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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 12:28:17 -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/t1256754599n9u20rl2k71i7ux.htm/, Retrieved Mon, 06 May 2024 04:35:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51707, Retrieved Mon, 06 May 2024 04:35:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordspart 2 model 1 bivariate eda
Estimated Impact118

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]
-   PD  [Bivariate Data Series] [part 2 model 1] [2009-10-28 18:19:40] [616e2df490b611f6cb7080068870ecbd]
- RMP       [Bivariate Explorative Data Analysis] [part 2 model 1 bi...] [2009-10-28 18:28:17] [88e98f4c87ea17c4967db8279bda8533] [Current]
-    D        [Bivariate Explorative Data Analysis] [part2 model 2 Biv...] [2009-10-28 18:56:15] [616e2df490b611f6cb7080068870ecbd]
-    D          [Bivariate Explorative Data Analysis] [part 2 model 3 bi...] [2009-10-28 19:22:48] [616e2df490b611f6cb7080068870ecbd]
- RMPD          [Kendall tau Rank Correlation] [ws4 part 3] [2009-10-28 19:54:27] [616e2df490b611f6cb7080068870ecbd]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
8.2
8
7.5
6.8
6.5
6.6
7.6
8
8.1
7.7
7.5
7.6
7.8
7.8
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.2
7.9
7.3
6.9
6.6
6.7
6.9
7
7.1
7.2
7.1
6.9
7
6.8
6.4
6.7
6.6
6.4
6.3
6.2
6.5
6.8
6.8
6.4
6.1
5.8
6.1
7.2
7.3
6.9
6.1
5.8
6.2
7.1
7.7
7.9
7.7
7.4
7.5
8
8.1
Dataseries Y:
Download CSV
Histogram 
9.9
9.8
9.3
8.3
8.0
8.5
10.4
11.1
10.9
10.0
9.2
9.2
9.5
9.6
9.5
9.1
8.9
9.0
10.1
10.3
10.2
9.6
9.2
9.3
9.4
9.4
9.2
9.0
9.0
9.0
9.8
10.0
9.8
9.3
9.0
9.0
9.1
9.1
9.1
9.2
8.8
8.3
8.4
8.1
7.7
7.9
7.9
8.0
7.9
7.6
7.1
6.8
6.5
6.9
8.2
8.7
8.3
7.9
7.5
7.8
8.3
8.4
8.2
7.7
7.2
7.3
8.1
8.5

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51707&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51707&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51707&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'George Udny Yule' @ 72.249.76.132






Model: Y[t] = c + b X[t] + e[t]
c2.12234811957570
b0.925204918032787

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 2.12234811957570 \tabularnewline
b & 0.925204918032787 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51707&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]2.12234811957570[/C][/ROW]
[ROW][C]b[/C][C]0.925204918032787[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51707&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51707&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]
c2.12234811957570
b0.925204918032787






Descriptive Statistics about e[t]
# observations68
minimum-1.76886451301832
Q1-0.401344021215043
median0.0399981918997103
mean2.31030727636985e-17
Q30.325525855834136
maximum1.77129942140791

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 68 \tabularnewline
minimum & -1.76886451301832 \tabularnewline
Q1 & -0.401344021215043 \tabularnewline
median & 0.0399981918997103 \tabularnewline
mean & 2.31030727636985e-17 \tabularnewline
Q3 & 0.325525855834136 \tabularnewline
maximum & 1.77129942140791 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51707&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]68[/C][/ROW]
[ROW][C]minimum[/C][C]-1.76886451301832[/C][/ROW]
[ROW][C]Q1[/C][C]-0.401344021215043[/C][/ROW]
[ROW][C]median[/C][C]0.0399981918997103[/C][/ROW]
[ROW][C]mean[/C][C]2.31030727636985e-17[/C][/ROW]
[ROW][C]Q3[/C][C]0.325525855834136[/C][/ROW]
[ROW][C]maximum[/C][C]1.77129942140791[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51707&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51707&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]
# observations68
minimum-1.76886451301832
Q1-0.401344021215043
median0.0399981918997103
mean2.31030727636985e-17
Q30.325525855834136
maximum1.77129942140791


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')