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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 14:20:16 -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/t1257801714r7gydu0hltueglm.htm/, Retrieved Thu, 28 Mar 2024 11:46:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=55060, Retrieved Thu, 28 Mar 2024 11:46:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWS5 bivariate eda met nieuwe reeksen
Estimated Impact258
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Trivariate Scatterplots] [WS5 trivariate] [2009-11-09 12:59:13] [c620fe7250af73a91c51407172a85dab]
- RMP   [Partial Correlation] [WS5 partiële corr...] [2009-11-09 19:36:40] [c620fe7250af73a91c51407172a85dab]
- RMPD      [Bivariate Explorative Data Analysis] [WS5 bivariate eda...] [2009-11-09 21:20:16] [b4ff140915b3f24d4faed3d78f95eba4] [Current]
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Dataseries X:
1,414
1,37
1,618
1,202
1,086
1,286
1,502
1,862
1,346
1,13
1,018
1,562
1,562
1,734
1,33
1,402
1,818
1,802
1,758
1,254
1,47
1,314
0,498
-0,118
-0,418
-0,606
-0,406
-0,09
0,082
0,254
0,226
0,026
0,126
-0,434
-0,834
-0,462
-0,778
-0,69
-0,286
0,118
0,562
1,15
1,222
1,398
0,954
1,374
2,106
3,278
3,018
2,69
1,314
-0,138
-0,098
0,586
0,898
0,162
-0,038
-1,058
-1,462
-1,394
Dataseries Y:
5,694
5,45
4,748
3,882
3,816
4,116
4,282
4,292
3,826
3,56
3,748
4,892
5,092
5,014
4,26
3,882
4,048
4,082
4,038
3,684
3,55
3,594
3,528
4,262
4,462
4,174
3,674
3,44
3,462
3,584
3,606
3,606
3,706
3,196
2,696
2,818
2,452
2,14
2,494
2,648
2,792
2,78
2,502
2,178
1,834
1,754
2,286
3,608
3,998
3,62
3,044
2,292
2,482
2,916
2,928
2,442
1,942
1,222
1,168
1,836




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=55060&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=55060&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55060&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'Gwilym Jenkins' @ 72.249.127.135







Model: Y[t] = c + b X[t] + e[t]
c3.03751724094850
b0.468252839071387

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 3.03751724094850 \tabularnewline
b & 0.468252839071387 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55060&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]3.03751724094850[/C][/ROW]
[ROW][C]b[/C][C]0.468252839071387[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55060&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55060&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]
c3.03751724094850
b0.468252839071387







Descriptive Statistics about e[t]
# observations60
minimum-1.92689664183259
Q1-0.555185282959553
median0.108754068258694
mean-1.00960906301850e-16
Q30.466070615067446
maximum1.99437324460456

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -1.92689664183259 \tabularnewline
Q1 & -0.555185282959553 \tabularnewline
median & 0.108754068258694 \tabularnewline
mean & -1.00960906301850e-16 \tabularnewline
Q3 & 0.466070615067446 \tabularnewline
maximum & 1.99437324460456 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55060&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]-1.92689664183259[/C][/ROW]
[ROW][C]Q1[/C][C]-0.555185282959553[/C][/ROW]
[ROW][C]median[/C][C]0.108754068258694[/C][/ROW]
[ROW][C]mean[/C][C]-1.00960906301850e-16[/C][/ROW]
[ROW][C]Q3[/C][C]0.466070615067446[/C][/ROW]
[ROW][C]maximum[/C][C]1.99437324460456[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55060&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55060&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-1.92689664183259
Q1-0.555185282959553
median0.108754068258694
mean-1.00960906301850e-16
Q30.466070615067446
maximum1.99437324460456



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')