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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 computationThu, 17 Dec 2009 02:40:23 -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/Dec/17/t126104287111fj3fm96acvv24.htm/, Retrieved Tue, 30 Apr 2024 06:40:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=68668, Retrieved Tue, 30 Apr 2024 06:40:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsSHW Paper: Bivariate EDA
Estimated Impact166

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] [WS 4 - Deel 1 - V...] [2009-10-26 08:28:42] [b103a1dc147def8132c7f643ad8c8f84]
-  M D      [Bivariate Explorative Data Analysis] [Paper: Bivariate EDA] [2009-12-17 09:40:23] [a45cc820faa25ce30779915639528ec2] [Current]
-    D        [Bivariate Explorative Data Analysis] [Paper Bivariate E...] [2009-12-17 13:23:43] [4395c69e961f9a13a0559fd2f0a72538]
- RMPD          [Central Tendency] [Paper Robustness ...] [2009-12-17 13:29:44] [4395c69e961f9a13a0559fd2f0a72538]
- RMP           [Bivariate Kernel Density Estimation] [Paper Bivariate K...] [2009-12-17 13:34:57] [4395c69e961f9a13a0559fd2f0a72538]
- RMP           [Pearson Correlation] [Paper Pearson Cor...] [2009-12-17 13:40:11] [4395c69e961f9a13a0559fd2f0a72538]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-0.8
-0.2
0.2
1
0
-0.2
1
0.4
1
1.7
3.1
3.3
3.1
3.5
6
5.7
4.7
4.2
3.6
4.4
2.5
-0.6
-1.9
-1.9
0.7
-0.9
-1.7
-3.1
-2.1
0.2
1.2
3.8
4
6.6
5.3
7.6
4.7
6.6
4.4
4.6
6
4.8
4
2.7
3
4.1
4
2.7
2.6
3.1
4.4
3
2
1.3
1.5
1.3
3.2
1.8
3.3
1
2.4
0.4
-0.1
1.3
-1.1
-4.4
-7.5
-12.2
-14.5
-16
-16.7
-16.3
-16.9
Dataseries Y:
Download CSV
Histogram 
15,5
15,1
11,7
16,3
16,7
15
14,9
14,6
15,3
17,9
16,4
15,4
17,9
15,9
13,9
17,8
17,9
17,4
16,7
16
16,6
19,1
17,8
17,2
18,6
16,3
15,1
19,2
17,7
19,1
18
17,5
17,8
21,1
17,2
19,4
19,8
17,6
16,2
19,5
19,9
20
17,3
18,9
18,6
21,4
18,6
19,8
20,8
19,6
17,7
19,8
22,2
20,7
17,9
20,9
21,2
21,4
23
21,3
23,9
22,4
18,3
22,8
22,3
17,8
16,4
16
16,4
17,7
16,6
16,2
18,3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68668&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]3 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=68668&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68668&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Model: Y[t] = c + b X[t] + e[t]
c18.1000957529787
b0.0886790440217543

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 18.1000957529787 \tabularnewline
b & 0.0886790440217543 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68668&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]18.1000957529787[/C][/ROW]
[ROW][C]b[/C][C]0.0886790440217543[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68668&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68668&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]
c18.1000957529787
b0.0886790440217543






Descriptive Statistics about e[t]
# observations73
minimum-6.41783156178305
Q1-1.4000957529787
median-0.213869760533016
mean-3.61677963073521e-17
Q31.37480928348874
maximum5.58707454136909

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 73 \tabularnewline
minimum & -6.41783156178305 \tabularnewline
Q1 & -1.4000957529787 \tabularnewline
median & -0.213869760533016 \tabularnewline
mean & -3.61677963073521e-17 \tabularnewline
Q3 & 1.37480928348874 \tabularnewline
maximum & 5.58707454136909 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68668&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]73[/C][/ROW]
[ROW][C]minimum[/C][C]-6.41783156178305[/C][/ROW]
[ROW][C]Q1[/C][C]-1.4000957529787[/C][/ROW]
[ROW][C]median[/C][C]-0.213869760533016[/C][/ROW]
[ROW][C]mean[/C][C]-3.61677963073521e-17[/C][/ROW]
[ROW][C]Q3[/C][C]1.37480928348874[/C][/ROW]
[ROW][C]maximum[/C][C]5.58707454136909[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68668&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68668&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]
# observations73
minimum-6.41783156178305
Q1-1.4000957529787
median-0.213869760533016
mean-3.61677963073521e-17
Q31.37480928348874
maximum5.58707454136909


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')