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Author's title

Author*Unverified author*
R Software Modulerwasp_edabi.wasp
Title produced by softwareBivariate Explorative Data Analysis
Date of computationWed, 28 Oct 2009 12:28:45 -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/t1256754562tgzdm4qhr9cu0mv.htm/, Retrieved Sun, 05 May 2024 23:57:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51704, Retrieved Sun, 05 May 2024 23:57:51 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact126
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] [WS 4 part 2 vraag 2] [2009-10-28 18:28:45] [58c0e7ecdfec19fc38e879e32991032d] [Current]
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Dataseries X:
2,140066163
2,091864062
1,987874348
1,974081026
2,041220329
2,104134154
2,128231706
2,116255515
2,054123734
2,014903021
2,066862759
2,116255515
2,163323026
2,104134154
1,931521412
1,871802177
1,916922612
1,960094784
2,028148247
2,066862759
2,079441542
2,066862759
2,066862759
2,041220329
2,091864062
2,128231706
2,116255515
2,174751721
2,219203484
2,208274414
2,208274414
2,208274414
2,197224577
2,197224577
2,2300144
2,282382386
2,302585093
2,282382386
2,197224577
2,197224577
2,197224577
2,219203484
2,240709689
2,240709689
2,2300144
2,219203484
2,261763098
2,32238772
2,332143895
2,312535424
2,197224577
2,186051277
2,208274414
2,251291799
2,261763098
2,251291799
2,219203484
2,219203484
2,302585093
2,388762789
2,406945108
2,341805806
2,140066163
2,079441542
2,116255515
2,2300144
2,282382386
2,292534757
2,2300144
2,197224577
2,208274414
2,240709689
2,240709689
2,208274414
2,066862759
2,066862759
2,104134154
2,163323026
2,208274414
2,197224577
2,151762203
2,128231706
2,163323026
2,219203484
2,240709689
2,219203484
2,054123734
2,041220329
2,066862759
2,151762203
2,197224577
2,208274414
2,174751721
2,128231706
2,128231706
2,041220329
1,987874348
1,945910149
1,902107526
1,945910149
1,960094784
1,931521412
1,960094784
2,014903021
2,091864062
2,140066163
2,186051277
2,197224577
2,219203484
2,163323026
2,079441542
2,091864062
2,116255515
2,091864062
2,128231706
2,186051277
2,240709689
2,272125886
2,312535424
2,341805806
2,370243741
Dataseries Y:
2,091864062
2,079441542
2,014903021
2,00148
2,041220329
2,066862759
2,041220329
1,960094784
1,824549292
1,757857918
1,808288771
1,931521412
1,987874348
1,974081026
1,808288771
1,757857918
1,808288771
1,85629799
1,916922612
1,916922612
1,871802177
1,824549292
1,840549633
1,85629799
1,887069649
1,902107526
1,85629799
1,916922612
1,945910149
1,931521412
1,960094784
1,974081026
1,960094784
1,945910149
1,931521412
1,902107526
1,887069649
1,931521412
1,987874348
2,066862759
2,104134154
2,104134154
2,104134154
2,091864062
2,066862759
2,041220329
2,041220329
2,028148247
2,014903021
2,014903021
1,960094784
2,014903021
2,014903021
2,054123734
2,054123734
2,054123734
2,028148247
2,014903021
2,041220329
2,091864062
2,079441542
2,028148247
1,887069649
1,871802177
1,916922612
2,014903021
2,079441542
2,104134154
2,091864062
2,066862759
2,066862759
2,028148247
2,014903021
2,028148247
1,987874348
2,014903021
2,028148247
2,014903021
2,028148247
2,054123734
2,066862759
2,054123734
2,014903021
1,887069649
1,840549633
1,840549633
1,791759469
1,840549633
1,85629799
1,840549633
1,840549633
1,85629799
1,902107526
1,902107526
1,916922612
1,824549292
1,757857918
1,722766598
1,686398954
1,740466175
1,757857918
1,704748092
1,686398954
1,686398954
1,686398954
1,704748092
1,722766598
1,740466175
1,757857918
1,686398954
1,589235205
1,648658626
1,704748092
1,774952351
1,840549633
1,871802177
1,85629799
1,85629799
1,887069649
1,916922612
1,974081026




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=51704&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=51704&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51704&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]
c0.583355377445252
b0.621619918621112

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 0.583355377445252 \tabularnewline
b & 0.621619918621112 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51704&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]0.583355377445252[/C][/ROW]
[ROW][C]b[/C][C]0.621619918621112[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51704&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51704&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]
c0.583355377445252
b0.621619918621112







Descriptive Statistics about e[t]
# observations121
minimum-0.286742454560649
Q1-0.0702052879106251
median0.00336990356788208
mean1.14804465578269e-19
Q30.0720855080309869
maximum0.195845353121994

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 121 \tabularnewline
minimum & -0.286742454560649 \tabularnewline
Q1 & -0.0702052879106251 \tabularnewline
median & 0.00336990356788208 \tabularnewline
mean & 1.14804465578269e-19 \tabularnewline
Q3 & 0.0720855080309869 \tabularnewline
maximum & 0.195845353121994 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51704&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]-0.286742454560649[/C][/ROW]
[ROW][C]Q1[/C][C]-0.0702052879106251[/C][/ROW]
[ROW][C]median[/C][C]0.00336990356788208[/C][/ROW]
[ROW][C]mean[/C][C]1.14804465578269e-19[/C][/ROW]
[ROW][C]Q3[/C][C]0.0720855080309869[/C][/ROW]
[ROW][C]maximum[/C][C]0.195845353121994[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51704&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51704&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-0.286742454560649
Q1-0.0702052879106251
median0.00336990356788208
mean1.14804465578269e-19
Q30.0720855080309869
maximum0.195845353121994



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')