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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 computationSat, 07 Nov 2009 13:36:23 -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/07/t1257626242bzhwi32mnvtfgkp.htm/, Retrieved Mon, 06 May 2024 14:20:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=54484, Retrieved Mon, 06 May 2024 14:20:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150

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] [workshop5/Bivaria...] [2009-11-07 20:36:23] [f94f05f163a3ee3ab544c4fef41db0eb] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1023.10
1141.00
1116.30
1135.60
1210.50
1230.00
1136.50
1068.70
1372.50
1049.90
1302.20
1305.90
1173.50
1277.40
1238.60
1508.60
1423.40
1375.10
1344.10
1287.50
1446.90
1451.00
1604.40
1501.50
1522.80
1328.00
1420.50
1648.00
1631.10
1396.60
1663.40
1283.00
1582.40
1785.20
1853.60
1994.10
2042.80
1586.10
1942.40
1763.60
1819.90
1836.00
1447.50
1509.50
1661.20
1456.20
1310.90
1542.10
1537.70
Dataseries Y:
Download CSV
Histogram 
10881.30
11301.20
13643.90
12517.00
13981.10
14275.70
13425.00
13565.70
16216.30
12970.00
14079.90
14235.00
12213.40
12581.00
14130.40
14210.80
14378.50
13142.80
13714.70
13621.90
15379.80
13306.30
14391.20
14909.90
14025.40
12951.20
14344.30
16093.40
15413.60
14705.70
15972.80
16241.40
16626.40
17136.20
15622.90
18003.90
16136.10
14423.70
16789.40
16782.20
14133.80
12607.00
12004.50
12175.40
13268.00
12299.30
11800.60
13873.30
12315.00

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54484&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54484&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54484&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Model: Y[t] = c + b X[t] + e[t]
c8445.11564620489
b3.91376532268688

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 8445.11564620489 \tabularnewline
b & 3.91376532268688 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54484&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]8445.11564620489[/C][/ROW]
[ROW][C]b[/C][C]3.91376532268688[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54484&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54484&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]
c8445.11564620489
b3.91376532268688






Descriptive Statistics about e[t]
# observations49
minimum-3023.788778658
Q1-817.689129423551
median137.811500835754
mean1.04351901269666e-13
Q3829.848124079747
maximum2774.92344478784

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 49 \tabularnewline
minimum & -3023.788778658 \tabularnewline
Q1 & -817.689129423551 \tabularnewline
median & 137.811500835754 \tabularnewline
mean & 1.04351901269666e-13 \tabularnewline
Q3 & 829.848124079747 \tabularnewline
maximum & 2774.92344478784 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54484&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]49[/C][/ROW]
[ROW][C]minimum[/C][C]-3023.788778658[/C][/ROW]
[ROW][C]Q1[/C][C]-817.689129423551[/C][/ROW]
[ROW][C]median[/C][C]137.811500835754[/C][/ROW]
[ROW][C]mean[/C][C]1.04351901269666e-13[/C][/ROW]
[ROW][C]Q3[/C][C]829.848124079747[/C][/ROW]
[ROW][C]maximum[/C][C]2774.92344478784[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54484&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54484&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]
# observations49
minimum-3023.788778658
Q1-817.689129423551
median137.811500835754
mean1.04351901269666e-13
Q3829.848124079747
maximum2774.92344478784


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = grey ;
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')