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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 computationSun, 01 Nov 2009 07:46:37 -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/01/t12570869371yic601lltr8ojd.htm/, Retrieved Tue, 07 May 2024 01:05:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=52330, Retrieved Tue, 07 May 2024 01:05:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Explorative Data Analysis] [Shwws5v1] [2009-11-01 14:46:37] [93b66894f6318f3da4fcda772f2ffa6f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
102.1
102.86
102.99
103.73
105.02
104.43
104.63
104.93
105.87
105.66
106.76
106
107.22
107.33
107.11
108.86
107.72
107.88
108.38
107.72
108.41
109.9
111.45
112.18
113.34
113.46
114.06
115.54
116.39
115.94
116.97
115.94
115.91
116.43
116.26
116.35
117.9
117.7
117.53
117.86
117.65
116.51
115.93
115.31
115
Dataseries Y:
Download CSV
Histogram 
100.35
100.35
100.36
100.39
100.34
100.34
100.35
100.43
100.47
100.67
100.75
100.78
100.79
100.67
100.64
100.64
100.76
100.79
100.79
100.9
100.98
101.11
101.18
101.22
101.23
101.09
101.26
101.28
101.43
101.53
101.54
101.54
101.79
102.18
102.37
102.46
102.46
102.03
102.26
102.33
102.44
102.5
102.52
102.66
102.72

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=52330&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=52330&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52330&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]
c86.1933134730192
b0.135872441268998

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 86.1933134730192 \tabularnewline
b & 0.135872441268998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52330&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]86.1933134730192[/C][/ROW]
[ROW][C]b[/C][C]0.135872441268998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52330&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52330&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]
c86.1933134730192
b0.135872441268998






Descriptive Statistics about e[t]
# observations45
minimum-0.612015337239248
Q1-0.155499810380284
median-0.0204087353751701
mean-1.40946282423116e-18
Q30.173183800686681
maximum0.90135578104601

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 45 \tabularnewline
minimum & -0.612015337239248 \tabularnewline
Q1 & -0.155499810380284 \tabularnewline
median & -0.0204087353751701 \tabularnewline
mean & -1.40946282423116e-18 \tabularnewline
Q3 & 0.173183800686681 \tabularnewline
maximum & 0.90135578104601 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52330&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]45[/C][/ROW]
[ROW][C]minimum[/C][C]-0.612015337239248[/C][/ROW]
[ROW][C]Q1[/C][C]-0.155499810380284[/C][/ROW]
[ROW][C]median[/C][C]-0.0204087353751701[/C][/ROW]
[ROW][C]mean[/C][C]-1.40946282423116e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.173183800686681[/C][/ROW]
[ROW][C]maximum[/C][C]0.90135578104601[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52330&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52330&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]
# observations45
minimum-0.612015337239248
Q1-0.155499810380284
median-0.0204087353751701
mean-1.40946282423116e-18
Q30.173183800686681
maximum0.90135578104601


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 10 ;
Parameters (R input):
par1 = 0 ; par2 = 10 ;
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')