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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_edabi.wasp
Title produced by softwareBivariate Explorative Data Analysis
Date of computationTue, 10 Nov 2009 10:35:44 -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/10/t1257874621bltv59nj0f4912w.htm/, Retrieved Mon, 06 May 2024 09:10:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=55322, Retrieved Mon, 06 May 2024 09:10:05 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact221
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]
- RMPD    [Bivariate Explorative Data Analysis] [] [2009-11-10 17:35:44] [6dfcce621b31349cab7f0d189e6f8a9d] [Current]
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Dataseries X:
492865
480961
461935
456608
441977
439148
488180
520564
501492
485025
464196
460170
467037
460070
447988
442867
436087
431328
484015
509673
512927
502831
470984
471067
476049
474605
470439
461251
454724
455626
516847
525192
522975
518585
509239
512238
519164
517009
509933
509127
500857
506971
569323
579714
577992
565464
547344
554788
562325
560854
555332
543599
536662
542722
593530
610763
612613
611324
594167
595454
590865
589379
584428
573100
567456
569028
620735
628884
628232
612117
595404
597141
Dataseries Y:
344744
338653
327532
326225
318672
317756
337302
349420
336923
330758
321002
320820
327032
324047
316735
315710
313427
310527
330962
339015
341332
339092
323308
325849
330675
332225
331735
328047
326165
327081
346764
344190
343333
345777
344094
348609
354846
356427
353467
355996
352487
355178
374556
375021
375787
372720
364431
370490
376974
377632
378205
370861
369167
371551
382842
381903
384502
392058
384359
388884
386586
387495
385705
378670
377367
376911
389827
387820
387267
380575
372402
376740




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 4 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55322&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55322&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55322&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 time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Model: Y[t] = c + b X[t] + e[t]
c129826.971818701
b0.425980889008533

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 129826.971818701 \tabularnewline
b & 0.425980889008533 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55322&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]129826.971818701[/C][/ROW]
[ROW][C]b[/C][C]0.425980889008533[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55322&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55322&T=1

As an alternative you can also use a QR Code:  

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

Model: Y[t] = c + b X[t] + e[t]
c129826.971818701
b0.425980889008533







Descriptive Statistics about e[t]
# observations72
minimum-11055.6970579379
Q1-4937.8439511111
median398.320576689062
mean2.10991717924319e-13
Q34695.07632554047
maximum11817.2091264120

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 72 \tabularnewline
minimum & -11055.6970579379 \tabularnewline
Q1 & -4937.8439511111 \tabularnewline
median & 398.320576689062 \tabularnewline
mean & 2.10991717924319e-13 \tabularnewline
Q3 & 4695.07632554047 \tabularnewline
maximum & 11817.2091264120 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55322&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]72[/C][/ROW]
[ROW][C]minimum[/C][C]-11055.6970579379[/C][/ROW]
[ROW][C]Q1[/C][C]-4937.8439511111[/C][/ROW]
[ROW][C]median[/C][C]398.320576689062[/C][/ROW]
[ROW][C]mean[/C][C]2.10991717924319e-13[/C][/ROW]
[ROW][C]Q3[/C][C]4695.07632554047[/C][/ROW]
[ROW][C]maximum[/C][C]11817.2091264120[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55322&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55322&T=2

As an alternative you can also use a QR Code:  

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

Descriptive Statistics about e[t]
# observations72
minimum-11055.6970579379
Q1-4937.8439511111
median398.320576689062
mean2.10991717924319e-13
Q34695.07632554047
maximum11817.2091264120



Parameters (Session):
par1 = red ; par2 = blue ; par3 = TRUE ; par4 = iptot ; par5 = ipkl ;
Parameters (R input):
par1 = 0 ; par2 = 36 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
x <- as.ts(x)
y <- as.ts(y)
mylm <- lm(y~x)
cbind(mylm$resid)
library(lattice)
bitmap(file='pic1.png')
plot(y,type='l',main='Run Sequence Plot of Y[t]',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic1a.png')
plot(x,type='l',main='Run Sequence Plot of X[t]',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic1b.png')
plot(x,y,main='Scatter Plot',xlab='X[t]',ylab='Y[t]')
grid()
dev.off()
bitmap(file='pic1c.png')
plot(mylm$resid,type='l',main='Run Sequence Plot of e[t]',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic2.png')
hist(mylm$resid,main='Histogram of e[t]')
dev.off()
bitmap(file='pic3.png')
if (par1 > 0)
{
densityplot(~mylm$resid,col='black',main=paste('Density Plot of e[t] bw = ',par1),bw=par1)
} else {
densityplot(~mylm$resid,col='black',main='Density Plot of e[t]')
}
dev.off()
bitmap(file='pic4.png')
qqnorm(mylm$resid,main='QQ plot of e[t]')
qqline(mylm$resid)
grid()
dev.off()
if (par2 > 0)
{
bitmap(file='pic5.png')
acf(mylm$resid,lag.max=par2,main='Residual Autocorrelation Function')
grid()
dev.off()
}
summary(x)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Model: Y[t] = c + b X[t] + e[t]',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'c',1,TRUE)
a<-table.element(a,mylm$coeff[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'b',1,TRUE)
a<-table.element(a,mylm$coeff[[2]])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Descriptive Statistics about e[t]',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'# observations',header=TRUE)
a<-table.element(a,length(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'minimum',header=TRUE)
a<-table.element(a,min(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,quantile(mylm$resid,0.25))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
a<-table.element(a,median(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
a<-table.element(a,mean(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,quantile(mylm$resid,0.75))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'maximum',header=TRUE)
a<-table.element(a,max(mylm$resid))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')