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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_harrell_davis.wasp
Title produced by softwareHarrell-Davis Quantiles
Date of computationTue, 02 Nov 2010 22:14:24 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/02/t12887360502e924zwtaa8vkwb.htm/, Retrieved Sat, 27 Apr 2024 19:20:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=92121, Retrieved Sat, 27 Apr 2024 19:20:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Central Tendency] [] [2010-10-20 18:18:38] [b98453cac15ba1066b407e146608df68]
F RMPD    [Harrell-Davis Quantiles] [Harrell-Davis Qua...] [2010-11-02 22:14:24] [7a87ed98a7b21a29d6a45388a9b7b229] [Current]
-   P       [Harrell-Davis Quantiles] [Harrell-Davis Qua...] [2010-11-02 22:23:27] [f4dc4aa51d65be851b8508203d9f6001]
-   P       [Harrell-Davis Quantiles] [Harrell-Davis Qua...] [2010-11-02 22:31:17] [f4dc4aa51d65be851b8508203d9f6001]
Feedback Forum
2010-11-06 15:10:36 [7d66e2e510b144c68ca0882fd178e17c] [reply] 
Je berekening is niet helemaal correct uitgevoerd. Bij de lowest quantile moet 0,001 staan, bij de highest 0,1 en bij de stepsize 0,001.

http://www.freestatistics.org/blog/index.php?v=date/2010/Oct/31/t1288533089untoeiqjr7hlvi5.htm/
2010-11-07 10:23:04 [50992571857ef0dcfd2a4644b51ec6d0] [reply] 
de student gebruikt de juiste gegevens. Het enige verschil dat hier is dat de student als highest quantile 0.999 gebruikt en in de oplossing wordt als highest quantile 0.01 gebruikt. Als lowest quantile gebruikt de student 0.02 en hij/zij neemt een stepsize van 0.1. Ik vind dit 3 x een zeer rare keuze van de student. Door deze gegevens in te vullen verkrijgt de student veel meer data (dan nodig) maar dit is echter zeker niet verkeerd. De student verkreeg de juiste informatie maar kreeg daarbovenop dus nog veel meer informatie. Hij/zij kon de juiste informatie selecteren en kon zo de bovenstaande bevindingen correct namaken en controleren. De student bespreekt echter enkel de berekening voor een type I fout van 0,2% maar niet de berekening voor een type I fout van 5%. Met de informatie uit de voorbeeldoplossing kunnen we enkel de berekening nakijken wanneer we een type I fout hebben van 0,2%. Dus als de student ook deze berekening had willen nakijken dan had hij/zij sowieso meer informatie nodig gehad. Met de informatie die de student verkreeg uit zijn/haar berekening kunnen we echter de berekening voor een type I fout van 5% niet nakijken.

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Dataset

Dataseries X:
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Histogram
Boxplots 
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1.3766765505352E-14
1.3766765505352E-14
1.3766765505352E-14
1.3766765505352E-14
-0.0050988000839864
0.005098800084038
1.3766765505352E-14
1.3766765505352E-14
1.3766765505352E-14
1.3766765505352E-14
1.3766765505352E-14
-0.0050988000839864
3.7747582837255E-14
0.005098800084038
1.3766765505352E-14
-0.0050988000839864
3.7747582837255E-14
3.7747582837255E-14
3.7747582837255E-14
3.7747582837255E-14
3.7747582837255E-14
0.00063877357653785
4.3076653355456E-14
0.0038240964384433
3.9523939676656E-14
3.9523939676656E-14
3.9523939676656E-14
3.9523939676656E-14
3.9523939676656E-14
3.9523939676656E-14
3.9523939676656E-14
3.9523939676656E-14
3.9523939676656E-14
3.9523939676656E-14
3.9523939676656E-14
3.9523939676656E-14
3.9523939676656E-14
-0.0038240964383607
4.3076653355456E-14
4.3076653355456E-14
4.3076653355456E-14
4.3076653355456E-14
0.0038240964384433
3.9523939676656E-14
3.9523939676656E-14
3.9523939676656E-14
-0.0038240964383607
4.3076653355456E-14
4.3076653355456E-14
4.3076653355456E-14
4.3076653355456E-14
0.0038240964384433
-0.0038240964383607
4.3076653355456E-14
4.3076653355456E-14
4.3076653355456E-14
0.0038240964384433
0.0094967475372392
0.0062814276885823
-2.1316282072803E-14
-0.0062814276886214
-1.7763568394003E-14
-1.7763568394003E-14
-1.7763568394003E-14
0.0062814276885823
-2.1316282072803E-14
-2.1316282072803E-14
-0.0062814276886214
-1.7763568394003E-14

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=92121&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=92121&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=92121&T=0

As an alternative you can also use a QR Code:  
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'George Udny Yule' @ 72.249.76.132






Harrell-Davis Quantiles
quantilesvaluestandard error
0.002-0.01361160423441010.00102086072099757
0.012-0.007935961798750360.000706635983265818
0.022-0.007139677274499859.2141949077331e-05
0.032-0.006960619629204320.000148778826300720
0.042-0.006718508580121520.000220541025942038
0.052-0.006375514070563720.000337985417573495
0.062-0.005996268186927280.000315442749138007
0.072-0.005678116930164870.000295056815815844
0.082-0.005345268833402940.000443794037214407
0.092-0.004719946294423510.00109251312719088
0.102-0.003440940577898960.00193356594428542
0.112-0.001802996553375370.00186705392629421
0.122-0.0006217492239760340.00098221285817972
0.132-0.0001358199244831010.000291150589226958
0.142-1.86254741295268e-055.02884810333207e-05
0.152-1.61153227251544e-065.21434870303227e-06
0.162-8.89734361186583e-083.33194999555296e-07
0.172-3.17785805445570e-091.34285310522809e-08
0.182-7.45312360656058e-113.48446647119539e-10
0.192-1.20742876459948e-125.93101864209576e-12
0.202-5.62279049974814e-146.89609967252671e-14
0.212-4.25397246251725e-142.90215018218468e-15
0.222-4.08646512337948e-142.53610125345181e-15
0.232-3.93744902111947e-142.27471186181238e-15
0.242-3.80801963313136e-142.00130233630801e-15
0.252-3.68525095833892e-142.2027101292957e-15
0.262-3.53096423818919e-143.22524512613180e-15
0.272-3.30555673485389e-144.70662944568422e-15
0.282-3.00821028300171e-145.68100379325799e-15
0.292-2.68906924658745e-145.46757422153907e-15
0.302-2.41138908590562e-144.32171382525662e-15
0.312-2.20546419004841e-143.04729830128908e-15
0.322-2.05965704330354e-142.25784982091175e-15
0.332-1.94048764478379e-142.14508496716495e-15
0.342-1.81509663034505e-142.52671160296561e-15
0.352-1.66812964269046e-142.95040202607112e-15
0.362-1.50752940974043e-143.03511219571785e-15
0.372-1.35263864663927e-142.76160947147456e-15
0.382-1.21735616295144e-142.33355986131986e-15
0.392-1.10662493576029e-141.86441555917127e-15
0.402-1.02186984453585e-141.37479343103818e-15
0.412-9.61767481355776e-159.4508699322894e-16
0.422-9.2004128864128e-156.77672047312744e-16
0.432-8.87529841751425e-155.90352318390947e-16
0.442-8.55806091678473e-156.5093798795789e-16
0.452-8.17562545760256e-158.5130662732351e-16
0.462-7.65952277970311e-151.17726110026484e-15
0.472-6.94751219001196e-151.62160778731435e-15
0.482-5.95018090209732e-152.31349351291564e-15
0.492-4.48052712317048e-153.50212985807223e-15
0.502-2.27623417113612e-155.18109338096233e-15
0.5127.80413603070006e-166.75383957859958e-15
0.5224.40918774288806e-157.3278726011834e-15
0.5327.97328118956618e-156.50751119850653e-15
0.5421.08685582268317e-144.81059951170257e-15
0.5521.29019659750399e-143.21293077322849e-15
0.5621.43189699866692e-142.38707401153294e-15
0.5721.55375849645742e-142.37697327148608e-15
0.5821.68818201337458e-142.81356451065462e-15
0.5921.84851569366326e-143.32424730464718e-15
0.6022.03253963404659e-143.67991159381119e-15
0.6122.22885812434859e-143.76065997335330e-15
0.6222.42164159284071e-143.52171202728994e-15
0.6322.59424628078066e-142.98546326038466e-15
0.6422.73277470624122e-142.24149880570614e-15
0.6522.83009189927125e-141.45222251072996e-15
0.6622.88921767333032e-148.1842193887032e-16
0.6722.92346868477565e-145.0652576837005e-16
0.6822.95292213933912e-146.10050228200239e-16
0.6922.99837440896815e-141.05465447201731e-15
0.7023.07194525738909e-141.56887559845823e-15
0.7123.16803685771533e-141.81106562646529e-15
0.7223.28861829952286e-142.82367046252621e-15
0.7324.95503948407528e-149.3612323715931e-14
0.7428.59422275440413e-134.42339745278547e-12
0.7523.11966666758972e-111.5387519377128e-10
0.7628.60746503195005e-103.87745969361225e-09
0.7721.73031906759433e-087.02568117315385e-08
0.7822.51881820601767e-079.07874221052217e-07
0.7922.64165170583736e-068.28876069411967e-06
0.8021.98725008619535e-055.29049437894411e-05
0.8120.0001069520445889990.000233354738645267
0.8220.0004122408867120630.00070263825672381
0.8320.001147001377556090.00142659746342605
0.8420.002349087608095220.00193151094541668
0.8520.003677165138598370.00173520129848315
0.8620.004677874512128390.00106256687574529
0.8720.005237804489910480.000525979548037094
0.8820.005551086071067040.000325080407574060
0.8920.005798882104290680.000291698422511875
0.9020.006061241140489180.000320622133656396
0.9120.006365180607954230.000347002240157863
0.9220.006664921529085640.000286478425438436
0.9320.006902534595705330.000203090249796877
0.9420.007126048448857220.000233071616400393
0.9520.007668152339290530.000703730113196285
0.9620.009178822734705720.0014922280702123
0.9720.01205508169917050.00219376274008825
0.9820.01779404203843810.0041084810295265
0.9920.02670838942686470.00252619108872092

\begin{tabular}{lllllllll}
\hline
Harrell-Davis Quantiles \tabularnewline
quantiles & value & standard error \tabularnewline
0.002 & -0.0136116042344101 & 0.00102086072099757 \tabularnewline
0.012 & -0.00793596179875036 & 0.000706635983265818 \tabularnewline
0.022 & -0.00713967727449985 & 9.2141949077331e-05 \tabularnewline
0.032 & -0.00696061962920432 & 0.000148778826300720 \tabularnewline
0.042 & -0.00671850858012152 & 0.000220541025942038 \tabularnewline
0.052 & -0.00637551407056372 & 0.000337985417573495 \tabularnewline
0.062 & -0.00599626818692728 & 0.000315442749138007 \tabularnewline
0.072 & -0.00567811693016487 & 0.000295056815815844 \tabularnewline
0.082 & -0.00534526883340294 & 0.000443794037214407 \tabularnewline
0.092 & -0.00471994629442351 & 0.00109251312719088 \tabularnewline
0.102 & -0.00344094057789896 & 0.00193356594428542 \tabularnewline
0.112 & -0.00180299655337537 & 0.00186705392629421 \tabularnewline
0.122 & -0.000621749223976034 & 0.00098221285817972 \tabularnewline
0.132 & -0.000135819924483101 & 0.000291150589226958 \tabularnewline
0.142 & -1.86254741295268e-05 & 5.02884810333207e-05 \tabularnewline
0.152 & -1.61153227251544e-06 & 5.21434870303227e-06 \tabularnewline
0.162 & -8.89734361186583e-08 & 3.33194999555296e-07 \tabularnewline
0.172 & -3.17785805445570e-09 & 1.34285310522809e-08 \tabularnewline
0.182 & -7.45312360656058e-11 & 3.48446647119539e-10 \tabularnewline
0.192 & -1.20742876459948e-12 & 5.93101864209576e-12 \tabularnewline
0.202 & -5.62279049974814e-14 & 6.89609967252671e-14 \tabularnewline
0.212 & -4.25397246251725e-14 & 2.90215018218468e-15 \tabularnewline
0.222 & -4.08646512337948e-14 & 2.53610125345181e-15 \tabularnewline
0.232 & -3.93744902111947e-14 & 2.27471186181238e-15 \tabularnewline
0.242 & -3.80801963313136e-14 & 2.00130233630801e-15 \tabularnewline
0.252 & -3.68525095833892e-14 & 2.2027101292957e-15 \tabularnewline
0.262 & -3.53096423818919e-14 & 3.22524512613180e-15 \tabularnewline
0.272 & -3.30555673485389e-14 & 4.70662944568422e-15 \tabularnewline
0.282 & -3.00821028300171e-14 & 5.68100379325799e-15 \tabularnewline
0.292 & -2.68906924658745e-14 & 5.46757422153907e-15 \tabularnewline
0.302 & -2.41138908590562e-14 & 4.32171382525662e-15 \tabularnewline
0.312 & -2.20546419004841e-14 & 3.04729830128908e-15 \tabularnewline
0.322 & -2.05965704330354e-14 & 2.25784982091175e-15 \tabularnewline
0.332 & -1.94048764478379e-14 & 2.14508496716495e-15 \tabularnewline
0.342 & -1.81509663034505e-14 & 2.52671160296561e-15 \tabularnewline
0.352 & -1.66812964269046e-14 & 2.95040202607112e-15 \tabularnewline
0.362 & -1.50752940974043e-14 & 3.03511219571785e-15 \tabularnewline
0.372 & -1.35263864663927e-14 & 2.76160947147456e-15 \tabularnewline
0.382 & -1.21735616295144e-14 & 2.33355986131986e-15 \tabularnewline
0.392 & -1.10662493576029e-14 & 1.86441555917127e-15 \tabularnewline
0.402 & -1.02186984453585e-14 & 1.37479343103818e-15 \tabularnewline
0.412 & -9.61767481355776e-15 & 9.4508699322894e-16 \tabularnewline
0.422 & -9.2004128864128e-15 & 6.77672047312744e-16 \tabularnewline
0.432 & -8.87529841751425e-15 & 5.90352318390947e-16 \tabularnewline
0.442 & -8.55806091678473e-15 & 6.5093798795789e-16 \tabularnewline
0.452 & -8.17562545760256e-15 & 8.5130662732351e-16 \tabularnewline
0.462 & -7.65952277970311e-15 & 1.17726110026484e-15 \tabularnewline
0.472 & -6.94751219001196e-15 & 1.62160778731435e-15 \tabularnewline
0.482 & -5.95018090209732e-15 & 2.31349351291564e-15 \tabularnewline
0.492 & -4.48052712317048e-15 & 3.50212985807223e-15 \tabularnewline
0.502 & -2.27623417113612e-15 & 5.18109338096233e-15 \tabularnewline
0.512 & 7.80413603070006e-16 & 6.75383957859958e-15 \tabularnewline
0.522 & 4.40918774288806e-15 & 7.3278726011834e-15 \tabularnewline
0.532 & 7.97328118956618e-15 & 6.50751119850653e-15 \tabularnewline
0.542 & 1.08685582268317e-14 & 4.81059951170257e-15 \tabularnewline
0.552 & 1.29019659750399e-14 & 3.21293077322849e-15 \tabularnewline
0.562 & 1.43189699866692e-14 & 2.38707401153294e-15 \tabularnewline
0.572 & 1.55375849645742e-14 & 2.37697327148608e-15 \tabularnewline
0.582 & 1.68818201337458e-14 & 2.81356451065462e-15 \tabularnewline
0.592 & 1.84851569366326e-14 & 3.32424730464718e-15 \tabularnewline
0.602 & 2.03253963404659e-14 & 3.67991159381119e-15 \tabularnewline
0.612 & 2.22885812434859e-14 & 3.76065997335330e-15 \tabularnewline
0.622 & 2.42164159284071e-14 & 3.52171202728994e-15 \tabularnewline
0.632 & 2.59424628078066e-14 & 2.98546326038466e-15 \tabularnewline
0.642 & 2.73277470624122e-14 & 2.24149880570614e-15 \tabularnewline
0.652 & 2.83009189927125e-14 & 1.45222251072996e-15 \tabularnewline
0.662 & 2.88921767333032e-14 & 8.1842193887032e-16 \tabularnewline
0.672 & 2.92346868477565e-14 & 5.0652576837005e-16 \tabularnewline
0.682 & 2.95292213933912e-14 & 6.10050228200239e-16 \tabularnewline
0.692 & 2.99837440896815e-14 & 1.05465447201731e-15 \tabularnewline
0.702 & 3.07194525738909e-14 & 1.56887559845823e-15 \tabularnewline
0.712 & 3.16803685771533e-14 & 1.81106562646529e-15 \tabularnewline
0.722 & 3.28861829952286e-14 & 2.82367046252621e-15 \tabularnewline
0.732 & 4.95503948407528e-14 & 9.3612323715931e-14 \tabularnewline
0.742 & 8.59422275440413e-13 & 4.42339745278547e-12 \tabularnewline
0.752 & 3.11966666758972e-11 & 1.5387519377128e-10 \tabularnewline
0.762 & 8.60746503195005e-10 & 3.87745969361225e-09 \tabularnewline
0.772 & 1.73031906759433e-08 & 7.02568117315385e-08 \tabularnewline
0.782 & 2.51881820601767e-07 & 9.07874221052217e-07 \tabularnewline
0.792 & 2.64165170583736e-06 & 8.28876069411967e-06 \tabularnewline
0.802 & 1.98725008619535e-05 & 5.29049437894411e-05 \tabularnewline
0.812 & 0.000106952044588999 & 0.000233354738645267 \tabularnewline
0.822 & 0.000412240886712063 & 0.00070263825672381 \tabularnewline
0.832 & 0.00114700137755609 & 0.00142659746342605 \tabularnewline
0.842 & 0.00234908760809522 & 0.00193151094541668 \tabularnewline
0.852 & 0.00367716513859837 & 0.00173520129848315 \tabularnewline
0.862 & 0.00467787451212839 & 0.00106256687574529 \tabularnewline
0.872 & 0.00523780448991048 & 0.000525979548037094 \tabularnewline
0.882 & 0.00555108607106704 & 0.000325080407574060 \tabularnewline
0.892 & 0.00579888210429068 & 0.000291698422511875 \tabularnewline
0.902 & 0.00606124114048918 & 0.000320622133656396 \tabularnewline
0.912 & 0.00636518060795423 & 0.000347002240157863 \tabularnewline
0.922 & 0.00666492152908564 & 0.000286478425438436 \tabularnewline
0.932 & 0.00690253459570533 & 0.000203090249796877 \tabularnewline
0.942 & 0.00712604844885722 & 0.000233071616400393 \tabularnewline
0.952 & 0.00766815233929053 & 0.000703730113196285 \tabularnewline
0.962 & 0.00917882273470572 & 0.0014922280702123 \tabularnewline
0.972 & 0.0120550816991705 & 0.00219376274008825 \tabularnewline
0.982 & 0.0177940420384381 & 0.0041084810295265 \tabularnewline
0.992 & 0.0267083894268647 & 0.00252619108872092 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=92121&T=1

[TABLE]
[ROW][C]Harrell-Davis Quantiles[/C][/ROW]
[ROW][C]quantiles[/C][C]value[/C][C]standard error[/C][/ROW]
[ROW][C]0.002[/C][C]-0.0136116042344101[/C][C]0.00102086072099757[/C][/ROW]
[ROW][C]0.012[/C][C]-0.00793596179875036[/C][C]0.000706635983265818[/C][/ROW]
[ROW][C]0.022[/C][C]-0.00713967727449985[/C][C]9.2141949077331e-05[/C][/ROW]
[ROW][C]0.032[/C][C]-0.00696061962920432[/C][C]0.000148778826300720[/C][/ROW]
[ROW][C]0.042[/C][C]-0.00671850858012152[/C][C]0.000220541025942038[/C][/ROW]
[ROW][C]0.052[/C][C]-0.00637551407056372[/C][C]0.000337985417573495[/C][/ROW]
[ROW][C]0.062[/C][C]-0.00599626818692728[/C][C]0.000315442749138007[/C][/ROW]
[ROW][C]0.072[/C][C]-0.00567811693016487[/C][C]0.000295056815815844[/C][/ROW]
[ROW][C]0.082[/C][C]-0.00534526883340294[/C][C]0.000443794037214407[/C][/ROW]
[ROW][C]0.092[/C][C]-0.00471994629442351[/C][C]0.00109251312719088[/C][/ROW]
[ROW][C]0.102[/C][C]-0.00344094057789896[/C][C]0.00193356594428542[/C][/ROW]
[ROW][C]0.112[/C][C]-0.00180299655337537[/C][C]0.00186705392629421[/C][/ROW]
[ROW][C]0.122[/C][C]-0.000621749223976034[/C][C]0.00098221285817972[/C][/ROW]
[ROW][C]0.132[/C][C]-0.000135819924483101[/C][C]0.000291150589226958[/C][/ROW]
[ROW][C]0.142[/C][C]-1.86254741295268e-05[/C][C]5.02884810333207e-05[/C][/ROW]
[ROW][C]0.152[/C][C]-1.61153227251544e-06[/C][C]5.21434870303227e-06[/C][/ROW]
[ROW][C]0.162[/C][C]-8.89734361186583e-08[/C][C]3.33194999555296e-07[/C][/ROW]
[ROW][C]0.172[/C][C]-3.17785805445570e-09[/C][C]1.34285310522809e-08[/C][/ROW]
[ROW][C]0.182[/C][C]-7.45312360656058e-11[/C][C]3.48446647119539e-10[/C][/ROW]
[ROW][C]0.192[/C][C]-1.20742876459948e-12[/C][C]5.93101864209576e-12[/C][/ROW]
[ROW][C]0.202[/C][C]-5.62279049974814e-14[/C][C]6.89609967252671e-14[/C][/ROW]
[ROW][C]0.212[/C][C]-4.25397246251725e-14[/C][C]2.90215018218468e-15[/C][/ROW]
[ROW][C]0.222[/C][C]-4.08646512337948e-14[/C][C]2.53610125345181e-15[/C][/ROW]
[ROW][C]0.232[/C][C]-3.93744902111947e-14[/C][C]2.27471186181238e-15[/C][/ROW]
[ROW][C]0.242[/C][C]-3.80801963313136e-14[/C][C]2.00130233630801e-15[/C][/ROW]
[ROW][C]0.252[/C][C]-3.68525095833892e-14[/C][C]2.2027101292957e-15[/C][/ROW]
[ROW][C]0.262[/C][C]-3.53096423818919e-14[/C][C]3.22524512613180e-15[/C][/ROW]
[ROW][C]0.272[/C][C]-3.30555673485389e-14[/C][C]4.70662944568422e-15[/C][/ROW]
[ROW][C]0.282[/C][C]-3.00821028300171e-14[/C][C]5.68100379325799e-15[/C][/ROW]
[ROW][C]0.292[/C][C]-2.68906924658745e-14[/C][C]5.46757422153907e-15[/C][/ROW]
[ROW][C]0.302[/C][C]-2.41138908590562e-14[/C][C]4.32171382525662e-15[/C][/ROW]
[ROW][C]0.312[/C][C]-2.20546419004841e-14[/C][C]3.04729830128908e-15[/C][/ROW]
[ROW][C]0.322[/C][C]-2.05965704330354e-14[/C][C]2.25784982091175e-15[/C][/ROW]
[ROW][C]0.332[/C][C]-1.94048764478379e-14[/C][C]2.14508496716495e-15[/C][/ROW]
[ROW][C]0.342[/C][C]-1.81509663034505e-14[/C][C]2.52671160296561e-15[/C][/ROW]
[ROW][C]0.352[/C][C]-1.66812964269046e-14[/C][C]2.95040202607112e-15[/C][/ROW]
[ROW][C]0.362[/C][C]-1.50752940974043e-14[/C][C]3.03511219571785e-15[/C][/ROW]
[ROW][C]0.372[/C][C]-1.35263864663927e-14[/C][C]2.76160947147456e-15[/C][/ROW]
[ROW][C]0.382[/C][C]-1.21735616295144e-14[/C][C]2.33355986131986e-15[/C][/ROW]
[ROW][C]0.392[/C][C]-1.10662493576029e-14[/C][C]1.86441555917127e-15[/C][/ROW]
[ROW][C]0.402[/C][C]-1.02186984453585e-14[/C][C]1.37479343103818e-15[/C][/ROW]
[ROW][C]0.412[/C][C]-9.61767481355776e-15[/C][C]9.4508699322894e-16[/C][/ROW]
[ROW][C]0.422[/C][C]-9.2004128864128e-15[/C][C]6.77672047312744e-16[/C][/ROW]
[ROW][C]0.432[/C][C]-8.87529841751425e-15[/C][C]5.90352318390947e-16[/C][/ROW]
[ROW][C]0.442[/C][C]-8.55806091678473e-15[/C][C]6.5093798795789e-16[/C][/ROW]
[ROW][C]0.452[/C][C]-8.17562545760256e-15[/C][C]8.5130662732351e-16[/C][/ROW]
[ROW][C]0.462[/C][C]-7.65952277970311e-15[/C][C]1.17726110026484e-15[/C][/ROW]
[ROW][C]0.472[/C][C]-6.94751219001196e-15[/C][C]1.62160778731435e-15[/C][/ROW]
[ROW][C]0.482[/C][C]-5.95018090209732e-15[/C][C]2.31349351291564e-15[/C][/ROW]
[ROW][C]0.492[/C][C]-4.48052712317048e-15[/C][C]3.50212985807223e-15[/C][/ROW]
[ROW][C]0.502[/C][C]-2.27623417113612e-15[/C][C]5.18109338096233e-15[/C][/ROW]
[ROW][C]0.512[/C][C]7.80413603070006e-16[/C][C]6.75383957859958e-15[/C][/ROW]
[ROW][C]0.522[/C][C]4.40918774288806e-15[/C][C]7.3278726011834e-15[/C][/ROW]
[ROW][C]0.532[/C][C]7.97328118956618e-15[/C][C]6.50751119850653e-15[/C][/ROW]
[ROW][C]0.542[/C][C]1.08685582268317e-14[/C][C]4.81059951170257e-15[/C][/ROW]
[ROW][C]0.552[/C][C]1.29019659750399e-14[/C][C]3.21293077322849e-15[/C][/ROW]
[ROW][C]0.562[/C][C]1.43189699866692e-14[/C][C]2.38707401153294e-15[/C][/ROW]
[ROW][C]0.572[/C][C]1.55375849645742e-14[/C][C]2.37697327148608e-15[/C][/ROW]
[ROW][C]0.582[/C][C]1.68818201337458e-14[/C][C]2.81356451065462e-15[/C][/ROW]
[ROW][C]0.592[/C][C]1.84851569366326e-14[/C][C]3.32424730464718e-15[/C][/ROW]
[ROW][C]0.602[/C][C]2.03253963404659e-14[/C][C]3.67991159381119e-15[/C][/ROW]
[ROW][C]0.612[/C][C]2.22885812434859e-14[/C][C]3.76065997335330e-15[/C][/ROW]
[ROW][C]0.622[/C][C]2.42164159284071e-14[/C][C]3.52171202728994e-15[/C][/ROW]
[ROW][C]0.632[/C][C]2.59424628078066e-14[/C][C]2.98546326038466e-15[/C][/ROW]
[ROW][C]0.642[/C][C]2.73277470624122e-14[/C][C]2.24149880570614e-15[/C][/ROW]
[ROW][C]0.652[/C][C]2.83009189927125e-14[/C][C]1.45222251072996e-15[/C][/ROW]
[ROW][C]0.662[/C][C]2.88921767333032e-14[/C][C]8.1842193887032e-16[/C][/ROW]
[ROW][C]0.672[/C][C]2.92346868477565e-14[/C][C]5.0652576837005e-16[/C][/ROW]
[ROW][C]0.682[/C][C]2.95292213933912e-14[/C][C]6.10050228200239e-16[/C][/ROW]
[ROW][C]0.692[/C][C]2.99837440896815e-14[/C][C]1.05465447201731e-15[/C][/ROW]
[ROW][C]0.702[/C][C]3.07194525738909e-14[/C][C]1.56887559845823e-15[/C][/ROW]
[ROW][C]0.712[/C][C]3.16803685771533e-14[/C][C]1.81106562646529e-15[/C][/ROW]
[ROW][C]0.722[/C][C]3.28861829952286e-14[/C][C]2.82367046252621e-15[/C][/ROW]
[ROW][C]0.732[/C][C]4.95503948407528e-14[/C][C]9.3612323715931e-14[/C][/ROW]
[ROW][C]0.742[/C][C]8.59422275440413e-13[/C][C]4.42339745278547e-12[/C][/ROW]
[ROW][C]0.752[/C][C]3.11966666758972e-11[/C][C]1.5387519377128e-10[/C][/ROW]
[ROW][C]0.762[/C][C]8.60746503195005e-10[/C][C]3.87745969361225e-09[/C][/ROW]
[ROW][C]0.772[/C][C]1.73031906759433e-08[/C][C]7.02568117315385e-08[/C][/ROW]
[ROW][C]0.782[/C][C]2.51881820601767e-07[/C][C]9.07874221052217e-07[/C][/ROW]
[ROW][C]0.792[/C][C]2.64165170583736e-06[/C][C]8.28876069411967e-06[/C][/ROW]
[ROW][C]0.802[/C][C]1.98725008619535e-05[/C][C]5.29049437894411e-05[/C][/ROW]
[ROW][C]0.812[/C][C]0.000106952044588999[/C][C]0.000233354738645267[/C][/ROW]
[ROW][C]0.822[/C][C]0.000412240886712063[/C][C]0.00070263825672381[/C][/ROW]
[ROW][C]0.832[/C][C]0.00114700137755609[/C][C]0.00142659746342605[/C][/ROW]
[ROW][C]0.842[/C][C]0.00234908760809522[/C][C]0.00193151094541668[/C][/ROW]
[ROW][C]0.852[/C][C]0.00367716513859837[/C][C]0.00173520129848315[/C][/ROW]
[ROW][C]0.862[/C][C]0.00467787451212839[/C][C]0.00106256687574529[/C][/ROW]
[ROW][C]0.872[/C][C]0.00523780448991048[/C][C]0.000525979548037094[/C][/ROW]
[ROW][C]0.882[/C][C]0.00555108607106704[/C][C]0.000325080407574060[/C][/ROW]
[ROW][C]0.892[/C][C]0.00579888210429068[/C][C]0.000291698422511875[/C][/ROW]
[ROW][C]0.902[/C][C]0.00606124114048918[/C][C]0.000320622133656396[/C][/ROW]
[ROW][C]0.912[/C][C]0.00636518060795423[/C][C]0.000347002240157863[/C][/ROW]
[ROW][C]0.922[/C][C]0.00666492152908564[/C][C]0.000286478425438436[/C][/ROW]
[ROW][C]0.932[/C][C]0.00690253459570533[/C][C]0.000203090249796877[/C][/ROW]
[ROW][C]0.942[/C][C]0.00712604844885722[/C][C]0.000233071616400393[/C][/ROW]
[ROW][C]0.952[/C][C]0.00766815233929053[/C][C]0.000703730113196285[/C][/ROW]
[ROW][C]0.962[/C][C]0.00917882273470572[/C][C]0.0014922280702123[/C][/ROW]
[ROW][C]0.972[/C][C]0.0120550816991705[/C][C]0.00219376274008825[/C][/ROW]
[ROW][C]0.982[/C][C]0.0177940420384381[/C][C]0.0041084810295265[/C][/ROW]
[ROW][C]0.992[/C][C]0.0267083894268647[/C][C]0.00252619108872092[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=92121&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=92121&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Harrell-Davis Quantiles
quantilesvaluestandard error
0.002-0.01361160423441010.00102086072099757
0.012-0.007935961798750360.000706635983265818
0.022-0.007139677274499859.2141949077331e-05
0.032-0.006960619629204320.000148778826300720
0.042-0.006718508580121520.000220541025942038
0.052-0.006375514070563720.000337985417573495
0.062-0.005996268186927280.000315442749138007
0.072-0.005678116930164870.000295056815815844
0.082-0.005345268833402940.000443794037214407
0.092-0.004719946294423510.00109251312719088
0.102-0.003440940577898960.00193356594428542
0.112-0.001802996553375370.00186705392629421
0.122-0.0006217492239760340.00098221285817972
0.132-0.0001358199244831010.000291150589226958
0.142-1.86254741295268e-055.02884810333207e-05
0.152-1.61153227251544e-065.21434870303227e-06
0.162-8.89734361186583e-083.33194999555296e-07
0.172-3.17785805445570e-091.34285310522809e-08
0.182-7.45312360656058e-113.48446647119539e-10
0.192-1.20742876459948e-125.93101864209576e-12
0.202-5.62279049974814e-146.89609967252671e-14
0.212-4.25397246251725e-142.90215018218468e-15
0.222-4.08646512337948e-142.53610125345181e-15
0.232-3.93744902111947e-142.27471186181238e-15
0.242-3.80801963313136e-142.00130233630801e-15
0.252-3.68525095833892e-142.2027101292957e-15
0.262-3.53096423818919e-143.22524512613180e-15
0.272-3.30555673485389e-144.70662944568422e-15
0.282-3.00821028300171e-145.68100379325799e-15
0.292-2.68906924658745e-145.46757422153907e-15
0.302-2.41138908590562e-144.32171382525662e-15
0.312-2.20546419004841e-143.04729830128908e-15
0.322-2.05965704330354e-142.25784982091175e-15
0.332-1.94048764478379e-142.14508496716495e-15
0.342-1.81509663034505e-142.52671160296561e-15
0.352-1.66812964269046e-142.95040202607112e-15
0.362-1.50752940974043e-143.03511219571785e-15
0.372-1.35263864663927e-142.76160947147456e-15
0.382-1.21735616295144e-142.33355986131986e-15
0.392-1.10662493576029e-141.86441555917127e-15
0.402-1.02186984453585e-141.37479343103818e-15
0.412-9.61767481355776e-159.4508699322894e-16
0.422-9.2004128864128e-156.77672047312744e-16
0.432-8.87529841751425e-155.90352318390947e-16
0.442-8.55806091678473e-156.5093798795789e-16
0.452-8.17562545760256e-158.5130662732351e-16
0.462-7.65952277970311e-151.17726110026484e-15
0.472-6.94751219001196e-151.62160778731435e-15
0.482-5.95018090209732e-152.31349351291564e-15
0.492-4.48052712317048e-153.50212985807223e-15
0.502-2.27623417113612e-155.18109338096233e-15
0.5127.80413603070006e-166.75383957859958e-15
0.5224.40918774288806e-157.3278726011834e-15
0.5327.97328118956618e-156.50751119850653e-15
0.5421.08685582268317e-144.81059951170257e-15
0.5521.29019659750399e-143.21293077322849e-15
0.5621.43189699866692e-142.38707401153294e-15
0.5721.55375849645742e-142.37697327148608e-15
0.5821.68818201337458e-142.81356451065462e-15
0.5921.84851569366326e-143.32424730464718e-15
0.6022.03253963404659e-143.67991159381119e-15
0.6122.22885812434859e-143.76065997335330e-15
0.6222.42164159284071e-143.52171202728994e-15
0.6322.59424628078066e-142.98546326038466e-15
0.6422.73277470624122e-142.24149880570614e-15
0.6522.83009189927125e-141.45222251072996e-15
0.6622.88921767333032e-148.1842193887032e-16
0.6722.92346868477565e-145.0652576837005e-16
0.6822.95292213933912e-146.10050228200239e-16
0.6922.99837440896815e-141.05465447201731e-15
0.7023.07194525738909e-141.56887559845823e-15
0.7123.16803685771533e-141.81106562646529e-15
0.7223.28861829952286e-142.82367046252621e-15
0.7324.95503948407528e-149.3612323715931e-14
0.7428.59422275440413e-134.42339745278547e-12
0.7523.11966666758972e-111.5387519377128e-10
0.7628.60746503195005e-103.87745969361225e-09
0.7721.73031906759433e-087.02568117315385e-08
0.7822.51881820601767e-079.07874221052217e-07
0.7922.64165170583736e-068.28876069411967e-06
0.8021.98725008619535e-055.29049437894411e-05
0.8120.0001069520445889990.000233354738645267
0.8220.0004122408867120630.00070263825672381
0.8320.001147001377556090.00142659746342605
0.8420.002349087608095220.00193151094541668
0.8520.003677165138598370.00173520129848315
0.8620.004677874512128390.00106256687574529
0.8720.005237804489910480.000525979548037094
0.8820.005551086071067040.000325080407574060
0.8920.005798882104290680.000291698422511875
0.9020.006061241140489180.000320622133656396
0.9120.006365180607954230.000347002240157863
0.9220.006664921529085640.000286478425438436
0.9320.006902534595705330.000203090249796877
0.9420.007126048448857220.000233071616400393
0.9520.007668152339290530.000703730113196285
0.9620.009178822734705720.0014922280702123
0.9720.01205508169917050.00219376274008825
0.9820.01779404203843810.0041084810295265
0.9920.02670838942686470.00252619108872092


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0.002 ; par2 = 0.999 ; par3 = 0.01 ;
Parameters (R input):
par1 = 0.002 ; par2 = 0.999 ; par3 = 0.01 ;
R code (references can be found in the software module):
par1 <- as(par1,'numeric')
par2 <- as(par2,'numeric')
par3 <- as(par3,'numeric')
library(Hmisc)
myseq <- seq(par1, par2, par3)
hd <- hdquantile(x, probs = myseq, se = TRUE, na.rm = FALSE, names = TRUE, weights=FALSE)
bitmap(file='test1.png')
plot(myseq,hd,col=2,main=main,xlab=xlab,ylab=ylab)
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Harrell-Davis Quantiles',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'quantiles',header=TRUE)
a<-table.element(a,'value',header=TRUE)
a<-table.element(a,'standard error',header=TRUE)
a<-table.row.end(a)
length(hd)
for (i in 1:length(hd))
{
a<-table.row.start(a)
a<-table.element(a,as(labels(hd)[i],'numeric'),header=TRUE)
a<-table.element(a,as.matrix(hd[i])[1,1])
a<-table.element(a,as.matrix(attr(hd,'se')[i])[1,1])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')