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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_edabi.wasp
Title produced by softwareBivariate Explorative Data Analysis
Date of computationFri, 30 Oct 2009 07:23:00 -0600
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/Oct/30/t1256909041meqbr4u3lhi6gqa.htm/, Retrieved Mon, 29 Apr 2024 06:11:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=52095, Retrieved Mon, 29 Apr 2024 06:11:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
- RMPD    [Bivariate Explorative Data Analysis] [WS4_P2_BivarEDA] [2009-10-30 13:23:00] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
109.87
95.74
123.06
123.39
120.28
115.33
110.4
114.49
132.03
123.16
118.82
128.32
112.24
104.53
132.57
122.52
131.8
124.55
120.96
122.6
145.52
118.57
134.25
136.7
121.37
111.63
134.42
137.65
137.86
119.77
130.69
128.28
147.45
128.42
136.9
143.95
135.64
122.48
136.83
153.04
142.71
123.46
144.37
146.15
147.61
158.51
147.4
165.05
154.64
126.2
157.36
154.15
123.21
113.07
110.45
113.57
122.44
114.93
111.85
126.04
Dataseries Y:
Download CSV
Histogram 
79.8
83.4
113.6
112.9
104
109.9
99
106.3
128.9
111.1
102.9
130
87
87.5
117.6
103.4
110.8
112.6
102.5
112.4
135.6
105.1
127.7
137
91
90.5
122.4
123.3
124.3
120
118.1
119
142.7
123.6
129.6
151.6
110.4
99.2
130.5
136.2
129.7
128
121.6
135.8
143.8
147.5
136.2
156.6
123.3
104.5
139.8
136.5
112.1
118.5
94.4
102.3
111.4
99.2
87.8
115.8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52095&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52095&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52095&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 time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Model: Y[t] = c + b X[t] + e[t]
c-19.5460387116701
b1.05414769523628

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -19.5460387116701 \tabularnewline
b & 1.05414769523628 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52095&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-19.5460387116701[/C][/ROW]
[ROW][C]b[/C][C]1.05414769523628[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52095&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52095&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]
c-19.5460387116701
b1.05414769523628






Descriptive Statistics about e[t]
# observations60
minimum-20.1673608796681
Q1-5.49293101990514
median0.590938500106292
mean3.35809938883935e-16
Q33.7752723103761
maximum19.4014779824078

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -20.1673608796681 \tabularnewline
Q1 & -5.49293101990514 \tabularnewline
median & 0.590938500106292 \tabularnewline
mean & 3.35809938883935e-16 \tabularnewline
Q3 & 3.7752723103761 \tabularnewline
maximum & 19.4014779824078 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52095&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]-20.1673608796681[/C][/ROW]
[ROW][C]Q1[/C][C]-5.49293101990514[/C][/ROW]
[ROW][C]median[/C][C]0.590938500106292[/C][/ROW]
[ROW][C]mean[/C][C]3.35809938883935e-16[/C][/ROW]
[ROW][C]Q3[/C][C]3.7752723103761[/C][/ROW]
[ROW][C]maximum[/C][C]19.4014779824078[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52095&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52095&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]
# observations60
minimum-20.1673608796681
Q1-5.49293101990514
median0.590938500106292
mean3.35809938883935e-16
Q33.7752723103761
maximum19.4014779824078


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')