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 12:19:44 -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/t1224526890jg5gi4ckl8ru12f.htm/, Retrieved Tue, 28 May 2024 16:49:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=17845, Retrieved Tue, 28 May 2024 16:49:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Percentiles] [Percentiles Tot. ...] [2008-10-20 18:19:44] [d592f629d96b926609f311957d74fcca] [Current]
Feedback Forum
2008-10-24 12:30:20 [c4ccf1f44d59ce687616256b9e80d6b0] [reply
Het antwoord is 90.7 < totale productie < 111.7. In de les werd ons aangeraden naar de 5e kolom te kijken. Indien de punten allemaal op de rechte vallen spreken we van een normale verdeling. Indien er bochten zitten in de punten dan is er een afwijking van de normale verdeling. Er is hier dus sprake van een afwijking van de normale verdeling.
2008-10-27 10:30:00 [Dorien Peeters] [reply
a 89,99141564
b 111,98368

De waarden tussen de cijfers a en b zijn aan het toeval te wijten. Waarden buiten deze cijfers zijn toe te schrijven aan externe factoren.
Er is dus sprake van afwijking in de verdeling( dit kan je weten door naar het aantal punten te kijken, als deze mooi op een rechte liggen heb je een normaalverdeling)

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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=17845&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=17845&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=17845&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=17845&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=17845&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=17845&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')