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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 computationTue, 03 Nov 2009 12:46:24 -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/03/t1257277817kk6au6itfwt90ph.htm/, Retrieved Wed, 01 May 2024 17:46:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53369, Retrieved Wed, 01 May 2024 17:46:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact155

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Trivariate Scatterplots] [] [2009-11-03 19:22:33] [134dc66689e3d457a82860db6471d419]
- RMPD    [Bivariate Explorative Data Analysis] [ x en y] [2009-11-03 19:46:24] [4f297b039e1043ebee7ff7a83b1eaaaa] [Current]
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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 
-7	
-6	
-6	
-3	
-2	
-5	
-11	
-11	
-11	
-10	
-14	
-8	
-9	
-5	
-1	
-2	
-5	
-4	
-6	
-2	
-2	
-2	
-2	
2	
1	
-8	
-1	
1	
-1	
2	
2	
1	
-1	
-2	
-2	
-1	
-8	
-4	
-6	
-3	
-3	
-7	
-9	
-11	
-13	
-11	
-9	
-17	
-22	
-25	
-20	
-24	
-24	
-22	
-19	
-18	
-17

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53369&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53369&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53369&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 time3 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Model: Y[t] = c + b X[t] + e[t]
c117.502783520977
b-1.17586721030177

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 117.502783520977 \tabularnewline
b & -1.17586721030177 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53369&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]117.502783520977[/C][/ROW]
[ROW][C]b[/C][C]-1.17586721030177[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53369&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53369&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]
c117.502783520977
b-1.17586721030177






Descriptive Statistics about e[t]
# observations57
minimum-11.0996227697541
Q1-5.38131802898305
median0.705095645686036
mean6.6295348840247e-16
Q35.36255554096803
maximum9.07778305427814

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 57 \tabularnewline
minimum & -11.0996227697541 \tabularnewline
Q1 & -5.38131802898305 \tabularnewline
median & 0.705095645686036 \tabularnewline
mean & 6.6295348840247e-16 \tabularnewline
Q3 & 5.36255554096803 \tabularnewline
maximum & 9.07778305427814 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53369&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]-11.0996227697541[/C][/ROW]
[ROW][C]Q1[/C][C]-5.38131802898305[/C][/ROW]
[ROW][C]median[/C][C]0.705095645686036[/C][/ROW]
[ROW][C]mean[/C][C]6.6295348840247e-16[/C][/ROW]
[ROW][C]Q3[/C][C]5.36255554096803[/C][/ROW]
[ROW][C]maximum[/C][C]9.07778305427814[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53369&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53369&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-11.0996227697541
Q1-5.38131802898305
median0.705095645686036
mean6.6295348840247e-16
Q35.36255554096803
maximum9.07778305427814


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')