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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, 28 Oct 2009 14:41:39 -0600
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/Oct/28/t1256762684n2lgixf2cvhtx1p.htm/, Retrieved Mon, 06 May 2024 07:34:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51817, Retrieved Mon, 06 May 2024 07:34:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
- RMPD  [Bivariate Explorative Data Analysis] [run sequence plot] [2009-10-27 18:21:30] [134dc66689e3d457a82860db6471d419]
-    D      [Bivariate Explorative Data Analysis] [eigen creatie] [2009-10-28 20:41:39] [4f297b039e1043ebee7ff7a83b1eaaaa] [Current]
- RMPD        [Pearson Correlation] [] [2009-11-02 10:05:54] [ba905ddf7cdf9ecb063c35348c4dab2e]
- RMPD        [Kendall tau Rank Correlation] [] [2009-11-02 10:09:25] [ba905ddf7cdf9ecb063c35348c4dab2e]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100.80  
101.33  
101.88  
101.85  
102.04  
102.22  
102.63  
102.65  
102.54  
102.37  
102.68  
102.76  
102.82  
103.31  
103.23  
103.60  
103.95  
103.93  
104.25  
104.38  
104.36  
104.32  
104.58  
104.68  
104.92  
105.46  
105.23  
105.58  
105.34  
105.28  
105.70  
105.67  
105.71  
106.19  
106.93  
107.44  
107.85  
108.71  
109.32  
109.49  
110.20  
110.62  
111.22  
110.88  
111.15  
111.29  
111.09  
111.24  
111.45  
111.75  
111.07  
111.17  
110.96  
110.50  
110.48  
110.66  
110.46
Dataseries Y:
Download CSV
Histogram 
97
98
95
89
89
89
90
86
92
91
95
99
98
95
96
94
98
98
98
98
102
101
92
99
101
99
102
102
101
99
98
98
99
92
96
94
97
97
93
91
89
87
89
91
83
78
75
80
76
76
78
81
82
83
89
89
88

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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51817&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51817&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51817&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'RServer@AstonUniversity' @ vre.aston.ac.uk






Model: Y[t] = c + b X[t] + e[t]
c238.465025356275
b-1.37537477433971

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 238.465025356275 \tabularnewline
b & -1.37537477433971 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51817&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]238.465025356275[/C][/ROW]
[ROW][C]b[/C][C]-1.37537477433971[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51817&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51817&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]
c238.465025356275
b-1.37537477433971






Descriptive Statistics about e[t]
# observations57
minimum-11.2828047703031
Q1-3.85344039554014
median1.45887221729000
mean1.04305955320329e-16
Q34.87082704820278
maximum8.7470433185122

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 57 \tabularnewline
minimum & -11.2828047703031 \tabularnewline
Q1 & -3.85344039554014 \tabularnewline
median & 1.45887221729000 \tabularnewline
mean & 1.04305955320329e-16 \tabularnewline
Q3 & 4.87082704820278 \tabularnewline
maximum & 8.7470433185122 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51817&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]57[/C][/ROW]
[ROW][C]minimum[/C][C]-11.2828047703031[/C][/ROW]
[ROW][C]Q1[/C][C]-3.85344039554014[/C][/ROW]
[ROW][C]median[/C][C]1.45887221729000[/C][/ROW]
[ROW][C]mean[/C][C]1.04305955320329e-16[/C][/ROW]
[ROW][C]Q3[/C][C]4.87082704820278[/C][/ROW]
[ROW][C]maximum[/C][C]8.7470433185122[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51817&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51817&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]
# observations57
minimum-11.2828047703031
Q1-3.85344039554014
median1.45887221729000
mean1.04305955320329e-16
Q34.87082704820278
maximum8.7470433185122


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')