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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, 04 Nov 2009 10:44:35 -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/04/t1257356765xgtn8g3wpvjei0v.htm/, Retrieved Mon, 29 Apr 2024 16:16:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53759, Retrieved Mon, 29 Apr 2024 16:16:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact155

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Bivariate Explorative Data Analysis] [workshop 5 (3)] [2009-11-04 17:44:35] [9a3898f49d4e2f0208d1968305d88f0a] [Current]
Feedback Forum
2009-11-08 13:21:31 [f1e24346ff4ab8a20729561498ad5c34] [reply] 
De Residual Autocorrelation Function zorgt voor een interessante grafiek. We zien hier dat er bijna geen significante autocorrelatie is tussen de reeksen en dat ze 'random' zijn. Er zit geen patroon in wat duidt op randomness. Niet alle lags liggen tussen de 95% betrouwbaarheidsintervallen maar dat wil niet meteen zeggen dat de gegevens niet random zijn.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3977.7
3983.4
4152.9
4286.1
4348.1
3949.3
4166.7
4217.9
4528.2
4232.2
4470.9
5121.2
4170.8
4398.6
4491.4
4251.8
4901.9
4745.2
4666.9
4210.4
5273.6
4095.3
4610.1
4718.1
4185.5
4314.7
4422.6
5059.2
5043.6
4436.6
4922.6
4454.8
5058.7
4768.9
5171.8
4989.3
5202.1
4838.4
4876.5
5875.5
5717.9
4778.8
6195.9
4625.4
5549.8
6397.6
5856.7
6343.8
6615.5
5904.6
6861
6553.5
5481
5435.3
5278
4671.8
4891.5
4241.6
4152.1
4484.4
Dataseries Y:
Download CSV
Histogram 
3956.2
3142.7
3884.3
3892.2
3613
3730.5
3481.3
3649.5
4215.2
4066.6
4196.8
4536.6
4441.6
3548.3
4735.9
4130.6
4356.2
4159.6
3988
4167.8
4902.2
3909.4
4697.6
4308.9
4420.4
3544.2
4433
4479.7
4533.2
4237.5
4207.4
4394
5148.4
4202.2
4682.5
4884.3
5288.9
4505.2
4611.5
5104
4586.6
4529.3
4504.1
4604.9
4795.4
5391.1
5213.9
5415
5990.3
4241.8
5677.6
5164.2
3962.3
4011
3310.3
3837.3
4145.3
3796.7
3849.6
4285

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=53759&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=53759&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53759&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]
c1502.47723793524
b0.5842488097053

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 1502.47723793524 \tabularnewline
b & 0.5842488097053 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53759&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]1502.47723793524[/C][/ROW]
[ROW][C]b[/C][C]0.5842488097053[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53759&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53759&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]
c1502.47723793524
b0.5842488097053






Descriptive Statistics about e[t]
# observations60
minimum-1275.84245555981
Q1-191.702814538111
median58.7930099597515
mean3.26405569239796e-15
Q3213.327509853475
maximum747.102029096817

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -1275.84245555981 \tabularnewline
Q1 & -191.702814538111 \tabularnewline
median & 58.7930099597515 \tabularnewline
mean & 3.26405569239796e-15 \tabularnewline
Q3 & 213.327509853475 \tabularnewline
maximum & 747.102029096817 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53759&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]-1275.84245555981[/C][/ROW]
[ROW][C]Q1[/C][C]-191.702814538111[/C][/ROW]
[ROW][C]median[/C][C]58.7930099597515[/C][/ROW]
[ROW][C]mean[/C][C]3.26405569239796e-15[/C][/ROW]
[ROW][C]Q3[/C][C]213.327509853475[/C][/ROW]
[ROW][C]maximum[/C][C]747.102029096817[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53759&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53759&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-1275.84245555981
Q1-191.702814538111
median58.7930099597515
mean3.26405569239796e-15
Q3213.327509853475
maximum747.102029096817


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')