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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, 27 Oct 2009 11:20:41 -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/27/t1256664089sudc3womr70xnp6.htm/, Retrieved Tue, 07 May 2024 12:22:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51066, Retrieved Tue, 07 May 2024 12:22:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133

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]
F RMPD  [Univariate Explorative Data Analysis] [Colombia Coffee] [2008-01-07 14:21:11] [74be16979710d4c4e7c6647856088456]
F RMPD    [Univariate Data Series] [] [2009-10-14 08:30:28] [74be16979710d4c4e7c6647856088456]
- RMPD      [Central Tendency] [ws 3 part 2.1 con...] [2009-10-20 19:44:01] [12f02da0296cb21dc23d82ae014a8b71]
- RMPD        [Bivariate Explorative Data Analysis] [WS 4 part 2.1] [2009-10-27 17:07:08] [12f02da0296cb21dc23d82ae014a8b71]
-    D            [Bivariate Explorative Data Analysis] [WS 4 part 2.2] [2009-10-27 17:20:41] [51d49d3536f6a59f2486a67bf50b2759] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9,253017
9,139274
9,181632
9,104646
9,111845
9,09212
9,111404
9,11548
9,1214
9,195937
9,195024
9,236885
9,243872
9,17885
9,222763
9,143025
9,129564
9,129564
9,154193
9,142811
9,151651
9,203618
9,225032
9,275847
9,281265
9,186048
9,265491
9,141312
9,149422
9,159258
9,142918
9,139811
9,168893
9,226214
9,228377
9,288227
9,315151
9,19766
9,301825
9,138952
9,17056
9,148252
9,188504
9,151121
9,165029
9,226804
9,243485
9,28452
9,329456
9,228475
9,300272
9,172431
9,180809
9,173365
9,167328
9,184099
9,185433
9,259892
9,282568
9,312807
Dataseries Y:
Download CSV
Histogram 
1901
1395
1639
1643
1751
1797
1373
1558
1555
2061
2010
2119
1985
1963
2017
1975
1589
1679
1392
1511
1449
1767
1899
2179
2217
2049
2343
2175
1607
1702
1764
1766
1615
1953
2091
2411
2550
2351
2786
2525
2474
2332
1978
1789
1904
1997
2207
2453
1948
1384
1989
2140
2100
2045
2083
2022
1950
1422
1859
2147

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=51066&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=51066&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51066&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-22693.6414694519
b2678.68704015050

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -22693.6414694519 \tabularnewline
b & 2678.68704015050 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51066&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-22693.6414694519[/C][/ROW]
[ROW][C]b[/C][C]2678.68704015050[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51066&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51066&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-22693.6414694519
b2678.68704015050






Descriptive Statistics about e[t]
# observations60
minimum-688.71122414141
Q1-186.374686617744
median15.6480355526641
mean-2.14377127161214e-15
Q3146.69537794018
maximum738.249186494395

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -688.71122414141 \tabularnewline
Q1 & -186.374686617744 \tabularnewline
median & 15.6480355526641 \tabularnewline
mean & -2.14377127161214e-15 \tabularnewline
Q3 & 146.69537794018 \tabularnewline
maximum & 738.249186494395 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51066&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]-688.71122414141[/C][/ROW]
[ROW][C]Q1[/C][C]-186.374686617744[/C][/ROW]
[ROW][C]median[/C][C]15.6480355526641[/C][/ROW]
[ROW][C]mean[/C][C]-2.14377127161214e-15[/C][/ROW]
[ROW][C]Q3[/C][C]146.69537794018[/C][/ROW]
[ROW][C]maximum[/C][C]738.249186494395[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51066&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51066&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-688.71122414141
Q1-186.374686617744
median15.6480355526641
mean-2.14377127161214e-15
Q3146.69537794018
maximum738.249186494395


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')