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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, 09 Nov 2009 11:53:35 -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/09/t1257792890l4wiud43ol7w4md.htm/, Retrieved Wed, 24 Apr 2024 14:12:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=54925, Retrieved Wed, 24 Apr 2024 14:12:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact180

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]
-    D    [Bivariate Explorative Data Analysis] [JJ Workshop 6, Bi...] [2009-11-09 18:53:35] [e31f2fa83f4a5291b9a51009566cf69b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
85
96.1
113.6
116.8
102.7
106.8
124.2
117.8
121.6
117.9
111.4
109.8
92.6
104.9
120.3
109.1
93.1
87.3
106.9
102.1
102.4
113.3
100.6
103.5
93.7
102.6
108.1
105.9
87.1
81.8
103.8
95.8
92.7
101.1
88
92.8
89.7
95.6
95.2
96.9
79.2
73.5
99.7
87.8
91.3
93.9
90
89.8
88.9
104.2
110.8
110.5
87.1
89.2
96.5
95.4
101
107.6
93.8
93.8
Dataseries Y:
Download CSV
Histogram 
94.3
99.4
115.7
116.8
99.8
96
115.9
109.1
117.3
109.8
112.8
110.7
100
113.3
122.4
112.5
104.2
92.5
117.2
109.3
106.1
118.8
105.3
106
102
112.9
116.5
114.8
100.5
85.4
114.6
109.9
100.7
115.5
100.7
99
102.3
108.8
105.9
113.2
95.7
80.9
113.9
98.1
102.8
104.7
95.9
94.6
101.6
103.9
110.3
114.1
96.8
87.4
111.4
97.4
102.9
112.7
97
95.1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54925&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54925&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54925&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 time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Model: Y[t] = c + b X[t] + e[t]
c39.3947409484311
b0.663042805057097

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 39.3947409484311 \tabularnewline
b & 0.663042805057097 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54925&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]39.3947409484311[/C][/ROW]
[ROW][C]b[/C][C]0.663042805057097[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54925&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54925&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]
c39.3947409484311
b0.663042805057097






Descriptive Statistics about e[t]
# observations60
minimum-14.2077125285291
Q1-3.52483682300298
median0.220003819309400
mean-5.01335084557297e-17
Q33.39526786708674
maximum9.55641124153616

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -14.2077125285291 \tabularnewline
Q1 & -3.52483682300298 \tabularnewline
median & 0.220003819309400 \tabularnewline
mean & -5.01335084557297e-17 \tabularnewline
Q3 & 3.39526786708674 \tabularnewline
maximum & 9.55641124153616 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54925&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]-14.2077125285291[/C][/ROW]
[ROW][C]Q1[/C][C]-3.52483682300298[/C][/ROW]
[ROW][C]median[/C][C]0.220003819309400[/C][/ROW]
[ROW][C]mean[/C][C]-5.01335084557297e-17[/C][/ROW]
[ROW][C]Q3[/C][C]3.39526786708674[/C][/ROW]
[ROW][C]maximum[/C][C]9.55641124153616[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54925&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54925&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-14.2077125285291
Q1-3.52483682300298
median0.220003819309400
mean-5.01335084557297e-17
Q33.39526786708674
maximum9.55641124153616


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
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')