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Author's title

Author*Unverified author*
R Software Modulerwasp_percentiles.wasp
Title produced by softwarePercentiles
Date of computationTue, 24 Sep 2019 15:25:27 +0200
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/Sep/24/t15693315820xl2re66qpruxb5.htm/, Retrieved Thu, 02 May 2024 21:08:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=318913, Retrieved Thu, 02 May 2024 21:08:32 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact131
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Percentiles] [Upvotes (Measured...] [2019-09-24 13:25:27] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
12.3
13.6
15.9
22.1
24.9
25.2
26.8
27.1
28.5
32.5
33.9
36.2
38.8
39.7
40.3
40.5
42.3
46.3
46.4
49.3
49.9
50.4
50.5
56.2
59.2
59.7
59.8
60.4
63.4
64.5
67.3
72.3
74.7
78.3
83.2
83.7




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 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 time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318913&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]0 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=318913&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318913&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 time0 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)
0.0538.9839.02539.739.739.7938.839.47538.8
0.139.944040.340.340.3639.74040
0.1540.4240.4540.540.541.3140.540.3540.5
0.241.9442.342.342.344.742.342.342.3
0.2546.346.32546.346.3546.37546.346.37546.3
0.346.9847.8549.349.349.0146.447.8547.85
0.3549.5449.7549.949.949.92549.349.4549.9
0.450.250.450.450.450.4250.450.450.4
0.4550.4851.92550.550.552.49550.554.77550.5
0.556.257.756.257.757.756.257.757.7
0.5559.359.57559.759.759.52559.259.32559.7
0.659.7459.859.859.859.7859.759.859.8
0.6560.1661.1560.460.460.3760.462.6560.4
0.762.863.9563.463.463.5163.463.9563.95
0.7564.566.664.565.965.264.565.267.3
0.868.372.372.372.369.367.372.372.3
0.8573.2675.674.774.773.6272.377.474.7
0.976.8680.7578.378.377.2278.380.7580.75
0.9582.2283.57583.283.282.46583.283.32583.7

\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 & 38.98 & 39.025 & 39.7 & 39.7 & 39.79 & 38.8 & 39.475 & 38.8 \tabularnewline
0.1 & 39.94 & 40 & 40.3 & 40.3 & 40.36 & 39.7 & 40 & 40 \tabularnewline
0.15 & 40.42 & 40.45 & 40.5 & 40.5 & 41.31 & 40.5 & 40.35 & 40.5 \tabularnewline
0.2 & 41.94 & 42.3 & 42.3 & 42.3 & 44.7 & 42.3 & 42.3 & 42.3 \tabularnewline
0.25 & 46.3 & 46.325 & 46.3 & 46.35 & 46.375 & 46.3 & 46.375 & 46.3 \tabularnewline
0.3 & 46.98 & 47.85 & 49.3 & 49.3 & 49.01 & 46.4 & 47.85 & 47.85 \tabularnewline
0.35 & 49.54 & 49.75 & 49.9 & 49.9 & 49.925 & 49.3 & 49.45 & 49.9 \tabularnewline
0.4 & 50.2 & 50.4 & 50.4 & 50.4 & 50.42 & 50.4 & 50.4 & 50.4 \tabularnewline
0.45 & 50.48 & 51.925 & 50.5 & 50.5 & 52.495 & 50.5 & 54.775 & 50.5 \tabularnewline
0.5 & 56.2 & 57.7 & 56.2 & 57.7 & 57.7 & 56.2 & 57.7 & 57.7 \tabularnewline
0.55 & 59.3 & 59.575 & 59.7 & 59.7 & 59.525 & 59.2 & 59.325 & 59.7 \tabularnewline
0.6 & 59.74 & 59.8 & 59.8 & 59.8 & 59.78 & 59.7 & 59.8 & 59.8 \tabularnewline
0.65 & 60.16 & 61.15 & 60.4 & 60.4 & 60.37 & 60.4 & 62.65 & 60.4 \tabularnewline
0.7 & 62.8 & 63.95 & 63.4 & 63.4 & 63.51 & 63.4 & 63.95 & 63.95 \tabularnewline
0.75 & 64.5 & 66.6 & 64.5 & 65.9 & 65.2 & 64.5 & 65.2 & 67.3 \tabularnewline
0.8 & 68.3 & 72.3 & 72.3 & 72.3 & 69.3 & 67.3 & 72.3 & 72.3 \tabularnewline
0.85 & 73.26 & 75.6 & 74.7 & 74.7 & 73.62 & 72.3 & 77.4 & 74.7 \tabularnewline
0.9 & 76.86 & 80.75 & 78.3 & 78.3 & 77.22 & 78.3 & 80.75 & 80.75 \tabularnewline
0.95 & 82.22 & 83.575 & 83.2 & 83.2 & 82.465 & 83.2 & 83.325 & 83.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318913&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]38.98[/C][C]39.025[/C][C]39.7[/C][C]39.7[/C][C]39.79[/C][C]38.8[/C][C]39.475[/C][C]38.8[/C][/ROW]
[ROW][C]0.1[/C][C]39.94[/C][C]40[/C][C]40.3[/C][C]40.3[/C][C]40.36[/C][C]39.7[/C][C]40[/C][C]40[/C][/ROW]
[ROW][C]0.15[/C][C]40.42[/C][C]40.45[/C][C]40.5[/C][C]40.5[/C][C]41.31[/C][C]40.5[/C][C]40.35[/C][C]40.5[/C][/ROW]
[ROW][C]0.2[/C][C]41.94[/C][C]42.3[/C][C]42.3[/C][C]42.3[/C][C]44.7[/C][C]42.3[/C][C]42.3[/C][C]42.3[/C][/ROW]
[ROW][C]0.25[/C][C]46.3[/C][C]46.325[/C][C]46.3[/C][C]46.35[/C][C]46.375[/C][C]46.3[/C][C]46.375[/C][C]46.3[/C][/ROW]
[ROW][C]0.3[/C][C]46.98[/C][C]47.85[/C][C]49.3[/C][C]49.3[/C][C]49.01[/C][C]46.4[/C][C]47.85[/C][C]47.85[/C][/ROW]
[ROW][C]0.35[/C][C]49.54[/C][C]49.75[/C][C]49.9[/C][C]49.9[/C][C]49.925[/C][C]49.3[/C][C]49.45[/C][C]49.9[/C][/ROW]
[ROW][C]0.4[/C][C]50.2[/C][C]50.4[/C][C]50.4[/C][C]50.4[/C][C]50.42[/C][C]50.4[/C][C]50.4[/C][C]50.4[/C][/ROW]
[ROW][C]0.45[/C][C]50.48[/C][C]51.925[/C][C]50.5[/C][C]50.5[/C][C]52.495[/C][C]50.5[/C][C]54.775[/C][C]50.5[/C][/ROW]
[ROW][C]0.5[/C][C]56.2[/C][C]57.7[/C][C]56.2[/C][C]57.7[/C][C]57.7[/C][C]56.2[/C][C]57.7[/C][C]57.7[/C][/ROW]
[ROW][C]0.55[/C][C]59.3[/C][C]59.575[/C][C]59.7[/C][C]59.7[/C][C]59.525[/C][C]59.2[/C][C]59.325[/C][C]59.7[/C][/ROW]
[ROW][C]0.6[/C][C]59.74[/C][C]59.8[/C][C]59.8[/C][C]59.8[/C][C]59.78[/C][C]59.7[/C][C]59.8[/C][C]59.8[/C][/ROW]
[ROW][C]0.65[/C][C]60.16[/C][C]61.15[/C][C]60.4[/C][C]60.4[/C][C]60.37[/C][C]60.4[/C][C]62.65[/C][C]60.4[/C][/ROW]
[ROW][C]0.7[/C][C]62.8[/C][C]63.95[/C][C]63.4[/C][C]63.4[/C][C]63.51[/C][C]63.4[/C][C]63.95[/C][C]63.95[/C][/ROW]
[ROW][C]0.75[/C][C]64.5[/C][C]66.6[/C][C]64.5[/C][C]65.9[/C][C]65.2[/C][C]64.5[/C][C]65.2[/C][C]67.3[/C][/ROW]
[ROW][C]0.8[/C][C]68.3[/C][C]72.3[/C][C]72.3[/C][C]72.3[/C][C]69.3[/C][C]67.3[/C][C]72.3[/C][C]72.3[/C][/ROW]
[ROW][C]0.85[/C][C]73.26[/C][C]75.6[/C][C]74.7[/C][C]74.7[/C][C]73.62[/C][C]72.3[/C][C]77.4[/C][C]74.7[/C][/ROW]
[ROW][C]0.9[/C][C]76.86[/C][C]80.75[/C][C]78.3[/C][C]78.3[/C][C]77.22[/C][C]78.3[/C][C]80.75[/C][C]80.75[/C][/ROW]
[ROW][C]0.95[/C][C]82.22[/C][C]83.575[/C][C]83.2[/C][C]83.2[/C][C]82.465[/C][C]83.2[/C][C]83.325[/C][C]83.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318913&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318913&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)
0.0538.9839.02539.739.739.7938.839.47538.8
0.139.944040.340.340.3639.74040
0.1540.4240.4540.540.541.3140.540.3540.5
0.241.9442.342.342.344.742.342.342.3
0.2546.346.32546.346.3546.37546.346.37546.3
0.346.9847.8549.349.349.0146.447.8547.85
0.3549.5449.7549.949.949.92549.349.4549.9
0.450.250.450.450.450.4250.450.450.4
0.4550.4851.92550.550.552.49550.554.77550.5
0.556.257.756.257.757.756.257.757.7
0.5559.359.57559.759.759.52559.259.32559.7
0.659.7459.859.859.859.7859.759.859.8
0.6560.1661.1560.460.460.3760.462.6560.4
0.762.863.9563.463.463.5163.463.9563.95
0.7564.566.664.565.965.264.565.267.3
0.868.372.372.372.369.367.372.372.3
0.8573.2675.674.774.773.6272.377.474.7
0.976.8680.7578.378.377.2278.380.7580.75
0.9582.2283.57583.283.282.46583.283.32583.7



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')