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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 computationTue, 27 Oct 2009 09:49:05 -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/27/t1256658625k1nql9jdnx6djn0.htm/, Retrieved Tue, 07 May 2024 05:47:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51021, Retrieved Tue, 07 May 2024 05:47:05 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
- RMPD    [Bivariate Explorative Data Analysis] [] [2009-10-27 15:49:05] [82bf023f1e4d9556a54030fcde33aa09] [Current]
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Dataseries X:
698756356
817674025
774453241
833361424
817960000
797045824
777517456
745290000
718561636
720546649
803155600
823288249
727057296
709050384
669101689
704955601
662702049
666156100
633126244
638219169
626550961
611177284
656845641
636603361
582063876
514019584
474281284
471932176
463110400
425349376
389154529
365880384
347673316
332697600
404854641
393268561
331676944
309197056
290293444
315311049
310323456
293436900
288082729
277189201
251032336
266211856
318622500
302551236
265266369
266603584
267224409
315275536
348718276
364504464
378691600
367220569
366722500
392317249
453945636
447618649
Dataseries Y:
32905
32481
30946
31924
31899
30889
30334
29438
28549
28749
32858
34780
32145
31682
29736
30629
28593
28753
27538
28325
27873
27836
30777
31189
29923
26950
25327
25190
25443
23883
22545
21650
20923
20882
25011
25335
22251
21293
20292
21561
21262
20242
20205
19631
18357
19011
22883
23170
21538
21194
20915
23212
24414
24980
25453
25159
24708
25956
29371
30068




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51021&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]2 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=51021&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51021&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Model: Y[t] = c + b X[t] + e[t]
c15626.9562238136
b2.09585578455139e-05

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 15626.9562238136 \tabularnewline
b & 2.09585578455139e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51021&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]15626.9562238136[/C][/ROW]
[ROW][C]b[/C][C]2.09585578455139e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51021&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51021&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]
c15626.9562238136
b2.09585578455139e-05







Descriptive Statistics about e[t]
# observations60
minimum-2531.23195896411
Q1-1268.14775607803
median-320.010549117149
mean1.55771691841740e-13
Q31221.49749167835
maximum5059.60242838909

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -2531.23195896411 \tabularnewline
Q1 & -1268.14775607803 \tabularnewline
median & -320.010549117149 \tabularnewline
mean & 1.55771691841740e-13 \tabularnewline
Q3 & 1221.49749167835 \tabularnewline
maximum & 5059.60242838909 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51021&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]-2531.23195896411[/C][/ROW]
[ROW][C]Q1[/C][C]-1268.14775607803[/C][/ROW]
[ROW][C]median[/C][C]-320.010549117149[/C][/ROW]
[ROW][C]mean[/C][C]1.55771691841740e-13[/C][/ROW]
[ROW][C]Q3[/C][C]1221.49749167835[/C][/ROW]
[ROW][C]maximum[/C][C]5059.60242838909[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51021&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51021&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-2531.23195896411
Q1-1268.14775607803
median-320.010549117149
mean1.55771691841740e-13
Q31221.49749167835
maximum5059.60242838909



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')