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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, 05 Nov 2009 01:51:09 -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/05/t1257411133ijsxzk1wcc4kk6n.htm/, Retrieved Thu, 02 May 2024 14:02:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53889, Retrieved Thu, 02 May 2024 14:02:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Partial Correlation] [WS5] [2009-11-04 17:31:42] [868ad9c0049635b9b2c3848f186e9622]
- RMPD  [Bivariate Explorative Data Analysis] [reeks X t.o.v Z] [2009-11-05 08:48:18] [cd6314e7e707a6546bd4604c9d1f2b69]
-    D      [Bivariate Explorative Data Analysis] [reeks Y t.o.v. Z] [2009-11-05 08:51:09] [ea241b681aafed79da4b5b99fad98471] [Current]
-    D        [Bivariate Explorative Data Analysis] [reeks e(t) t.o.v....] [2009-11-05 08:57:02] [cd6314e7e707a6546bd4604c9d1f2b69]
- RMPD          [Pearson Correlation] [check correlatie ...] [2009-11-05 08:59:04] [cd6314e7e707a6546bd4604c9d1f2b69]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.75
1.75
1.55
1.5
1.5
1.1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1.21
1.25
1.25
1.45
1.5
1.5
1.64
1.75
1.93
2
2.17
2.25
2.39
2.5
2.5
2.65
2.75
2.75
2.9
3
3
3
3
3
3
3
3
3
3
3
3
3.18
3.25
3.25
3.23
2.92
2.25
Dataseries Y:
Download CSV
Histogram 
3,75
3,75
3,55
3,5
3,5
3,1
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3,21
3,25
3,25
3,45
3,5
3,5
3,64
3,75
3,93
4
4,17
4,25
4,39
4,5
4,5
4,65
4,75
4,75
4,9
5
5
5
5
5
5
5
5
5
5
5
5
5,18
5,25
5,25
4,49
3,92
3,25

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53889&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]5 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=53889&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53889&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Model: Y[t] = c + b X[t] + e[t]
c2.05074104289218
b0.95103503800079

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 2.05074104289218 \tabularnewline
b & 0.95103503800079 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53889&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]2.05074104289218[/C][/ROW]
[ROW][C]b[/C][C]0.95103503800079[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53889&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53889&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]
c2.05074104289218
b0.95103503800079






Descriptive Statistics about e[t]
# observations72
minimum-0.940569878393956
Q1-0.00177608089296866
median0.015361655806755
mean9.32112701067399e-19
Q30.0802402304557087
maximum0.108395083605254

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 72 \tabularnewline
minimum & -0.940569878393956 \tabularnewline
Q1 & -0.00177608089296866 \tabularnewline
median & 0.015361655806755 \tabularnewline
mean & 9.32112701067399e-19 \tabularnewline
Q3 & 0.0802402304557087 \tabularnewline
maximum & 0.108395083605254 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53889&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]72[/C][/ROW]
[ROW][C]minimum[/C][C]-0.940569878393956[/C][/ROW]
[ROW][C]Q1[/C][C]-0.00177608089296866[/C][/ROW]
[ROW][C]median[/C][C]0.015361655806755[/C][/ROW]
[ROW][C]mean[/C][C]9.32112701067399e-19[/C][/ROW]
[ROW][C]Q3[/C][C]0.0802402304557087[/C][/ROW]
[ROW][C]maximum[/C][C]0.108395083605254[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53889&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53889&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]
# observations72
minimum-0.940569878393956
Q1-0.00177608089296866
median0.015361655806755
mean9.32112701067399e-19
Q30.0802402304557087
maximum0.108395083605254


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')