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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 computationMon, 09 Nov 2009 05:39:24 -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/09/t1257770405dp589gs7ugdhyj9.htm/, Retrieved Tue, 23 Apr 2024 21:52:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=54774, Retrieved Tue, 23 Apr 2024 21:52:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact157
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Explorative Data Analysis] [workshop 6] [2009-11-09 12:39:24] [e81f30a5c3daacfe71a556c99a478849] [Current]
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Dataseries X:
2.28
2.26
2.71
2.77
2.77
2.64
2.56
2.07
2.32
2.16
2.23
2.40
2.84
2.77
2.93
2.91
2.69
2.38
2.58
3.19
2.82
2.72
2.53
2.70
2.42
2.50
2.31
2.41
2.56
2.76
2.71
2.44
2.46
2.12
1.99
1.86
1.88
1.82
1.74
1.71
1.38
1.27
1.19
1.28
1.19
1.22
1.47
1.46
1.96
1.88
2.03
2.04
1.90
1.80
1.92
1.92
1.97
2.46
2.36
2.53
2.31
1.98
1.46
1.26
1.58
1.74
1.89
1.85
1.62
1.30
1.42
1.15
0.42
0.74
1.02
1.51
1.86
1.59
1.03
0.44
0.82
0.86
0.58
0.59
0.95
0.98
1.23
1.17
0.84
0.74
0.65
0.91
1.19
1.30
1.53
1.94
1.79
1.95
2.26
2.04
2.16
2.75
2.79
2.88
3.36
2.97
3.10
2.49
2.20
2.25
2.09
2.79
3.14
2.93
2.65
2.67
2.26
2.35
2.13
2.18
2.90
2.63
2.67
1.81
1.33
0.88
1.28
1.26
1.26
1.29
1.10
1.37
1.21
1.74
1.76
1.48
1.04
1.62
1.49
1.79
1.80
1.58
1.86
1.74
1.59
1.26
1.13
1.92
2.61
2.26
2.41
2.26
2.03
2.86
2.55
2.27
2.26
2.57
3.07
2.76
2.51
2.87
3.14
3.11
3.16
2.47
2.57
2.89
2.63
2.38
1.69
1.96
2.19
1.87
1.6
1.63
1.22
1.21
1.49
1.64
Dataseries Y:
6.9
6.8
6.7
6.6
6.5
6.5
7.0
7.5
7.6
7.6
7.6
7.8
8.0
8.0
8.0
7.9
7.9
8.0
8.5
9.2
9.4
9.5
9.5
9.6
9.7
9.7
9.6
9.5
9.4
9.3
9.6
10.2
10.2
10.1
9.9
9.8
9.8
9.7
9.5
9.3
9.1
9.0
9.5
10.0
10.2
10.1
10.0
9.9
10.0
9.9
9.7
9.5
9.2
9.0
9.3
9.8
9.8
9.6
9.4
9.3
9.2
9.2
9.0
8.8
8.7
8.7
9.1
9.7
9.8
9.6
9.4
9.4
9.5
9.4
9.3
9.2
9.0
8.9
9.2
9.8
9.9
9.6
9.2
9.1
9.1
9.0
8.9
8.7
8.5
8.3
8.5
8.7
8.4
8.1
7.8
7.7
7.5
7.2
6.8
6.7
6.4
6.3
6.8
7.3
7.1
7.0
6.8
6.6
6.3
6.1
6.1
6.3
6.3
6.0
6.2
6.4
6.8
7.5
7.5
7.6
7.6
7.4
7.3
7.1
6.9
6.8
7.5
7.6
7.8
8.0
8.1
8.2
8.3
8.2
8.0
7.9
7.6
7.6
8.3
8.4
8.4
8.4
8.4
8.6
8.9
8.8
8.3
7.5
7.2
7.4
8.8
9.3
9.3
8.7
8.2
8.3
8.5
8.6
8.5
8.2
8.1
7.9
8.6
8.7
8.7
8.5
8.4
8.5
8.7
8.7
8.6
8.5
8.3
8.0
8.2
8.1
8.1
8.0
7.9
7.9




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=54774&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=54774&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54774&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]
c9.40654352465934
b-0.509315226482804

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 9.40654352465934 \tabularnewline
b & -0.509315226482804 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54774&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]9.40654352465934[/C][/ROW]
[ROW][C]b[/C][C]-0.509315226482804[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54774&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54774&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]
c9.40654352465934
b-0.509315226482804







Descriptive Statistics about e[t]
# observations180
minimum-2.24207470131028
Q1-0.746390549133755
median0.0447568790042258
mean2.26640417467289e-17
Q30.795004870816653
maximum2.04637193248836

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 180 \tabularnewline
minimum & -2.24207470131028 \tabularnewline
Q1 & -0.746390549133755 \tabularnewline
median & 0.0447568790042258 \tabularnewline
mean & 2.26640417467289e-17 \tabularnewline
Q3 & 0.795004870816653 \tabularnewline
maximum & 2.04637193248836 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54774&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]180[/C][/ROW]
[ROW][C]minimum[/C][C]-2.24207470131028[/C][/ROW]
[ROW][C]Q1[/C][C]-0.746390549133755[/C][/ROW]
[ROW][C]median[/C][C]0.0447568790042258[/C][/ROW]
[ROW][C]mean[/C][C]2.26640417467289e-17[/C][/ROW]
[ROW][C]Q3[/C][C]0.795004870816653[/C][/ROW]
[ROW][C]maximum[/C][C]2.04637193248836[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54774&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54774&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]
# observations180
minimum-2.24207470131028
Q1-0.746390549133755
median0.0447568790042258
mean2.26640417467289e-17
Q30.795004870816653
maximum2.04637193248836



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')