Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_percentiles.wasp
Title produced by softwarePercentiles
Date of computationSun, 16 Dec 2007 06:21:14 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2007/Dec/16/t11978104597zg4802ifgx18cz.htm/, Retrieved Thu, 02 May 2024 09:56:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=4165, Retrieved Thu, 02 May 2024 09:56:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsChristel Stuer Steven Coomans Nele Rombaut Gregory De Meulenaer Gudrun Verhelst Mathias Bruneel
Estimated Impact192
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Percentiles] [Paper: QQ plot fa...] [2007-12-16 13:21:14] [2d443d719c26b75b5a69a7433280dbf3] [Current]
-    D    [Percentiles] [QQ-plot Wt] [2008-12-21 12:53:48] [74be16979710d4c4e7c6647856088456]
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Dataseries X:
608
651
691
627
634
703
475
337
803
722
590
724
627
696
825
675
641
784
412
352
839
729
696
634
694
638
762
635
721
854
418
367
824
687
601
676
740
690
683
594
729
732
386
330
707
716
657
653




Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time55 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of compuational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 55 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4165&T=0

[TABLE]
[ROW][C]Summary of compuational 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]55 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=4165&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4165&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 compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time55 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)
0.05343343.75352352357.25337345.25337
0.1382.2384.1386386404.2386368.9386
0.15429.4437.95475475480.75418455.05418
0.2592.4593.2594594596.8594590.8594
0.25608612.75608617.5622.25608622.25608
0.3629.8631.9634634634627629.1634
0.35634.8635.45635635636.35635637.55635
0.4643647651651649641645651
0.45655.4657.9657657659.7657674.1657
0.5676679.5676679.5679.5676679.5679.5
0.55688.2689.85690690689.55687687.15690
0.6693.4694.8694694694.4694695.2694
0.65697.4701.95703703699.85696697.05703
0.7712.4717.5716716715.1716719.5716
0.75722723.5722723722.5722722.5724
0.8729729.6729729729729731.4729
0.85738.4754.3740740739.6740747.7762
0.9787.8805.1803803789.7784821.9803
0.95824.6832.7825825824.65825831.3839

\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
0.05 & 343 & 343.75 & 352 & 352 & 357.25 & 337 & 345.25 & 337 \tabularnewline
0.1 & 382.2 & 384.1 & 386 & 386 & 404.2 & 386 & 368.9 & 386 \tabularnewline
0.15 & 429.4 & 437.95 & 475 & 475 & 480.75 & 418 & 455.05 & 418 \tabularnewline
0.2 & 592.4 & 593.2 & 594 & 594 & 596.8 & 594 & 590.8 & 594 \tabularnewline
0.25 & 608 & 612.75 & 608 & 617.5 & 622.25 & 608 & 622.25 & 608 \tabularnewline
0.3 & 629.8 & 631.9 & 634 & 634 & 634 & 627 & 629.1 & 634 \tabularnewline
0.35 & 634.8 & 635.45 & 635 & 635 & 636.35 & 635 & 637.55 & 635 \tabularnewline
0.4 & 643 & 647 & 651 & 651 & 649 & 641 & 645 & 651 \tabularnewline
0.45 & 655.4 & 657.9 & 657 & 657 & 659.7 & 657 & 674.1 & 657 \tabularnewline
0.5 & 676 & 679.5 & 676 & 679.5 & 679.5 & 676 & 679.5 & 679.5 \tabularnewline
0.55 & 688.2 & 689.85 & 690 & 690 & 689.55 & 687 & 687.15 & 690 \tabularnewline
0.6 & 693.4 & 694.8 & 694 & 694 & 694.4 & 694 & 695.2 & 694 \tabularnewline
0.65 & 697.4 & 701.95 & 703 & 703 & 699.85 & 696 & 697.05 & 703 \tabularnewline
0.7 & 712.4 & 717.5 & 716 & 716 & 715.1 & 716 & 719.5 & 716 \tabularnewline
0.75 & 722 & 723.5 & 722 & 723 & 722.5 & 722 & 722.5 & 724 \tabularnewline
0.8 & 729 & 729.6 & 729 & 729 & 729 & 729 & 731.4 & 729 \tabularnewline
0.85 & 738.4 & 754.3 & 740 & 740 & 739.6 & 740 & 747.7 & 762 \tabularnewline
0.9 & 787.8 & 805.1 & 803 & 803 & 789.7 & 784 & 821.9 & 803 \tabularnewline
0.95 & 824.6 & 832.7 & 825 & 825 & 824.65 & 825 & 831.3 & 839 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4165&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]0.05[/C][C]343[/C][C]343.75[/C][C]352[/C][C]352[/C][C]357.25[/C][C]337[/C][C]345.25[/C][C]337[/C][/ROW]
[ROW][C]0.1[/C][C]382.2[/C][C]384.1[/C][C]386[/C][C]386[/C][C]404.2[/C][C]386[/C][C]368.9[/C][C]386[/C][/ROW]
[ROW][C]0.15[/C][C]429.4[/C][C]437.95[/C][C]475[/C][C]475[/C][C]480.75[/C][C]418[/C][C]455.05[/C][C]418[/C][/ROW]
[ROW][C]0.2[/C][C]592.4[/C][C]593.2[/C][C]594[/C][C]594[/C][C]596.8[/C][C]594[/C][C]590.8[/C][C]594[/C][/ROW]
[ROW][C]0.25[/C][C]608[/C][C]612.75[/C][C]608[/C][C]617.5[/C][C]622.25[/C][C]608[/C][C]622.25[/C][C]608[/C][/ROW]
[ROW][C]0.3[/C][C]629.8[/C][C]631.9[/C][C]634[/C][C]634[/C][C]634[/C][C]627[/C][C]629.1[/C][C]634[/C][/ROW]
[ROW][C]0.35[/C][C]634.8[/C][C]635.45[/C][C]635[/C][C]635[/C][C]636.35[/C][C]635[/C][C]637.55[/C][C]635[/C][/ROW]
[ROW][C]0.4[/C][C]643[/C][C]647[/C][C]651[/C][C]651[/C][C]649[/C][C]641[/C][C]645[/C][C]651[/C][/ROW]
[ROW][C]0.45[/C][C]655.4[/C][C]657.9[/C][C]657[/C][C]657[/C][C]659.7[/C][C]657[/C][C]674.1[/C][C]657[/C][/ROW]
[ROW][C]0.5[/C][C]676[/C][C]679.5[/C][C]676[/C][C]679.5[/C][C]679.5[/C][C]676[/C][C]679.5[/C][C]679.5[/C][/ROW]
[ROW][C]0.55[/C][C]688.2[/C][C]689.85[/C][C]690[/C][C]690[/C][C]689.55[/C][C]687[/C][C]687.15[/C][C]690[/C][/ROW]
[ROW][C]0.6[/C][C]693.4[/C][C]694.8[/C][C]694[/C][C]694[/C][C]694.4[/C][C]694[/C][C]695.2[/C][C]694[/C][/ROW]
[ROW][C]0.65[/C][C]697.4[/C][C]701.95[/C][C]703[/C][C]703[/C][C]699.85[/C][C]696[/C][C]697.05[/C][C]703[/C][/ROW]
[ROW][C]0.7[/C][C]712.4[/C][C]717.5[/C][C]716[/C][C]716[/C][C]715.1[/C][C]716[/C][C]719.5[/C][C]716[/C][/ROW]
[ROW][C]0.75[/C][C]722[/C][C]723.5[/C][C]722[/C][C]723[/C][C]722.5[/C][C]722[/C][C]722.5[/C][C]724[/C][/ROW]
[ROW][C]0.8[/C][C]729[/C][C]729.6[/C][C]729[/C][C]729[/C][C]729[/C][C]729[/C][C]731.4[/C][C]729[/C][/ROW]
[ROW][C]0.85[/C][C]738.4[/C][C]754.3[/C][C]740[/C][C]740[/C][C]739.6[/C][C]740[/C][C]747.7[/C][C]762[/C][/ROW]
[ROW][C]0.9[/C][C]787.8[/C][C]805.1[/C][C]803[/C][C]803[/C][C]789.7[/C][C]784[/C][C]821.9[/C][C]803[/C][/ROW]
[ROW][C]0.95[/C][C]824.6[/C][C]832.7[/C][C]825[/C][C]825[/C][C]824.65[/C][C]825[/C][C]831.3[/C][C]839[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4165&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4165&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)
0.05343343.75352352357.25337345.25337
0.1382.2384.1386386404.2386368.9386
0.15429.4437.95475475480.75418455.05418
0.2592.4593.2594594596.8594590.8594
0.25608612.75608617.5622.25608622.25608
0.3629.8631.9634634634627629.1634
0.35634.8635.45635635636.35635637.55635
0.4643647651651649641645651
0.45655.4657.9657657659.7657674.1657
0.5676679.5676679.5679.5676679.5679.5
0.55688.2689.85690690689.55687687.15690
0.6693.4694.8694694694.4694695.2694
0.65697.4701.95703703699.85696697.05703
0.7712.4717.5716716715.1716719.5716
0.75722723.5722723722.5722722.5724
0.8729729.6729729729729731.4729
0.85738.4754.3740740739.6740747.7762
0.9787.8805.1803803789.7784821.9803
0.95824.6832.7825825824.65825831.3839



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)
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')