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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, 02 Nov 2009 07:29:33 -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/02/t1257172408fbnofvgetkv2bgx.htm/, Retrieved Fri, 03 May 2024 22:13:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=52671, Retrieved Fri, 03 May 2024 22:13:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
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 Explorative Data Analysis] [WS 5 - 5] [2009-11-02 14:29:33] [a18540c86166a2b66550d1fef0503cc2] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,062
0,15
0,326
0,766
0,866
0,69
0,162
-0,102
-0,102
0,162
0,426
0,526
0,426
0,438
0,35
0,338
0,162
0,25
0,25
0,238
0,15
0,15
0,15
0,05
0,05
-0,05
-0,05
-0,25
-0,338
-0,262
-0,198
-0,11
-0,21
-0,21
-0,31
-0,322
-0,334
-0,434
-0,622
-0,898
-0,81
-0,458
-0,194
0,07
0,146
-0,03
-0,218
-0,306
-0,218
-0,142
0,034
0,134
0,322
0,058
-0,194
-0,27
0,006
0,382
0,658
0,682
Dataseries Y:
Download CSV
Histogram 
-0,192
-0,54
-0,536
-0,476
0,024
0,02
0,208
0,352
0,152
0,308
-0,036
0,164
-0,336
-0,088
-0,04
-0,088
-0,092
-0,04
0,06
0,012
0,46
0,36
0,16
0,06
0,26
0,16
0,06
0,36
0,408
0,312
0,068
-0,08
-0,68
-0,78
-0,48
-0,528
-0,676
-0,676
-0,528
-0,432
-0,48
-0,672
-0,916
-1,16
-0,856
-0,26
0,288
0,336
0,388
0,392
0,596
0,296
0,648
1,092
1,284
1,18
0,884
0,388
-0,808
-1,212

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=52671&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=52671&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52671&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]
c-0.032320997766396
b-0.00439644568221104

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -0.032320997766396 \tabularnewline
b & -0.00439644568221104 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52671&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-0.032320997766396[/C][/ROW]
[ROW][C]b[/C][C]-0.00439644568221104[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52671&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52671&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-0.032320997766396
b-0.00439644568221104






Descriptive Statistics about e[t]
# observations60
minimum-1.17668062627834
Q1-0.449591456105366
median0.0577414325071564
mean1.72008674664825e-17
Q30.349120768095152
maximum1.31546808730405

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -1.17668062627834 \tabularnewline
Q1 & -0.449591456105366 \tabularnewline
median & 0.0577414325071564 \tabularnewline
mean & 1.72008674664825e-17 \tabularnewline
Q3 & 0.349120768095152 \tabularnewline
maximum & 1.31546808730405 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52671&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]-1.17668062627834[/C][/ROW]
[ROW][C]Q1[/C][C]-0.449591456105366[/C][/ROW]
[ROW][C]median[/C][C]0.0577414325071564[/C][/ROW]
[ROW][C]mean[/C][C]1.72008674664825e-17[/C][/ROW]
[ROW][C]Q3[/C][C]0.349120768095152[/C][/ROW]
[ROW][C]maximum[/C][C]1.31546808730405[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52671&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52671&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]
# observations60
minimum-1.17668062627834
Q1-0.449591456105366
median0.0577414325071564
mean1.72008674664825e-17
Q30.349120768095152
maximum1.31546808730405


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')