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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, 12 Nov 2009 10:22:24 -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/12/t1258047098ql5m69jtcbzlk94.htm/, Retrieved Fri, 03 May 2024 23:22:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=56281, Retrieved Fri, 03 May 2024 23:22:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsws6bivariateeda
Estimated Impact133

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Box-Cox Normality Plot] [3/11/2009] [2009-11-02 22:22:24] [b98453cac15ba1066b407e146608df68]
- RMPD    [Bivariate Explorative Data Analysis] [] [2009-11-12 17:22:24] [2b548c9d2e9bba6e1eaf65bd4d551f41] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
96,80
114,10
110,30
103,90
101,60
94,60
95,90
104,70
102,80
98,10
113,90
80,90
95,70
113,20
105,90
108,80
102,30
99,00
100,70
115,50
100,70
109,90
114,60
85,40
100,50
114,80
116,50
112,90
102,00
106,00
105,30
118,80
106,10
109,30
117,20
92,50
104,20
112,50
122,40
113,30
100,00
110,70
112,80
109,80
117,30
109,10
115,90
96,00
99,80
116,80
115,70
99,40
94,30
91,00
93,20
103,10
94,10
91,80
102,70
82,60
Dataseries Y:
Download CSV
Histogram 
8,00
8,10
7,70
7,50
7,60
7,80
7,80
7,80
7,50
7,50
7,10
7,50
7,50
7,60
7,70
7,70
7,90
8,10
8,20
8,20
8,20
7,90
7,30
6,90
6,60
6,70
6,90
7,00
7,10
7,20
7,10
6,90
7,00
6,80
6,40
6,70
6,60
6,40
6,30
6,20
6,50
6,80
6,80
6,40
6,10
5,80
6,10
7,20
7,30
6,90
6,10
5,80
6,20
7,10
7,70
7,90
7,70
7,40
7,50
8,00

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'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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56281&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56281&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56281&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'Gwilym Jenkins' @ 72.249.127.135






Model: Y[t] = c + b X[t] + e[t]
c9.66287356011315
b-0.0238024768072979

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 9.66287356011315 \tabularnewline
b & -0.0238024768072979 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56281&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]9.66287356011315[/C][/ROW]
[ROW][C]b[/C][C]-0.0238024768072979[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56281&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56281&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]
c9.66287356011315
b-0.0238024768072979






Descriptive Statistics about e[t]
# observations60
minimum-1.49690736546773
Q1-0.455393618749367
median0.0208409012050208
mean-8.07459567958579e-18
Q30.396576550819609
maximum1.28631251112976

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -1.49690736546773 \tabularnewline
Q1 & -0.455393618749367 \tabularnewline
median & 0.0208409012050208 \tabularnewline
mean & -8.07459567958579e-18 \tabularnewline
Q3 & 0.396576550819609 \tabularnewline
maximum & 1.28631251112976 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56281&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]-1.49690736546773[/C][/ROW]
[ROW][C]Q1[/C][C]-0.455393618749367[/C][/ROW]
[ROW][C]median[/C][C]0.0208409012050208[/C][/ROW]
[ROW][C]mean[/C][C]-8.07459567958579e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.396576550819609[/C][/ROW]
[ROW][C]maximum[/C][C]1.28631251112976[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56281&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56281&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-1.49690736546773
Q1-0.455393618749367
median0.0208409012050208
mean-8.07459567958579e-18
Q30.396576550819609
maximum1.28631251112976


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')