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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 computationTue, 27 Oct 2009 13:01:52 -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/27/t1256670201zceu8pxp56xtfks.htm/, Retrieved Tue, 07 May 2024 06:40:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51132, Retrieved Tue, 07 May 2024 06:40:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsSHWWS4V2 model 2
Estimated Impact124
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Explorative Data Analysis] [Bivariate EDA wer...] [2009-10-27 18:39:55] [4395c69e961f9a13a0559fd2f0a72538]
-    D    [Bivariate Explorative Data Analysis] [Bivariate EDA lnY...] [2009-10-27 19:01:52] [d1081bd6cdf1fed9ed45c42dbd523bf1] [Current]
- RMP       [Bivariate Kernel Density Estimation] [Bivariate Kernel ...] [2009-10-27 19:07:17] [4395c69e961f9a13a0559fd2f0a72538]
- RMPD        [Pearson Correlation] [Pearson Correlati...] [2009-10-27 19:09:52] [4395c69e961f9a13a0559fd2f0a72538]
- RM D          [Kendall tau Rank Correlation] [Kendall Rank Corr...] [2009-10-27 19:13:14] [4395c69e961f9a13a0559fd2f0a72538]
-    D            [Kendall tau Rank Correlation] [Kendall Rank Corr...] [2009-10-28 17:25:16] [4395c69e961f9a13a0559fd2f0a72538]
-    D              [Kendall tau Rank Correlation] [Kendall Rank Corr...] [2009-10-28 17:51:25] [4395c69e961f9a13a0559fd2f0a72538]
-    D          [Pearson Correlation] [Pearson Correlati...] [2009-10-28 17:20:47] [4395c69e961f9a13a0559fd2f0a72538]
-    D            [Pearson Correlation] [Pearson correlati...] [2009-10-28 17:48:03] [4395c69e961f9a13a0559fd2f0a72538]
-    D        [Bivariate Kernel Density Estimation] [Bivariate Kernel ...] [2009-10-28 17:18:05] [4395c69e961f9a13a0559fd2f0a72538]
-    D          [Bivariate Kernel Density Estimation] [Bivariate Kernel ...] [2009-10-28 17:44:30] [4395c69e961f9a13a0559fd2f0a72538]
-    D      [Bivariate Explorative Data Analysis] [Bivariate EDA Wer...] [2009-10-28 17:11:58] [4395c69e961f9a13a0559fd2f0a72538]
-    D        [Bivariate Explorative Data Analysis] [Bivariate EDA Wer...] [2009-10-28 17:39:05] [4395c69e961f9a13a0559fd2f0a72538]
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Dataseries X:
1,987874348
2,028148247
2,014903021
2,028148247
2,066862759
2,066862759
2,091864062
2,104134154
2,079441542
2,014903021
1,916922612
1,871802177
1,887069649
2,028148247
2,079441542
2,091864062
2,041220329
2,014903021
2,028148247
2,054123734
2,054123734
2,054123734
2,014903021
2,014903021
1,960094784
2,014903021
2,014903021
2,028148247
2,041220329
2,041220329
2,066862759
2,091864062
2,104134154
2,104134154
2,104134154
2,066862759
1,987874348
1,931521412
1,887069649
1,902107526
1,931521412
1,945910149
1,960094784
1,974081026
1,960094784
1,931521412
1,945910149
1,916922612
1,85629799
1,902107526
1,887069649
1,85629799
1,840549633
1,824549292
1,871802177
1,916922612
1,916922612
1,85629799
1,808288771
1,757857918
1,808288771
1,974081026
1,987874348
1,931521412
1,808288771
1,757857918
1,824549292
1,960094784
2,041220329
2,066862759
2,041220329
2,00148
2,014903021
Dataseries Y:
2,066862759
2,208274414
2,240709689
2,240709689
2,208274414
2,197224577
2,2300144
2,292534757
2,282382386
2,2300144
2,116255515
2,079441542
2,140066163
2,341805806
2,406945108
2,388762789
2,302585093
2,219203484
2,219203484
2,251291799
2,261763098
2,251291799
2,208274414
2,186051277
2,197224577
2,312535424
2,332143895
2,32238772
2,261763098
2,219203484
2,2300144
2,240709689
2,240709689
2,219203484
2,197224577
2,197224577
2,197224577
2,282382386
2,302585093
2,282382386
2,2300144
2,197224577
2,197224577
2,208274414
2,208274414
2,208274414
2,219203484
2,174751721
2,116255515
2,128231706
2,091864062
2,041220329
2,066862759
2,066862759
2,079441542
2,066862759
2,028148247
1,960094784
1,916922612
1,871802177
1,931521412
2,104134154
2,163323026
2,116255515
2,066862759
2,014903021
2,054123734
2,116255515
2,128231706
2,104134154
2,041220329
1,974081026
1,987874348




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=51132&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=51132&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51132&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]
c0.563495668829972
b0.814587445233579

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 0.563495668829972 \tabularnewline
b & 0.814587445233579 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51132&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]0.563495668829972[/C][/ROW]
[ROW][C]b[/C][C]0.814587445233579[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51132&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51132&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]
c0.563495668829972
b0.814587445233579







Descriptive Statistics about e[t]
# observations73
minimum-0.219795122716076
Q1-0.0388617093322088
median0.00437311869701271
mean-5.3059072071294e-19
Q30.0370603056558041
maximum0.201906179813291

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 73 \tabularnewline
minimum & -0.219795122716076 \tabularnewline
Q1 & -0.0388617093322088 \tabularnewline
median & 0.00437311869701271 \tabularnewline
mean & -5.3059072071294e-19 \tabularnewline
Q3 & 0.0370603056558041 \tabularnewline
maximum & 0.201906179813291 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51132&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]73[/C][/ROW]
[ROW][C]minimum[/C][C]-0.219795122716076[/C][/ROW]
[ROW][C]Q1[/C][C]-0.0388617093322088[/C][/ROW]
[ROW][C]median[/C][C]0.00437311869701271[/C][/ROW]
[ROW][C]mean[/C][C]-5.3059072071294e-19[/C][/ROW]
[ROW][C]Q3[/C][C]0.0370603056558041[/C][/ROW]
[ROW][C]maximum[/C][C]0.201906179813291[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51132&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51132&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]
# observations73
minimum-0.219795122716076
Q1-0.0388617093322088
median0.00437311869701271
mean-5.3059072071294e-19
Q30.0370603056558041
maximum0.201906179813291



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')