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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 computationSat, 31 Oct 2009 02:44:53 -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/31/t1256978757o64wz92fnweddzm.htm/, Retrieved Sun, 05 May 2024 20:41:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=52178, Retrieved Sun, 05 May 2024 20:41:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsws5p1c
Estimated Impact140

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] [] [2009-10-31 08:44:53] [9ea4b07b6662a0f40f92decdf1e3b5d5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,004878
0,006087
-0,008573
-0,020843
-0,050898
-0,046106
-0,058765
-0,066974
-0,084296
-0,107374
-0,096766
-0,090798
-0,114517
-0,088466
-0,024909
-0,030021
-0,069683
-0,080991
-0,036431
-0,067246
-0,030336
-0,027045
0,030321
0,048418
0,044207
0,051770
0,053257
0,002175
0,020245
0,049833
-0,017758
0,000774
-0,033878
-0,021983
0,061207
0,082120
0,033938
0,070980
0,048644
0,083932
0,069020
0,068482
0,021265
0,012754
0,003019
0,098848
0,075917
0,173120
0,388413
0,385660
0,276458
0,107809
0,066988
0,065613
-0,023869
-0,101971
-0,177417
-0,205792
-0,351004
-0,371444
Dataseries Y:
Download CSV
Histogram 
-0,103068
-0,124585
-0,109079
-0,095073
-0,104573
-0,110079
-0,145089
-0,149583
-0,147078
-0,175085
-0,158591
-0,147090
-0,169591
-0,095077
-0,010079
0,003914
0,006920
0,029425
0,103939
0,040433
0,018929
0,064935
0,168440
0,206940
0,257448
0,277948
0,283943
0,256440
0,285439
0,279937
0,257436
0,299437
0,274447
0,273947
0,279945
0,332450
0,292942
0,226428
0,171413
0,143910
0,085403
0,066399
0,018884
0,010889
-0,112132
-0,141644
-0,167146
-0,061136
-0,004637
-0,007622
-0,068591
-0,203080
-0,247574
-0,258066
-0,242540
-0,281540
-0,313020
-0,296505
-0,367494
-0,402512

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=52178&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=52178&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52178&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]
c3.73751426146105e-08
b0.621254278194284

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 3.73751426146105e-08 \tabularnewline
b & 0.621254278194284 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52178&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]3.73751426146105e-08[/C][/ROW]
[ROW][C]b[/C][C]0.621254278194284[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52178&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52178&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]
c3.73751426146105e-08
b0.621254278194284






Descriptive Statistics about e[t]
# observations60
minimum-0.298828394330304
Q1-0.133632784302843
median-0.0185758354198198
mean8.67361737988404e-19
Q30.156417201987608
maximum0.298956111813535

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -0.298828394330304 \tabularnewline
Q1 & -0.133632784302843 \tabularnewline
median & -0.0185758354198198 \tabularnewline
mean & 8.67361737988404e-19 \tabularnewline
Q3 & 0.156417201987608 \tabularnewline
maximum & 0.298956111813535 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52178&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]-0.298828394330304[/C][/ROW]
[ROW][C]Q1[/C][C]-0.133632784302843[/C][/ROW]
[ROW][C]median[/C][C]-0.0185758354198198[/C][/ROW]
[ROW][C]mean[/C][C]8.67361737988404e-19[/C][/ROW]
[ROW][C]Q3[/C][C]0.156417201987608[/C][/ROW]
[ROW][C]maximum[/C][C]0.298956111813535[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52178&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52178&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-0.298828394330304
Q1-0.133632784302843
median-0.0185758354198198
mean8.67361737988404e-19
Q30.156417201987608
maximum0.298956111813535


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')