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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 computationMon, 09 Nov 2009 03:56:58 -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/09/t1257764301qzfivybh04gqjb1.htm/, Retrieved Fri, 19 Apr 2024 15:10:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=54726, Retrieved Fri, 19 Apr 2024 15:10:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact209
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Explorative Data Analysis] [3/11/2009] [2009-11-02 22:01:32] [b98453cac15ba1066b407e146608df68]
-   PD    [Bivariate Explorative Data Analysis] [Bivariate EDA icp...] [2009-11-09 10:56:58] [4f297b039e1043ebee7ff7a83b1eaaaa] [Current]
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Dataseries X:
124.06
124.58
122.00
124.02
124.16
124.29
123.93
124.62
121.81
124.14
124.31
125.15
125.35
125.48
124.17
125.33
124.46
123.39
123.14
122.24
119.31
120.87
120.43
119.41
118.85
119.08
117.25
118.51
118.42
118.56
117.97
117.98
115.25
117.23
117.08
116.83
117.17
117.73
115.74
116.99
116.90
116.49
115.84
115.92
113.32
114.84
114.75
114.84
115.03
115.03
112.99
114.15
113.77
113.57
113.38
112.71
110.27
111.73
112.12
112.31
111.73
111.83
109.99
111.15
111.25
110.87
110.27
110.18
108.15
109.60
109.60
109.41
109.80
109.60
107.76
109.02
108.62
109.02
109.22
108.92
106.69
107.76
107.66
107.85
107.95
107.85
106.30
107.37
107.66
107.46
107.37
107.18
105.43
106.39
106.50
106.50
106.69
106.50
105.14
106.50
106.20
105.72
104.76
104.55
102.71
104.36
104.65
104.46
104.65
103.88
102.32
103.39
103.00
102.71
102.51
102.04
100.00
Dataseries Y:
241.66
251.25
230.26
240.91
211.20
188.19
177.01
167.85
174.03
170.09
203.42
254.97
342.84
386.29
440.51
433.58
408.13
370.32
355.51
332.62
314.62
301.73
306.31
282.98
266.48
249.97
259.87
246.24
238.36
238.04
224.19
214.71
203.11
221.00
211.73
209.39
217.48
242.19
244.64
232.07
235.80
230.37
209.82
206.41
209.60
192.24
186.17
193.41
202.36
203.00
190.64
185.43
171.58
179.57
180.42
162.10
157.95
146.66
154.43
163.38
150.92
151.98
144.74
140.37
143.36
135.79
134.73
126.42
124.72
117.90
114.07
112.26
105.44
110.77
107.68
105.76
102.03
100.22
111.62
118.11
111.72
103.42
97.13
103.10
104.91
100.22
98.52
95.32
96.92
96.60
92.55
82.75
80.84
79.13
79.77
85.10
96.39
97.56
96.39
101.18
103.52
100.11
99.26
104.48
101.29
100.33
115.24
113.64
115.35
108.42
105.65
108.64
104.80
95.43
104.48
103.84
100.01




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=54726&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=54726&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54726&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]
c-976.371333859081
b10.1865981783424

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -976.371333859081 \tabularnewline
b & 10.1865981783424 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54726&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-976.371333859081[/C][/ROW]
[ROW][C]b[/C][C]10.1865981783424[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54726&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54726&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]
c-976.371333859081
b10.1865981783424







Descriptive Statistics about e[t]
# observations117
minimum-125.232531125953
Q1-22.0332796751506
median-1.21760094176433
mean-6.84756145423187e-16
Q320.1045120639707
maximum152.011438054301

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 117 \tabularnewline
minimum & -125.232531125953 \tabularnewline
Q1 & -22.0332796751506 \tabularnewline
median & -1.21760094176433 \tabularnewline
mean & -6.84756145423187e-16 \tabularnewline
Q3 & 20.1045120639707 \tabularnewline
maximum & 152.011438054301 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54726&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]117[/C][/ROW]
[ROW][C]minimum[/C][C]-125.232531125953[/C][/ROW]
[ROW][C]Q1[/C][C]-22.0332796751506[/C][/ROW]
[ROW][C]median[/C][C]-1.21760094176433[/C][/ROW]
[ROW][C]mean[/C][C]-6.84756145423187e-16[/C][/ROW]
[ROW][C]Q3[/C][C]20.1045120639707[/C][/ROW]
[ROW][C]maximum[/C][C]152.011438054301[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54726&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54726&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]
# observations117
minimum-125.232531125953
Q1-22.0332796751506
median-1.21760094176433
mean-6.84756145423187e-16
Q320.1045120639707
maximum152.011438054301



Parameters (Session):
par1 = 3 ; par2 = TRUE ; par3 = TRUE ;
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')