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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 14:27:26 -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/t1256761721buqe7ir7b6dvz8p.htm/, Retrieved Mon, 06 May 2024 05:22:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51813, Retrieved Mon, 06 May 2024 05:22:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact172
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] [run sequence plot] [2009-10-27 18:21:30] [134dc66689e3d457a82860db6471d419]
-    D      [Bivariate Explorative Data Analysis] [ln graf] [2009-10-28 20:27:26] [4f297b039e1043ebee7ff7a83b1eaaaa] [Current]
- RMPD        [Pearson Correlation] [] [2009-11-02 10:17:46] [ba905ddf7cdf9ecb063c35348c4dab2e]
- RMPD        [Kendall tau Rank Correlation] [] [2009-11-02 10:19:44] [ba905ddf7cdf9ecb063c35348c4dab2e]
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Dataseries X:
4.613138356
4.618382517
4.62379565
4.623501143
4.625364893
4.627127353
4.631130288
4.631325143
4.630252966
4.628593701
4.631617356
4.632396172
4.632979887
4.637734177
4.636959508
4.64053733
4.643910014
4.643717596
4.646791861
4.648038086
4.64784646
4.647463098
4.649952329
4.650908078
4.653198155
4.658331734
4.656148431
4.659468959
4.657193214
4.656623468
4.660604893
4.66032103
4.660699496
4.665229942
4.672174415
4.676932552
4.680741373
4.688683786
4.694279361
4.695833221
4.702296897
4.706100905
4.711510222
4.708448535
4.71088064
4.712139407
4.710340684
4.711690029
4.71357606
4.716264233
4.710160633
4.711060561
4.709169776
4.705015521
4.704834509
4.706462437
4.704653464
Dataseries Y:
-1.098612289
-0.693147181
-1.609437912
-2.397895273
-2.397895273
-2.397895273
-2.302585093
-2.63905733
-2.079441542
-2.197224577
-1.609437912
0
-0.693147181
-1.609437912
-1.386294361
-1.791759469
-0.693147181
-0.693147181
-0.693147181
-0.693147181
0.693147181
0
-2.079441542
0
0
0
0.693147181
0.693147181
0
0
-0.693147181
-0.693147181
0
-2.079441542
-1.386294361
-1.791759469
-1.098612289
-1.098612289
-1.945910149
-2.197224577
-2.397895273
-2.564949357
-2.397895273
-2.197224577
-2.833213344
-3.091042453
-3.218875825
-2.995732274
-3.17805383
-3.17805383
-3.091042453
-2.944438979
-2.890371758
-2.833213344
-2.397895273
-2.397895273
-2.48490665




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=51813&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=51813&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51813&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]
c84.7367843907454
b-18.4956787270994

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 84.7367843907454 \tabularnewline
b & -18.4956787270994 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51813&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]84.7367843907454[/C][/ROW]
[ROW][C]b[/C][C]-18.4956787270994[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51813&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51813&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]
c84.7367843907454
b-18.4956787270994







Descriptive Statistics about e[t]
# observations57
minimum-1.71633979507955
Q1-0.681315699311674
median-0.115572121110776
mean2.14686295970189e-17
Q30.738091959904905
maximum2.13640369481105

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 57 \tabularnewline
minimum & -1.71633979507955 \tabularnewline
Q1 & -0.681315699311674 \tabularnewline
median & -0.115572121110776 \tabularnewline
mean & 2.14686295970189e-17 \tabularnewline
Q3 & 0.738091959904905 \tabularnewline
maximum & 2.13640369481105 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51813&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]57[/C][/ROW]
[ROW][C]minimum[/C][C]-1.71633979507955[/C][/ROW]
[ROW][C]Q1[/C][C]-0.681315699311674[/C][/ROW]
[ROW][C]median[/C][C]-0.115572121110776[/C][/ROW]
[ROW][C]mean[/C][C]2.14686295970189e-17[/C][/ROW]
[ROW][C]Q3[/C][C]0.738091959904905[/C][/ROW]
[ROW][C]maximum[/C][C]2.13640369481105[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51813&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51813&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]
# observations57
minimum-1.71633979507955
Q1-0.681315699311674
median-0.115572121110776
mean2.14686295970189e-17
Q30.738091959904905
maximum2.13640369481105



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')