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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_percentiles.wasp
Title produced by softwarePercentiles
Date of computationFri, 22 Jan 2016 09:48:31 +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/2016/Jan/22/t1453456124xa9eiwwmnalqup8.htm/, Retrieved Tue, 07 May 2024 10:49:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=291081, Retrieved Tue, 07 May 2024 10:49:49 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact55
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Percentiles] [Vraag5c] [2016-01-22 09:48:31] [c12205d6bf4e176e94a944db40434bc4] [Current]
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Dataseries X:
10.24
10.89
9
12.25
13.69
7.29
12.96
12.25
14.44
11.56
13.69
12.25
7.84
14.44
18.49
10.89
12.96
12.96
10.89
7.84




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=291081&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=291081&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=291081&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'Gertrude Mary Cox' @ cox.wessa.net







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)
0.17.847.847.847.847.847.847.847.84
0.299.24899.629.99299.9929
0.310.8910.8910.8910.8910.8910.8910.8910.89
0.410.8911.15810.8911.22511.29210.8911.29210.89
0.512.2512.2512.2512.2512.2512.2512.2512.25
0.612.2512.67612.2512.60512.53412.2512.53412.96
0.712.9612.9612.9612.9612.9612.9612.9612.96
0.813.6913.6913.6913.6913.6913.6913.6913.69
0.914.4414.4414.4414.4414.4414.4414.4414.44

\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
0.1 & 7.84 & 7.84 & 7.84 & 7.84 & 7.84 & 7.84 & 7.84 & 7.84 \tabularnewline
0.2 & 9 & 9.248 & 9 & 9.62 & 9.992 & 9 & 9.992 & 9 \tabularnewline
0.3 & 10.89 & 10.89 & 10.89 & 10.89 & 10.89 & 10.89 & 10.89 & 10.89 \tabularnewline
0.4 & 10.89 & 11.158 & 10.89 & 11.225 & 11.292 & 10.89 & 11.292 & 10.89 \tabularnewline
0.5 & 12.25 & 12.25 & 12.25 & 12.25 & 12.25 & 12.25 & 12.25 & 12.25 \tabularnewline
0.6 & 12.25 & 12.676 & 12.25 & 12.605 & 12.534 & 12.25 & 12.534 & 12.96 \tabularnewline
0.7 & 12.96 & 12.96 & 12.96 & 12.96 & 12.96 & 12.96 & 12.96 & 12.96 \tabularnewline
0.8 & 13.69 & 13.69 & 13.69 & 13.69 & 13.69 & 13.69 & 13.69 & 13.69 \tabularnewline
0.9 & 14.44 & 14.44 & 14.44 & 14.44 & 14.44 & 14.44 & 14.44 & 14.44 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=291081&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]0.1[/C][C]7.84[/C][C]7.84[/C][C]7.84[/C][C]7.84[/C][C]7.84[/C][C]7.84[/C][C]7.84[/C][C]7.84[/C][/ROW]
[ROW][C]0.2[/C][C]9[/C][C]9.248[/C][C]9[/C][C]9.62[/C][C]9.992[/C][C]9[/C][C]9.992[/C][C]9[/C][/ROW]
[ROW][C]0.3[/C][C]10.89[/C][C]10.89[/C][C]10.89[/C][C]10.89[/C][C]10.89[/C][C]10.89[/C][C]10.89[/C][C]10.89[/C][/ROW]
[ROW][C]0.4[/C][C]10.89[/C][C]11.158[/C][C]10.89[/C][C]11.225[/C][C]11.292[/C][C]10.89[/C][C]11.292[/C][C]10.89[/C][/ROW]
[ROW][C]0.5[/C][C]12.25[/C][C]12.25[/C][C]12.25[/C][C]12.25[/C][C]12.25[/C][C]12.25[/C][C]12.25[/C][C]12.25[/C][/ROW]
[ROW][C]0.6[/C][C]12.25[/C][C]12.676[/C][C]12.25[/C][C]12.605[/C][C]12.534[/C][C]12.25[/C][C]12.534[/C][C]12.96[/C][/ROW]
[ROW][C]0.7[/C][C]12.96[/C][C]12.96[/C][C]12.96[/C][C]12.96[/C][C]12.96[/C][C]12.96[/C][C]12.96[/C][C]12.96[/C][/ROW]
[ROW][C]0.8[/C][C]13.69[/C][C]13.69[/C][C]13.69[/C][C]13.69[/C][C]13.69[/C][C]13.69[/C][C]13.69[/C][C]13.69[/C][/ROW]
[ROW][C]0.9[/C][C]14.44[/C][C]14.44[/C][C]14.44[/C][C]14.44[/C][C]14.44[/C][C]14.44[/C][C]14.44[/C][C]14.44[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=291081&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=291081&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)
0.17.847.847.847.847.847.847.847.84
0.299.24899.629.99299.9929
0.310.8910.8910.8910.8910.8910.8910.8910.89
0.410.8911.15810.8911.22511.29210.8911.29210.89
0.512.2512.2512.2512.2512.2512.2512.2512.25
0.612.2512.67612.2512.60512.53412.2512.53412.96
0.712.9612.9612.9612.9612.9612.9612.9612.96
0.813.6913.6913.6913.6913.6913.6913.6913.69
0.914.4414.4414.4414.4414.4414.4414.4414.44



Parameters (Session):
par1 = grey ; par2 = no ;
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')