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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 10:09:47 -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/t1256746369v7n42xi100gr86r.htm/, Retrieved Mon, 06 May 2024 07:51:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51501, Retrieved Mon, 06 May 2024 07:51:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWS4D2BDMLDG
Estimated Impact81

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] [Workshop 4: deel ...] [2009-10-28 16:09:47] [3d2053c5f7c50d3c075d87ce0bd87294] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
21.4
26.4
26.4
29.4
34.4
24.4
26.4
25.4
31.4
27.4
27.4
29.4
32.4
26.4
22.4
19.4
21.4
23.4
23.4
25.4
28.4
27.4
21.4
17.4
24.4
26.4
22.4
14.4
18.4
25.4
29.4
26.4
26.4
20.4
26.4
29.4
33.4
32.4
35.4
34.4
36.4
32.4
34.4
31.4
27.4
27.4
30.4
32.4
32.4
27.4
31.4
29.4
27.4
25.4
26.4
23.4
18.4
22.4
17.4
17.4
11.4
9.4
6.4
0
7.8
7.9
12
16.9
12.3
Dataseries Y:
Download CSV
Histogram 
47,55488374
47,67122995
47,67904605
47,62021269
47,48687256
46,96105925
46,7149091
46,72041365
46,78353218
46,29983446
46,5672109
46,67279085
46,90208423
47,0160966
47,06088688
47,13837027
47,07677776
46,5694131
46,27723586
46,30202072
46,52739752
47,08021506
47,27495952
47,33488406
47,21709178
47,52098049
47,7468916
47,9299205
47,8987386
47,35400791
46,77492507
46,45602732
47,15154956
47,39519251
47,69417862
47,70741143
47,99242208
48,13229019
47,99905205
48,17338792
48,14847428
47,2007091
46,55630021
46,2269147
47,01554674
47,55402508
47,7442208
48,05078385
48,08094838
48,27774396
48,12614946
48,36071091
48,05595741
47,49759203
46,92351513
46,54004011
47,68247594
48,42168898
49,06947273
49,55331973
50,03937738
50,3807464
50,91083342
51,06153373
51,07554092
50,72471066
49,90197248
49,59503448
50,52323508

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51501&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51501&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51501&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'George Udny Yule' @ 72.249.76.132






Model: Y[t] = c + b X[t] + e[t]
c50.5138928355349
b-0.111799551534695

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 50.5138928355349 \tabularnewline
b & -0.111799551534695 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51501&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]50.5138928355349[/C][/ROW]
[ROW][C]b[/C][C]-0.111799551534695[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51501&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51501&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]
c50.5138928355349
b-0.111799551534695






Descriptive Statistics about e[t]
# observations69
minimum-1.62054746962302
Q1-0.811388051949536
median-0.111688052741362
mean9.15506599676745e-17
Q30.818884297258636
maximum1.70408512032803

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 69 \tabularnewline
minimum & -1.62054746962302 \tabularnewline
Q1 & -0.811388051949536 \tabularnewline
median & -0.111688052741362 \tabularnewline
mean & 9.15506599676745e-17 \tabularnewline
Q3 & 0.818884297258636 \tabularnewline
maximum & 1.70408512032803 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51501&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]69[/C][/ROW]
[ROW][C]minimum[/C][C]-1.62054746962302[/C][/ROW]
[ROW][C]Q1[/C][C]-0.811388051949536[/C][/ROW]
[ROW][C]median[/C][C]-0.111688052741362[/C][/ROW]
[ROW][C]mean[/C][C]9.15506599676745e-17[/C][/ROW]
[ROW][C]Q3[/C][C]0.818884297258636[/C][/ROW]
[ROW][C]maximum[/C][C]1.70408512032803[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51501&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51501&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]
# observations69
minimum-1.62054746962302
Q1-0.811388051949536
median-0.111688052741362
mean9.15506599676745e-17
Q30.818884297258636
maximum1.70408512032803


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')