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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 13:46:28 -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/t1256759267j7no0r7u520g1ov.htm/, Retrieved Mon, 06 May 2024 06:52:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51786, Retrieved Mon, 06 May 2024 06:52:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsIlseWS4P2
Estimated Impact137

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 Bivariate dat...] [2009-10-27 22:25:17] [8733f8ed033058987ec00f5e71b74854]
-    D      [Bivariate Explorative Data Analysis] [WS4 ln Y[t] = c +...] [2009-10-28 19:46:28] [c6e373ff11c42d4585d53e9e88ed5606] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-0.22234
-0.26167
-0.29327
-0.27148
-0.26344
-0.27771
-0.25758
-0.23854
-0.19598
-0.1854
-0.20636
-0.20343
-0.18357
-0.16433
-0.17025
-0.19087
-0.17714
-0.18399
-0.20465
-0.24451
-0.23507
-0.23776
-0.24772
-0.24114
-0.23198
-0.25317
-0.27862
-0.26229
-0.26804
-0.28081
-0.30129
-0.30092
-0.29409
-0.31598
-0.3091
-0.32902
-0.35256
-0.38417
-0.37638
-0.38649
-0.38852
-0.44
-0.45432
-0.44193
-0.44167
-0.45552
-0.4038
-0.36256
-0.28683
-0.24153
-0.29632
-0.28058
-0.24569
-0.2662
-0.27687
-0.31115
-0.33761
-0.34274
-0.35543
-0.35543
Dataseries Y:
Download CSV
Histogram 
0.732368
0.751416
0.760806
0.756122
0.741937
0.737164
0.741937
0.737164
0.732368
0.727549
0.732368
0.737164
0.746688
0.788457
0.883768
0.900161
0.916291
0.951658
1.011601
1.022451
1.064711
1.108563
1.131402
1.172482
1.211941
1.255616
1.283708
1.300192
1.319086
1.34025
1.358409
1.381282
1.406097
1.420696
1.465568
1.519513
1.532557
1.519513
1.549688
1.512927
1.458615
1.472472
1.528228
1.549688
1.543298
1.591274
1.558145
1.562346
1.644805
1.229641
0.996949
0.828552
0.693147
0.494696
0.262364
0.076961
0
0
0
0

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=51786&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=51786&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51786&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]
c0.395435966755222
b-2.18491296781043

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 0.395435966755222 \tabularnewline
b & -2.18491296781043 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51786&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]0.395435966755222[/C][/ROW]
[ROW][C]b[/C][C]-2.18491296781043[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51786&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51786&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]
c0.395435966755222
b-2.18491296781043






Descriptive Statistics about e[t]
# observations60
minimum-1.17201958290408
Q1-0.179577541388410
median0.147181135003239
mean6.45461693353037e-18
Q30.305131204912260
maximum0.622670446687712

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -1.17201958290408 \tabularnewline
Q1 & -0.179577541388410 \tabularnewline
median & 0.147181135003239 \tabularnewline
mean & 6.45461693353037e-18 \tabularnewline
Q3 & 0.305131204912260 \tabularnewline
maximum & 0.622670446687712 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51786&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]-1.17201958290408[/C][/ROW]
[ROW][C]Q1[/C][C]-0.179577541388410[/C][/ROW]
[ROW][C]median[/C][C]0.147181135003239[/C][/ROW]
[ROW][C]mean[/C][C]6.45461693353037e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.305131204912260[/C][/ROW]
[ROW][C]maximum[/C][C]0.622670446687712[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51786&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51786&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-1.17201958290408
Q1-0.179577541388410
median0.147181135003239
mean6.45461693353037e-18
Q30.305131204912260
maximum0.622670446687712


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')