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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 computationMon, 02 Nov 2009 10:18:15 -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/02/t12571823396d9of44zllt9ct2.htm/, Retrieved Tue, 07 May 2024 23:00:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=52820, Retrieved Tue, 07 May 2024 23:00:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact98

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] [] [2009-11-02 17:18:15] [fbab597368601c68e80be601720d8ff9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-0,93
-1,19
-1,46
-0,91
-0,23
-0,61
-0,55
-0,91
-1,08
-0,07
-0,42
-0,76
-0,74
-0,24
0,58
0,31
0,21
0,34
0,51
0,79
1,04
0,13
0,00
0,39
0,12
0,10
-0,77
-0,23
-0,27
-0,27
-0,57
-0,44
-0,86
-1,21
-0,78
-0,60
-0,86
-0,80
-0,91
-0,90
-1,26
-1,25
-1,31
-1,54
-1,20
-0,29
0,48
0,66
1,08
0,98
1,93
1,47
2,71
3,27
3,57
3,13
3,08
2,63
1,37
1,19
1,08
1,12
-0,02
0,15
-0,79
-1,53
-2,43
-1,47
-1,80
Dataseries Y:
Download CSV
Histogram 
-0,73
-0,78
-0,83
-0,94
-0,95
-0,94
-0,97
-1,15
-1,13
-1,06
-1,02
-0,97
-1,03
-0,89
-0,66
-0,56
-0,56
-0,76
-0,64
-0,58
-0,47
-0,55
-0,72
-0,58
-0,74
-0,75
-0,75
-0,60
-0,77
-0,42
-0,45
-0,10
-0,05
0,02
0,19
0,39
0,59
0,57
0,64
0,75
0,84
1,00
1,06
0,97
1,07
1,15
1,20
1,18
1,20
1,06
1,16
1,05
1,22
1,12
1,45
1,55
1,45
1,35
1,04
0,61
0,52
0,38
0,15
-0,15
-0,40
-0,44
-0,58
-0,61
-0,64

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52820&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52820&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52820&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Model: Y[t] = c + b X[t] + e[t]
c0.00034224064427009
b0.3403651113659

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 0.00034224064427009 \tabularnewline
b & 0.3403651113659 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52820&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]0.00034224064427009[/C][/ROW]
[ROW][C]b[/C][C]0.3403651113659[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52820&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52820&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]
c0.00034224064427009
b0.3403651113659






Descriptive Statistics about e[t]
# observations69
minimum-1.03651668284866
Q1-0.713084634076971
median-0.0276850401856506
mean1.14612641884970e-17
Q30.502753094419544
maximum1.50553605524506

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 69 \tabularnewline
minimum & -1.03651668284866 \tabularnewline
Q1 & -0.713084634076971 \tabularnewline
median & -0.0276850401856506 \tabularnewline
mean & 1.14612641884970e-17 \tabularnewline
Q3 & 0.502753094419544 \tabularnewline
maximum & 1.50553605524506 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52820&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]69[/C][/ROW]
[ROW][C]minimum[/C][C]-1.03651668284866[/C][/ROW]
[ROW][C]Q1[/C][C]-0.713084634076971[/C][/ROW]
[ROW][C]median[/C][C]-0.0276850401856506[/C][/ROW]
[ROW][C]mean[/C][C]1.14612641884970e-17[/C][/ROW]
[ROW][C]Q3[/C][C]0.502753094419544[/C][/ROW]
[ROW][C]maximum[/C][C]1.50553605524506[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52820&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52820&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]
# observations69
minimum-1.03651668284866
Q1-0.713084634076971
median-0.0276850401856506
mean1.14612641884970e-17
Q30.502753094419544
maximum1.50553605524506


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')