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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 computationWed, 28 Oct 2009 10:04:11 -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/t125674592715ar2dfo9jajj9d.htm/, Retrieved Sun, 05 May 2024 21:50:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51486, Retrieved Sun, 05 May 2024 21:50:07 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWS5 Bivariate eda y=ywaarden en x=zwaarden
Estimated Impact93
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Explorative Data Analysis] [] [2009-10-28 16:04:11] [4563e36d4b7005634fe3557528d9fcab] [Current]
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Dataseries X:
14271
14013
15912
14290
14744
14721
13918
13263
15660
15629
15113
14526
15132
14908
16167
14122
13985
14236
13921
12394
15454
16146
15107
14593
14695
14513
17071
15179
15460
17173
15938
15003
18216
17847
18029
17281
16706
16750
18912
17763
16736
18061
16713
16769
19514
19251
19951
19052
19555
19083
22534
18854
19801
20346
18169
19087
20842
21602
22360
20334
21215
20530
23152
20134
21193
21628
20823
20493
22106
24178
24958
21620
Dataseries Y:
4071
4351
4871
4649
4922
4879
4853
4545
4733
5191
4983
4593
4656
4513
4857
4681
4897
4547
4692
4390
5341
5415
4890
5120
4422
4797
5689
5171
4265
5215
4874
4590
4994
4988
5110
5141
4395
4523
5306
5365
5496
5647
5443
5546
5912
5665
5963
5861
5366
5619
6721
6054
6619
6856
6193
6317
6618
6585
6852
6586
6154
6193
7606
6588
7143
7629
7041
7146
7200
7739
7953
7082




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51486&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51486&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51486&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 time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Model: Y[t] = c + b X[t] + e[t]
c167.406463057477
b0.306304713357724

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 167.406463057477 \tabularnewline
b & 0.306304713357724 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51486&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]167.406463057477[/C][/ROW]
[ROW][C]b[/C][C]0.306304713357724[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51486&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51486&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]
c167.406463057477
b0.306304713357724







Descriptive Statistics about e[t]
# observations72
minimum-889.533004411614
Q1-244.111371285354
median99.8737076540166
mean-1.26237754817361e-14
Q3292.380669414601
maximum836.835196441668

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 72 \tabularnewline
minimum & -889.533004411614 \tabularnewline
Q1 & -244.111371285354 \tabularnewline
median & 99.8737076540166 \tabularnewline
mean & -1.26237754817361e-14 \tabularnewline
Q3 & 292.380669414601 \tabularnewline
maximum & 836.835196441668 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51486&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]72[/C][/ROW]
[ROW][C]minimum[/C][C]-889.533004411614[/C][/ROW]
[ROW][C]Q1[/C][C]-244.111371285354[/C][/ROW]
[ROW][C]median[/C][C]99.8737076540166[/C][/ROW]
[ROW][C]mean[/C][C]-1.26237754817361e-14[/C][/ROW]
[ROW][C]Q3[/C][C]292.380669414601[/C][/ROW]
[ROW][C]maximum[/C][C]836.835196441668[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51486&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51486&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]
# observations72
minimum-889.533004411614
Q1-244.111371285354
median99.8737076540166
mean-1.26237754817361e-14
Q3292.380669414601
maximum836.835196441668



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')