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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 computationWed, 28 Oct 2009 14:04:10 -0600
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/Oct/28/t1256760301n1lgpaxziv1pt29.htm/, Retrieved Mon, 06 May 2024 00:26:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51799, Retrieved Mon, 06 May 2024 00:26:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
- RMPD  [Bivariate Explorative Data Analysis] [Workshop 4] [2009-10-28 19:51:33] [85be98bd9ebcfd4d73e77f8552419c9a]
-    D      [Bivariate Explorative Data Analysis] [Workshop 4] [2009-10-28 20:04:10] [5cd0e65b1f56b3935a0672588b930e12] [Current]
-    D        [Bivariate Explorative Data Analysis] [Workshop 4] [2009-10-28 20:19:41] [85be98bd9ebcfd4d73e77f8552419c9a]
-  M D        [Bivariate Explorative Data Analysis] [] [2009-11-02 13:42:39] [3425351e86519d261a643e224a0c8ee1]
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Dataseries X:
3.04
3.12
3.21
3.34
3.45
3.74
4.02
4.24
4.87
5.62
6.02
5.98
5.89
5.76
5.58
5.39
5.19
5.16
5.2
5.25
5.26
5.21
5.18
5.13
5.03
5.01
4.87
4.86
4.82
4.69
4.65
4.61
4.47
4.37
4.29
4.2
4.19
4.09
3.88
3.87
3.74
3.61
3.43
3.29
3.18
3.07
3.02
2.97
2.98
3.01
3.06
3.12
3.16
3.19
3.21
3.27
3.36
3.45
3.52
3.58
Dataseries Y:
-0.355434285
-0.342738227
-0.337614889
-0.311154064
-0.276874024
-0.266202421
-0.245687006
-0.280582004
-0.296319266
-0.241532853
-0.286831767
-0.362557929
-0.403796501
-0.455524953
-0.441668099
-0.441926312
-0.454319274
-0.439995996
-0.388522449
-0.38648635
-0.376379233
-0.384173883
-0.352556294
-0.329015309
-0.309100484
-0.3159773
-0.294086537
-0.300918624
-0.301288893
-0.280808417
-0.268040242
-0.262287267
-0.278616621
-0.253168575
-0.231983761
-0.241140784
-0.247719327
-0.237756046
-0.235071912
-0.244514032
-0.204653059
-0.183986542
-0.177141795
-0.19086878
-0.170248728
-0.164327623
-0.183570737
-0.203430389
-0.206363871
-0.18539989
-0.195978388
-0.238544032
-0.25758361
-0.27770798
-0.263440944
-0.271476757
-0.293266972
-0.261671305
-0.222342622
-0.200324644




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51799&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]5 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=51799&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51799&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Model: Y[t] = c + b X[t] + e[t]
c-0.0506764749722267
b-0.0558483644071373

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -0.0506764749722267 \tabularnewline
b & -0.0558483644071373 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51799&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-0.0506764749722267[/C][/ROW]
[ROW][C]b[/C][C]-0.0558483644071373[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51799&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51799&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]
c-0.0506764749722267
b-0.0558483644071373







Descriptive Statistics about e[t]
# observations60
minimum-0.134978782230076
Q1-0.0346845514714706
median0.0198788002027715
mean3.46944695195361e-18
Q30.0380564059779538
maximum0.123011429940339

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -0.134978782230076 \tabularnewline
Q1 & -0.0346845514714706 \tabularnewline
median & 0.0198788002027715 \tabularnewline
mean & 3.46944695195361e-18 \tabularnewline
Q3 & 0.0380564059779538 \tabularnewline
maximum & 0.123011429940339 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51799&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]-0.134978782230076[/C][/ROW]
[ROW][C]Q1[/C][C]-0.0346845514714706[/C][/ROW]
[ROW][C]median[/C][C]0.0198788002027715[/C][/ROW]
[ROW][C]mean[/C][C]3.46944695195361e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.0380564059779538[/C][/ROW]
[ROW][C]maximum[/C][C]0.123011429940339[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51799&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51799&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]
# observations60
minimum-0.134978782230076
Q1-0.0346845514714706
median0.0198788002027715
mean3.46944695195361e-18
Q30.0380564059779538
maximum0.123011429940339



Parameters (Session):
par1 = 0 ; par2 = 36 ;
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')