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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 computationWed, 04 Nov 2009 03:10:11 -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/04/t1257329481842dhz3sjmgig9i.htm/, Retrieved Mon, 29 Apr 2024 09:09:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53502, Retrieved Mon, 29 Apr 2024 09:09:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact187

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Box-Cox Linearity Plot] [3/11/2009] [2009-11-02 21:47:57] [b98453cac15ba1066b407e146608df68]
- RMPD    [Bivariate Explorative Data Analysis] [Workshop 5.7] [2009-11-04 10:10:11] [852eae237d08746109043531619a60c9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4,25
4,25
4,25
3,85
3,75
3,75
3,55
3,5
3,5
3,1
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3,21
3,25
3,25
3,45
3,5
3,5
3,64
3,75
3,93
4
4,17
4,25
4,39
4,5
4,5
4,65
4,2
4,75
4,9
5
5
5
5
5
5
5
5
5
5
5
5
5,18
5,25
5,25
4,49
3,92
3,25
3
3
Dataseries Y:
Download CSV
Histogram 
123,1
136,6
112,1
95,1
96,3
105,7
115,8
105,7
105,7
111,1
82,4
60
107,3
99,3
113,5
108,9
100,2
103,9
138,9
120,2
100,2
143,2
70,9
85,2
133
136,6
117,9
106,3
122,3
125,5
148,4
126,3
99,6
140,4
80,3
92,6
138,5
110,9
119,6
105
109
129,4
148,6
101,4
134,8
143,7
81,6
90,3
141,5
140,7
140,2
100,2
125,7
119,6
134,7
109
116,3
146,9
97,4
89,4
132,1
139,8
129
112,5
121,9
121,7
123,1
131,6
119,3
132,5
98,3
85,1
131,7
129,3
90,7
78,6
68,9
79,1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53502&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]6 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=53502&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53502&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 time6 seconds
R Server'George Udny Yule' @ 72.249.76.132






Model: Y[t] = c + b X[t] + e[t]
c92.0807207390146
b5.74317334407533

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 92.0807207390146 \tabularnewline
b & 5.74317334407533 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53502&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]92.0807207390146[/C][/ROW]
[ROW][C]b[/C][C]5.74317334407533[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53502&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53502&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]
c92.0807207390146
b5.74317334407533






Descriptive Statistics about e[t]
# observations78
minimum-49.3102407712406
Q1-10.5889307752689
median1.00341254060871
mean-3.52015425355909e-16
Q315.1825822154661
maximum39.0897592287594

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 78 \tabularnewline
minimum & -49.3102407712406 \tabularnewline
Q1 & -10.5889307752689 \tabularnewline
median & 1.00341254060871 \tabularnewline
mean & -3.52015425355909e-16 \tabularnewline
Q3 & 15.1825822154661 \tabularnewline
maximum & 39.0897592287594 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53502&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]78[/C][/ROW]
[ROW][C]minimum[/C][C]-49.3102407712406[/C][/ROW]
[ROW][C]Q1[/C][C]-10.5889307752689[/C][/ROW]
[ROW][C]median[/C][C]1.00341254060871[/C][/ROW]
[ROW][C]mean[/C][C]-3.52015425355909e-16[/C][/ROW]
[ROW][C]Q3[/C][C]15.1825822154661[/C][/ROW]
[ROW][C]maximum[/C][C]39.0897592287594[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53502&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53502&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]
# observations78
minimum-49.3102407712406
Q1-10.5889307752689
median1.00341254060871
mean-3.52015425355909e-16
Q315.1825822154661
maximum39.0897592287594


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 24 ;
Parameters (R input):
par1 = 0 ; par2 = 24 ;
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')