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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 computationFri, 27 Nov 2009 08:43:22 -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/27/t1259336709i3gg9vyobklsblj.htm/, Retrieved Mon, 29 Apr 2024 07:12:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60911, Retrieved Mon, 29 Apr 2024 07:12:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Explorative Data Analysis] [Workshop 4 Part 2...] [2009-11-27 15:43:22] [fd0d141bb5380cffe1d2cd25afb62297] [Current]
-    D    [Bivariate Explorative Data Analysis] [Workshop 4 Part 2...] [2009-11-27 16:48:24] [436fa41bc7abf585320106c16117fff6]
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Dataseries X:
2.00148
1.960095
1.916923
1.931521
1.974081
2.00148
1.987874
1.931521
1.931521
1.916923
1.960095
1.974081
1.960095
1.94591
1.931521
1.960095
1.987874
2.014903
2.014903
2.014903
1.987874
1.94591
1.902108
1.871802
1.871802
1.871802
1.88707
1.916923
1.931521
1.931521
1.916923
1.916923
1.871802
1.808289
1.808289
1.774952
1.740466
1.774952
1.774952
1.808289
1.84055
1.824549
1.774952
1.740466
1.686399
1.722767
1.824549
1.84055
1.791759
1.722767
1.704748
1.774952
1.871802
1.916923
1.916923
1.871802
1.824549
1.824549
1.871802
1.902108
Dataseries Y:
3.242592
3.165475
3.091042
3.058707
3.030134
3.015535
3.010621
3.015535
2.985682
2.970414
3.139833
3.157
3.157
3.131137
3.086487
3.068053
3.020425
3.005683
2.965273
2.95491
2.933857
2.933857
3.11795
3.148453
3.135494
3.063391
2.99072
2.933857
2.923162
2.912351
2.923162
2.99072
2.95491
2.912351
3.049273
3.020425
2.949688
2.895912
2.833213
2.839078
2.85647
2.821379
2.727853
2.66026
2.595255
2.727853
3.095578
3.165475
3.100092
2.970414
2.809403
2.850707
2.985682
3.054001
3.068053
3.025291
2.949688
2.97553
3.157
3.178054




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=60911&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=60911&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60911&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]
c1.38447017629253
b0.854340569132985

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 1.38447017629253 \tabularnewline
b & 0.854340569132985 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60911&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]1.38447017629253[/C][/ROW]
[ROW][C]b[/C][C]0.854340569132985[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60911&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60911&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]
c1.38447017629253
b0.854340569132985







Descriptive Statistics about e[t]
# observations60
minimum-0.229974257737829
Q1-0.088810404749843
median-0.00546115612128843
mean-5.78286333749456e-18
Q30.0846458097826394
maximum0.208548289189752

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -0.229974257737829 \tabularnewline
Q1 & -0.088810404749843 \tabularnewline
median & -0.00546115612128843 \tabularnewline
mean & -5.78286333749456e-18 \tabularnewline
Q3 & 0.0846458097826394 \tabularnewline
maximum & 0.208548289189752 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60911&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]-0.229974257737829[/C][/ROW]
[ROW][C]Q1[/C][C]-0.088810404749843[/C][/ROW]
[ROW][C]median[/C][C]-0.00546115612128843[/C][/ROW]
[ROW][C]mean[/C][C]-5.78286333749456e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.0846458097826394[/C][/ROW]
[ROW][C]maximum[/C][C]0.208548289189752[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60911&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60911&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-0.229974257737829
Q1-0.088810404749843
median-0.00546115612128843
mean-5.78286333749456e-18
Q30.0846458097826394
maximum0.208548289189752



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')