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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_boxcoxlin.wasp
Title produced by softwareBox-Cox Linearity Plot
Date of computationTue, 11 Nov 2008 08:18:08 -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/11/t1226416941fgr1vbm4srdzau7.htm/, Retrieved Mon, 20 May 2024 09:44:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23589, Retrieved Mon, 20 May 2024 09:44:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Box-Cox Linearity Plot] [Box Cox] [2008-11-11 15:18:08] [8758b22b4a10c08c31202f233362e983] [Current]
Feedback Forum
2008-11-19 20:48:51 [Nathalie Koulouris] [reply] 
De student heeft hier de juiste berekeningmethode gebruikt maar geen verdere motivatie gegeven.
2008-11-21 16:05:38 [Matthieu Blondeau] [reply] 
Er is hier een deviatie van ongeveer 13. De Residual Standard Deviation: 312,4346 tov 299,9478.
2008-11-23 17:01:01 [Michaël De Kuyer] [reply] 
Bij deze methode gaat men proberen de relatie tussen twee tijdreeksen door de waarden te transformeren. Bij deze toepassing is te zien op de linear fit dat de punten dichter bij de recht komen te liggen. Bovendien neemt de standaardafwijking af.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
11008,9
9996,6
9419,5
11958,8
12594,6
11890,6
10871,7
11835,7
11542,2
13093,7
11180,2
12035,7
12112
10875,2
9897,3
11672,1
12385,7
11405,6
9830,9
11025,1
10853,8
12252,6
11839,4
11669,1
11601,4
11178,4
9516,4
12102,8
12989
11610,2
10205,5
11356,2
11307,1
12648,6
11947,2
11714,1
12192,5
11268,8
9097,4
12639,8
13040,1
11687,3
11191,7
11391,9
11793,1
13933,2
12778,1
11810,3
13698,4
11956,6
10723,8
13938,9
13979,8
13807,4
12973,9
12509,8
12934,1
14908,3
13772,1
13012,6
14049,9
11816,5
11593,2
14466,2
13615,9
14733,9
13880,7
13527,5
13584
16170,2
13260,6
14741,9
15486,5
13154,5
12621,2
15031,6
15452,4
15428
13105,9
14716,8
14180
16202,2
14392,4
15140,6
15960,1
14351,3
13230,2
15202,1
17157,3
16159,1
13405,7
17224,7
17338,4
17370,6
18817,8
16593,2
17979,5
Dataseries Y:
Download CSV
Histogram 
3202,1
3650,2
2805,1
3957,5
3941,3
3905,4
3546,9
3208,7
3402
3661,1
3073,9
3419,2
3532,8
3693,1
2622,9
3130,8
3487,5
3349,7
3044,2
3266
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3419,5
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3505,1
3845,3
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3954
3460,1
3454,1
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3418
3349,5
3423,4
3242,8
3277,2
3833
2606,3
3643,8
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3669,3
3191,5
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3970,7
3601,2
3610
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3892,2
3613
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3649,5
4215,2
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4196,8
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4735,9
4130,6
4356,2
4159,6
3988
4167,8
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3909,4
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4308,9
4420,4
3544,2
4433
4479,7
4533,2
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4394
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4202,2
4682,5
4884,3
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4505,2
4611,5
5081,1
4523,1
4412,8
4647,4
4778,6
4495,3
4633,5
4360,5
4517,9

Tables (Output of Computation)





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=23589&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=23589&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23589&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Box-Cox Linearity Plot
# observations x97
maximum correlation0.85988615818112
optimal lambda(x)-0.65
Residual SD (orginial)312.434631723634
Residual SD (transformed)299.947870190242

\begin{tabular}{lllllllll}
\hline
Box-Cox Linearity Plot \tabularnewline
# observations x & 97 \tabularnewline
maximum correlation & 0.85988615818112 \tabularnewline
optimal lambda(x) & -0.65 \tabularnewline
Residual SD (orginial) & 312.434631723634 \tabularnewline
Residual SD (transformed) & 299.947870190242 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23589&T=1

[TABLE]
[ROW][C]Box-Cox Linearity Plot[/C][/ROW]
[ROW][C]# observations x[/C][C]97[/C][/ROW]
[ROW][C]maximum correlation[/C][C]0.85988615818112[/C][/ROW]
[ROW][C]optimal lambda(x)[/C][C]-0.65[/C][/ROW]
[ROW][C]Residual SD (orginial)[/C][C]312.434631723634[/C][/ROW]
[ROW][C]Residual SD (transformed)[/C][C]299.947870190242[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23589&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23589&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 Linearity Plot
# observations x97
maximum correlation0.85988615818112
optimal lambda(x)-0.65
Residual SD (orginial)312.434631723634
Residual SD (transformed)299.947870190242


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(x1,y)
if (mx < abs(c[i]))
{
mx <- abs(c[i])
mxli <- l[i]
}
}
c
mx
mxli
if (mxli != 0)
{
x1 <- (x^mxli - 1) / mxli
} else {
x1 <- log(x)
}
r<-lm(y~x)
se <- sqrt(var(r$residuals))
r1 <- lm(y~x1)
se1 <- sqrt(var(r1$residuals))
bitmap(file='test1.png')
plot(l,c,main='Box-Cox Linearity Plot',xlab='Lambda',ylab='correlation')
grid()
dev.off()
bitmap(file='test2.png')
plot(x,y,main='Linear Fit of Original Data',xlab='x',ylab='y')
abline(r)
grid()
mtext(paste('Residual Standard Deviation = ',se))
dev.off()
bitmap(file='test3.png')
plot(x1,y,main='Linear Fit of Transformed Data',xlab='x',ylab='y')
abline(r1)
grid()
mtext(paste('Residual Standard Deviation = ',se1))
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Box-Cox Linearity 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(x)',header=TRUE)
a<-table.element(a,mxli)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Residual SD (orginial)',header=TRUE)
a<-table.element(a,se)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Residual SD (transformed)',header=TRUE)
a<-table.element(a,se1)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')