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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 07:30:47 -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/t1257431534jzpto1vakb0sqn4.htm/, Retrieved Thu, 02 May 2024 23:08:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=54120, Retrieved Thu, 02 May 2024 23:08:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact127

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] [3/11/2009] [2009-11-02 22:01:32] [b98453cac15ba1066b407e146608df68]
-   PD    [Bivariate Explorative Data Analysis] [Shwws6v1] [2009-11-05 14:30:47] [93b66894f6318f3da4fcda772f2ffa6f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100,35
100,35
100,36
100,39
100,34
100,34
100,35
100,43
100,47
100,67
100,75
100,78
100,79
100,67
100,64
100,64
100,76
100,79
100,79
100,9
100,98
101,11
101,18
101,22
101,23
101,09
101,26
101,28
101,43
101,53
101,54
101,54
101,79
102,18
102,37
102,46
102,46
102,03
102,26
102,33
102,44
102,5
102,52
102,66
102,72
Dataseries Y:
Download CSV
Histogram 
102,1
102,86
102,99
103,73
105,02
104,43
104,63
104,93
105,87
105,66
106,76
106
107,22
107,33
107,11
108,86
107,72
107,88
108,38
107,72
108,41
109,9
111,45
112,18
113,34
113,46
114,06
115,54
116,39
115,94
116,97
115,94
115,91
116,43
116,26
116,35
117,9
117,7
117,53
117,86
117,65
116,51
115,93
115,31
115

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time71 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 & 71 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54120&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]71 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=54120&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54120&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 time71 seconds
R Server'George Udny Yule' @ 72.249.76.132






Model: Y[t] = c + b X[t] + e[t]
c-496.182843436178
b5.99546433915312

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -496.182843436178 \tabularnewline
b & 5.99546433915312 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54120&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-496.182843436178[/C][/ROW]
[ROW][C]b[/C][C]5.99546433915312[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54120&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54120&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-496.182843436178
b5.99546433915312






Descriptive Statistics about e[t]
# observations45
minimum-4.67125348163068
Q1-1.31284096292712
median-0.220007307065209
mean1.41379963292110e-16
Q31.50194302709900
maximum4.50221516674980

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 45 \tabularnewline
minimum & -4.67125348163068 \tabularnewline
Q1 & -1.31284096292712 \tabularnewline
median & -0.220007307065209 \tabularnewline
mean & 1.41379963292110e-16 \tabularnewline
Q3 & 1.50194302709900 \tabularnewline
maximum & 4.50221516674980 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54120&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]45[/C][/ROW]
[ROW][C]minimum[/C][C]-4.67125348163068[/C][/ROW]
[ROW][C]Q1[/C][C]-1.31284096292712[/C][/ROW]
[ROW][C]median[/C][C]-0.220007307065209[/C][/ROW]
[ROW][C]mean[/C][C]1.41379963292110e-16[/C][/ROW]
[ROW][C]Q3[/C][C]1.50194302709900[/C][/ROW]
[ROW][C]maximum[/C][C]4.50221516674980[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54120&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54120&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]
# observations45
minimum-4.67125348163068
Q1-1.31284096292712
median-0.220007307065209
mean1.41379963292110e-16
Q31.50194302709900
maximum4.50221516674980


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')