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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_boxcoxnorm.wasp
Title produced by softwareBox-Cox Normality Plot
Date of computationMon, 10 Nov 2008 04:55:49 -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/10/t1226318233f53llulqcijib33.htm/, Retrieved Sun, 19 May 2024 10:46:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22970, Retrieved Sun, 19 May 2024 10:46:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact239

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Bivariate Kernel Density Estimation] [Various EDA topic...] [2008-11-07 10:38:01] [e5d91604aae608e98a8ea24759233f66]
F RMPD  [Trivariate Scatterplots] [Various EDA topic...] [2008-11-07 10:42:57] [e5d91604aae608e98a8ea24759233f66]
F RMPD    [Box-Cox Linearity Plot] [Various EDA topic...] [2008-11-07 11:06:19] [e5d91604aae608e98a8ea24759233f66]
F RM D        [Box-Cox Normality Plot] [Various EDA topic...] [2008-11-10 11:55:49] [55ca0ca4a201c9689dcf5fae352c92eb] [Current]
F RMPD          [Maximum-likelihood Fitting - Normal Distribution] [Various EDA topic...] [2008-11-10 12:02:10] [e5d91604aae608e98a8ea24759233f66]
- RMPD          [Testing Variance - Critical Value (Region)] [Various types of ...] [2008-11-10 12:36:06] [e5d91604aae608e98a8ea24759233f66]
-   P             [Testing Variance - Critical Value (Region)] [Various types of ...] [2008-11-10 12:44:47] [e5d91604aae608e98a8ea24759233f66]
- RMPD            [Notched Boxplots] [Various types of ...] [2008-11-10 13:05:18] [e5d91604aae608e98a8ea24759233f66]
- RMPD          [Testing Variance - p-value (probability)] [Various types of ...] [2008-11-10 12:39:28] [e5d91604aae608e98a8ea24759233f66]
Feedback Forum
2008-11-22 18:31:01 [Kenny Simons] [reply] 
Zelfde uitleg als bij vraag 3.

Een Box-Cox linearity plot is een manier om een tijdreeks te transformeren, zodat je een verband lineair kan maken. Om nu een verband lineair te maken, moet je gaan zoeken of er een lambda parameter bestaat, zodat je de tijdreeks op een juiste manier kan transformeren.

Grafisch moet je de lambdawaarde kiezen met de maximumwaarde, als je geen maximum kan aflezen, dan kan je uiteraard ook geen conclusies trekken.

2008-11-23 14:28:03 [Chi-Kwong Man] [reply] 
Idem uitleg vraag 3.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1946,81
1765,9
1635,25
1833,42
1910,43
1959,67
1969,6
2061,41
2093,48
2120,88
2174,56
2196,72
2350,44
2440,25
2408,64
2472,81
2407,6
2454,62
2448,05
2497,84
2645,64
2756,76
2849,27
2921,44
3080,58
3106,22
3119,31
3061,26
3097,31
3161,69
3257,16
3277,01
3295,32
3363,99
3494,17
3667,03
3813,06
3917,96
3895,51
3733,22
3801,06
3570,12
3701,61
3862,27
3970,1
4138,52
4199,75
4290,89
4443,91
4502,64
4356,98
4591,27
4696,96
4621,4
4562,84
4202,52
4296,49
4435,23
4105,18
4116,68

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22970&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22970&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22970&T=0

As an alternative you can also use a QR Code:  
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 time2 seconds
R Server'George Udny Yule' @ 72.249.76.132






Box-Cox Normality Plot
# observations x60
maximum correlation0.940430237322678
optimal lambda0.31

\begin{tabular}{lllllllll}
\hline
Box-Cox Normality Plot \tabularnewline
# observations x & 60 \tabularnewline
maximum correlation & 0.940430237322678 \tabularnewline
optimal lambda & 0.31 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22970&T=1

[TABLE]
[ROW][C]Box-Cox Normality Plot[/C][/ROW]
[ROW][C]# observations x[/C][C]60[/C][/ROW]
[ROW][C]maximum correlation[/C][C]0.940430237322678[/C][/ROW]
[ROW][C]optimal lambda[/C][C]0.31[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22970&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22970&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Box-Cox Normality Plot
# observations x60
maximum correlation0.940430237322678
optimal lambda0.31


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
R code (references can be found in the software module):
n <- length(x)
c <- array(NA,dim=c(401))
l <- array(NA,dim=c(401))
mx <- 0
mxli <- -999
for (i in 1:401)
{
l[i] <- (i-201)/100
if (l[i] != 0)
{
x1 <- (x^l[i] - 1) / l[i]
} else {
x1 <- log(x)
}
c[i] <- cor(qnorm(ppoints(x), mean=0, sd=1),x1)
if (mx < c[i])
{
mx <- c[i]
mxli <- l[i]
}
}
c
mx
mxli
if (mxli != 0)
{
x1 <- (x^mxli - 1) / mxli
} else {
x1 <- log(x)
}
bitmap(file='test1.png')
plot(l,c,main='Box-Cox Normality Plot',xlab='Lambda',ylab='correlation')
mtext(paste('Optimal Lambda =',mxli))
grid()
dev.off()
bitmap(file='test2.png')
hist(x,main='Histogram of Original Data',xlab='X',ylab='frequency')
grid()
dev.off()
bitmap(file='test3.png')
hist(x1,main='Histogram of Transformed Data',xlab='X',ylab='frequency')
grid()
dev.off()
bitmap(file='test4.png')
qqnorm(x)
qqline(x)
grid()
mtext('Original Data')
dev.off()
bitmap(file='test5.png')
qqnorm(x1)
qqline(x1)
grid()
mtext('Transformed Data')
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Box-Cox Normality Plot',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'# observations x',header=TRUE)
a<-table.element(a,n)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'maximum correlation',header=TRUE)
a<-table.element(a,mx)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'optimal lambda',header=TRUE)
a<-table.element(a,mxli)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')