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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, 11 Nov 2009 05:41:44 -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/11/t12579433654qw3rplrvj7k6mg.htm/, Retrieved Thu, 25 Apr 2024 00:46:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=55542, Retrieved Thu, 25 Apr 2024 00:46:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113

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] [] [2009-11-11 12:41:44] [c4328af89eba9af53ee195d6fed304d9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100,00
104,02
112,82
133,11
102,26
103,44
98,49
111,32
112,82
114,67
121,37
129,34
107,12
112,32
121,71
119,11
114,84
123,72
117,69
121,54
114,92
123,55
126,32
139,40
151,55
163,79
164,29
194,22
233,36
202,01
189,10
190,36
213,75
194,13
207,21
232,19
218,11
227,66
239,56
223,55
225,48
226,82
209,05
214,08
202,85
230,93
240,74
232,36
250,29
288,18
337,97
280,64
278,96
282,65
294,38
294,89
227,66
205,87
168,48
166,14
Dataseries Y:
Download CSV
Histogram 
100
101,73
102,97
100,18
99,58
100,79
102,60
99,08
99,31
100,24
101,64
105,56
108,99
106,24
108,25
106,18
104,04
107,16
107,14
111,00
109,75
110,51
108,55
112,37
112,26
115,12
115,18
117,17
117,87
114,22
114,22
114,98
117,26
120,14
123,91
125,96
127,53
129,32
126,52
127,78
133,32
137,66
135,32
130,87
132,56
137,36
139,02
133,07
132,02
123,98
119,65
119,29
124,65
125,87
114,82
114,16
115,82
104,70
87,12
79,92

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55542&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]5 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=55542&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55542&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Model: Y[t] = c + b X[t] + e[t]
c89.488532700819
b0.138226276218814

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 89.488532700819 \tabularnewline
b & 0.138226276218814 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55542&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]89.488532700819[/C][/ROW]
[ROW][C]b[/C][C]0.138226276218814[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55542&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55542&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]
c89.488532700819
b0.138226276218814






Descriptive Statistics about e[t]
# observations60
minimum-32.5334462318127
Q1-3.91715031616978
median0.398723972073682
mean2.46908974747366e-17
Q34.45619831560636
maximum16.9352642556380

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -32.5334462318127 \tabularnewline
Q1 & -3.91715031616978 \tabularnewline
median & 0.398723972073682 \tabularnewline
mean & 2.46908974747366e-17 \tabularnewline
Q3 & 4.45619831560636 \tabularnewline
maximum & 16.9352642556380 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55542&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]-32.5334462318127[/C][/ROW]
[ROW][C]Q1[/C][C]-3.91715031616978[/C][/ROW]
[ROW][C]median[/C][C]0.398723972073682[/C][/ROW]
[ROW][C]mean[/C][C]2.46908974747366e-17[/C][/ROW]
[ROW][C]Q3[/C][C]4.45619831560636[/C][/ROW]
[ROW][C]maximum[/C][C]16.9352642556380[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55542&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55542&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-32.5334462318127
Q1-3.91715031616978
median0.398723972073682
mean2.46908974747366e-17
Q34.45619831560636
maximum16.9352642556380


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')