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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 13:42:45 -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/t1258145013xpz8duj3vsr1cc8.htm/, Retrieved Sun, 05 May 2024 19:30:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57118, Retrieved Sun, 05 May 2024 19:30:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact129

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] [3/11/2009] [2009-11-02 22:01:32] [b98453cac15ba1066b407e146608df68]
-   PD  [Bivariate Explorative Data Analysis] [workshop 6,6] [2009-11-10 21:16:38] [35f0fff14d789f48983afb62e692bd0d]
-    D      [Bivariate Explorative Data Analysis] [Workshop 6] [2009-11-13 20:42:45] [ee8fc1691ecec7724e0ca78f0c288737] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
87.1
110.5
110.8
104.2
88.9
89.8
90
93.9
91.3
87.8
99.7
73.5
79.2
96.9
95.2
95.6
89.7
92.8
88
101.1
92.7
95.8
103.8
81.8
87.1
105.9
108.1
102.6
93.7
103.5
100.6
113.3
102.4
102.1
106.9
87.3
93.1
109.1
120.3
104.9
92.6
109.8
111.4
117.9
121.6
117.8
124.2
106.8
102.7
116.8
113.6
96.1
85
83.2
84.9
83
79.6
83.2
83.8
82.8
71.4
Dataseries Y:
Download CSV
Histogram 
122.6
115.4
109
129.1
102.8
96.2
127.7
128.9
126.5
119.8
113.2
114.1
134.1
130
121.8
132.1
105.3
103
117.1
126.3
138.1
119.5
138
135.5
178.6
162.2
176.9
204.9
132.2
142.5
164.3
174.9
175.4
143
158.7
155.4
176.6
163.3
178.9
182.7
158.9
115.5
169.4
168
159.8
129
157.9
169.5
169.1
183.6
168.9
186.2
227.1
126.4
169.3
175
133.9
110.1
104.3
108.7
112.6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57118&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]2 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=57118&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57118&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Model: Y[t] = c + b X[t] + e[t]
c61.1818777463889
b0.848467938121139

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 61.1818777463889 \tabularnewline
b & 0.848467938121139 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57118&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]61.1818777463889[/C][/ROW]
[ROW][C]b[/C][C]0.848467938121139[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57118&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57118&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]
c61.1818777463889
b0.848467938121139






Descriptive Statistics about e[t]
# observations61
minimum-46.1921252902111
Q1-20.1560254555213
median-5.37441019806761
mean1.37176813624087e-15
Q317.7017464622735
maximum93.7983475133143

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 61 \tabularnewline
minimum & -46.1921252902111 \tabularnewline
Q1 & -20.1560254555213 \tabularnewline
median & -5.37441019806761 \tabularnewline
mean & 1.37176813624087e-15 \tabularnewline
Q3 & 17.7017464622735 \tabularnewline
maximum & 93.7983475133143 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57118&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]-46.1921252902111[/C][/ROW]
[ROW][C]Q1[/C][C]-20.1560254555213[/C][/ROW]
[ROW][C]median[/C][C]-5.37441019806761[/C][/ROW]
[ROW][C]mean[/C][C]1.37176813624087e-15[/C][/ROW]
[ROW][C]Q3[/C][C]17.7017464622735[/C][/ROW]
[ROW][C]maximum[/C][C]93.7983475133143[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57118&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57118&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]
# observations61
minimum-46.1921252902111
Q1-20.1560254555213
median-5.37441019806761
mean1.37176813624087e-15
Q317.7017464622735
maximum93.7983475133143


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')