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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, 09 Nov 2009 11:21:28 -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/09/t1257790997lb4hpr9zyx2h39q.htm/, Retrieved Tue, 23 Apr 2024 19:14:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=54907, Retrieved Tue, 23 Apr 2024 19:14:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact221

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] [3/11/2009] [2009-11-02 22:01:32] [b98453cac15ba1066b407e146608df68]
-   PD    [Bivariate Explorative Data Analysis] [] [2009-11-09 18:21:28] [791a4a78a0a7ca497fb8791b982a539e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
785.8
819.3
849.4
880.4
900.1
937.2
948.9
952.6
947.3
974.2
1000.8
1032.8
1050.7
1057.3
1075.4
1118.4
1179.8
1227
1257.8
1251.5
1236.3
1170.6
1213.1
1265.5
1300.8
1348.4
1371.9
1403.3
1451.8
1474.2
1438.2
1513.6
1562.2
1546.2
1527.5
1418.7
1448.5
1492.1
1395.4
1403.7
1316.6
1274.5
1264.4
1323.9
1332.1
1250.2
1096.7
1080.8
1039.2
792
746.6
688.8
715.8
672.9
629.5
681.2
755.4
760.6
765.9
836.8
904.9
Dataseries Y:
Download CSV
Histogram 
5
1.3
0
1.3
3
1.3
-1
-1.3
-2
-0.3
0.7
-6
-1
3
-2
-1.3
1.7
4
5.3
-3
1.3
8.7
7.3
7.3
7.7
4.7
4.7
3.7
8.3
8
8.3
12.7
11.7
9.7
9.3
9.3
7.7
8.3
7.7
7
4.3
-0.3
4.7
2
0.3
-1.7
1.3
-12.3
-14.3
-15.7
-16.7
-19
-27.3
-26.7
-26.3
-28.6
-22.9
-21.9
-17.6
-17.6
-20.8

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=54907&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=54907&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54907&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]
c-39.823757984658
b0.0339544325578863

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -39.823757984658 \tabularnewline
b & 0.0339544325578863 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54907&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-39.823757984658[/C][/ROW]
[ROW][C]b[/C][C]0.0339544325578863[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54907&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54907&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]
c-39.823757984658
b0.0339544325578863






Descriptive Statistics about e[t]
# observations61
minimum-11.9060014737742
Q1-3.75116631036814
median-0.854106986656876
mean5.13876282145916e-17
Q33.88593179842405
maximum18.1423648806709

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 61 \tabularnewline
minimum & -11.9060014737742 \tabularnewline
Q1 & -3.75116631036814 \tabularnewline
median & -0.854106986656876 \tabularnewline
mean & 5.13876282145916e-17 \tabularnewline
Q3 & 3.88593179842405 \tabularnewline
maximum & 18.1423648806709 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54907&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]61[/C][/ROW]
[ROW][C]minimum[/C][C]-11.9060014737742[/C][/ROW]
[ROW][C]Q1[/C][C]-3.75116631036814[/C][/ROW]
[ROW][C]median[/C][C]-0.854106986656876[/C][/ROW]
[ROW][C]mean[/C][C]5.13876282145916e-17[/C][/ROW]
[ROW][C]Q3[/C][C]3.88593179842405[/C][/ROW]
[ROW][C]maximum[/C][C]18.1423648806709[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54907&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54907&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]
# observations61
minimum-11.9060014737742
Q1-3.75116631036814
median-0.854106986656876
mean5.13876282145916e-17
Q33.88593179842405
maximum18.1423648806709


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')