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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 08:53:34 -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/t1256741751f36gmflz3w1pczw.htm/, Retrieved Mon, 06 May 2024 04:21:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51378, Retrieved Mon, 06 May 2024 04:21:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsY[t] = c + b X[t] + e[t] cvm
Estimated Impact118
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 ...] [2009-10-28 14:53:34] [a5ada8bd39e806b5b90f09589c89554a] [Current]
-    D    [Bivariate Explorative Data Analysis] [Workshop 4: Part ...] [2009-10-28 14:57:24] [03d5b865e91ca35b5a5d21b8d6da5aba]
-    D      [Bivariate Explorative Data Analysis] [Workshop 4: Part ...] [2009-10-28 15:10:24] [03d5b865e91ca35b5a5d21b8d6da5aba]
-    D      [Bivariate Explorative Data Analysis] [Workshop 4: Part ...] [2009-10-28 15:14:02] [03d5b865e91ca35b5a5d21b8d6da5aba]
-    D        [Bivariate Explorative Data Analysis] [Workshop 4: Part ...] [2009-10-28 15:21:40] [03d5b865e91ca35b5a5d21b8d6da5aba]
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Dataseries X:
-2806,152717
-2947,419023
-4456,870921
-4174,40494
-3784,050335
-3781,328125
-3336,329208
-3415,384571
-3999,205373
-3669,639501
-3413,184571
-4105,516208
-2646,919781
-2918,385762
-4402,526609
-3761,961494
-4175,90494
-3795,694755
-3377,129099
-3691,828341
-4189,293781
-3526,095405
-3903,60559
-4170,10494
-2536,953367
-2973,852284
-4024,260845
-4173,22715
-3815,228017
-3424,351201
-3420,351201
-3375,85131
-3774,172545
-3214,340583
-3356,784679
-3861,150119
-2730,741774
-3075,618698
-3795,539176
-4498,793023
-3809,805806
-3170,218481
-3460,851093
-3448,706673
-3506,950985
-3996,649794
-3437,340042
-3525,962036
-3174,218481
-2658,153042
-3901,7278
-3772,250335
-2894,596921
-2789,208297
-2461,042424
-2795,330507
-3247,473845
-2950,119023
-2907,219131
-3403,56236
-2273,576227
Dataseries Y:
87,4
96,8
114,1
110,3
103,9
101,6
94,6
95,9
104,7
102,8
98,1
113,9
80,9
95,7
113,2
105,9
108,8
102,3
99
100,7
115,5
100,7
109,9
114,6
85,4
100,5
114,8
116,5
112,9
102
106
105,3
118,8
106,1
109,3
117,2
92,5
104,2
112,5
122,4
113,3
100
110,7
112,8
109,8
117,3
109,1
115,9
96
99,8
116,8
115,7
99,4
94,3
91
93,2
103,1
94,1
91,8
102,7
82,6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51378&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51378&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51378&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'RServer@AstonUniversity' @ vre.aston.ac.uk







Model: Y[t] = c + b X[t] + e[t]
c51.3068077687487
b-0.0152645541487493

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 51.3068077687487 \tabularnewline
b & -0.0152645541487493 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51378&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]51.3068077687487[/C][/ROW]
[ROW][C]b[/C][C]-0.0152645541487493[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51378&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51378&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]
c51.3068077687487
b-0.0152645541487493







Descriptive Statistics about e[t]
# observations61
minimum-10.8108580932188
Q1-4.52221863815257
median-0.0756822343325843
mean-1.76305492291028e-16
Q33.83820520934483
maximum10.7709538062950

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 61 \tabularnewline
minimum & -10.8108580932188 \tabularnewline
Q1 & -4.52221863815257 \tabularnewline
median & -0.0756822343325843 \tabularnewline
mean & -1.76305492291028e-16 \tabularnewline
Q3 & 3.83820520934483 \tabularnewline
maximum & 10.7709538062950 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51378&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]61[/C][/ROW]
[ROW][C]minimum[/C][C]-10.8108580932188[/C][/ROW]
[ROW][C]Q1[/C][C]-4.52221863815257[/C][/ROW]
[ROW][C]median[/C][C]-0.0756822343325843[/C][/ROW]
[ROW][C]mean[/C][C]-1.76305492291028e-16[/C][/ROW]
[ROW][C]Q3[/C][C]3.83820520934483[/C][/ROW]
[ROW][C]maximum[/C][C]10.7709538062950[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51378&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51378&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]
# observations61
minimum-10.8108580932188
Q1-4.52221863815257
median-0.0756822343325843
mean-1.76305492291028e-16
Q33.83820520934483
maximum10.7709538062950



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')