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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 10:13:55 -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/t12567464915jpq23x9uayi0mp.htm/, Retrieved Mon, 06 May 2024 07:56:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51504, Retrieved Mon, 06 May 2024 07:56:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact90

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] [ws4 part2 ex3] [2009-10-27 18:11:21] [95cead3ebb75668735f848316249436a]
-    D      [Bivariate Explorative Data Analysis] [Ws 4 part 2 ex 3] [2009-10-28 16:13:55] [ba02bcb7e07025bbb7f8a074d38ad767] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
268,96
316,84
497,29
519,84
334,89
501,76
571,21
453,69
529
457,96
449,44
436,81
320,41
428,49
492,84
392,04
313,29
384,16
432,64
392,04
345,96
441
345,96
357,21
299,29
400
396,01
380,25
262,44
309,76
392,04
376,36
295,84
445,21
316,84
306,25
324
364,81
313,29
368,64
228,01
265,69
345,96
295,84
316,84
364,81
275,56
256
278,89
302,76
320,41
316,84
193,21
252,81
320,41
237,16
268,96
320,41
234,09
213,16
222,01
225
278,89
265,69
136,89
228,01
240,25
225
237,16
256
216,09
219,04
Dataseries Y:
Download CSV
Histogram 
324
384,16
542,89
561,69
412,09
519,84
590,49
462,25
552,25
492,84
436,81
492,84
380,25
445,21
484
368,64
316,84
368,64
396,01
384,16
327,61
416,16
327,61
345,96
309,76
376,36
372,49
345,96
285,61
268,96
361
349,69
292,41
462,25
316,84
327,61
361
357,21
282,24
327,61
246,49
228,01
334,89
272,25
285,61
338,56
268,96
246,49
285,61
275,56
278,89
275,56
207,36
210,25
306,25
204,49
237,16
295,84
213,16
201,64
222,01
198,81
243,36
213,16
141,61
182,25
201,64
187,69
207,36
234,09
204,49
210,25

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=51504&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=51504&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51504&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]
c-37.4031110400014
b1.09167704971872

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -37.4031110400014 \tabularnewline
b & 1.09167704971872 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51504&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-37.4031110400014[/C][/ROW]
[ROW][C]b[/C][C]1.09167704971872[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51504&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51504&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]
c-37.4031110400014
b1.09167704971872






Descriptive Statistics about e[t]
# observations72
minimum-39.4845642997666
Q1-22.3817732545611
median-6.50640371087587
mean1.98309010031163e-15
Q313.9307818025522
maximum83.9013838596977

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 72 \tabularnewline
minimum & -39.4845642997666 \tabularnewline
Q1 & -22.3817732545611 \tabularnewline
median & -6.50640371087587 \tabularnewline
mean & 1.98309010031163e-15 \tabularnewline
Q3 & 13.9307818025522 \tabularnewline
maximum & 83.9013838596977 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51504&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]72[/C][/ROW]
[ROW][C]minimum[/C][C]-39.4845642997666[/C][/ROW]
[ROW][C]Q1[/C][C]-22.3817732545611[/C][/ROW]
[ROW][C]median[/C][C]-6.50640371087587[/C][/ROW]
[ROW][C]mean[/C][C]1.98309010031163e-15[/C][/ROW]
[ROW][C]Q3[/C][C]13.9307818025522[/C][/ROW]
[ROW][C]maximum[/C][C]83.9013838596977[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51504&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51504&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]
# observations72
minimum-39.4845642997666
Q1-22.3817732545611
median-6.50640371087587
mean1.98309010031163e-15
Q313.9307818025522
maximum83.9013838596977


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')