) & 2630.6 & 63.4533914094111 & 41.4572009717646 \tabularnewline
Winsorized Mean ( 2 / 20 ) & 2623.23333333333 & 58.0935703759092 & 45.155312650936 \tabularnewline
Winsorized Mean ( 3 / 20 ) & 2627.68333333333 & 54.2041781890013 & 48.4775052611446 \tabularnewline
Winsorized Mean ( 4 / 20 ) & 2623.75 & 52.6616726427933 & 49.8227623303389 \tabularnewline
Winsorized Mean ( 5 / 20 ) & 2627.08333333333 & 51.7598337591238 & 50.7552505975786 \tabularnewline
Winsorized Mean ( 6 / 20 ) & 2623.78333333333 & 51.089768629039 & 51.356336185128 \tabularnewline
Winsorized Mean ( 7 / 20 ) & 2622.96666666667 & 49.2487572854581 & 53.2595503164333 \tabularnewline
Winsorized Mean ( 8 / 20 ) & 2631.5 & 47.4813641706178 & 55.4217437928714 \tabularnewline
Winsorized Mean ( 9 / 20 ) & 2628.2 & 46.5299114051083 & 56.4840963722845 \tabularnewline
Winsorized Mean ( 10 / 20 ) & 2628.53333333333 & 46.2397367856238 & 56.8457676461204 \tabularnewline
Winsorized Mean ( 11 / 20 ) & 2629.81666666667 & 45.0692216601819 & 58.3506120095718 \tabularnewline
Winsorized Mean ( 12 / 20 ) & 2626.41666666667 & 43.0632650591172 & 60.9897243755466 \tabularnewline
Winsorized Mean ( 13 / 20 ) & 2622.95 & 39.1703178367885 & 66.9626938165037 \tabularnewline
Winsorized Mean ( 14 / 20 ) & 2608.25 & 35.8362694237931 & 72.7824084911104 \tabularnewline
Winsorized Mean ( 15 / 20 ) & 2607.25 & 34.6400508294795 & 75.2669218886124 \tabularnewline
Winsorized Mean ( 16 / 20 ) & 2606.98333333333 & 33.0065178605772 & 78.9838947672544 \tabularnewline
Winsorized Mean ( 17 / 20 ) & 2598.48333333333 & 31.2320953604243 & 83.1991354837498 \tabularnewline
Winsorized Mean ( 18 / 20 ) & 2596.08333333333 & 30.6789120571349 & 84.6211015729149 \tabularnewline
Winsorized Mean ( 19 / 20 ) & 2608.75 & 26.7638092029301 & 97.4730457917923 \tabularnewline
Winsorized Mean ( 20 / 20 ) & 2609.75 & 25.3262176856294 & 103.045390843372 \tabularnewline
Trimmed Mean ( 1 / 20 ) & 2627 & 59.3731573089424 & 44.2455836790127 \tabularnewline
Trimmed Mean ( 2 / 20 ) & 2623.14285714286 & 54.2169968351771 & 48.3822972548142 \tabularnewline
Trimmed Mean ( 3 / 20 ) & 2623.09259259259 & 51.5548005745061 & 50.8796962331713 \tabularnewline
Trimmed Mean ( 4 / 20 ) & 2621.32692307692 & 50.1694092430464 & 52.2495074713332 \tabularnewline
Trimmed Mean ( 5 / 20 ) & 2620.6 & 49.0285714285714 & 53.4504662004662 \tabularnewline
Trimmed Mean ( 6 / 20 ) & 2618.97916666667 & 47.870220995325 & 54.709986965016 \tabularnewline
Trimmed Mean ( 7 / 20 ) & 2617.93478260870 & 46.5791249523588 & 56.2040352901933 \tabularnewline
Trimmed Mean ( 8 / 20 ) & 2616.95454545455 & 45.4356985240150 & 57.5968815373524 \tabularnewline
Trimmed Mean ( 9 / 20 ) & 2614.35714285714 & 44.3994880926608 & 58.8825965155507 \tabularnewline
Trimmed Mean ( 10 / 20 ) & 2612.05 & 43.2547010932531 & 60.3876557687606 \tabularnewline
Trimmed Mean ( 11 / 20 ) & 2609.44736842105 & 41.7590067859647 & 62.4882526970947 \tabularnewline
Trimmed Mean ( 12 / 20 ) & 2606.36111111111 & 40.0238054713588 & 65.120272308345 \tabularnewline
Trimmed Mean ( 13 / 20 ) & 2603.41176470588 & 38.199591173981 & 68.152869826501 \tabularnewline
Trimmed Mean ( 14 / 20 ) & 2600.59375 & 36.799255432044 & 70.6697382723526 \tabularnewline
Trimmed Mean ( 15 / 20 ) & 2599.5 & 35.7881618518698 & 72.6357506361893 \tabularnewline
Trimmed Mean ( 16 / 20 ) & 2598.39285714286 & 34.601217952908 & 75.0954160249287 \tabularnewline
Trimmed Mean ( 17 / 20 ) & 2597.15384615385 & 33.2897162364084 & 78.0167012452147 \tabularnewline
Trimmed Mean ( 18 / 20 ) & 2596.95833333333 & 31.8390699266211 & 81.5651443122708 \tabularnewline
Trimmed Mean ( 19 / 20 ) & 2597.09090909091 & 29.6282821690636 & 87.655804486791 \tabularnewline
Trimmed Mean ( 20 / 20 ) & 2595.25 & 27.7654532600648 & 93.4704712252173 \tabularnewline
Median & 2583 &  &  \tabularnewline
Midrange & 3267.5 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 2589.16129032258 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 2599.5 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 2589.16129032258 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 2599.5 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 2599.5 &  &  \tabularnewline
Midmean - Closest Observation & 2589.16129032258 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 2599.5 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 2600.59375 &  &  \tabularnewline
Number of observations & 60 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=17227&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]2648.35[/C][C]73.1838286951829[/C][C]36.1876393626604[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]2592.95554790755[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]2539.30571453546[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]2707.35188268290[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 20 )[/C][C]2630.6[/C][C]63.4533914094111[/C][C]41.4572009717646[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 20 )[/C][C]2623.23333333333[/C][C]58.0935703759092[/C][C]45.155312650936[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 20 )[/C][C]2627.68333333333[/C][C]54.2041781890013[/C][C]48.4775052611446[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 20 )[/C][C]2623.75[/C][C]52.6616726427933[/C][C]49.8227623303389[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 20 )[/C][C]2627.08333333333[/C][C]51.7598337591238[/C][C]50.7552505975786[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 20 )[/C][C]2623.78333333333[/C][C]51.089768629039[/C][C]51.356336185128[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 20 )[/C][C]2622.96666666667[/C][C]49.2487572854581[/C][C]53.2595503164333[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 20 )[/C][C]2631.5[/C][C]47.4813641706178[/C][C]55.4217437928714[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 20 )[/C][C]2628.2[/C][C]46.5299114051083[/C][C]56.4840963722845[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 20 )[/C][C]2628.53333333333[/C][C]46.2397367856238[/C][C]56.8457676461204[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 20 )[/C][C]2629.81666666667[/C][C]45.0692216601819[/C][C]58.3506120095718[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 20 )[/C][C]2626.41666666667[/C][C]43.0632650591172[/C][C]60.9897243755466[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 20 )[/C][C]2622.95[/C][C]39.1703178367885[/C][C]66.9626938165037[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 20 )[/C][C]2608.25[/C][C]35.8362694237931[/C][C]72.7824084911104[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 20 )[/C][C]2607.25[/C][C]34.6400508294795[/C][C]75.2669218886124[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 20 )[/C][C]2606.98333333333[/C][C]33.0065178605772[/C][C]78.9838947672544[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 20 )[/C][C]2598.48333333333[/C][C]31.2320953604243[/C][C]83.1991354837498[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 20 )[/C][C]2596.08333333333[/C][C]30.6789120571349[/C][C]84.6211015729149[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 20 )[/C][C]2608.75[/C][C]26.7638092029301[/C][C]97.4730457917923[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 20 )[/C][C]2609.75[/C][C]25.3262176856294[/C][C]103.045390843372[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 20 )[/C][C]2627[/C][C]59.3731573089424[/C][C]44.2455836790127[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 20 )[/C][C]2623.14285714286[/C][C]54.2169968351771[/C][C]48.3822972548142[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 20 )[/C][C]2623.09259259259[/C][C]51.5548005745061[/C][C]50.8796962331713[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 20 )[/C][C]2621.32692307692[/C][C]50.1694092430464[/C][C]52.2495074713332[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 20 )[/C][C]2620.6[/C][C]49.0285714285714[/C][C]53.4504662004662[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 20 )[/C][C]2618.97916666667[/C][C]47.870220995325[/C][C]54.709986965016[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 20 )[/C][C]2617.93478260870[/C][C]46.5791249523588[/C][C]56.2040352901933[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 20 )[/C][C]2616.95454545455[/C][C]45.4356985240150[/C][C]57.5968815373524[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 20 )[/C][C]2614.35714285714[/C][C]44.3994880926608[/C][C]58.8825965155507[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 20 )[/C][C]2612.05[/C][C]43.2547010932531[/C][C]60.3876557687606[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 20 )[/C][C]2609.44736842105[/C][C]41.7590067859647[/C][C]62.4882526970947[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 20 )[/C][C]2606.36111111111[/C][C]40.0238054713588[/C][C]65.120272308345[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 20 )[/C][C]2603.41176470588[/C][C]38.199591173981[/C][C]68.152869826501[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 20 )[/C][C]2600.59375[/C][C]36.799255432044[/C][C]70.6697382723526[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 20 )[/C][C]2599.5[/C][C]35.7881618518698[/C][C]72.6357506361893[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 20 )[/C][C]2598.39285714286[/C][C]34.601217952908[/C][C]75.0954160249287[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 20 )[/C][C]2597.15384615385[/C][C]33.2897162364084[/C][C]78.0167012452147[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 20 )[/C][C]2596.95833333333[/C][C]31.8390699266211[/C][C]81.5651443122708[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 20 )[/C][C]2597.09090909091[/C][C]29.6282821690636[/C][C]87.655804486791[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 20 )[/C][C]2595.25[/C][C]27.7654532600648[/C][C]93.4704712252173[/C][/ROW]
[ROW][C]Median[/C][C]2583[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]3267.5[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]2589.16129032258[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]2599.5[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]2589.16129032258[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]2599.5[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]2599.5[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]2589.16129032258[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]2599.5[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]2600.59375[/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=17227&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=17227&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 Mean2648.3573.183828695182936.1876393626604
Geometric Mean2592.95554790755
Harmonic Mean2539.30571453546
Quadratic Mean2707.35188268290
Winsorized Mean ( 1 / 20 )2630.663.453391409411141.4572009717646
Winsorized Mean ( 2 / 20 )2623.2333333333358.093570375909245.155312650936
Winsorized Mean ( 3 / 20 )2627.6833333333354.204178189001348.4775052611446
Winsorized Mean ( 4 / 20 )2623.7552.661672642793349.8227623303389
Winsorized Mean ( 5 / 20 )2627.0833333333351.759833759123850.7552505975786
Winsorized Mean ( 6 / 20 )2623.7833333333351.08976862903951.356336185128
Winsorized Mean ( 7 / 20 )2622.9666666666749.248757285458153.2595503164333
Winsorized Mean ( 8 / 20 )2631.547.481364170617855.4217437928714
Winsorized Mean ( 9 / 20 )2628.246.529911405108356.4840963722845
Winsorized Mean ( 10 / 20 )2628.5333333333346.239736785623856.8457676461204
Winsorized Mean ( 11 / 20 )2629.8166666666745.069221660181958.3506120095718
Winsorized Mean ( 12 / 20 )2626.4166666666743.063265059117260.9897243755466
Winsorized Mean ( 13 / 20 )2622.9539.170317836788566.9626938165037
Winsorized Mean ( 14 / 20 )2608.2535.836269423793172.7824084911104
Winsorized Mean ( 15 / 20 )2607.2534.640050829479575.2669218886124
Winsorized Mean ( 16 / 20 )2606.9833333333333.006517860577278.9838947672544
Winsorized Mean ( 17 / 20 )2598.4833333333331.232095360424383.1991354837498
Winsorized Mean ( 18 / 20 )2596.0833333333330.678912057134984.6211015729149
Winsorized Mean ( 19 / 20 )2608.7526.763809202930197.4730457917923
Winsorized Mean ( 20 / 20 )2609.7525.3262176856294103.045390843372
Trimmed Mean ( 1 / 20 )262759.373157308942444.2455836790127
Trimmed Mean ( 2 / 20 )2623.1428571428654.216996835177148.3822972548142
Trimmed Mean ( 3 / 20 )2623.0925925925951.554800574506150.8796962331713
Trimmed Mean ( 4 / 20 )2621.3269230769250.169409243046452.2495074713332
Trimmed Mean ( 5 / 20 )2620.649.028571428571453.4504662004662
Trimmed Mean ( 6 / 20 )2618.9791666666747.87022099532554.709986965016
Trimmed Mean ( 7 / 20 )2617.9347826087046.579124952358856.2040352901933
Trimmed Mean ( 8 / 20 )2616.9545454545545.435698524015057.5968815373524
Trimmed Mean ( 9 / 20 )2614.3571428571444.399488092660858.8825965155507
Trimmed Mean ( 10 / 20 )2612.0543.254701093253160.3876557687606
Trimmed Mean ( 11 / 20 )2609.4473684210541.759006785964762.4882526970947
Trimmed Mean ( 12 / 20 )2606.3611111111140.023805471358865.120272308345
Trimmed Mean ( 13 / 20 )2603.4117647058838.19959117398168.152869826501
Trimmed Mean ( 14 / 20 )2600.5937536.79925543204470.6697382723526
Trimmed Mean ( 15 / 20 )2599.535.788161851869872.6357506361893
Trimmed Mean ( 16 / 20 )2598.3928571428634.60121795290875.0954160249287
Trimmed Mean ( 17 / 20 )2597.1538461538533.289716236408478.0167012452147
Trimmed Mean ( 18 / 20 )2596.9583333333331.839069926621181.5651443122708
Trimmed Mean ( 19 / 20 )2597.0909090909129.628282169063687.655804486791
Trimmed Mean ( 20 / 20 )2595.2527.765453260064893.4704712252173
Median2583
Midrange3267.5
Midmean - Weighted Average at Xnp2589.16129032258
Midmean - Weighted Average at X(n+1)p2599.5
Midmean - Empirical Distribution Function2589.16129032258
Midmean - Empirical Distribution Function - Averaging2599.5
Midmean - Empirical Distribution Function - Interpolation2599.5
Midmean - Closest Observation2589.16129032258
Midmean - True Basic - Statistics Graphics Toolkit2599.5
Midmean - MS Excel (old versions)2600.59375
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')