Free Statistics

of Irreproducible Research!

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 computationMon, 09 Nov 2009 13:38:54 -0700
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/Nov/09/t1257799207irx6nbmzytysdjk.htm/, Retrieved Fri, 29 Mar 2024 00:19:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=55037, Retrieved Fri, 29 Mar 2024 00:19:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsws6.beda
Estimated Impact217
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Bivariate Explorative Data Analysis] [] [2009-11-09 20:38:54] [9ea4b07b6662a0f40f92decdf1e3b5d5] [Current]
Feedback Forum
2009-11-12 19:19:08 [Joris Van Mol] [reply
Volgende grafieken konden ook interessant zijn voor jouw reeks. (ik heb ze geblogd met jouw reeks van de depositorente)

Tuckey lambda PPCC plot:
http://www.freestatistics.org/blog/index.php?v=date/2009/Nov/12/t1258051783gyrfh4txkbxjbyp.htm/

mean plot:
http://www.freestatistics.org/blog/index.php?v=date/2009/Nov/12/t12580518994e79l3pp8ghez41.htm/

standard deviation plot:
http://www.freestatistics.org/blog/index.php?v=date/2009/Nov/12/t1258051959qs3io8d802u86a8.htm/
2009-11-19 14:46:18 [f1e24346ff4ab8a20729561498ad5c34] [reply
Bij de Run Sequence Plot van e[t] zien we alles wat we nog niet verklaard hebben in ons model. Het model moet dus duidelijk nog uitgebreid worden met saisonaliteit en trend.

Post a new message
Dataseries X:
2756.76
2849.27
2921.44
2981.85
3080.58
3106.22
3119.31
3061.26
3097.31
3161.69
3257.16
3277.01
3295.32
3363.99
3494.17
3667.03
3813.06
3917.96
3895.51
3801.06
3570.12
3701.61
3862.27
3970.1
4138.52
4199.75
4290.89
4443.91
4502.64
4356.98
4591.27
4696.96
4621.4
4562.84
4202.52
4296.49
4435.23
4105.18
4116.68
3844.49
3720.98
3674.4
3857.62
3801.06
3504.37
3032.6
3047.03
2962.34
2197.82
2014.45
1862.83
1905.41
1810.99
1670.07
1864.44
2052.02
2029.6
2070.83
2293.41
2443.27
Dataseries Y:
0.86
0.88
0.88
0.88
0.87
0.88
0.87
0.85
0.84
0.83
0.86
0.87
0.85
0.89
0.98
1.01
1
1.01
1.05
1
0.99
1.02
1.11
1.15
1.18
1.2
1.22
1.2
1.23
1.23
1.21
1.25
1.2
1.2
1.21
1.25
1.23
1.2
1.18
1.16
1.12
1.11
1.1
1.08
1.01
1.01
0.99
1.07
1.13
1.09
0.95
0.79
0.73
0.7
0.65
0.61
0.53
0.51
0.41
0.42




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=55037&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=55037&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55037&T=0

As an alternative you can also use a 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]
c0.295153746317019
b0.000203125468506030

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 0.295153746317019 \tabularnewline
b & 0.000203125468506030 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55037&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]0.295153746317019[/C][/ROW]
[ROW][C]b[/C][C]0.000203125468506030[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55037&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55037&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Model: Y[t] = c + b X[t] + e[t]
c0.295153746317019
b0.000203125468506030







Descriptive Statistics about e[t]
# observations60
minimum-0.371444109753745
Q1-0.0608171931208675
median0.00147450303162627
mean9.886116809447e-18
Q30.0552447615381137
maximum0.38841303649106

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -0.371444109753745 \tabularnewline
Q1 & -0.0608171931208675 \tabularnewline
median & 0.00147450303162627 \tabularnewline
mean & 9.886116809447e-18 \tabularnewline
Q3 & 0.0552447615381137 \tabularnewline
maximum & 0.38841303649106 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55037&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]-0.371444109753745[/C][/ROW]
[ROW][C]Q1[/C][C]-0.0608171931208675[/C][/ROW]
[ROW][C]median[/C][C]0.00147450303162627[/C][/ROW]
[ROW][C]mean[/C][C]9.886116809447e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.0552447615381137[/C][/ROW]
[ROW][C]maximum[/C][C]0.38841303649106[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55037&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55037&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Descriptive Statistics about e[t]
# observations60
minimum-0.371444109753745
Q1-0.0608171931208675
median0.00147450303162627
mean9.886116809447e-18
Q30.0552447615381137
maximum0.38841303649106



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')