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Description of Statistical Computation

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, 27 Oct 2009 13:38:29 -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/27/t12566724523drcrfdd5x6hzww.htm/, Retrieved Sun, 24 May 2026 00:44:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51177, Retrieved Sun, 24 May 2026 00:44:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsws4deel2 bivariate explotative data analysis
Estimated Impact577

Tree of Dependent Computations

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] [WS3Part1-EDA] [2009-10-27 11:32:51] [90f6d58d515a4caed6fb4b8be4e11eaa]
-    D      [Bivariate Explorative Data Analysis] [] [2009-10-27 19:38:29] [2b548c9d2e9bba6e1eaf65bd4d551f41] [Current]
-    D        [Bivariate Explorative Data Analysis] [] [2009-10-28 14:38:29] [90f6d58d515a4caed6fb4b8be4e11eaa]
-  M D          [Bivariate Explorative Data Analysis] [] [2009-11-03 18:18:43] [90f6d58d515a4caed6fb4b8be4e11eaa]
-    D        [Bivariate Explorative Data Analysis] [] [2009-10-28 14:45:34] [90f6d58d515a4caed6fb4b8be4e11eaa]
-    D          [Bivariate Explorative Data Analysis] [WS4.2.2] [2009-10-29 10:26:21] [dff692ae32125bdbbfc005d665e23b83]
-  M D          [Bivariate Explorative Data Analysis] [] [2009-11-03 18:25:53] [90f6d58d515a4caed6fb4b8be4e11eaa]
-  M D          [Bivariate Explorative Data Analysis] [] [2009-11-03 18:34:29] [90f6d58d515a4caed6fb4b8be4e11eaa]
-  M D          [Bivariate Explorative Data Analysis] [] [2009-11-03 18:40:24] [90f6d58d515a4caed6fb4b8be4e11eaa]
-  M D          [Bivariate Explorative Data Analysis] [] [2009-11-03 19:09:25] [90f6d58d515a4caed6fb4b8be4e11eaa]
-    D        [Bivariate Explorative Data Analysis] [] [2009-10-28 14:56:46] [90f6d58d515a4caed6fb4b8be4e11eaa]
-    D        [Bivariate Explorative Data Analysis] [] [2009-10-28 15:03:02] [90f6d58d515a4caed6fb4b8be4e11eaa]
-    D        [Bivariate Explorative Data Analysis] [] [2009-10-28 15:08:46] [90f6d58d515a4caed6fb4b8be4e11eaa]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
8
8.1
7.7
7.5
7.6
7.8
7.8
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.2
7.9
7.3
6.9
6.6
6.7
6.9
7
7.1
7.2
7.1
6.9
7
6.8
6.4
6.7
6.6
6.4
6.3
6.2
6.5
6.8
6.8
6.4
6.1
5.8
6.1
7.2
7.3
6.9
6.1
5.8
6.2
7.1
7.7
7.9
7.7
7.4
7.5
8
Dataseries Y:
Download CSV
Histogram 
4
3.8
4.7
4.3
3.9
4
4.3
4.8
4.4
4.3
4.7
4.7
4.9
5
4.2
4.3
4.8
4.8
4.8
4.2
4.6
4.8
4.5
4.4
4.3
3.9
3.7
4
4.1
3.7
3.8
3.8
3.8
3.3
3.3
3.3
3.2
3.4
4.2
4.9
5.1
5.5
5.6
6.4
6.1
7.1
7.8
7.9
7.4
7.5
6.8
5.2
4.7
4.1
3.9
2.6
2.7
1.8
1
0.3

Tables (Output of Computation)





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=51177&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=51177&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51177&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 time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Model: Y[t] = c + b X[t] + e[t]
c9.93160104746916
b-0.763411719377527

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 9.93160104746916 \tabularnewline
b & -0.763411719377527 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51177&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]9.93160104746916[/C][/ROW]
[ROW][C]b[/C][C]-0.763411719377527[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51177&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51177&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:

Model: Y[t] = c + b X[t] + e[t]
c9.93160104746916
b-0.763411719377527






Descriptive Statistics about e[t]
# observations60
minimum-3.52430729244895
Q1-0.748207253919846
median0.0939868478622882
mean-4.08382818302873e-18
Q30.823560382940097
maximum3.46496333204903

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -3.52430729244895 \tabularnewline
Q1 & -0.748207253919846 \tabularnewline
median & 0.0939868478622882 \tabularnewline
mean & -4.08382818302873e-18 \tabularnewline
Q3 & 0.823560382940097 \tabularnewline
maximum & 3.46496333204903 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51177&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]-3.52430729244895[/C][/ROW]
[ROW][C]Q1[/C][C]-0.748207253919846[/C][/ROW]
[ROW][C]median[/C][C]0.0939868478622882[/C][/ROW]
[ROW][C]mean[/C][C]-4.08382818302873e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.823560382940097[/C][/ROW]
[ROW][C]maximum[/C][C]3.46496333204903[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51177&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51177&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Descriptive Statistics about e[t]
# observations60
minimum-3.52430729244895
Q1-0.748207253919846
median0.0939868478622882
mean-4.08382818302873e-18
Q30.823560382940097
maximum3.46496333204903


Figures (Output of Computation)

Input Parameters & R Code

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')