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Author's title

Author*Unverified author*
R Software Modulerwasp_edabi.wasp
Title produced by softwareBivariate Explorative Data Analysis
Date of computationMon, 09 Nov 2009 12:39:03 -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/t1257795584f1h5m5g63vuk94z.htm/, Retrieved Thu, 28 Mar 2024 23:23:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=54957, Retrieved Thu, 28 Mar 2024 23:23:44 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsData x: Z(t) Data y: X(t)
Estimated Impact155
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]
-   PD  [Bivariate Data Series] [] [2009-10-30 22:08:48] [b5ba85a7ae9f50cb97d92cbc56161b32]
- RMPD    [Bivariate Explorative Data Analysis] [workshop 5] [2009-11-02 20:33:10] [309ee52d0058ff0a6f7eec15e07b2d9f]
-    D        [Bivariate Explorative Data Analysis] [e(t)'] [2009-11-09 19:39:03] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
1.6891
1.7236
1.8072
1.7847
1.6813
1.6469
1.689
1.7169
1.8036
1.7955
1.7172
1.7348
1.7094
1.6963
1.6695
1.6537
1.6662
1.6793
1.7922
1.8045
1.7927
1.7831
1.7847
1.8076
1.8218
1.8112
1.795
1.7813
1.7866
1.7552
1.7184
1.7114
1.6967
1.6867
1.6337
1.6626
1.6374
1.626
1.637
1.6142
1.7033
1.7483
1.7135
1.7147
1.7396
1.7049
1.6867
1.7462
1.7147
1.667
1.6806
1.6738
1.6571
1.5875
1.6002
1.6144
1.6009
1.5937
1.603
1.5979
Dataseries Y:
1.5291
1.5358
1.5355
1.5287
1.5334
1.5225
1.5135
1.5144
1.4913
1.4793
1.4663
1.4749
1.4745
1.4775
1.4678
1.4658
1.4572
1.4721
1.4624
1.4636
1.4649
1.465
1.4673
1.4679
1.4621
1.4674
1.4695
1.4964
1.5155
1.5411
1.5476
1.54
1.5474
1.5485
1.559
1.5544
1.5657
1.5734
1.567
1.5547
1.54
1.5192
1.527
1.5387
1.5431
1.5426
1.5216
1.5364
1.5469
1.5501
1.5494
1.5475
1.5448
1.5391
1.5578
1.5528
1.5496
1.549
1.5449
1.5479




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54957&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54957&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54957&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 time8 seconds
R Server'George Udny Yule' @ 72.249.76.132







Model: Y[t] = c + b X[t] + e[t]
c2.07896963451149
b-0.328760672175972

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 2.07896963451149 \tabularnewline
b & -0.328760672175972 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54957&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]2.07896963451149[/C][/ROW]
[ROW][C]b[/C][C]-0.328760672175972[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54957&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54957&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]
c2.07896963451149
b-0.328760672175972







Descriptive Statistics about e[t]
# observations60
minimum-0.0739886025318874
Q1-0.0179405983140138
median0.00509815346479483
mean-4.97928318043440e-19
Q30.0235295400008766
maximum0.0506666522449246

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -0.0739886025318874 \tabularnewline
Q1 & -0.0179405983140138 \tabularnewline
median & 0.00509815346479483 \tabularnewline
mean & -4.97928318043440e-19 \tabularnewline
Q3 & 0.0235295400008766 \tabularnewline
maximum & 0.0506666522449246 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54957&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]-0.0739886025318874[/C][/ROW]
[ROW][C]Q1[/C][C]-0.0179405983140138[/C][/ROW]
[ROW][C]median[/C][C]0.00509815346479483[/C][/ROW]
[ROW][C]mean[/C][C]-4.97928318043440e-19[/C][/ROW]
[ROW][C]Q3[/C][C]0.0235295400008766[/C][/ROW]
[ROW][C]maximum[/C][C]0.0506666522449246[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54957&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54957&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-0.0739886025318874
Q1-0.0179405983140138
median0.00509815346479483
mean-4.97928318043440e-19
Q30.0235295400008766
maximum0.0506666522449246



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')