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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 09:36:08 -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/t1256744378ix5s9kplgv84itd.htm/, Retrieved Sun, 05 May 2024 20:51:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51445, Retrieved Sun, 05 May 2024 20:51:19 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsbhschhwstws4p2vr2
Estimated Impact121
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] [Workshop 4 part 1] [2009-10-27 21:04:25] [786e067c4f7cec17385c4742b96b6dfa]
-    D      [Bivariate Explorative Data Analysis] [workshop 4 part 2...] [2009-10-28 15:36:08] [682632737e024f9e62885141c5f654cd] [Current]
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Dataseries X:
2,178545315
2,169645049
2,152165991
2,171404635
2,169409898
2,177824972
2,179494341
2,182528775
2,196424914
2,189265669
2,196507792
2,203304916
2,20561031
2,175105741
2,180956951
2,187887178
2,180355296
2,185655459
2,192567453
2,201041896
2,193375081
2,193958978
2,194236749
2,187774455
2,207688213
2,204445581
2,19080776
2,188956593
2,180068501
2,183526073
2,187464315
2,190079454
2,184719815
2,190583796
2,191814172
2,209139536
2,220186568
2,216271493
2,215716855
2,212746765
2,203984244
2,215320251
2,223054413
2,222196046
2,220866133
2,2254902
2,22297645
2,213597437
2,226032503
2,228066553
2,219689289
2,214525955
2,196811543
2,206825876
2,213623993
2,207391978
2,201178556
2,204608289
2,203848464
2,216060286
Dataseries Y:
2,109240969
2,110252917
2,110791661
2,111363344
2,111497749
2,111699278
2,111766433
2,111632112
2,111934276
2,112236231
2,112169148
2,112135603
2,112102055
2,111531343
2,111262514
2,111430552
2,111060782
2,110959881
2,110657038
2,110858957
2,110556043
2,110959881
2,110858957
2,111228898
2,111363344
2,111430552
2,111632112
2,111699278
2,111900713
2,112437417
2,112034951
2,112102055
2,112504459
2,113140837
2,113676012
2,114210528
2,115310895
2,116740535
2,118066222
2,118892725
2,119717659
2,12169113
2,125481266
2,128496199
2,135132651
2,13801834
2,139942023
2,140350889
2,140633725
2,140508043
2,140853581
2,141919874
2,143951116
2,145507171
2,147738141
2,150971232
2,151584339
2,151247237
2,150326536
2,149742415




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51445&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51445&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51445&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'Gwilym Jenkins' @ 72.249.127.135







Model: Y[t] = c + b X[t] + e[t]
c0.972292803752618
b0.522750156170503

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 0.972292803752618 \tabularnewline
b & 0.522750156170503 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51445&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]0.972292803752618[/C][/ROW]
[ROW][C]b[/C][C]0.522750156170503[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51445&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51445&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]
c0.972292803752618
b0.522750156170503







Descriptive Statistics about e[t]
# observations60
minimum-0.0175847839022737
Q1-0.00833005692698212
median-0.00282095171483614
mean-2.01989123488575e-19
Q30.00414684180970985
maximum0.028625101339217

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -0.0175847839022737 \tabularnewline
Q1 & -0.00833005692698212 \tabularnewline
median & -0.00282095171483614 \tabularnewline
mean & -2.01989123488575e-19 \tabularnewline
Q3 & 0.00414684180970985 \tabularnewline
maximum & 0.028625101339217 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51445&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]-0.0175847839022737[/C][/ROW]
[ROW][C]Q1[/C][C]-0.00833005692698212[/C][/ROW]
[ROW][C]median[/C][C]-0.00282095171483614[/C][/ROW]
[ROW][C]mean[/C][C]-2.01989123488575e-19[/C][/ROW]
[ROW][C]Q3[/C][C]0.00414684180970985[/C][/ROW]
[ROW][C]maximum[/C][C]0.028625101339217[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51445&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51445&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-0.0175847839022737
Q1-0.00833005692698212
median-0.00282095171483614
mean-2.01989123488575e-19
Q30.00414684180970985
maximum0.028625101339217



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')