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Description of Statistical Computation

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 computationMon, 28 Dec 2009 04:22:26 -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/Dec/28/t1261999411d62045ubgpiwmqe.htm/, Retrieved Sun, 05 May 2024 06:38:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=70937, Retrieved Sun, 05 May 2024 06:38:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact123

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Explorative Data Analysis] [paper bivar2] [2009-12-28 11:22:26] [f47dffd5f5a8c03c3681db4cc9472742] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
110,75
110,64
110,46
110,66
110,48
110,5
110,96
111,17
111,07
111,75
111,45
111,24
111,09
111,29
111,15
110,88
111,22
110,62
110,2
109,49
109,32
108,71
107,85
107,44
106,93
106,19
105,71
105,67
105,7
105,28
105,34
105,58
105,23
105,46
104,92
104,68
104,58
104,32
104,36
104,38
104,25
103,93
103,95
103,6
103,23
103,31
102,82
102,76
102,68
102,37
102,54
102,65
102,63
102,22
102,04
101,85
101,88
101,33
100,8
Dataseries Y:
Download CSV
Histogram 
286.445
288.576
293.299
295.881
292.710
271.993
267.430
273.963
273.046
268.347
264.319
255.765
246.263
245.098
246.969
248.333
247.934
226.839
225.554
237.085
237.080
245.039
248.541
247.105
243.422
250.643
254.663
260.993
258.556
235.372
246.057
253.353
255.198
264.176
269.034
265.861
269.826
278.506
292.300
290.726
289.802
271.311
274.352
275.216
276.836
280.408
280.190
282.656
281.477
288.186
292.300
291.186
287.259
264.993
267.140
270.150
275.037
277.103
277.128

Tables (Output of Computation)





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=70937&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=70937&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70937&T=0

As an alternative you can also use a QR Code:  
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]
c473.95869778321
b-1.94708715996814

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 473.95869778321 \tabularnewline
b & -1.94708715996814 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70937&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]473.95869778321[/C][/ROW]
[ROW][C]b[/C][C]-1.94708715996814[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70937&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70937&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Model: Y[t] = c + b X[t] + e[t]
c473.95869778321
b-1.94708715996814






Descriptive Statistics about e[t]
# observations59
minimum-33.8356927547202
Q1-11.7815764663517
median-0.552457925655145
mean2.85109429444612e-15
Q312.5525748937757
maximum37.3869673388651

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 59 \tabularnewline
minimum & -33.8356927547202 \tabularnewline
Q1 & -11.7815764663517 \tabularnewline
median & -0.552457925655145 \tabularnewline
mean & 2.85109429444612e-15 \tabularnewline
Q3 & 12.5525748937757 \tabularnewline
maximum & 37.3869673388651 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70937&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]59[/C][/ROW]
[ROW][C]minimum[/C][C]-33.8356927547202[/C][/ROW]
[ROW][C]Q1[/C][C]-11.7815764663517[/C][/ROW]
[ROW][C]median[/C][C]-0.552457925655145[/C][/ROW]
[ROW][C]mean[/C][C]2.85109429444612e-15[/C][/ROW]
[ROW][C]Q3[/C][C]12.5525748937757[/C][/ROW]
[ROW][C]maximum[/C][C]37.3869673388651[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70937&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70937&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Descriptive Statistics about e[t]
# observations59
minimum-33.8356927547202
Q1-11.7815764663517
median-0.552457925655145
mean2.85109429444612e-15
Q312.5525748937757
maximum37.3869673388651


Figures (Output of Computation)

Input Parameters & R Code

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')