Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_percentiles.wasp
Title produced by softwarePercentiles
Date of computationThu, 13 Nov 2008 02:20:33 -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/2008/Nov/13/t12265680695aoxcsuo93536ed.htm/, Retrieved Sun, 19 May 2024 11:36:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24510, Retrieved Sun, 19 May 2024 11:36:08 +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] [Stefan Temmerman] [2008-11-13 09:20:33] [7866e091edc3e3e9f6a037e9d19fcaa2] [Current]
Feedback Forum
2008-11-24 21:10:42 [5faab2fc6fb120339944528a32d48a04] [reply
Bij het bekijken van de QQ-plot kunnen we vaststellen dat er teveel schommelingen zijn om een normaalverdeling te zijn. De student kwam tot de juiste conclusie.

Post a new message
Dataseries X:
62
48
53
26
24
32
71
62
62
61
22
61
68
60
51
31
60
67
74
39
60
59
22
67
69
63
35
34
54
59
39
59
34
59
22
47
65




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=24510&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=24510&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24510&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)
0.052222222222222222
0.123.423.6242425.22422.424
0.1528.7529.5313131.43127.531
0.232.833.23434343232.834
0.2534.2534.53535353434.534.5
0.33939393939393939
0.3546.647.3474747.64747.747
0.450.451.4515151.85152.651
0.4553.6554.55454555458.554
0.55959595959595959
0.555959595959595959
0.66060606060606060
0.6560.0560.7616160.46060.361
0.76161.6616161.26161.462
0.756262626262626262
0.862.663.8636362.86364.263
0.8565.967676766.2656767
0.967.368.2686867.46768.868
0.9569.371.3717169.46973.771

\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 & 22 & 22 & 22 & 22 & 22 & 22 & 22 & 22 \tabularnewline
0.1 & 23.4 & 23.6 & 24 & 24 & 25.2 & 24 & 22.4 & 24 \tabularnewline
0.15 & 28.75 & 29.5 & 31 & 31 & 31.4 & 31 & 27.5 & 31 \tabularnewline
0.2 & 32.8 & 33.2 & 34 & 34 & 34 & 32 & 32.8 & 34 \tabularnewline
0.25 & 34.25 & 34.5 & 35 & 35 & 35 & 34 & 34.5 & 34.5 \tabularnewline
0.3 & 39 & 39 & 39 & 39 & 39 & 39 & 39 & 39 \tabularnewline
0.35 & 46.6 & 47.3 & 47 & 47 & 47.6 & 47 & 47.7 & 47 \tabularnewline
0.4 & 50.4 & 51.4 & 51 & 51 & 51.8 & 51 & 52.6 & 51 \tabularnewline
0.45 & 53.65 & 54.5 & 54 & 54 & 55 & 54 & 58.5 & 54 \tabularnewline
0.5 & 59 & 59 & 59 & 59 & 59 & 59 & 59 & 59 \tabularnewline
0.55 & 59 & 59 & 59 & 59 & 59 & 59 & 59 & 59 \tabularnewline
0.6 & 60 & 60 & 60 & 60 & 60 & 60 & 60 & 60 \tabularnewline
0.65 & 60.05 & 60.7 & 61 & 61 & 60.4 & 60 & 60.3 & 61 \tabularnewline
0.7 & 61 & 61.6 & 61 & 61 & 61.2 & 61 & 61.4 & 62 \tabularnewline
0.75 & 62 & 62 & 62 & 62 & 62 & 62 & 62 & 62 \tabularnewline
0.8 & 62.6 & 63.8 & 63 & 63 & 62.8 & 63 & 64.2 & 63 \tabularnewline
0.85 & 65.9 & 67 & 67 & 67 & 66.2 & 65 & 67 & 67 \tabularnewline
0.9 & 67.3 & 68.2 & 68 & 68 & 67.4 & 67 & 68.8 & 68 \tabularnewline
0.95 & 69.3 & 71.3 & 71 & 71 & 69.4 & 69 & 73.7 & 71 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24510&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]22[/C][C]22[/C][C]22[/C][C]22[/C][C]22[/C][C]22[/C][C]22[/C][C]22[/C][/ROW]
[ROW][C]0.1[/C][C]23.4[/C][C]23.6[/C][C]24[/C][C]24[/C][C]25.2[/C][C]24[/C][C]22.4[/C][C]24[/C][/ROW]
[ROW][C]0.15[/C][C]28.75[/C][C]29.5[/C][C]31[/C][C]31[/C][C]31.4[/C][C]31[/C][C]27.5[/C][C]31[/C][/ROW]
[ROW][C]0.2[/C][C]32.8[/C][C]33.2[/C][C]34[/C][C]34[/C][C]34[/C][C]32[/C][C]32.8[/C][C]34[/C][/ROW]
[ROW][C]0.25[/C][C]34.25[/C][C]34.5[/C][C]35[/C][C]35[/C][C]35[/C][C]34[/C][C]34.5[/C][C]34.5[/C][/ROW]
[ROW][C]0.3[/C][C]39[/C][C]39[/C][C]39[/C][C]39[/C][C]39[/C][C]39[/C][C]39[/C][C]39[/C][/ROW]
[ROW][C]0.35[/C][C]46.6[/C][C]47.3[/C][C]47[/C][C]47[/C][C]47.6[/C][C]47[/C][C]47.7[/C][C]47[/C][/ROW]
[ROW][C]0.4[/C][C]50.4[/C][C]51.4[/C][C]51[/C][C]51[/C][C]51.8[/C][C]51[/C][C]52.6[/C][C]51[/C][/ROW]
[ROW][C]0.45[/C][C]53.65[/C][C]54.5[/C][C]54[/C][C]54[/C][C]55[/C][C]54[/C][C]58.5[/C][C]54[/C][/ROW]
[ROW][C]0.5[/C][C]59[/C][C]59[/C][C]59[/C][C]59[/C][C]59[/C][C]59[/C][C]59[/C][C]59[/C][/ROW]
[ROW][C]0.55[/C][C]59[/C][C]59[/C][C]59[/C][C]59[/C][C]59[/C][C]59[/C][C]59[/C][C]59[/C][/ROW]
[ROW][C]0.6[/C][C]60[/C][C]60[/C][C]60[/C][C]60[/C][C]60[/C][C]60[/C][C]60[/C][C]60[/C][/ROW]
[ROW][C]0.65[/C][C]60.05[/C][C]60.7[/C][C]61[/C][C]61[/C][C]60.4[/C][C]60[/C][C]60.3[/C][C]61[/C][/ROW]
[ROW][C]0.7[/C][C]61[/C][C]61.6[/C][C]61[/C][C]61[/C][C]61.2[/C][C]61[/C][C]61.4[/C][C]62[/C][/ROW]
[ROW][C]0.75[/C][C]62[/C][C]62[/C][C]62[/C][C]62[/C][C]62[/C][C]62[/C][C]62[/C][C]62[/C][/ROW]
[ROW][C]0.8[/C][C]62.6[/C][C]63.8[/C][C]63[/C][C]63[/C][C]62.8[/C][C]63[/C][C]64.2[/C][C]63[/C][/ROW]
[ROW][C]0.85[/C][C]65.9[/C][C]67[/C][C]67[/C][C]67[/C][C]66.2[/C][C]65[/C][C]67[/C][C]67[/C][/ROW]
[ROW][C]0.9[/C][C]67.3[/C][C]68.2[/C][C]68[/C][C]68[/C][C]67.4[/C][C]67[/C][C]68.8[/C][C]68[/C][/ROW]
[ROW][C]0.95[/C][C]69.3[/C][C]71.3[/C][C]71[/C][C]71[/C][C]69.4[/C][C]69[/C][C]73.7[/C][C]71[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24510&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24510&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)
0.052222222222222222
0.123.423.6242425.22422.424
0.1528.7529.5313131.43127.531
0.232.833.23434343232.834
0.2534.2534.53535353434.534.5
0.33939393939393939
0.3546.647.3474747.64747.747
0.450.451.4515151.85152.651
0.4553.6554.55454555458.554
0.55959595959595959
0.555959595959595959
0.66060606060606060
0.6560.0560.7616160.46060.361
0.76161.6616161.26161.462
0.756262626262626262
0.862.663.8636362.86364.263
0.8565.967676766.2656767
0.967.368.2686867.46768.868
0.9569.371.3717169.46973.771



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')