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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 12:59:38 -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/t12577968389xt8qucf986z0ek.htm/, Retrieved Fri, 29 Mar 2024 13:07:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=54973, Retrieved Fri, 29 Mar 2024 13:07:42 +0000
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Original text written by user:Bivariate EDA van leningsbedrag en Amerikaanse inflatie
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact164
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Box-Cox Linearity Plot] [3/11/2009] [2009-11-02 21:47:57] [b98453cac15ba1066b407e146608df68]
- RMPD    [Bivariate Explorative Data Analysis] [Shw5: Bivariate E...] [2009-11-09 19:59:38] [7a39e26d7a09dd77604df90cb29f8d39] [Current]
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Dataseries X:
24710.92
23983.59
24434.12
23939.23
24290.02
24117.63
23724.64
22989.44
23716.86
25058.83
25059.00
23579.18
24209.03
24173.67
24706.39
24522.12
24766.15
25940.04
24985.78
24788.00
26544.56
28019.08
27285.71
29161.16
28357.73
27979.91
27543.95
27397.53
27623.59
27736.07
27803.79
27779.55
27524.13
27582.72
28638.95
28825.78
30132.61
29326.85
29075.62
28230.63
28118.36
28173.29
27396.91
24578.55
24504.77
27582.37
26920.31
25426.68
25390.80
25041.16
22769.42
22921.89
26267.63
27364.67
28382.59
29132.81
28214.51
28865.73
24405.35
Dataseries Y:
0.527
0.472
0.000
0.052
0.313
0.364
0.363
-0.155
0.052
0.568
0.668
1.378
0.252
-0.402
-0.050
0.555
0.050
0.150
0.450
0.299
0.199
0.496
0.444
-0.393
-0.444
0.198
0.494
0.133
0.388
0.484
0.278
0.369
0.165
0.155
0.087
0.414
0.360
0.975
0.270
0.359
0.169
0.381
0.154
0.486
0.925
0.728
-0.014
0.046
-0.819
-1.674
-0.788
0.279
0.396
-0.141
-0.019
0.099
0.742
0.005
0.448




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=54973&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=54973&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54973&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Model: Y[t] = c + b X[t] + e[t]
c-0.218814966510909
b1.65300724442378e-05

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -0.218814966510909 \tabularnewline
b & 1.65300724442378e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54973&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-0.218814966510909[/C][/ROW]
[ROW][C]b[/C][C]1.65300724442378e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54973&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54973&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]
c-0.218814966510909
b1.65300724442378e-05







Descriptive Statistics about e[t]
# observations59
minimum-1.86911722237684
Q1-0.159621194599425
median0.0706379468061831
mean5.2749192137621e-18
Q30.247996632191237
maximum1.20704941293519

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 59 \tabularnewline
minimum & -1.86911722237684 \tabularnewline
Q1 & -0.159621194599425 \tabularnewline
median & 0.0706379468061831 \tabularnewline
mean & 5.2749192137621e-18 \tabularnewline
Q3 & 0.247996632191237 \tabularnewline
maximum & 1.20704941293519 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54973&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]59[/C][/ROW]
[ROW][C]minimum[/C][C]-1.86911722237684[/C][/ROW]
[ROW][C]Q1[/C][C]-0.159621194599425[/C][/ROW]
[ROW][C]median[/C][C]0.0706379468061831[/C][/ROW]
[ROW][C]mean[/C][C]5.2749192137621e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.247996632191237[/C][/ROW]
[ROW][C]maximum[/C][C]1.20704941293519[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54973&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54973&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]
# observations59
minimum-1.86911722237684
Q1-0.159621194599425
median0.0706379468061831
mean5.2749192137621e-18
Q30.247996632191237
maximum1.20704941293519



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')