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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 06:08:56 -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/t12574265697gx4wm7lc4hjutz.htm/, Retrieved Thu, 02 May 2024 16:16:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=54044, Retrieved Thu, 02 May 2024 16:16:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact126

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] [Assumpties BDM] [2009-11-05 13:08:56] [9be6fbb216efe5bb8ca600257c6e1971] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.3
106.3
94
102.8
102
105.1
92.4
81.4
105.8
120.3
100.7
88.8
94.3
99.9
103.4
103.3
98.8
104.2
91.2
74.7
108.5
114.5
96.9
89.6
97.1
100.3
122.6
115.4
109
129.1
102.8
96.2
127.7
128.9
126.5
119.8
113.2
114.1
134.1
130
121.8
132.1
105.3
103
117.1
126.3
138.1
119.5
138
135.5
178.6
162.2
176.9
204.9
132.2
142.5
164.3
174.9
175.4
143
Dataseries Y:
Download CSV
Histogram 
89.3
90.3
91.1
90.1
86.7
85.1
83.4
82
80.4
81.9
93.8
94.8
92.3
87.5
83.2
82
80.3
81.8
85.1
84.2
84.4
84.5
93.3
93.2
100.3
111.4
114.9
109.5
109.9
105.8
110.8
108.8
116.1
109.8
113.8
113.8
117.4
119.5
122.6
120.7
119
126.1
133.9
138.1
140.4
148.2
148.2
155.9
171.1
171.9
188.8
214.9
228.5
220
225.4
220.7
219.7
232.1
223.5
218.9

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=54044&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=54044&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54044&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]
c-58.4890589701895
b1.54658385932975

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -58.4890589701895 \tabularnewline
b & 1.54658385932975 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54044&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-58.4890589701895[/C][/ROW]
[ROW][C]b[/C][C]1.54658385932975[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54044&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54044&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-58.4890589701895
b1.54658385932975






Descriptive Statistics about e[t]
# observations60
minimum-45.6649793071797
Q1-19.0359419086691
median-0.601935664316554
mean1.34776449260225e-15
Q315.0631264749884
maximum79.4306727667962

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -45.6649793071797 \tabularnewline
Q1 & -19.0359419086691 \tabularnewline
median & -0.601935664316554 \tabularnewline
mean & 1.34776449260225e-15 \tabularnewline
Q3 & 15.0631264749884 \tabularnewline
maximum & 79.4306727667962 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54044&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]-45.6649793071797[/C][/ROW]
[ROW][C]Q1[/C][C]-19.0359419086691[/C][/ROW]
[ROW][C]median[/C][C]-0.601935664316554[/C][/ROW]
[ROW][C]mean[/C][C]1.34776449260225e-15[/C][/ROW]
[ROW][C]Q3[/C][C]15.0631264749884[/C][/ROW]
[ROW][C]maximum[/C][C]79.4306727667962[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54044&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54044&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-45.6649793071797
Q1-19.0359419086691
median-0.601935664316554
mean1.34776449260225e-15
Q315.0631264749884
maximum79.4306727667962


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 50 ; par2 = 50 ; par3 = 0 ; par4 = 0 ; par5 = 0 ; par6 = Y ; par7 = Y ;
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')