Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_percentiles.wasp
Title produced by softwarePercentiles
Date of computationMon, 20 Oct 2008 03:57:11 -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/t122449675772mjkysoakdoz8z.htm/, Retrieved Fri, 17 May 2024 08:36:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=17179, Retrieved Fri, 17 May 2024 08:36:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact174
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Percentiles and Normal Probability Plot] [Q6 P(a
F RM D    [Percentiles] [Q6 Percentiles] [2008-10-20 09:57:11] [284c7cdb9fcda2adcbb08e211682c8d6] [Current]
-           [Percentiles] [Compute a and b i...] [2008-10-21 06:44:08] [cd4f4e2e27ac3340ba571e82a95c1ca6]
Feedback Forum
2008-10-27 11:53:49 [Olivier Uyttendaele] [reply
Je schreef in je document a= 108,42
b= 109,16 zonder verdere uitleg.

Dit zijn de waarden van p 0.8 uit de tabel. Waarschijnlijk veronderstelde je dat de 80% betrouwbaarheidsinterval zo af te lezen valt.

Je moet echter op een andere manier te werk gaan. Het model was correct gereproduceerd. Maar om aan een 80% betrouwbaarheidsinterval te komen, laat je aan begin en einde elk 10% vallen en lees je dan eigenlijk gewoon af uit de tabel. a wordt dan de waarde die je vindt bij p 0.1 en b de waarde die je vindt bij 0.9.
a = 89.98 en b = 111.67
Er is dus een kans van 80% dat de waarden van de totale productie tussen deze 2 waarden ligt.

Wat je hier doet is eigenlijk de extreme waarden weglaten in de geordende reeks aan kop en staart.

2008-10-27 21:36:42 [Koen Van den Heuvel] [reply
Een echte analyse van de vraag is er niet er worden gewoon 2 resultaten gegeven zonder verdere uitleg.
Dit zijn echter de resultaten indien we P(a < Total Production < b) = 60% zouden willen bekomen. Er werd langs beide kanten 20% van de staart weggelaten ipv 10%. We berekenen hier de uiterste waarden waartussen 80% van de getallen van de totale productie liggen.

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Dataseries X:
110.40
96.40
101.90
106.20
81.00
94.70
101.00
109.40
102.30
90.70
96.20
96.10
106.00
103.10
102.00
104.70
86.00
92.10
106.90
112.60
101.70
92.00
97.40
97.00
105.40
102.70
98.10
104.50
87.40
89.90
109.80
111.70
98.60
96.90
95.10
97.00
112.70
102.90
97.40
111.40
87.40
96.80
114.10
110.30
103.90
101.60
94.60
95.90
104.70
102.80
98.10
113.90
80.90
95.70
113.20
105.90
108.80
102.30
99.00
100.70
115.50




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=17179&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=17179&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=17179&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Percentiles - Ungrouped Data
pWeighted Average at XnpWeighted Average at X(n+1)pEmpirical Distribution FunctionEmpirical Distribution Function - AveragingEmpirical Distribution Function - InterpolationClosest ObservationTrue Basic - Statistics Graphics ToolkitMS Excel (old versions)
0.0280.92280.92481818280.980.97680.9
0.0483.283.4868686.568183.681
0.0686.92487.00887.487.487.487.486.39287.4
0.0887.487.487.487.489.487.487.487.4
0.189.9890.0690.790.790.789.990.5489.9
0.1291.11691.272929292.0290.791.42890.7
0.1492.05492.06892.192.193.192.192.03292.1
0.169494.494.694.694.6694.692.394.6
0.1894.69894.76494.794.795.0294.795.03694.7
0.295.2295.3495.795.795.795.195.4695.1
0.2295.78495.82895.995.995.9495.795.77295.9
0.2496.02896.07696.196.196.1496.195.92496.1
0.2696.18696.22496.296.296.3296.296.37696.2
0.2896.43296.54496.896.896.7296.496.65696.4
0.396.8396.8696.996.996.996.896.8496.9
0.3296.95296.9849797979796.91697
0.349797.032979797.169797.36897
0.3697.38497.497.497.497.497.497.497.4
0.3897.52697.79298.198.197.9697.497.70898.1
0.498.198.198.198.198.198.198.198.1
0.4298.4198.61698.698.698.6898.698.98498.6
0.4498.93699.476999999.6899100.22499
0.46100.718100.856101101100.88100.7100.844101
0.48101.168101.456101.6101.6101.48101101.144101.6
0.5101.65101.7101.7101.7101.7101.7101.7101.7
0.52101.844101.924101.9101.9101.92101.9101.976101.9
0.54101.994102.144102102102.12102102.156102
0.56102.3102.3102.3102.3102.3102.3102.3102.3
0.58102.452102.684102.7102.7102.62102.3102.316102.7
0.6102.76102.82102.8102.8102.8102.8102.88102.8
0.62102.882102.988102.9102.9102.94102.9103.012102.9
0.64103.132103.644103.9103.9103.42103.1103.356103.9
0.66104.056104.452104.5104.5104.26103.9103.948104.5
0.68104.596104.7104.7104.7104.66104.5104.7104.7
0.7104.7104.98104.7104.7104.7104.7105.12104.7
0.72105.344105.72105.4105.4105.5105.4105.58105.9
0.74105.914105.988106106105.94105.9105.912106
0.76106.072106.284106.2106.2106.12106106.816106.2
0.78106.606107.584106.9106.9106.76106.9108.116106.9
0.8108.42109.16108.8108.8108.8108.8109.04109.4
0.82109.408109.736109.8109.8109.48109.4109.464109.8
0.84109.92110.308110.3110.3110109.8110.392110.3
0.86110.346110.72110.4110.4110.36110.3111.08110.4
0.88111.08111.568111.4111.4111.2111.4111.532111.7
0.9111.67112.42111.7111.7111.7111.7111.88112.6
0.92112.612112.72112.7112.7112.62112.6113.18112.7
0.94112.87113.396113.2113.2112.9112.7113.704113.2
0.96113.592114.004113.9113.9113.62113.9113.996114.1
0.98114.056115.164114.1114.1114.06114.1114.436115.5

\begin{tabular}{lllllllll}
\hline
Percentiles - Ungrouped Data \tabularnewline
p & Weighted Average at Xnp & Weighted Average at X(n+1)p & Empirical Distribution Function & Empirical Distribution Function - Averaging & Empirical Distribution Function - Interpolation & Closest Observation & True Basic - Statistics Graphics Toolkit & MS Excel (old versions) \tabularnewline
0.02 & 80.922 & 80.924 & 81 & 81 & 82 & 80.9 & 80.976 & 80.9 \tabularnewline
0.04 & 83.2 & 83.4 & 86 & 86 & 86.56 & 81 & 83.6 & 81 \tabularnewline
0.06 & 86.924 & 87.008 & 87.4 & 87.4 & 87.4 & 87.4 & 86.392 & 87.4 \tabularnewline
0.08 & 87.4 & 87.4 & 87.4 & 87.4 & 89.4 & 87.4 & 87.4 & 87.4 \tabularnewline
0.1 & 89.98 & 90.06 & 90.7 & 90.7 & 90.7 & 89.9 & 90.54 & 89.9 \tabularnewline
0.12 & 91.116 & 91.272 & 92 & 92 & 92.02 & 90.7 & 91.428 & 90.7 \tabularnewline
0.14 & 92.054 & 92.068 & 92.1 & 92.1 & 93.1 & 92.1 & 92.032 & 92.1 \tabularnewline
0.16 & 94 & 94.4 & 94.6 & 94.6 & 94.66 & 94.6 & 92.3 & 94.6 \tabularnewline
0.18 & 94.698 & 94.764 & 94.7 & 94.7 & 95.02 & 94.7 & 95.036 & 94.7 \tabularnewline
0.2 & 95.22 & 95.34 & 95.7 & 95.7 & 95.7 & 95.1 & 95.46 & 95.1 \tabularnewline
0.22 & 95.784 & 95.828 & 95.9 & 95.9 & 95.94 & 95.7 & 95.772 & 95.9 \tabularnewline
0.24 & 96.028 & 96.076 & 96.1 & 96.1 & 96.14 & 96.1 & 95.924 & 96.1 \tabularnewline
0.26 & 96.186 & 96.224 & 96.2 & 96.2 & 96.32 & 96.2 & 96.376 & 96.2 \tabularnewline
0.28 & 96.432 & 96.544 & 96.8 & 96.8 & 96.72 & 96.4 & 96.656 & 96.4 \tabularnewline
0.3 & 96.83 & 96.86 & 96.9 & 96.9 & 96.9 & 96.8 & 96.84 & 96.9 \tabularnewline
0.32 & 96.952 & 96.984 & 97 & 97 & 97 & 97 & 96.916 & 97 \tabularnewline
0.34 & 97 & 97.032 & 97 & 97 & 97.16 & 97 & 97.368 & 97 \tabularnewline
0.36 & 97.384 & 97.4 & 97.4 & 97.4 & 97.4 & 97.4 & 97.4 & 97.4 \tabularnewline
0.38 & 97.526 & 97.792 & 98.1 & 98.1 & 97.96 & 97.4 & 97.708 & 98.1 \tabularnewline
0.4 & 98.1 & 98.1 & 98.1 & 98.1 & 98.1 & 98.1 & 98.1 & 98.1 \tabularnewline
0.42 & 98.41 & 98.616 & 98.6 & 98.6 & 98.68 & 98.6 & 98.984 & 98.6 \tabularnewline
0.44 & 98.936 & 99.476 & 99 & 99 & 99.68 & 99 & 100.224 & 99 \tabularnewline
0.46 & 100.718 & 100.856 & 101 & 101 & 100.88 & 100.7 & 100.844 & 101 \tabularnewline
0.48 & 101.168 & 101.456 & 101.6 & 101.6 & 101.48 & 101 & 101.144 & 101.6 \tabularnewline
0.5 & 101.65 & 101.7 & 101.7 & 101.7 & 101.7 & 101.7 & 101.7 & 101.7 \tabularnewline
0.52 & 101.844 & 101.924 & 101.9 & 101.9 & 101.92 & 101.9 & 101.976 & 101.9 \tabularnewline
0.54 & 101.994 & 102.144 & 102 & 102 & 102.12 & 102 & 102.156 & 102 \tabularnewline
0.56 & 102.3 & 102.3 & 102.3 & 102.3 & 102.3 & 102.3 & 102.3 & 102.3 \tabularnewline
0.58 & 102.452 & 102.684 & 102.7 & 102.7 & 102.62 & 102.3 & 102.316 & 102.7 \tabularnewline
0.6 & 102.76 & 102.82 & 102.8 & 102.8 & 102.8 & 102.8 & 102.88 & 102.8 \tabularnewline
0.62 & 102.882 & 102.988 & 102.9 & 102.9 & 102.94 & 102.9 & 103.012 & 102.9 \tabularnewline
0.64 & 103.132 & 103.644 & 103.9 & 103.9 & 103.42 & 103.1 & 103.356 & 103.9 \tabularnewline
0.66 & 104.056 & 104.452 & 104.5 & 104.5 & 104.26 & 103.9 & 103.948 & 104.5 \tabularnewline
0.68 & 104.596 & 104.7 & 104.7 & 104.7 & 104.66 & 104.5 & 104.7 & 104.7 \tabularnewline
0.7 & 104.7 & 104.98 & 104.7 & 104.7 & 104.7 & 104.7 & 105.12 & 104.7 \tabularnewline
0.72 & 105.344 & 105.72 & 105.4 & 105.4 & 105.5 & 105.4 & 105.58 & 105.9 \tabularnewline
0.74 & 105.914 & 105.988 & 106 & 106 & 105.94 & 105.9 & 105.912 & 106 \tabularnewline
0.76 & 106.072 & 106.284 & 106.2 & 106.2 & 106.12 & 106 & 106.816 & 106.2 \tabularnewline
0.78 & 106.606 & 107.584 & 106.9 & 106.9 & 106.76 & 106.9 & 108.116 & 106.9 \tabularnewline
0.8 & 108.42 & 109.16 & 108.8 & 108.8 & 108.8 & 108.8 & 109.04 & 109.4 \tabularnewline
0.82 & 109.408 & 109.736 & 109.8 & 109.8 & 109.48 & 109.4 & 109.464 & 109.8 \tabularnewline
0.84 & 109.92 & 110.308 & 110.3 & 110.3 & 110 & 109.8 & 110.392 & 110.3 \tabularnewline
0.86 & 110.346 & 110.72 & 110.4 & 110.4 & 110.36 & 110.3 & 111.08 & 110.4 \tabularnewline
0.88 & 111.08 & 111.568 & 111.4 & 111.4 & 111.2 & 111.4 & 111.532 & 111.7 \tabularnewline
0.9 & 111.67 & 112.42 & 111.7 & 111.7 & 111.7 & 111.7 & 111.88 & 112.6 \tabularnewline
0.92 & 112.612 & 112.72 & 112.7 & 112.7 & 112.62 & 112.6 & 113.18 & 112.7 \tabularnewline
0.94 & 112.87 & 113.396 & 113.2 & 113.2 & 112.9 & 112.7 & 113.704 & 113.2 \tabularnewline
0.96 & 113.592 & 114.004 & 113.9 & 113.9 & 113.62 & 113.9 & 113.996 & 114.1 \tabularnewline
0.98 & 114.056 & 115.164 & 114.1 & 114.1 & 114.06 & 114.1 & 114.436 & 115.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=17179&T=1

[TABLE]
[ROW][C]Percentiles - Ungrouped Data[/C][/ROW]
[ROW][C]p[/C][C]Weighted Average at Xnp[/C][C]Weighted Average at X(n+1)p[/C][C]Empirical Distribution Function[/C][C]Empirical Distribution Function - Averaging[/C][C]Empirical Distribution Function - Interpolation[/C][C]Closest Observation[/C][C]True Basic - Statistics Graphics Toolkit[/C][C]MS Excel (old versions)[/C][/ROW]
[ROW][C]0.02[/C][C]80.922[/C][C]80.924[/C][C]81[/C][C]81[/C][C]82[/C][C]80.9[/C][C]80.976[/C][C]80.9[/C][/ROW]
[ROW][C]0.04[/C][C]83.2[/C][C]83.4[/C][C]86[/C][C]86[/C][C]86.56[/C][C]81[/C][C]83.6[/C][C]81[/C][/ROW]
[ROW][C]0.06[/C][C]86.924[/C][C]87.008[/C][C]87.4[/C][C]87.4[/C][C]87.4[/C][C]87.4[/C][C]86.392[/C][C]87.4[/C][/ROW]
[ROW][C]0.08[/C][C]87.4[/C][C]87.4[/C][C]87.4[/C][C]87.4[/C][C]89.4[/C][C]87.4[/C][C]87.4[/C][C]87.4[/C][/ROW]
[ROW][C]0.1[/C][C]89.98[/C][C]90.06[/C][C]90.7[/C][C]90.7[/C][C]90.7[/C][C]89.9[/C][C]90.54[/C][C]89.9[/C][/ROW]
[ROW][C]0.12[/C][C]91.116[/C][C]91.272[/C][C]92[/C][C]92[/C][C]92.02[/C][C]90.7[/C][C]91.428[/C][C]90.7[/C][/ROW]
[ROW][C]0.14[/C][C]92.054[/C][C]92.068[/C][C]92.1[/C][C]92.1[/C][C]93.1[/C][C]92.1[/C][C]92.032[/C][C]92.1[/C][/ROW]
[ROW][C]0.16[/C][C]94[/C][C]94.4[/C][C]94.6[/C][C]94.6[/C][C]94.66[/C][C]94.6[/C][C]92.3[/C][C]94.6[/C][/ROW]
[ROW][C]0.18[/C][C]94.698[/C][C]94.764[/C][C]94.7[/C][C]94.7[/C][C]95.02[/C][C]94.7[/C][C]95.036[/C][C]94.7[/C][/ROW]
[ROW][C]0.2[/C][C]95.22[/C][C]95.34[/C][C]95.7[/C][C]95.7[/C][C]95.7[/C][C]95.1[/C][C]95.46[/C][C]95.1[/C][/ROW]
[ROW][C]0.22[/C][C]95.784[/C][C]95.828[/C][C]95.9[/C][C]95.9[/C][C]95.94[/C][C]95.7[/C][C]95.772[/C][C]95.9[/C][/ROW]
[ROW][C]0.24[/C][C]96.028[/C][C]96.076[/C][C]96.1[/C][C]96.1[/C][C]96.14[/C][C]96.1[/C][C]95.924[/C][C]96.1[/C][/ROW]
[ROW][C]0.26[/C][C]96.186[/C][C]96.224[/C][C]96.2[/C][C]96.2[/C][C]96.32[/C][C]96.2[/C][C]96.376[/C][C]96.2[/C][/ROW]
[ROW][C]0.28[/C][C]96.432[/C][C]96.544[/C][C]96.8[/C][C]96.8[/C][C]96.72[/C][C]96.4[/C][C]96.656[/C][C]96.4[/C][/ROW]
[ROW][C]0.3[/C][C]96.83[/C][C]96.86[/C][C]96.9[/C][C]96.9[/C][C]96.9[/C][C]96.8[/C][C]96.84[/C][C]96.9[/C][/ROW]
[ROW][C]0.32[/C][C]96.952[/C][C]96.984[/C][C]97[/C][C]97[/C][C]97[/C][C]97[/C][C]96.916[/C][C]97[/C][/ROW]
[ROW][C]0.34[/C][C]97[/C][C]97.032[/C][C]97[/C][C]97[/C][C]97.16[/C][C]97[/C][C]97.368[/C][C]97[/C][/ROW]
[ROW][C]0.36[/C][C]97.384[/C][C]97.4[/C][C]97.4[/C][C]97.4[/C][C]97.4[/C][C]97.4[/C][C]97.4[/C][C]97.4[/C][/ROW]
[ROW][C]0.38[/C][C]97.526[/C][C]97.792[/C][C]98.1[/C][C]98.1[/C][C]97.96[/C][C]97.4[/C][C]97.708[/C][C]98.1[/C][/ROW]
[ROW][C]0.4[/C][C]98.1[/C][C]98.1[/C][C]98.1[/C][C]98.1[/C][C]98.1[/C][C]98.1[/C][C]98.1[/C][C]98.1[/C][/ROW]
[ROW][C]0.42[/C][C]98.41[/C][C]98.616[/C][C]98.6[/C][C]98.6[/C][C]98.68[/C][C]98.6[/C][C]98.984[/C][C]98.6[/C][/ROW]
[ROW][C]0.44[/C][C]98.936[/C][C]99.476[/C][C]99[/C][C]99[/C][C]99.68[/C][C]99[/C][C]100.224[/C][C]99[/C][/ROW]
[ROW][C]0.46[/C][C]100.718[/C][C]100.856[/C][C]101[/C][C]101[/C][C]100.88[/C][C]100.7[/C][C]100.844[/C][C]101[/C][/ROW]
[ROW][C]0.48[/C][C]101.168[/C][C]101.456[/C][C]101.6[/C][C]101.6[/C][C]101.48[/C][C]101[/C][C]101.144[/C][C]101.6[/C][/ROW]
[ROW][C]0.5[/C][C]101.65[/C][C]101.7[/C][C]101.7[/C][C]101.7[/C][C]101.7[/C][C]101.7[/C][C]101.7[/C][C]101.7[/C][/ROW]
[ROW][C]0.52[/C][C]101.844[/C][C]101.924[/C][C]101.9[/C][C]101.9[/C][C]101.92[/C][C]101.9[/C][C]101.976[/C][C]101.9[/C][/ROW]
[ROW][C]0.54[/C][C]101.994[/C][C]102.144[/C][C]102[/C][C]102[/C][C]102.12[/C][C]102[/C][C]102.156[/C][C]102[/C][/ROW]
[ROW][C]0.56[/C][C]102.3[/C][C]102.3[/C][C]102.3[/C][C]102.3[/C][C]102.3[/C][C]102.3[/C][C]102.3[/C][C]102.3[/C][/ROW]
[ROW][C]0.58[/C][C]102.452[/C][C]102.684[/C][C]102.7[/C][C]102.7[/C][C]102.62[/C][C]102.3[/C][C]102.316[/C][C]102.7[/C][/ROW]
[ROW][C]0.6[/C][C]102.76[/C][C]102.82[/C][C]102.8[/C][C]102.8[/C][C]102.8[/C][C]102.8[/C][C]102.88[/C][C]102.8[/C][/ROW]
[ROW][C]0.62[/C][C]102.882[/C][C]102.988[/C][C]102.9[/C][C]102.9[/C][C]102.94[/C][C]102.9[/C][C]103.012[/C][C]102.9[/C][/ROW]
[ROW][C]0.64[/C][C]103.132[/C][C]103.644[/C][C]103.9[/C][C]103.9[/C][C]103.42[/C][C]103.1[/C][C]103.356[/C][C]103.9[/C][/ROW]
[ROW][C]0.66[/C][C]104.056[/C][C]104.452[/C][C]104.5[/C][C]104.5[/C][C]104.26[/C][C]103.9[/C][C]103.948[/C][C]104.5[/C][/ROW]
[ROW][C]0.68[/C][C]104.596[/C][C]104.7[/C][C]104.7[/C][C]104.7[/C][C]104.66[/C][C]104.5[/C][C]104.7[/C][C]104.7[/C][/ROW]
[ROW][C]0.7[/C][C]104.7[/C][C]104.98[/C][C]104.7[/C][C]104.7[/C][C]104.7[/C][C]104.7[/C][C]105.12[/C][C]104.7[/C][/ROW]
[ROW][C]0.72[/C][C]105.344[/C][C]105.72[/C][C]105.4[/C][C]105.4[/C][C]105.5[/C][C]105.4[/C][C]105.58[/C][C]105.9[/C][/ROW]
[ROW][C]0.74[/C][C]105.914[/C][C]105.988[/C][C]106[/C][C]106[/C][C]105.94[/C][C]105.9[/C][C]105.912[/C][C]106[/C][/ROW]
[ROW][C]0.76[/C][C]106.072[/C][C]106.284[/C][C]106.2[/C][C]106.2[/C][C]106.12[/C][C]106[/C][C]106.816[/C][C]106.2[/C][/ROW]
[ROW][C]0.78[/C][C]106.606[/C][C]107.584[/C][C]106.9[/C][C]106.9[/C][C]106.76[/C][C]106.9[/C][C]108.116[/C][C]106.9[/C][/ROW]
[ROW][C]0.8[/C][C]108.42[/C][C]109.16[/C][C]108.8[/C][C]108.8[/C][C]108.8[/C][C]108.8[/C][C]109.04[/C][C]109.4[/C][/ROW]
[ROW][C]0.82[/C][C]109.408[/C][C]109.736[/C][C]109.8[/C][C]109.8[/C][C]109.48[/C][C]109.4[/C][C]109.464[/C][C]109.8[/C][/ROW]
[ROW][C]0.84[/C][C]109.92[/C][C]110.308[/C][C]110.3[/C][C]110.3[/C][C]110[/C][C]109.8[/C][C]110.392[/C][C]110.3[/C][/ROW]
[ROW][C]0.86[/C][C]110.346[/C][C]110.72[/C][C]110.4[/C][C]110.4[/C][C]110.36[/C][C]110.3[/C][C]111.08[/C][C]110.4[/C][/ROW]
[ROW][C]0.88[/C][C]111.08[/C][C]111.568[/C][C]111.4[/C][C]111.4[/C][C]111.2[/C][C]111.4[/C][C]111.532[/C][C]111.7[/C][/ROW]
[ROW][C]0.9[/C][C]111.67[/C][C]112.42[/C][C]111.7[/C][C]111.7[/C][C]111.7[/C][C]111.7[/C][C]111.88[/C][C]112.6[/C][/ROW]
[ROW][C]0.92[/C][C]112.612[/C][C]112.72[/C][C]112.7[/C][C]112.7[/C][C]112.62[/C][C]112.6[/C][C]113.18[/C][C]112.7[/C][/ROW]
[ROW][C]0.94[/C][C]112.87[/C][C]113.396[/C][C]113.2[/C][C]113.2[/C][C]112.9[/C][C]112.7[/C][C]113.704[/C][C]113.2[/C][/ROW]
[ROW][C]0.96[/C][C]113.592[/C][C]114.004[/C][C]113.9[/C][C]113.9[/C][C]113.62[/C][C]113.9[/C][C]113.996[/C][C]114.1[/C][/ROW]
[ROW][C]0.98[/C][C]114.056[/C][C]115.164[/C][C]114.1[/C][C]114.1[/C][C]114.06[/C][C]114.1[/C][C]114.436[/C][C]115.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=17179&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=17179&T=1

As an alternative you can also use a QR Code:  

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

Percentiles - Ungrouped Data
pWeighted Average at XnpWeighted Average at X(n+1)pEmpirical Distribution FunctionEmpirical Distribution Function - AveragingEmpirical Distribution Function - InterpolationClosest ObservationTrue Basic - Statistics Graphics ToolkitMS Excel (old versions)
0.0280.92280.92481818280.980.97680.9
0.0483.283.4868686.568183.681
0.0686.92487.00887.487.487.487.486.39287.4
0.0887.487.487.487.489.487.487.487.4
0.189.9890.0690.790.790.789.990.5489.9
0.1291.11691.272929292.0290.791.42890.7
0.1492.05492.06892.192.193.192.192.03292.1
0.169494.494.694.694.6694.692.394.6
0.1894.69894.76494.794.795.0294.795.03694.7
0.295.2295.3495.795.795.795.195.4695.1
0.2295.78495.82895.995.995.9495.795.77295.9
0.2496.02896.07696.196.196.1496.195.92496.1
0.2696.18696.22496.296.296.3296.296.37696.2
0.2896.43296.54496.896.896.7296.496.65696.4
0.396.8396.8696.996.996.996.896.8496.9
0.3296.95296.9849797979796.91697
0.349797.032979797.169797.36897
0.3697.38497.497.497.497.497.497.497.4
0.3897.52697.79298.198.197.9697.497.70898.1
0.498.198.198.198.198.198.198.198.1
0.4298.4198.61698.698.698.6898.698.98498.6
0.4498.93699.476999999.6899100.22499
0.46100.718100.856101101100.88100.7100.844101
0.48101.168101.456101.6101.6101.48101101.144101.6
0.5101.65101.7101.7101.7101.7101.7101.7101.7
0.52101.844101.924101.9101.9101.92101.9101.976101.9
0.54101.994102.144102102102.12102102.156102
0.56102.3102.3102.3102.3102.3102.3102.3102.3
0.58102.452102.684102.7102.7102.62102.3102.316102.7
0.6102.76102.82102.8102.8102.8102.8102.88102.8
0.62102.882102.988102.9102.9102.94102.9103.012102.9
0.64103.132103.644103.9103.9103.42103.1103.356103.9
0.66104.056104.452104.5104.5104.26103.9103.948104.5
0.68104.596104.7104.7104.7104.66104.5104.7104.7
0.7104.7104.98104.7104.7104.7104.7105.12104.7
0.72105.344105.72105.4105.4105.5105.4105.58105.9
0.74105.914105.988106106105.94105.9105.912106
0.76106.072106.284106.2106.2106.12106106.816106.2
0.78106.606107.584106.9106.9106.76106.9108.116106.9
0.8108.42109.16108.8108.8108.8108.8109.04109.4
0.82109.408109.736109.8109.8109.48109.4109.464109.8
0.84109.92110.308110.3110.3110109.8110.392110.3
0.86110.346110.72110.4110.4110.36110.3111.08110.4
0.88111.08111.568111.4111.4111.2111.4111.532111.7
0.9111.67112.42111.7111.7111.7111.7111.88112.6
0.92112.612112.72112.7112.7112.62112.6113.18112.7
0.94112.87113.396113.2113.2112.9112.7113.704113.2
0.96113.592114.004113.9113.9113.62113.9113.996114.1
0.98114.056115.164114.1114.1114.06114.1114.436115.5



Parameters (Session):
Parameters (R input):
R code (references can be found in the software module):
x <-sort(x[!is.na(x)])
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]
}
}
}
}
lx <- length(x)
qval <- array(NA,dim=c(99,8))
mystep <- 25
mystart <- 25
if (lx>10){
mystep=10
mystart=10
}
if (lx>20){
mystep=5
mystart=5
}
if (lx>50){
mystep=2
mystart=2
}
if (lx>=100){
mystep=1
mystart=1
}
for (perc in seq(mystart,99,mystep)) {
qval[perc,1] <- q1(x,lx,perc/100,i,f)
qval[perc,2] <- q2(x,lx,perc/100,i,f)
qval[perc,3] <- q3(x,lx,perc/100,i,f)
qval[perc,4] <- q4(x,lx,perc/100,i,f)
qval[perc,5] <- q5(x,lx,perc/100,i,f)
qval[perc,6] <- q6(x,lx,perc/100,i,f)
qval[perc,7] <- q7(x,lx,perc/100,i,f)
qval[perc,8] <- q8(x,lx,perc/100,i,f)
}
bitmap(file='test1.png')
myqqnorm <- qqnorm(x,col=2)
qqline(x)
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Percentiles - Ungrouped Data',9,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p',1,TRUE)
a<-table.element(a,hyperlink('method_1.htm', 'Weighted Average at Xnp',''),1,TRUE)
a<-table.element(a,hyperlink('method_2.htm','Weighted Average at X(n+1)p',''),1,TRUE)
a<-table.element(a,hyperlink('method_3.htm','Empirical Distribution Function',''),1,TRUE)
a<-table.element(a,hyperlink('method_4.htm','Empirical Distribution Function - Averaging',''),1,TRUE)
a<-table.element(a,hyperlink('method_5.htm','Empirical Distribution Function - Interpolation',''),1,TRUE)
a<-table.element(a,hyperlink('method_6.htm','Closest Observation',''),1,TRUE)
a<-table.element(a,hyperlink('method_7.htm','True Basic - Statistics Graphics Toolkit',''),1,TRUE)
a<-table.element(a,hyperlink('method_8.htm','MS Excel (old versions)',''),1,TRUE)
a<-table.row.end(a)
for (perc in seq(mystart,99,mystep)) {
a<-table.row.start(a)
a<-table.element(a,round(perc/100,2),1,TRUE)
for (j in 1:8) {
a<-table.element(a,round(qval[perc,j],6))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')