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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 computationSun, 09 Nov 2008 11:32:27 -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/09/t1226255575h1zypyr7zdnlyki.htm/, Retrieved Thu, 16 May 2024 01:39:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22810, Retrieved Thu, 16 May 2024 01:39:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact176

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Kernel Density Estimation] [Q1 Bivariate Dens...] [2007-11-03 14:50:57] [e2ec4dc832988c648c062d4cdc574d44]
- RMPD  [Hierarchical Clustering] [WS4 Q2 dendrogram] [2007-11-05 10:02:49] [74be16979710d4c4e7c6647856088456]
F RMPD    [Box-Cox Linearity Plot] [] [2008-11-04 20:21:52] [077ffec662d24c06be4c491541a44245]
F   PD        [Box-Cox Linearity Plot] [] [2008-11-09 18:32:27] [6d40a467de0f28bd2350f82ac9522c51] [Current]
Feedback Forum
2008-11-19 14:37:19 [2df1bcd103d52957f4a39bd4617794c8] [reply] 
Student gebruikt correct de Box-Cox Linearity Plot methode.

Door de r code aan te passen transformeren we de functie.

De oorspronkelijke grafiek wijkt niet veel af van de getransformeerde grafische voorstelling. We nemen dit visueel waar, maar ook wanneer we de cijfergegevens van de correlatie vergelijken stellen we hetzelfde vast.
2008-11-22 13:42:37 [Jeroen Michel] [reply] 
De student besluit hier een correcte analyse en besluit ook dat de orignele grafiek nihil tot weinig afwijkt van de getransformeerde grafiek. Dit kan je besluiten door de standaardafwijkingen met elkaar te vergelijken.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
299,63
305,945
382,252
348,846
335,367
373,617
312,612
312,232
337,161
331,476
350,103
345,127
297,256
295,979
361,007
321,803
354,937
349,432
290,979
349,576
327,625
349,377
336,777
339,134
323,321
318,86
373,583
333,03
408,556
414,646
291,514
348,857
349,368
375,765
364,136
349,53
348,167
332,856
360,551
346,969
392,815
372,02
371,027
342,672
367,343
390,786
343,785
362,6
349,468
340,624
369,536
407,782
392,239
404,824
373,669
344,902
396,7
398,911
366,009
392,484
Dataseries Y:
Download CSV
Histogram 
154,783
187,646
237,863
215,54
231,745
199,548
164,147
159,388
203,514
224,901
211,539
211,16
181,712
203,908
240,774
232,819
255,221
246,7
206,263
211,679
236,601
237,43
233,767
219,52
222,625
216,238
248,587
221,376
242,453
246,539
189,351
185,956
213,175
228,732
212,93
218,254
227,103
219,026
264,529
262,057
258,779
231,928
211,167
205,439
224,883
228,624
209,435
215,607
287,356
306,015
338,546
344,16
328,412
342,006
277,668
290,477
314,967
324,627
290,646
315,033

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=22810&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=22810&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22810&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 x60
maximum correlation0.66020391970702
optimal lambda(x)2
Residual SD (orginial)33.9432237204243
Residual SD (transformed)33.8964112430596

\begin{tabular}{lllllllll}
\hline
Box-Cox Linearity Plot \tabularnewline
# observations x & 60 \tabularnewline
maximum correlation & 0.66020391970702 \tabularnewline
optimal lambda(x) & 2 \tabularnewline
Residual SD (orginial) & 33.9432237204243 \tabularnewline
Residual SD (transformed) & 33.8964112430596 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22810&T=1

[TABLE]
[ROW][C]Box-Cox Linearity Plot[/C][/ROW]
[ROW][C]# observations x[/C][C]60[/C][/ROW]
[ROW][C]maximum correlation[/C][C]0.66020391970702[/C][/ROW]
[ROW][C]optimal lambda(x)[/C][C]2[/C][/ROW]
[ROW][C]Residual SD (orginial)[/C][C]33.9432237204243[/C][/ROW]
[ROW][C]Residual SD (transformed)[/C][C]33.8964112430596[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22810&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22810&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 x60
maximum correlation0.66020391970702
optimal lambda(x)2
Residual SD (orginial)33.9432237204243
Residual SD (transformed)33.8964112430596


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')