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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 computationWed, 11 Nov 2009 06:01:01 -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/2009/Nov/11/t1257944624jlb384i0oioci83.htm/, Retrieved Fri, 26 Apr 2024 16:43:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=55559, Retrieved Fri, 26 Apr 2024 16:43:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact185
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Box-Cox Linearity Plot] [3/11/2009] [2009-11-02 21:47:57] [b98453cac15ba1066b407e146608df68]
F    D    [Box-Cox Linearity Plot] [WS6: Linearity Bo...] [2009-11-11 13:01:01] [b8ce264f75295a954feffaf60221d1b0] [Current]
Feedback Forum
2009-11-18 18:37:37 [Joris Mols] [reply
Tukey: http://www.freestatistics.org/blog/index.php?v=date/2009/Nov/18/t1258568913dth9nnkphrp28ue.htm/

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Dataseries X:
100,00
106,54
127,63
141,72
147,95
142,16
147,95
155,82
164,13
159,16
147,14
159,16
178,85
200,44
189,43
160,16
157,02
168,91
173,19
175,83
158,78
166,96
171,24
179,55
191,00
196,41
206,80
208,94
224,86
217,31
229,96
252,36
255,25
290,37
269,67
240,53
252,86
265,51
299,31
297,42
277,09
313,59
335,75
370,67
375,33
358,65
334,80
335,05
364,07
350,47
350,16
393,46
405,29
406,86
426,12
422,97
373,63
335,18
329,89
346,32
100,00
106,54
127,63
141,72
147,95
142,16
147,95
155,82
164,13
159,16
147,14
159,16
178,85
200,44
189,43
160,16
157,02
168,91
173,19
175,83
158,78
166,96
171,24
179,55
191,00
196,41
206,80
208,94
224,86
217,31
229,96
252,36
255,25
290,37
269,67
240,53
252,86
265,51
299,31
297,42
277,09
313,59
335,75
370,67
375,33
358,65
334,80
335,05
364,07
350,47
350,16
393,46
405,29
406,86
426,12
422,97
373,63
335,18
329,89
346,32
Dataseries Y:
100,00
100,28
100,00
98,62
98,35
98,35
104,68
104,13
103,58
104,68
104,41
105,79
107,99
108,54
107,99
109,09
107,99
109,09
115,43
115,98
115,70
115,15
112,95
115,15
117,36
117,91
118,46
116,80
116,53
117,63
121,49
123,69
124,52
127,27
125,34
127,00
127,00
127,55
127,27
125,62
125,34
125,62
130,03
130,03
129,75
128,10
126,45
128,10
128,93
128,65
127,55
126,72
127,27
127,00
131,13
131,13
129,75
124,79
122,04
121,76
100,00
100,28
100,00
98,62
98,35
98,35
104,68
104,13
103,58
104,68
104,41
105,79
107,99
108,54
107,99
109,09
107,99
109,09
115,43
115,98
115,70
115,15
112,95
115,15
117,36
117,91
118,46
116,80
116,53
117,63
121,49
123,69
124,52
127,27
125,34
127,00
127,00
127,55
127,27
125,62
125,34
125,62
130,03
130,03
129,75
128,10
126,45
128,10
128,93
128,65
127,55
126,72
127,27
127,00
131,13
131,13
129,75
124,79
122,04
121,76




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=55559&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=55559&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55559&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







Box-Cox Linearity Plot
# observations x120
maximum correlation0.93028446349599
optimal lambda(x)-0.57
Residual SD (orginial)4.77838868247073
Residual SD (transformed)3.79708466695867

\begin{tabular}{lllllllll}
\hline
Box-Cox Linearity Plot \tabularnewline
# observations x & 120 \tabularnewline
maximum correlation & 0.93028446349599 \tabularnewline
optimal lambda(x) & -0.57 \tabularnewline
Residual SD (orginial) & 4.77838868247073 \tabularnewline
Residual SD (transformed) & 3.79708466695867 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55559&T=1

[TABLE]
[ROW][C]Box-Cox Linearity Plot[/C][/ROW]
[ROW][C]# observations x[/C][C]120[/C][/ROW]
[ROW][C]maximum correlation[/C][C]0.93028446349599[/C][/ROW]
[ROW][C]optimal lambda(x)[/C][C]-0.57[/C][/ROW]
[ROW][C]Residual SD (orginial)[/C][C]4.77838868247073[/C][/ROW]
[ROW][C]Residual SD (transformed)[/C][C]3.79708466695867[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55559&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55559&T=1

As an alternative you can also use a QR Code:  

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

Box-Cox Linearity Plot
# observations x120
maximum correlation0.93028446349599
optimal lambda(x)-0.57
Residual SD (orginial)4.77838868247073
Residual SD (transformed)3.79708466695867



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')