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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 computationWed, 28 Oct 2009 08:56:46 -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/28/t1256741876muny8jz78gm9ndh.htm/, Retrieved Sun, 05 May 2024 23:46:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51380, Retrieved Sun, 05 May 2024 23:46:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153

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] [90f6d58d515a4caed6fb4b8be4e11eaa]
-    D        [Bivariate Explorative Data Analysis] [] [2009-10-28 14:56:46] [2b548c9d2e9bba6e1eaf65bd4d551f41] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,1658
0,0339
0,6563
0,1120
-0,2154
0,0270
0,3270
0,8270
0,2120
0,1120
0,2116
0,5120
0,7120
0,8846
0,1563
0,2563
0,8969
1,0339
1,1012
0,5012
0,9012
0,8969
0,1638
-0,2451
-0,5888
-0,9063
-0,9451
-0,5662
-0,3884
-0,7118
-0,6884
-0,8451
-0,7662
-1,4251
-1,7575
-1,5063
-1,6888
-1,6575
-0,9438
-0,3315
0,1275
0,7749
0,8749
1,3425
0,7793
1,5029
2,4793
3,4882
3,0638
2,8549
1,4793
-0,3971
-0,5315
-0,3884
-0,1437
-1,3031
-1,3437
-2,4616
-3,1880
-3,5342
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'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=51380&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=51380&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51380&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]
c4.45667000000877
b1.00000263072324

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 4.45667000000877 \tabularnewline
b & 1.00000263072324 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51380&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]4.45667000000877[/C][/ROW]
[ROW][C]b[/C][C]1.00000263072324[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51380&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51380&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]
c4.45667000000877
b1.00000263072324






Descriptive Statistics about e[t]
# observations60
minimum-0.757872896961197
Q1-0.412970476958892
median-0.0448736519787655
mean2.22622846083690e-17
Q30.349632778824185
maximum1.14043104465143

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -0.757872896961197 \tabularnewline
Q1 & -0.412970476958892 \tabularnewline
median & -0.0448736519787655 \tabularnewline
mean & 2.22622846083690e-17 \tabularnewline
Q3 & 0.349632778824185 \tabularnewline
maximum & 1.14043104465143 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51380&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]-0.757872896961197[/C][/ROW]
[ROW][C]Q1[/C][C]-0.412970476958892[/C][/ROW]
[ROW][C]median[/C][C]-0.0448736519787655[/C][/ROW]
[ROW][C]mean[/C][C]2.22622846083690e-17[/C][/ROW]
[ROW][C]Q3[/C][C]0.349632778824185[/C][/ROW]
[ROW][C]maximum[/C][C]1.14043104465143[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51380&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51380&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-0.757872896961197
Q1-0.412970476958892
median-0.0448736519787655
mean2.22622846083690e-17
Q30.349632778824185
maximum1.14043104465143


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')