Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_percentiles.wasp
Title produced by softwarePercentiles
Date of computationTue, 19 Feb 2019 15:38:39 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2019/Feb/19/t1550587274hb5pwzpzfospr98.htm/, Retrieved Sat, 04 May 2024 16:20:01 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sat, 04 May 2024 16:20:01 +0200
QR Codes:

Original text written by user:
IsPrivate?This computation is private
User-defined keywords
Estimated Impact0
Dataseries X:
,96
1,51
1,26
1,04
1,25
1,18
,98
1,51
1,18
1,17
1,00
,89
1,19
1,00
1,16
,96
1,28
1,13
1,35
1,09
1,04
1,17
1,18
1,29
2,06
1,21
1,00
1,29
1,30
1,06
1,21
1,14
1,22




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time2 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]2 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R ServerBig Analytics Cloud Computing Center







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)
0.050.93550.9390.960.960.960.960.9110.96
0.10.9660.9680.980.980.9840.960.9720.96
0.150.9991111111
0.211111.016111
0.251.041.041.041.041.041.041.041.04
0.31.0581.0661.061.061.0781.061.0841.06
0.351.1121.1261.131.131.1321.131.0941.13
0.41.1441.1521.161.161.1561.141.1481.16
0.451.16851.171.171.171.171.171.171.17
0.51.1751.181.181.181.181.181.181.18
0.551.181.181.181.181.181.181.181.18
0.61.1881.1981.191.191.1941.191.2021.19
0.651.211.2111.211.211.211.211.2191.21
0.71.2231.2441.251.251.2321.221.2261.25
0.751.25751.271.261.261.261.261.271.27
0.81.2841.291.291.291.2861.281.291.29
0.851.29051.2991.31.31.2921.291.2911.3
0.91.3351.4461.351.351.341.351.4141.51
0.951.511.6751.511.511.511.511.8951.51

\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 & 0.9355 & 0.939 & 0.96 & 0.96 & 0.96 & 0.96 & 0.911 & 0.96 \tabularnewline
0.1 & 0.966 & 0.968 & 0.98 & 0.98 & 0.984 & 0.96 & 0.972 & 0.96 \tabularnewline
0.15 & 0.999 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \tabularnewline
0.2 & 1 & 1 & 1 & 1 & 1.016 & 1 & 1 & 1 \tabularnewline
0.25 & 1.04 & 1.04 & 1.04 & 1.04 & 1.04 & 1.04 & 1.04 & 1.04 \tabularnewline
0.3 & 1.058 & 1.066 & 1.06 & 1.06 & 1.078 & 1.06 & 1.084 & 1.06 \tabularnewline
0.35 & 1.112 & 1.126 & 1.13 & 1.13 & 1.132 & 1.13 & 1.094 & 1.13 \tabularnewline
0.4 & 1.144 & 1.152 & 1.16 & 1.16 & 1.156 & 1.14 & 1.148 & 1.16 \tabularnewline
0.45 & 1.1685 & 1.17 & 1.17 & 1.17 & 1.17 & 1.17 & 1.17 & 1.17 \tabularnewline
0.5 & 1.175 & 1.18 & 1.18 & 1.18 & 1.18 & 1.18 & 1.18 & 1.18 \tabularnewline
0.55 & 1.18 & 1.18 & 1.18 & 1.18 & 1.18 & 1.18 & 1.18 & 1.18 \tabularnewline
0.6 & 1.188 & 1.198 & 1.19 & 1.19 & 1.194 & 1.19 & 1.202 & 1.19 \tabularnewline
0.65 & 1.21 & 1.211 & 1.21 & 1.21 & 1.21 & 1.21 & 1.219 & 1.21 \tabularnewline
0.7 & 1.223 & 1.244 & 1.25 & 1.25 & 1.232 & 1.22 & 1.226 & 1.25 \tabularnewline
0.75 & 1.2575 & 1.27 & 1.26 & 1.26 & 1.26 & 1.26 & 1.27 & 1.27 \tabularnewline
0.8 & 1.284 & 1.29 & 1.29 & 1.29 & 1.286 & 1.28 & 1.29 & 1.29 \tabularnewline
0.85 & 1.2905 & 1.299 & 1.3 & 1.3 & 1.292 & 1.29 & 1.291 & 1.3 \tabularnewline
0.9 & 1.335 & 1.446 & 1.35 & 1.35 & 1.34 & 1.35 & 1.414 & 1.51 \tabularnewline
0.95 & 1.51 & 1.675 & 1.51 & 1.51 & 1.51 & 1.51 & 1.895 & 1.51 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]0.9355[/C][C]0.939[/C][C]0.96[/C][C]0.96[/C][C]0.96[/C][C]0.96[/C][C]0.911[/C][C]0.96[/C][/ROW]
[ROW][C]0.1[/C][C]0.966[/C][C]0.968[/C][C]0.98[/C][C]0.98[/C][C]0.984[/C][C]0.96[/C][C]0.972[/C][C]0.96[/C][/ROW]
[ROW][C]0.15[/C][C]0.999[/C][C]1[/C][C]1[/C][C]1[/C][C]1[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.2[/C][C]1[/C][C]1[/C][C]1[/C][C]1[/C][C]1.016[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.25[/C][C]1.04[/C][C]1.04[/C][C]1.04[/C][C]1.04[/C][C]1.04[/C][C]1.04[/C][C]1.04[/C][C]1.04[/C][/ROW]
[ROW][C]0.3[/C][C]1.058[/C][C]1.066[/C][C]1.06[/C][C]1.06[/C][C]1.078[/C][C]1.06[/C][C]1.084[/C][C]1.06[/C][/ROW]
[ROW][C]0.35[/C][C]1.112[/C][C]1.126[/C][C]1.13[/C][C]1.13[/C][C]1.132[/C][C]1.13[/C][C]1.094[/C][C]1.13[/C][/ROW]
[ROW][C]0.4[/C][C]1.144[/C][C]1.152[/C][C]1.16[/C][C]1.16[/C][C]1.156[/C][C]1.14[/C][C]1.148[/C][C]1.16[/C][/ROW]
[ROW][C]0.45[/C][C]1.1685[/C][C]1.17[/C][C]1.17[/C][C]1.17[/C][C]1.17[/C][C]1.17[/C][C]1.17[/C][C]1.17[/C][/ROW]
[ROW][C]0.5[/C][C]1.175[/C][C]1.18[/C][C]1.18[/C][C]1.18[/C][C]1.18[/C][C]1.18[/C][C]1.18[/C][C]1.18[/C][/ROW]
[ROW][C]0.55[/C][C]1.18[/C][C]1.18[/C][C]1.18[/C][C]1.18[/C][C]1.18[/C][C]1.18[/C][C]1.18[/C][C]1.18[/C][/ROW]
[ROW][C]0.6[/C][C]1.188[/C][C]1.198[/C][C]1.19[/C][C]1.19[/C][C]1.194[/C][C]1.19[/C][C]1.202[/C][C]1.19[/C][/ROW]
[ROW][C]0.65[/C][C]1.21[/C][C]1.211[/C][C]1.21[/C][C]1.21[/C][C]1.21[/C][C]1.21[/C][C]1.219[/C][C]1.21[/C][/ROW]
[ROW][C]0.7[/C][C]1.223[/C][C]1.244[/C][C]1.25[/C][C]1.25[/C][C]1.232[/C][C]1.22[/C][C]1.226[/C][C]1.25[/C][/ROW]
[ROW][C]0.75[/C][C]1.2575[/C][C]1.27[/C][C]1.26[/C][C]1.26[/C][C]1.26[/C][C]1.26[/C][C]1.27[/C][C]1.27[/C][/ROW]
[ROW][C]0.8[/C][C]1.284[/C][C]1.29[/C][C]1.29[/C][C]1.29[/C][C]1.286[/C][C]1.28[/C][C]1.29[/C][C]1.29[/C][/ROW]
[ROW][C]0.85[/C][C]1.2905[/C][C]1.299[/C][C]1.3[/C][C]1.3[/C][C]1.292[/C][C]1.29[/C][C]1.291[/C][C]1.3[/C][/ROW]
[ROW][C]0.9[/C][C]1.335[/C][C]1.446[/C][C]1.35[/C][C]1.35[/C][C]1.34[/C][C]1.35[/C][C]1.414[/C][C]1.51[/C][/ROW]
[ROW][C]0.95[/C][C]1.51[/C][C]1.675[/C][C]1.51[/C][C]1.51[/C][C]1.51[/C][C]1.51[/C][C]1.895[/C][C]1.51[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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)
0.050.93550.9390.960.960.960.960.9110.96
0.10.9660.9680.980.980.9840.960.9720.96
0.150.9991111111
0.211111.016111
0.251.041.041.041.041.041.041.041.04
0.31.0581.0661.061.061.0781.061.0841.06
0.351.1121.1261.131.131.1321.131.0941.13
0.41.1441.1521.161.161.1561.141.1481.16
0.451.16851.171.171.171.171.171.171.17
0.51.1751.181.181.181.181.181.181.18
0.551.181.181.181.181.181.181.181.18
0.61.1881.1981.191.191.1941.191.2021.19
0.651.211.2111.211.211.211.211.2191.21
0.71.2231.2441.251.251.2321.221.2261.25
0.751.25751.271.261.261.261.261.271.27
0.81.2841.291.291.291.2861.281.291.29
0.851.29051.2991.31.31.2921.291.2911.3
0.91.3351.4461.351.351.341.351.4141.51
0.951.511.6751.511.511.511.511.8951.51



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, 'Weighted Average at Xnp',1,TRUE)
a<-table.element(a, 'Weighted Average at X(n+1)p',1,TRUE)
a<-table.element(a, 'Empirical Distribution Function',1,TRUE)
a<-table.element(a, 'Empirical Distribution Function - Averaging',1,TRUE)
a<-table.element(a, 'Empirical Distribution Function - Interpolation',1,TRUE)
a<-table.element(a, 'Closest Observation',1,TRUE)
a<-table.element(a, 'True Basic - Statistics Graphics Toolkit',1,TRUE)
a<-table.element(a, '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,signif(qval[perc,j],6))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')