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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 11:07:35 -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/t1256749694didr9dqre3r97ae.htm/, Retrieved Mon, 06 May 2024 01:24:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51590, Retrieved Mon, 06 May 2024 01:24:28 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsPart 2.3
Estimated Impact81
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] [WS4 bivariate EDA...] [2009-10-28 15:49:46] [37a8d600db9abe09a2528d150ccff095]
-   PD      [Bivariate Explorative Data Analysis] [Workshop4] [2009-10-28 17:07:35] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
1089
1521
2025
2116
2025
2025
2401
2500
2916
3481
3364
3136
2304
2500
2704
2809
3025
1849
1764
1444
1681
1681
1521
1156
729
225
196
961
1681
1849
2116
1764
2025
2025
1600
1225
1296
1444
1521
1024
576
441
144
841
1296
961
784
900
1444
729
1600
1600
1936
2209
2025
1764
1444
2116
1369
1681
1600
1089
1156
1296
1296
1444
1764
1225
625
576
484
729
289
900
900
1156
1369
1296
1089
1089
1089
1369
1600
1225
1369
1849
1764
1089
1521
1600
1369
1936
1764
1849
1600
900
900
961
324
576
484
676
784
529
289
144
81
361
441
324
324
225
576
324
361
900
1089
1225
1296
2209
2116
Dataseries Y:
3844
4096
3844
4096
4096
4761
4761
4225
3136
3364
2809
3844
3025
3600
3481
3364
2809
3249
3249
2809
2916
2809
3249
3249
3025
2401
2500
2401
2916
3364
3364
2704
3136
2704
3481
2809
2704
2809
2601
2500
3136
2704
2116
2304
2116
2304
2304
2401
2809
2304
2601
2304
2500
3025
2704
2809
2704
3025
2809
2809
3136
2916
2704
3025
2916
3481
3136
3136
2601
2809
2704
2601
2116
2401
2116
3025
3249
2809
2704
2809
2500
2916
2809
2500
2601
2704
2209
2601
2401
2809
2704
2025
2809
2601
2304
2304
2304
2304
1600
1849
1600
1521
1521
1296
1681
1521
1600
1521
2116
1600
1369
1369
1936
1681
1600
1296
1444
1849
1764
2025
2116




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51590&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51590&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51590&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'George Udny Yule' @ 72.249.76.132







Model: Y[t] = c + b X[t] + e[t]
c1838.89919727032
b0.606378454783185

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 1838.89919727032 \tabularnewline
b & 0.606378454783185 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51590&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]1838.89919727032[/C][/ROW]
[ROW][C]b[/C][C]0.606378454783185[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51590&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51590&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]
c1838.89919727032
b0.606378454783185







Descriptive Statistics about e[t]
# observations121
minimum-1153.38920388638
Q1-359.092960164433
median-0.104724923420392
mean7.43436533638457e-15
Q3291.234325330668
maximum1694.18443179373

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 121 \tabularnewline
minimum & -1153.38920388638 \tabularnewline
Q1 & -359.092960164433 \tabularnewline
median & -0.104724923420392 \tabularnewline
mean & 7.43436533638457e-15 \tabularnewline
Q3 & 291.234325330668 \tabularnewline
maximum & 1694.18443179373 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51590&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]121[/C][/ROW]
[ROW][C]minimum[/C][C]-1153.38920388638[/C][/ROW]
[ROW][C]Q1[/C][C]-359.092960164433[/C][/ROW]
[ROW][C]median[/C][C]-0.104724923420392[/C][/ROW]
[ROW][C]mean[/C][C]7.43436533638457e-15[/C][/ROW]
[ROW][C]Q3[/C][C]291.234325330668[/C][/ROW]
[ROW][C]maximum[/C][C]1694.18443179373[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51590&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51590&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]
# observations121
minimum-1153.38920388638
Q1-359.092960164433
median-0.104724923420392
mean7.43436533638457e-15
Q3291.234325330668
maximum1694.18443179373



Parameters (Session):
par1 = 0 ; par2 = 1 ;
Parameters (R input):
par1 = 0 ; par2 = 1 ;
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')