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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 computationFri, 13 Nov 2009 08:34:19 -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/13/t1258126539lmekwbr51d911p1.htm/, Retrieved Sat, 27 Apr 2024 14:44:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=56825, Retrieved Sat, 27 Apr 2024 14:44:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact244

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Box-Cox Linearity Plot] [3/11/2009] [2009-11-02 21:47:57] [b98453cac15ba1066b407e146608df68]
- RMPD  [Bivariate Explorative Data Analysis] [] [2009-11-09 13:21:04] [023d83ebdf42a2acf423907b4076e8a1]
-   PD      [Bivariate Explorative Data Analysis] [W6 Bivariate EDA] [2009-11-13 15:34:19] [852eae237d08746109043531619a60c9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
105.9
117.6
113.6
115.9
118.9
77.6
81.2
123.1
136.6
112.1
95.1
96.3
105.7
115
105.7
105.7
111.1
82.4
60
107.3
99.3
113.5
108.9
100.2
103.9
138.7
120.2
100.2
143.2
70.9
85.2
133
136.6
117.9
106.3
122.3
125.5
148.4
126.3
99.6
140.4
80.3
92.6
138.5
110.9
119.6
105
109
129.4
148.6
101.4
134.8
143.7
81.6
90.3
141.5
140.7
140.2
100.2
125.7
119.6
134.7
109
116.3
146.9
97.4
89.4
132.1
139.8
129
112.5
121.9
121.7
123.1
131.6
119.3
132.5
98.3
85.1
131.7
129.3
90.7
78.6
68.9
79.1
Dataseries Y:
Download CSV
Histogram 
107.25
105.80
102.90
100.00
98.55
108.70
110.14
113.04
115.94
117.39
118.84
120.29
118.84
115.94
114.49
110.14
110.14
120.29
121.74
121.74
121.74
121.74
124.64
128.99
127.54
120.29
108.70
104.35
107.25
127.54
134.78
134.78
126.09
118.84
120.29
123.19
124.64
123.19
118.84
117.39
114.49
124.64
126.09
126.09
123.19
121.74
123.19
126.09
126.09
124.64
123.19
120.29
115.94
118.84
117.39
117.39
115.94
114.49
114.49
115.94
115.94
114.49
115.94
111.59
104.35
108.70
105.80
101.45
101.45
101.45
104.35
105.80
102.90
98.55
92.75
88.41
94.20
111.59
114.49
108.70
100.00
95.65
100.00
111.59
115.94

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=56825&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=56825&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56825&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]
c120.974164067820
b-0.0582932921274122

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 120.974164067820 \tabularnewline
b & -0.0582932921274122 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56825&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]120.974164067820[/C][/ROW]
[ROW][C]b[/C][C]-0.0582932921274122[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56825&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56825&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]
c120.974164067820
b-0.0582932921274122






Descriptive Statistics about e[t]
# observations85
minimum-25.6097743170201
Q1-7.55090443152784
median1.70021414686830
mean4.83832872663462e-16
Q37.38212458864091
maximum21.5588437851255

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 85 \tabularnewline
minimum & -25.6097743170201 \tabularnewline
Q1 & -7.55090443152784 \tabularnewline
median & 1.70021414686830 \tabularnewline
mean & 4.83832872663462e-16 \tabularnewline
Q3 & 7.38212458864091 \tabularnewline
maximum & 21.5588437851255 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=56825&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]85[/C][/ROW]
[ROW][C]minimum[/C][C]-25.6097743170201[/C][/ROW]
[ROW][C]Q1[/C][C]-7.55090443152784[/C][/ROW]
[ROW][C]median[/C][C]1.70021414686830[/C][/ROW]
[ROW][C]mean[/C][C]4.83832872663462e-16[/C][/ROW]
[ROW][C]Q3[/C][C]7.38212458864091[/C][/ROW]
[ROW][C]maximum[/C][C]21.5588437851255[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=56825&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=56825&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]
# observations85
minimum-25.6097743170201
Q1-7.55090443152784
median1.70021414686830
mean4.83832872663462e-16
Q37.38212458864091
maximum21.5588437851255


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 50 ; par2 = 50 ; par3 = 0 ; par4 = 0 ; par5 = 0 ; par6 = Y ; par7 = Y ;
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')