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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, 11 Nov 2009 07:23:09 -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/11/t1257949551cqp0ymxvyph80cv.htm/, Retrieved Fri, 26 Apr 2024 14:45:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=55616, Retrieved Fri, 26 Apr 2024 14:45:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact132
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Explorative Data Analysis] [] [2009-11-11 14:23:09] [c4328af89eba9af53ee195d6fed304d9] [Current]
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Dataseries X:
195,00
231,00
238,00
290,00
265,00
252,00
213,00
218,00
213,00
222,00
209,00
211,00
180,00
190,00
183,50
202,00
194,00
186,00
185,50
192,50
184,50
194,00
219,00
263,00
265,00
292,00
288,00
339,00
376,00
338,00
326,00
316,00
343,00
313,00
324,00
326,00
334,00
342,00
353,00
350,50
368,00
371,00
366,00
363,00
330,00
352,00
370,00
347,00
371,00
410,50
579,00
426,00
406,00
435,00
465,00
367,00
300,00
201,00
199,50
174,00
Dataseries Y:
416,25
398,35
400,00
427,25
391,25
397,20
394,80
391,50
407,65
418,10
429,10
452,85
427,75
420,90
433,45
427,15
427,90
415,35
432,60
431,65
439,60
466,10
459,50
499,75
530,00
568,25
564,25
587,00
661,00
625,00
622,95
637,25
621,05
600,60
614,10
648,75
639,75
660,20
670,40
658,25
673,60
666,50
654,75
665,75
672,00
742,50
790,25
784,25
846,75
914,75
988,50
887,75
853,00
888,25
937,50
912,50
822,25
880,00
729,50
778,00




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55616&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]5 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=55616&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55616&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Model: Y[t] = c + b X[t] + e[t]
c146.828919775016
b1.5602115798661

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 146.828919775016 \tabularnewline
b & 1.5602115798661 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55616&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]146.828919775016[/C][/ROW]
[ROW][C]b[/C][C]1.5602115798661[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55616&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55616&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]
c146.828919775016
b1.5602115798661







Descriptive Statistics about e[t]
# observations60
minimum-172.040277936185
Q1-57.8527779361848
median-28.6024216079925
mean2.42398693709826e-15
Q311.8734525846186
maximum419.568552671898

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -172.040277936185 \tabularnewline
Q1 & -57.8527779361848 \tabularnewline
median & -28.6024216079925 \tabularnewline
mean & 2.42398693709826e-15 \tabularnewline
Q3 & 11.8734525846186 \tabularnewline
maximum & 419.568552671898 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55616&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]-172.040277936185[/C][/ROW]
[ROW][C]Q1[/C][C]-57.8527779361848[/C][/ROW]
[ROW][C]median[/C][C]-28.6024216079925[/C][/ROW]
[ROW][C]mean[/C][C]2.42398693709826e-15[/C][/ROW]
[ROW][C]Q3[/C][C]11.8734525846186[/C][/ROW]
[ROW][C]maximum[/C][C]419.568552671898[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55616&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55616&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-172.040277936185
Q1-57.8527779361848
median-28.6024216079925
mean2.42398693709826e-15
Q311.8734525846186
maximum419.568552671898



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')