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of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_centraltendency.wasp
Title produced by softwareCentral Tendency
Date of computationTue, 15 Aug 2017 10:20:59 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Aug/15/t1502785286svqtdufx70nq0b4.htm/, Retrieved Sun, 19 May 2024 22:06:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=307261, Retrieved Sun, 19 May 2024 22:06:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact94
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Central Tendency] [Aantal verkochte ...] [2017-08-15 08:20:59] [6bb7048e855cced252efb5418d255fa6] [Current]
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Dataseries X:
503334
503737
504101
504504
504894
505297
505687
506090
506493
506883
507286
507676
508079
508482
508846
509249
509639
510042
510432
510835
511238
511628
512031
512421
512824
513227
513604
514007
514397
514800
515190
515593
515996
516386
516789
517179
517582
517985
518349
518752
519142
519545
519935
520338
520741
521131
521534
521924
522327
522730
523094
523497
523887
524290
524680
525083
525486
525876
526279
526669
527072
527475
527839
528242
528632
529035
529425
529828
530231
530621
531024
531414
531817
532220
532597
533000
533390
533793
534183
534586
534989
535379
535782
536172
536575
536978
537342
537745
538135
538538
538928
539331
539734
540124
540527
540917
541320
541723
542087
542490
542880
543283
543673
544076
544479
544869
545272
545662




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=307261&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=307261&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307261&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center







Central Tendency - Ungrouped Data
MeasureValueS.E.Value/S.E.
Arithmetic Mean5244921192.6439.79
Geometric Mean524347
Harmonic Mean524202
Quadratic Mean524638
Winsorized Mean ( 1 / 36 )5244931191.39440.235
Winsorized Mean ( 2 / 36 )5244921189.1441.084
Winsorized Mean ( 3 / 36 )5244921185.6442.387
Winsorized Mean ( 4 / 36 )5244921181.01444.106
Winsorized Mean ( 5 / 36 )5244921175.27446.275
Winsorized Mean ( 6 / 36 )5244921168.7448.781
Winsorized Mean ( 7 / 36 )5244921160.91451.794
Winsorized Mean ( 8 / 36 )5244931152.27455.181
Winsorized Mean ( 9 / 36 )5244921142.69458.997
Winsorized Mean ( 10 / 36 )5244951132.53463.117
Winsorized Mean ( 11 / 36 )5244941121.13467.825
Winsorized Mean ( 12 / 36 )5244941108.65473.09
Winsorized Mean ( 13 / 36 )5244951095.52478.762
Winsorized Mean ( 14 / 36 )5244901082.01484.735
Winsorized Mean ( 15 / 36 )5244921067.22491.455
Winsorized Mean ( 16 / 36 )5244901051.63498.739
Winsorized Mean ( 17 / 36 )5244901034.99506.759
Winsorized Mean ( 18 / 36 )5244901018.13515.152
Winsorized Mean ( 19 / 36 )524490999.935524.525
Winsorized Mean ( 20 / 36 )524493981.298534.489
Winsorized Mean ( 21 / 36 )524490961.935545.245
Winsorized Mean ( 22 / 36 )524498942.513556.489
Winsorized Mean ( 23 / 36 )524495921.728569.035
Winsorized Mean ( 24 / 36 )524495899.903582.836
Winsorized Mean ( 25 / 36 )524498877.753597.546
Winsorized Mean ( 26 / 36 )524492855.308613.22
Winsorized Mean ( 27 / 36 )524495831.831630.531
Winsorized Mean ( 28 / 36 )524492807.706649.36
Winsorized Mean ( 29 / 36 )524492782.539670.243
Winsorized Mean ( 30 / 36 )524492757.568692.337
Winsorized Mean ( 31 / 36 )524492731.134717.368
Winsorized Mean ( 32 / 36 )524496704.519744.473
Winsorized Mean ( 33 / 36 )524492677.303774.383
Winsorized Mean ( 34 / 36 )524500649.947806.989
Winsorized Mean ( 35 / 36 )524496621.548843.854
Winsorized Mean ( 36 / 36 )524496592.097885.828
Trimmed Mean ( 1 / 36 )5244921181.63443.873
Trimmed Mean ( 2 / 36 )5244921170.56448.071
Trimmed Mean ( 3 / 36 )5244921159.34452.406
Trimmed Mean ( 4 / 36 )5244921148.01456.869
Trimmed Mean ( 5 / 36 )5244921136.58461.466
Trimmed Mean ( 6 / 36 )5244931125.05466.193
Trimmed Mean ( 7 / 36 )5244931113.39471.078
Trimmed Mean ( 8 / 36 )5244931101.63476.107
Trimmed Mean ( 9 / 36 )5244931089.74481.299
Trimmed Mean ( 10 / 36 )5244931077.74486.662
Trimmed Mean ( 11 / 36 )5244931065.54492.232
Trimmed Mean ( 12 / 36 )5244931053.21497.996
Trimmed Mean ( 13 / 36 )5244931040.76503.952
Trimmed Mean ( 14 / 36 )5244921028.17510.124
Trimmed Mean ( 15 / 36 )5244921015.36516.56
Trimmed Mean ( 16 / 36 )5244931002.38523.245
Trimmed Mean ( 17 / 36 )524493989.243530.196
Trimmed Mean ( 18 / 36 )524493975.964537.41
Trimmed Mean ( 19 / 36 )524493962.467544.947
Trimmed Mean ( 20 / 36 )524493948.821552.784
Trimmed Mean ( 21 / 36 )524493934.982560.967
Trimmed Mean ( 22 / 36 )524494920.942569.519
Trimmed Mean ( 23 / 36 )524493906.597578.53
Trimmed Mean ( 24 / 36 )524493892.022587.982
Trimmed Mean ( 25 / 36 )524493877.256597.879
Trimmed Mean ( 26 / 36 )524493862.242608.289
Trimmed Mean ( 27 / 36 )524493846.909619.302
Trimmed Mean ( 28 / 36 )524492831.294630.935
Trimmed Mean ( 29 / 36 )524493815.379643.25
Trimmed Mean ( 30 / 36 )524493799.217656.258
Trimmed Mean ( 31 / 36 )524493782.656670.145
Trimmed Mean ( 32 / 36 )524493765.817684.88
Trimmed Mean ( 33 / 36 )524492748.613700.619
Trimmed Mean ( 34 / 36 )524492731.019717.481
Trimmed Mean ( 35 / 36 )524492712.911735.705
Trimmed Mean ( 36 / 36 )524492694.338755.384
Median524485
Midrange524498
Midmean - Weighted Average at Xnp524295
Midmean - Weighted Average at X(n+1)p524493
Midmean - Empirical Distribution Function524295
Midmean - Empirical Distribution Function - Averaging524493
Midmean - Empirical Distribution Function - Interpolation524493
Midmean - Closest Observation524295
Midmean - True Basic - Statistics Graphics Toolkit524493
Midmean - MS Excel (old versions)524493
Number of observations108

\begin{tabular}{lllllllll}
\hline
Central Tendency - Ungrouped Data \tabularnewline
Measure & Value & S.E. & Value/S.E. \tabularnewline
Arithmetic Mean & 524492 & 1192.6 & 439.79 \tabularnewline
Geometric Mean & 524347 &  &  \tabularnewline
Harmonic Mean & 524202 &  &  \tabularnewline
Quadratic Mean & 524638 &  &  \tabularnewline
Winsorized Mean ( 1 / 36 ) & 524493 & 1191.39 & 440.235 \tabularnewline
Winsorized Mean ( 2 / 36 ) & 524492 & 1189.1 & 441.084 \tabularnewline
Winsorized Mean ( 3 / 36 ) & 524492 & 1185.6 & 442.387 \tabularnewline
Winsorized Mean ( 4 / 36 ) & 524492 & 1181.01 & 444.106 \tabularnewline
Winsorized Mean ( 5 / 36 ) & 524492 & 1175.27 & 446.275 \tabularnewline
Winsorized Mean ( 6 / 36 ) & 524492 & 1168.7 & 448.781 \tabularnewline
Winsorized Mean ( 7 / 36 ) & 524492 & 1160.91 & 451.794 \tabularnewline
Winsorized Mean ( 8 / 36 ) & 524493 & 1152.27 & 455.181 \tabularnewline
Winsorized Mean ( 9 / 36 ) & 524492 & 1142.69 & 458.997 \tabularnewline
Winsorized Mean ( 10 / 36 ) & 524495 & 1132.53 & 463.117 \tabularnewline
Winsorized Mean ( 11 / 36 ) & 524494 & 1121.13 & 467.825 \tabularnewline
Winsorized Mean ( 12 / 36 ) & 524494 & 1108.65 & 473.09 \tabularnewline
Winsorized Mean ( 13 / 36 ) & 524495 & 1095.52 & 478.762 \tabularnewline
Winsorized Mean ( 14 / 36 ) & 524490 & 1082.01 & 484.735 \tabularnewline
Winsorized Mean ( 15 / 36 ) & 524492 & 1067.22 & 491.455 \tabularnewline
Winsorized Mean ( 16 / 36 ) & 524490 & 1051.63 & 498.739 \tabularnewline
Winsorized Mean ( 17 / 36 ) & 524490 & 1034.99 & 506.759 \tabularnewline
Winsorized Mean ( 18 / 36 ) & 524490 & 1018.13 & 515.152 \tabularnewline
Winsorized Mean ( 19 / 36 ) & 524490 & 999.935 & 524.525 \tabularnewline
Winsorized Mean ( 20 / 36 ) & 524493 & 981.298 & 534.489 \tabularnewline
Winsorized Mean ( 21 / 36 ) & 524490 & 961.935 & 545.245 \tabularnewline
Winsorized Mean ( 22 / 36 ) & 524498 & 942.513 & 556.489 \tabularnewline
Winsorized Mean ( 23 / 36 ) & 524495 & 921.728 & 569.035 \tabularnewline
Winsorized Mean ( 24 / 36 ) & 524495 & 899.903 & 582.836 \tabularnewline
Winsorized Mean ( 25 / 36 ) & 524498 & 877.753 & 597.546 \tabularnewline
Winsorized Mean ( 26 / 36 ) & 524492 & 855.308 & 613.22 \tabularnewline
Winsorized Mean ( 27 / 36 ) & 524495 & 831.831 & 630.531 \tabularnewline
Winsorized Mean ( 28 / 36 ) & 524492 & 807.706 & 649.36 \tabularnewline
Winsorized Mean ( 29 / 36 ) & 524492 & 782.539 & 670.243 \tabularnewline
Winsorized Mean ( 30 / 36 ) & 524492 & 757.568 & 692.337 \tabularnewline
Winsorized Mean ( 31 / 36 ) & 524492 & 731.134 & 717.368 \tabularnewline
Winsorized Mean ( 32 / 36 ) & 524496 & 704.519 & 744.473 \tabularnewline
Winsorized Mean ( 33 / 36 ) & 524492 & 677.303 & 774.383 \tabularnewline
Winsorized Mean ( 34 / 36 ) & 524500 & 649.947 & 806.989 \tabularnewline
Winsorized Mean ( 35 / 36 ) & 524496 & 621.548 & 843.854 \tabularnewline
Winsorized Mean ( 36 / 36 ) & 524496 & 592.097 & 885.828 \tabularnewline
Trimmed Mean ( 1 / 36 ) & 524492 & 1181.63 & 443.873 \tabularnewline
Trimmed Mean ( 2 / 36 ) & 524492 & 1170.56 & 448.071 \tabularnewline
Trimmed Mean ( 3 / 36 ) & 524492 & 1159.34 & 452.406 \tabularnewline
Trimmed Mean ( 4 / 36 ) & 524492 & 1148.01 & 456.869 \tabularnewline
Trimmed Mean ( 5 / 36 ) & 524492 & 1136.58 & 461.466 \tabularnewline
Trimmed Mean ( 6 / 36 ) & 524493 & 1125.05 & 466.193 \tabularnewline
Trimmed Mean ( 7 / 36 ) & 524493 & 1113.39 & 471.078 \tabularnewline
Trimmed Mean ( 8 / 36 ) & 524493 & 1101.63 & 476.107 \tabularnewline
Trimmed Mean ( 9 / 36 ) & 524493 & 1089.74 & 481.299 \tabularnewline
Trimmed Mean ( 10 / 36 ) & 524493 & 1077.74 & 486.662 \tabularnewline
Trimmed Mean ( 11 / 36 ) & 524493 & 1065.54 & 492.232 \tabularnewline
Trimmed Mean ( 12 / 36 ) & 524493 & 1053.21 & 497.996 \tabularnewline
Trimmed Mean ( 13 / 36 ) & 524493 & 1040.76 & 503.952 \tabularnewline
Trimmed Mean ( 14 / 36 ) & 524492 & 1028.17 & 510.124 \tabularnewline
Trimmed Mean ( 15 / 36 ) & 524492 & 1015.36 & 516.56 \tabularnewline
Trimmed Mean ( 16 / 36 ) & 524493 & 1002.38 & 523.245 \tabularnewline
Trimmed Mean ( 17 / 36 ) & 524493 & 989.243 & 530.196 \tabularnewline
Trimmed Mean ( 18 / 36 ) & 524493 & 975.964 & 537.41 \tabularnewline
Trimmed Mean ( 19 / 36 ) & 524493 & 962.467 & 544.947 \tabularnewline
Trimmed Mean ( 20 / 36 ) & 524493 & 948.821 & 552.784 \tabularnewline
Trimmed Mean ( 21 / 36 ) & 524493 & 934.982 & 560.967 \tabularnewline
Trimmed Mean ( 22 / 36 ) & 524494 & 920.942 & 569.519 \tabularnewline
Trimmed Mean ( 23 / 36 ) & 524493 & 906.597 & 578.53 \tabularnewline
Trimmed Mean ( 24 / 36 ) & 524493 & 892.022 & 587.982 \tabularnewline
Trimmed Mean ( 25 / 36 ) & 524493 & 877.256 & 597.879 \tabularnewline
Trimmed Mean ( 26 / 36 ) & 524493 & 862.242 & 608.289 \tabularnewline
Trimmed Mean ( 27 / 36 ) & 524493 & 846.909 & 619.302 \tabularnewline
Trimmed Mean ( 28 / 36 ) & 524492 & 831.294 & 630.935 \tabularnewline
Trimmed Mean ( 29 / 36 ) & 524493 & 815.379 & 643.25 \tabularnewline
Trimmed Mean ( 30 / 36 ) & 524493 & 799.217 & 656.258 \tabularnewline
Trimmed Mean ( 31 / 36 ) & 524493 & 782.656 & 670.145 \tabularnewline
Trimmed Mean ( 32 / 36 ) & 524493 & 765.817 & 684.88 \tabularnewline
Trimmed Mean ( 33 / 36 ) & 524492 & 748.613 & 700.619 \tabularnewline
Trimmed Mean ( 34 / 36 ) & 524492 & 731.019 & 717.481 \tabularnewline
Trimmed Mean ( 35 / 36 ) & 524492 & 712.911 & 735.705 \tabularnewline
Trimmed Mean ( 36 / 36 ) & 524492 & 694.338 & 755.384 \tabularnewline
Median & 524485 &  &  \tabularnewline
Midrange & 524498 &  &  \tabularnewline
Midmean - Weighted Average at Xnp & 524295 &  &  \tabularnewline
Midmean - Weighted Average at X(n+1)p & 524493 &  &  \tabularnewline
Midmean - Empirical Distribution Function & 524295 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Averaging & 524493 &  &  \tabularnewline
Midmean - Empirical Distribution Function - Interpolation & 524493 &  &  \tabularnewline
Midmean - Closest Observation & 524295 &  &  \tabularnewline
Midmean - True Basic - Statistics Graphics Toolkit & 524493 &  &  \tabularnewline
Midmean - MS Excel (old versions) & 524493 &  &  \tabularnewline
Number of observations & 108 &  &  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=307261&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]524492[/C][C]1192.6[/C][C]439.79[/C][/ROW]
[ROW][C]Geometric Mean[/C][C]524347[/C][C][/C][C][/C][/ROW]
[ROW][C]Harmonic Mean[/C][C]524202[/C][C][/C][C][/C][/ROW]
[ROW][C]Quadratic Mean[/C][C]524638[/C][C][/C][C][/C][/ROW]
[ROW][C]Winsorized Mean ( 1 / 36 )[/C][C]524493[/C][C]1191.39[/C][C]440.235[/C][/ROW]
[ROW][C]Winsorized Mean ( 2 / 36 )[/C][C]524492[/C][C]1189.1[/C][C]441.084[/C][/ROW]
[ROW][C]Winsorized Mean ( 3 / 36 )[/C][C]524492[/C][C]1185.6[/C][C]442.387[/C][/ROW]
[ROW][C]Winsorized Mean ( 4 / 36 )[/C][C]524492[/C][C]1181.01[/C][C]444.106[/C][/ROW]
[ROW][C]Winsorized Mean ( 5 / 36 )[/C][C]524492[/C][C]1175.27[/C][C]446.275[/C][/ROW]
[ROW][C]Winsorized Mean ( 6 / 36 )[/C][C]524492[/C][C]1168.7[/C][C]448.781[/C][/ROW]
[ROW][C]Winsorized Mean ( 7 / 36 )[/C][C]524492[/C][C]1160.91[/C][C]451.794[/C][/ROW]
[ROW][C]Winsorized Mean ( 8 / 36 )[/C][C]524493[/C][C]1152.27[/C][C]455.181[/C][/ROW]
[ROW][C]Winsorized Mean ( 9 / 36 )[/C][C]524492[/C][C]1142.69[/C][C]458.997[/C][/ROW]
[ROW][C]Winsorized Mean ( 10 / 36 )[/C][C]524495[/C][C]1132.53[/C][C]463.117[/C][/ROW]
[ROW][C]Winsorized Mean ( 11 / 36 )[/C][C]524494[/C][C]1121.13[/C][C]467.825[/C][/ROW]
[ROW][C]Winsorized Mean ( 12 / 36 )[/C][C]524494[/C][C]1108.65[/C][C]473.09[/C][/ROW]
[ROW][C]Winsorized Mean ( 13 / 36 )[/C][C]524495[/C][C]1095.52[/C][C]478.762[/C][/ROW]
[ROW][C]Winsorized Mean ( 14 / 36 )[/C][C]524490[/C][C]1082.01[/C][C]484.735[/C][/ROW]
[ROW][C]Winsorized Mean ( 15 / 36 )[/C][C]524492[/C][C]1067.22[/C][C]491.455[/C][/ROW]
[ROW][C]Winsorized Mean ( 16 / 36 )[/C][C]524490[/C][C]1051.63[/C][C]498.739[/C][/ROW]
[ROW][C]Winsorized Mean ( 17 / 36 )[/C][C]524490[/C][C]1034.99[/C][C]506.759[/C][/ROW]
[ROW][C]Winsorized Mean ( 18 / 36 )[/C][C]524490[/C][C]1018.13[/C][C]515.152[/C][/ROW]
[ROW][C]Winsorized Mean ( 19 / 36 )[/C][C]524490[/C][C]999.935[/C][C]524.525[/C][/ROW]
[ROW][C]Winsorized Mean ( 20 / 36 )[/C][C]524493[/C][C]981.298[/C][C]534.489[/C][/ROW]
[ROW][C]Winsorized Mean ( 21 / 36 )[/C][C]524490[/C][C]961.935[/C][C]545.245[/C][/ROW]
[ROW][C]Winsorized Mean ( 22 / 36 )[/C][C]524498[/C][C]942.513[/C][C]556.489[/C][/ROW]
[ROW][C]Winsorized Mean ( 23 / 36 )[/C][C]524495[/C][C]921.728[/C][C]569.035[/C][/ROW]
[ROW][C]Winsorized Mean ( 24 / 36 )[/C][C]524495[/C][C]899.903[/C][C]582.836[/C][/ROW]
[ROW][C]Winsorized Mean ( 25 / 36 )[/C][C]524498[/C][C]877.753[/C][C]597.546[/C][/ROW]
[ROW][C]Winsorized Mean ( 26 / 36 )[/C][C]524492[/C][C]855.308[/C][C]613.22[/C][/ROW]
[ROW][C]Winsorized Mean ( 27 / 36 )[/C][C]524495[/C][C]831.831[/C][C]630.531[/C][/ROW]
[ROW][C]Winsorized Mean ( 28 / 36 )[/C][C]524492[/C][C]807.706[/C][C]649.36[/C][/ROW]
[ROW][C]Winsorized Mean ( 29 / 36 )[/C][C]524492[/C][C]782.539[/C][C]670.243[/C][/ROW]
[ROW][C]Winsorized Mean ( 30 / 36 )[/C][C]524492[/C][C]757.568[/C][C]692.337[/C][/ROW]
[ROW][C]Winsorized Mean ( 31 / 36 )[/C][C]524492[/C][C]731.134[/C][C]717.368[/C][/ROW]
[ROW][C]Winsorized Mean ( 32 / 36 )[/C][C]524496[/C][C]704.519[/C][C]744.473[/C][/ROW]
[ROW][C]Winsorized Mean ( 33 / 36 )[/C][C]524492[/C][C]677.303[/C][C]774.383[/C][/ROW]
[ROW][C]Winsorized Mean ( 34 / 36 )[/C][C]524500[/C][C]649.947[/C][C]806.989[/C][/ROW]
[ROW][C]Winsorized Mean ( 35 / 36 )[/C][C]524496[/C][C]621.548[/C][C]843.854[/C][/ROW]
[ROW][C]Winsorized Mean ( 36 / 36 )[/C][C]524496[/C][C]592.097[/C][C]885.828[/C][/ROW]
[ROW][C]Trimmed Mean ( 1 / 36 )[/C][C]524492[/C][C]1181.63[/C][C]443.873[/C][/ROW]
[ROW][C]Trimmed Mean ( 2 / 36 )[/C][C]524492[/C][C]1170.56[/C][C]448.071[/C][/ROW]
[ROW][C]Trimmed Mean ( 3 / 36 )[/C][C]524492[/C][C]1159.34[/C][C]452.406[/C][/ROW]
[ROW][C]Trimmed Mean ( 4 / 36 )[/C][C]524492[/C][C]1148.01[/C][C]456.869[/C][/ROW]
[ROW][C]Trimmed Mean ( 5 / 36 )[/C][C]524492[/C][C]1136.58[/C][C]461.466[/C][/ROW]
[ROW][C]Trimmed Mean ( 6 / 36 )[/C][C]524493[/C][C]1125.05[/C][C]466.193[/C][/ROW]
[ROW][C]Trimmed Mean ( 7 / 36 )[/C][C]524493[/C][C]1113.39[/C][C]471.078[/C][/ROW]
[ROW][C]Trimmed Mean ( 8 / 36 )[/C][C]524493[/C][C]1101.63[/C][C]476.107[/C][/ROW]
[ROW][C]Trimmed Mean ( 9 / 36 )[/C][C]524493[/C][C]1089.74[/C][C]481.299[/C][/ROW]
[ROW][C]Trimmed Mean ( 10 / 36 )[/C][C]524493[/C][C]1077.74[/C][C]486.662[/C][/ROW]
[ROW][C]Trimmed Mean ( 11 / 36 )[/C][C]524493[/C][C]1065.54[/C][C]492.232[/C][/ROW]
[ROW][C]Trimmed Mean ( 12 / 36 )[/C][C]524493[/C][C]1053.21[/C][C]497.996[/C][/ROW]
[ROW][C]Trimmed Mean ( 13 / 36 )[/C][C]524493[/C][C]1040.76[/C][C]503.952[/C][/ROW]
[ROW][C]Trimmed Mean ( 14 / 36 )[/C][C]524492[/C][C]1028.17[/C][C]510.124[/C][/ROW]
[ROW][C]Trimmed Mean ( 15 / 36 )[/C][C]524492[/C][C]1015.36[/C][C]516.56[/C][/ROW]
[ROW][C]Trimmed Mean ( 16 / 36 )[/C][C]524493[/C][C]1002.38[/C][C]523.245[/C][/ROW]
[ROW][C]Trimmed Mean ( 17 / 36 )[/C][C]524493[/C][C]989.243[/C][C]530.196[/C][/ROW]
[ROW][C]Trimmed Mean ( 18 / 36 )[/C][C]524493[/C][C]975.964[/C][C]537.41[/C][/ROW]
[ROW][C]Trimmed Mean ( 19 / 36 )[/C][C]524493[/C][C]962.467[/C][C]544.947[/C][/ROW]
[ROW][C]Trimmed Mean ( 20 / 36 )[/C][C]524493[/C][C]948.821[/C][C]552.784[/C][/ROW]
[ROW][C]Trimmed Mean ( 21 / 36 )[/C][C]524493[/C][C]934.982[/C][C]560.967[/C][/ROW]
[ROW][C]Trimmed Mean ( 22 / 36 )[/C][C]524494[/C][C]920.942[/C][C]569.519[/C][/ROW]
[ROW][C]Trimmed Mean ( 23 / 36 )[/C][C]524493[/C][C]906.597[/C][C]578.53[/C][/ROW]
[ROW][C]Trimmed Mean ( 24 / 36 )[/C][C]524493[/C][C]892.022[/C][C]587.982[/C][/ROW]
[ROW][C]Trimmed Mean ( 25 / 36 )[/C][C]524493[/C][C]877.256[/C][C]597.879[/C][/ROW]
[ROW][C]Trimmed Mean ( 26 / 36 )[/C][C]524493[/C][C]862.242[/C][C]608.289[/C][/ROW]
[ROW][C]Trimmed Mean ( 27 / 36 )[/C][C]524493[/C][C]846.909[/C][C]619.302[/C][/ROW]
[ROW][C]Trimmed Mean ( 28 / 36 )[/C][C]524492[/C][C]831.294[/C][C]630.935[/C][/ROW]
[ROW][C]Trimmed Mean ( 29 / 36 )[/C][C]524493[/C][C]815.379[/C][C]643.25[/C][/ROW]
[ROW][C]Trimmed Mean ( 30 / 36 )[/C][C]524493[/C][C]799.217[/C][C]656.258[/C][/ROW]
[ROW][C]Trimmed Mean ( 31 / 36 )[/C][C]524493[/C][C]782.656[/C][C]670.145[/C][/ROW]
[ROW][C]Trimmed Mean ( 32 / 36 )[/C][C]524493[/C][C]765.817[/C][C]684.88[/C][/ROW]
[ROW][C]Trimmed Mean ( 33 / 36 )[/C][C]524492[/C][C]748.613[/C][C]700.619[/C][/ROW]
[ROW][C]Trimmed Mean ( 34 / 36 )[/C][C]524492[/C][C]731.019[/C][C]717.481[/C][/ROW]
[ROW][C]Trimmed Mean ( 35 / 36 )[/C][C]524492[/C][C]712.911[/C][C]735.705[/C][/ROW]
[ROW][C]Trimmed Mean ( 36 / 36 )[/C][C]524492[/C][C]694.338[/C][C]755.384[/C][/ROW]
[ROW][C]Median[/C][C]524485[/C][C][/C][C][/C][/ROW]
[ROW][C]Midrange[/C][C]524498[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at Xnp[/C][C]524295[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Weighted Average at X(n+1)p[/C][C]524493[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function[/C][C]524295[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Averaging[/C][C]524493[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Empirical Distribution Function - Interpolation[/C][C]524493[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - Closest Observation[/C][C]524295[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - True Basic - Statistics Graphics Toolkit[/C][C]524493[/C][C][/C][C][/C][/ROW]
[ROW][C]Midmean - MS Excel (old versions)[/C][C]524493[/C][C][/C][C][/C][/ROW]
[ROW][C]Number of observations[/C][C]108[/C][C][/C][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=307261&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307261&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 Mean5244921192.6439.79
Geometric Mean524347
Harmonic Mean524202
Quadratic Mean524638
Winsorized Mean ( 1 / 36 )5244931191.39440.235
Winsorized Mean ( 2 / 36 )5244921189.1441.084
Winsorized Mean ( 3 / 36 )5244921185.6442.387
Winsorized Mean ( 4 / 36 )5244921181.01444.106
Winsorized Mean ( 5 / 36 )5244921175.27446.275
Winsorized Mean ( 6 / 36 )5244921168.7448.781
Winsorized Mean ( 7 / 36 )5244921160.91451.794
Winsorized Mean ( 8 / 36 )5244931152.27455.181
Winsorized Mean ( 9 / 36 )5244921142.69458.997
Winsorized Mean ( 10 / 36 )5244951132.53463.117
Winsorized Mean ( 11 / 36 )5244941121.13467.825
Winsorized Mean ( 12 / 36 )5244941108.65473.09
Winsorized Mean ( 13 / 36 )5244951095.52478.762
Winsorized Mean ( 14 / 36 )5244901082.01484.735
Winsorized Mean ( 15 / 36 )5244921067.22491.455
Winsorized Mean ( 16 / 36 )5244901051.63498.739
Winsorized Mean ( 17 / 36 )5244901034.99506.759
Winsorized Mean ( 18 / 36 )5244901018.13515.152
Winsorized Mean ( 19 / 36 )524490999.935524.525
Winsorized Mean ( 20 / 36 )524493981.298534.489
Winsorized Mean ( 21 / 36 )524490961.935545.245
Winsorized Mean ( 22 / 36 )524498942.513556.489
Winsorized Mean ( 23 / 36 )524495921.728569.035
Winsorized Mean ( 24 / 36 )524495899.903582.836
Winsorized Mean ( 25 / 36 )524498877.753597.546
Winsorized Mean ( 26 / 36 )524492855.308613.22
Winsorized Mean ( 27 / 36 )524495831.831630.531
Winsorized Mean ( 28 / 36 )524492807.706649.36
Winsorized Mean ( 29 / 36 )524492782.539670.243
Winsorized Mean ( 30 / 36 )524492757.568692.337
Winsorized Mean ( 31 / 36 )524492731.134717.368
Winsorized Mean ( 32 / 36 )524496704.519744.473
Winsorized Mean ( 33 / 36 )524492677.303774.383
Winsorized Mean ( 34 / 36 )524500649.947806.989
Winsorized Mean ( 35 / 36 )524496621.548843.854
Winsorized Mean ( 36 / 36 )524496592.097885.828
Trimmed Mean ( 1 / 36 )5244921181.63443.873
Trimmed Mean ( 2 / 36 )5244921170.56448.071
Trimmed Mean ( 3 / 36 )5244921159.34452.406
Trimmed Mean ( 4 / 36 )5244921148.01456.869
Trimmed Mean ( 5 / 36 )5244921136.58461.466
Trimmed Mean ( 6 / 36 )5244931125.05466.193
Trimmed Mean ( 7 / 36 )5244931113.39471.078
Trimmed Mean ( 8 / 36 )5244931101.63476.107
Trimmed Mean ( 9 / 36 )5244931089.74481.299
Trimmed Mean ( 10 / 36 )5244931077.74486.662
Trimmed Mean ( 11 / 36 )5244931065.54492.232
Trimmed Mean ( 12 / 36 )5244931053.21497.996
Trimmed Mean ( 13 / 36 )5244931040.76503.952
Trimmed Mean ( 14 / 36 )5244921028.17510.124
Trimmed Mean ( 15 / 36 )5244921015.36516.56
Trimmed Mean ( 16 / 36 )5244931002.38523.245
Trimmed Mean ( 17 / 36 )524493989.243530.196
Trimmed Mean ( 18 / 36 )524493975.964537.41
Trimmed Mean ( 19 / 36 )524493962.467544.947
Trimmed Mean ( 20 / 36 )524493948.821552.784
Trimmed Mean ( 21 / 36 )524493934.982560.967
Trimmed Mean ( 22 / 36 )524494920.942569.519
Trimmed Mean ( 23 / 36 )524493906.597578.53
Trimmed Mean ( 24 / 36 )524493892.022587.982
Trimmed Mean ( 25 / 36 )524493877.256597.879
Trimmed Mean ( 26 / 36 )524493862.242608.289
Trimmed Mean ( 27 / 36 )524493846.909619.302
Trimmed Mean ( 28 / 36 )524492831.294630.935
Trimmed Mean ( 29 / 36 )524493815.379643.25
Trimmed Mean ( 30 / 36 )524493799.217656.258
Trimmed Mean ( 31 / 36 )524493782.656670.145
Trimmed Mean ( 32 / 36 )524493765.817684.88
Trimmed Mean ( 33 / 36 )524492748.613700.619
Trimmed Mean ( 34 / 36 )524492731.019717.481
Trimmed Mean ( 35 / 36 )524492712.911735.705
Trimmed Mean ( 36 / 36 )524492694.338755.384
Median524485
Midrange524498
Midmean - Weighted Average at Xnp524295
Midmean - Weighted Average at X(n+1)p524493
Midmean - Empirical Distribution Function524295
Midmean - Empirical Distribution Function - Averaging524493
Midmean - Empirical Distribution Function - Interpolation524493
Midmean - Closest Observation524295
Midmean - True Basic - Statistics Graphics Toolkit524493
Midmean - MS Excel (old versions)524493
Number of observations108



Parameters (Session):
par1 = Aantal verkochte exemplaren van 'La Libre' ; par2 = Niet gekend ; par3 = Cijferreeks verkochte exemplaren La Libre. ; par4 = 12 ;
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,'Arithmetic Mean',header=TRUE)
a<-table.element(a,signif(arm,6))
a<-table.element(a, signif(armse,6))
a<-table.element(a,signif(armose,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Geometric Mean',header=TRUE)
a<-table.element(a,signif(geo,6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Harmonic Mean',header=TRUE)
a<-table.element(a,signif(har,6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Quadratic Mean',header=TRUE)
a<-table.element(a,signif(qua,6))
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, mylabel,header=TRUE)
a<-table.element(a,signif(win[j,1],6))
a<-table.element(a,signif(win[j,2],6))
a<-table.element(a,signif(win[j,1]/win[j,2],6))
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, mylabel,header=TRUE)
a<-table.element(a,signif(tri[j,1],6))
a<-table.element(a,signif(tri[j,2],6))
a<-table.element(a,signif(tri[j,1]/tri[j,2],6))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a, 'Median',header=TRUE)
a<-table.element(a,signif(median(x),6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Midrange',header=TRUE)
a<-table.element(a,signif(midr,6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'Weighted Average at Xnp',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[1],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'Weighted Average at X(n+1)p',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[2],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'Empirical Distribution Function',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[3],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'Empirical Distribution Function - Averaging',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[4],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'Empirical Distribution Function - Interpolation',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[5],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'Closest Observation',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[6],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'True Basic - Statistics Graphics Toolkit',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[7],6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.row.start(a)
mymid <- 'Midmean'
mylabel <- paste(mymid,'MS Excel (old versions)',sep=' - ')
a<-table.element(a,mylabel,header=TRUE)
a<-table.element(a,signif(midm[8],6))
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,signif(length(x),6))
a<-table.element(a,'')
a<-table.element(a,'')
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')