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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 09:42:33 -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/t1256658298byov6602dzcg784.htm/, Retrieved Tue, 07 May 2024 12:46:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51017, Retrieved Tue, 07 May 2024 12:46:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110

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] [] [2009-10-27 15:42:33] [82bf023f1e4d9556a54030fcde33aa09] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
32905
32481
30946
31924
31899
30889
30334
29438
28549
28749
32858
34780
32145
31682
29736
30629
28593
28753
27538
28325
27873
27836
30777
31189
29923
26950
25327
25190
25443
23883
22545
21650
20923
20882
25011
25335
22251
21293
20292
21561
21262
20242
20205
19631
18357
19011
22883
23170
21538
21194
20915
23212
24414
24980
25453
25159
24708
25956
29371
30068
Dataseries Y:
Download CSV
Histogram 
10.18240634
10.26098716
10.23383392
10.27048899
10.261162
10.24821137
10.23580833
10.21464198
10.19638102
10.19776036
10.25202951
10.26440847
10.20225792
10.18971857
10.1607233
10.18682269
10.15591802
10.15851729
10.1330902
10.13709615
10.12787034
10.11544881
10.1514798
10.13582868
10.09104538
10.02888596
9.988655565
9.986172919
9.976738014
9.934210725
9.889743535
9.858908509
9.833386925
9.811372264
9.909519325
9.895001649
9.809835996
9.774744677
9.743201423
9.784535083
9.776562859
9.748586591
9.739379125
9.720105434
9.670546159
9.6999015
9.789758787
9.763880598
9.698122523
9.700636704
9.701799673
9.784478766
9.834887461
9.857024678
9.876116356
9.860736615
9.860057995
9.89379069
9.966744002
9.959726099

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51017&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51017&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51017&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'Gwilym Jenkins' @ 72.249.127.135






Model: Y[t] = c + b X[t] + e[t]
c8.87242564097912
b4.23586731931773e-05

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 8.87242564097912 \tabularnewline
b & 4.23586731931773e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51017&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]8.87242564097912[/C][/ROW]
[ROW][C]b[/C][C]4.23586731931773e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51017&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51017&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]
c8.87242564097912
b4.23586731931773e-05






Descriptive Statistics about e[t]
# observations60
minimum-0.186340127551578
Q1-0.0568486181611021
median0.0119715203091051
mean1.03338019565025e-17
Q30.0515357861388468
maximum0.114657618028859

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -0.186340127551578 \tabularnewline
Q1 & -0.0568486181611021 \tabularnewline
median & 0.0119715203091051 \tabularnewline
mean & 1.03338019565025e-17 \tabularnewline
Q3 & 0.0515357861388468 \tabularnewline
maximum & 0.114657618028859 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51017&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]-0.186340127551578[/C][/ROW]
[ROW][C]Q1[/C][C]-0.0568486181611021[/C][/ROW]
[ROW][C]median[/C][C]0.0119715203091051[/C][/ROW]
[ROW][C]mean[/C][C]1.03338019565025e-17[/C][/ROW]
[ROW][C]Q3[/C][C]0.0515357861388468[/C][/ROW]
[ROW][C]maximum[/C][C]0.114657618028859[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51017&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51017&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-0.186340127551578
Q1-0.0568486181611021
median0.0119715203091051
mean1.03338019565025e-17
Q30.0515357861388468
maximum0.114657618028859


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')