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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, 04 Nov 2009 10:16:47 -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/04/t1257355192nzrdzoyxilbwk4r.htm/, Retrieved Mon, 29 Apr 2024 08:32:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53736, Retrieved Mon, 29 Apr 2024 08:32:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKVN WS5
Estimated Impact150
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] [] [2009-10-28 15:16:32] [5482608004c1d7bbf873930172393a2d]
-    D    [Bivariate Explorative Data Analysis] [] [2009-10-28 16:44:36] [5482608004c1d7bbf873930172393a2d]
- RMPD      [Partial Correlation] [] [2009-11-03 18:08:02] [5482608004c1d7bbf873930172393a2d]
- RMPD          [Bivariate Explorative Data Analysis] [WS5 Bivariate EDA...] [2009-11-04 17:16:47] [f1100e00818182135823a11ccbd0f3b9] [Current]
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Dataseries X:
1169
2154
2249
2687
4359
5382
4459
6398
4596
3024
1887
2070
1351
2218
2461
3028
4784
4975
4607
6249
4809
3157
1910
2228
1594
2467
2222
3607
4685
4962
5770
5480
5000
3228
1993
2288
1580
2111
2192
3601
4665
4876
5813
5589
5331
3075
2002
2306
1507
1992
2487
3490
4647
5594
5611
5788
6204
3013
1931
2549
Dataseries Y:
1,00
1,00
0,99
0,97
0,96
0,96
1,09
1,10
1,10
1,09
1,07
1,08
1,09
1,09
1,07
1,07
1,05
1,06
1,20
1,22
1,21
1,19
1,15
1,17
1,18
1,18
1,17
1,14
1,13
1,14
1,25
1,28
1,29
1,28
1,25
1,25
1,24
1,24
1,23
1,20
1,19
1,20
1,30
1,32
1,32
1,29
1,25
1,25
1,25
1,24
1,22
1,21
1,20
1,20
1,30
1,31
1,30
1,23
1,19
1,17




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53736&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]10 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=53736&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53736&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 time10 seconds
R Server'George Udny Yule' @ 72.249.76.132







Model: Y[t] = c + b X[t] + e[t]
c1.12215794696057
b1.42439283164866e-05

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 1.12215794696057 \tabularnewline
b & 1.42439283164866e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53736&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]1.12215794696057[/C][/ROW]
[ROW][C]b[/C][C]1.42439283164866e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53736&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53736&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]
c1.12215794696057
b1.42439283164866e-05







Descriptive Statistics about e[t]
# observations60
minimum-0.238818769159905
Q1-0.0725400554791834
median0.0161493125352431
mean7.8592234355306e-18
Q30.0894692913157242
maximum0.124041973466230

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -0.238818769159905 \tabularnewline
Q1 & -0.0725400554791834 \tabularnewline
median & 0.0161493125352431 \tabularnewline
mean & 7.8592234355306e-18 \tabularnewline
Q3 & 0.0894692913157242 \tabularnewline
maximum & 0.124041973466230 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53736&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]-0.238818769159905[/C][/ROW]
[ROW][C]Q1[/C][C]-0.0725400554791834[/C][/ROW]
[ROW][C]median[/C][C]0.0161493125352431[/C][/ROW]
[ROW][C]mean[/C][C]7.8592234355306e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.0894692913157242[/C][/ROW]
[ROW][C]maximum[/C][C]0.124041973466230[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53736&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53736&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-0.238818769159905
Q1-0.0725400554791834
median0.0161493125352431
mean7.8592234355306e-18
Q30.0894692913157242
maximum0.124041973466230



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')