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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, 04 Nov 2009 10:07:50 -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/04/t125735451428f2ydkzf24ejcm.htm/, Retrieved Fri, 01 Nov 2024 00:16:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53729, Retrieved Fri, 01 Nov 2024 00:16:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact173
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Explorative Data Analysis] [ws5 2] [2009-11-04 17:07:50] [58c0e7ecdfec19fc38e879e32991032d] [Current]
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Dataseries X:
8.3
8.1
7.4
7.3
7.7
8
8
7.7
6.9
6.6
6.9
7.5
7.9
7.7
6.5
6.1
6.4
6.8
7.1
7.3
7.2
7
7
7
7.3
7.5
7.2
7.7
8
7.9
8
8
7.9
7.9
8
8.1
8.1
8.2
8
8.3
8.5
8.6
8.7
8.7
8.5
8.4
8.5
8.7
8.7
8.6
7.9
8.1
8.2
8.5
8.6
8.5
8.3
8.2
8.7
9.3
9.3
8.8
7.4
7.2
7.5
8.3
8.8
8.9
8.6
8.4
8.4
8.4
8.4
8.3
7.6
7.6
7.9
8
8.2
8.3
8.2
8.1
8
7.8
7.6
7.5
6.8
6.9
7.1
7.3
7.4
7.6
7.6
7.5
7.5
6.8
6.4
6.2
6
6.3
6.3
6.1
6.1
6.3
6.6
6.8
7
7.1
7.3
6.8
6.3
6.4
6.7
6.8
7.2
7.5
7.7
7.8
8.1
8.4
8.7
Dataseries Y:
8.1
8
7.5
7.4
7.7
7.9
7.7
7.1
6.2
5.8
6.1
6.9
7.3
7.2
6.1
5.8
6.1
6.4
6.8
6.8
6.5
6.2
6.3
6.4
6.6
6.7
6.4
6.8
7
6.9
7.1
7.2
7.1
7
6.9
6.7
6.6
6.9
7.3
7.9
8.2
8.2
8.2
8.1
7.9
7.7
7.7
7.6
7.5
7.5
7.1
7.5
7.5
7.8
7.8
7.8
7.6
7.5
7.7
8.1
8
7.6
6.6
6.5
6.8
7.5
8
8.2
8.1
7.9
7.9
7.6
7.5
7.6
7.3
7.5
7.6
7.5
7.6
7.8
7.9
7.8
7.5
6.6
6.3
6.3
6
6.3
6.4
6.3
6.3
6.4
6.7
6.7
6.8
6.2
5.8
5.6
5.4
5.7
5.8
5.5
5.4
5.4
5.4
5.5
5.6
5.7
5.8
5.4
4.9
5.2
5.5
5.9
6.3
6.5
6.4
6.4
6.6
6.8
7.2




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=53729&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=53729&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53729&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]
c-0.551592199794728
b0.970723001482495

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -0.551592199794728 \tabularnewline
b & 0.970723001482495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53729&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-0.551592199794728[/C][/ROW]
[ROW][C]b[/C][C]0.970723001482495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53729&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53729&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-0.551592199794728
b0.970723001482495







Descriptive Statistics about e[t]
# observations121
minimum-0.802481012658228
Q1-0.228830311323982
median0.0565311894172652
mean1.37998327755779e-17
Q30.206302086897024
maximum0.868241988824267

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 121 \tabularnewline
minimum & -0.802481012658228 \tabularnewline
Q1 & -0.228830311323982 \tabularnewline
median & 0.0565311894172652 \tabularnewline
mean & 1.37998327755779e-17 \tabularnewline
Q3 & 0.206302086897024 \tabularnewline
maximum & 0.868241988824267 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53729&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]121[/C][/ROW]
[ROW][C]minimum[/C][C]-0.802481012658228[/C][/ROW]
[ROW][C]Q1[/C][C]-0.228830311323982[/C][/ROW]
[ROW][C]median[/C][C]0.0565311894172652[/C][/ROW]
[ROW][C]mean[/C][C]1.37998327755779e-17[/C][/ROW]
[ROW][C]Q3[/C][C]0.206302086897024[/C][/ROW]
[ROW][C]maximum[/C][C]0.868241988824267[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53729&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53729&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]
# observations121
minimum-0.802481012658228
Q1-0.228830311323982
median0.0565311894172652
mean1.37998327755779e-17
Q30.206302086897024
maximum0.868241988824267



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')