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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 computationSun, 01 Nov 2009 13:28:22 -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/01/t1257107337ex0tuj5yoohm0k4.htm/, Retrieved Tue, 07 May 2024 01:26:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=52405, Retrieved Tue, 07 May 2024 01:26:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact161

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] [WS 4 module 2] [2009-10-26 21:23:44] [830e13ac5e5ac1e5b21c6af0c149b21d]
- RMPD  [Partial Correlation] [WS 5 ] [2009-10-30 20:36:39] [023d83ebdf42a2acf423907b4076e8a1]
- RMPD      [Bivariate Explorative Data Analysis] [WS 5 X - Z] [2009-11-01 20:28:22] [51118f1042b56b16d340924f16263174] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.01
100.88
100.55
100.82
101.5
102.16
102.39
102.54
102.85
103.47
103.57
103.69
103.5
103.47
103.45
103.48
103.93
103.89
104.4
104.79
104.77
105.13
105.26
104.96
104.75
105.01
105.15
105.2
105.77
105.78
106.26
106.13
106.12
106.57
106.44
106.54
107.1
108.1
108.4
108.84
109.62
110.42
110.67
111.66
112.28
112.87
112.18
112.36
112.16
111.49
111.25
111.36
111.74
111.1
111.33
111.25
111.04
110.97
111.31
111.02
Dataseries Y:
Download CSV
Histogram 
0,818465
0,800641
0,769764
0,745823
0,762253
0,768403
0,757518
0,772917
0,787774
0,82203
0,830772
0,813537
0,815927
0,832293
0,848464
0,843455
0,826241
0,837661
0,831947
0,81493
0,783085
0,790514
0,788395
0,780579
0,785731
0,792959
0,776337
0,75683
0,76929
0,764877
0,755173
0,739864
0,740138
0,745212
0,729076
0,734107
0,719632
0,702889
0,681013
0,686342
0,67944
0,678058
0,644039
0,63488
0,642797
0,642963
0,634115
0,66778
0,695894
0,750638
0,785423
0,74355
0,755344
0,782167
0,766284
0,75815
0,732601
0,71347
0,709824
0,700869

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52405&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52405&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52405&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'George Udny Yule' @ 72.249.76.132






Model: Y[t] = c + b X[t] + e[t]
c1.90954284143585
b-0.0108217064145584

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 1.90954284143585 \tabularnewline
b & -0.0108217064145584 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52405&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]1.90954284143585[/C][/ROW]
[ROW][C]b[/C][C]-0.0108217064145584[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52405&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52405&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]
c1.90954284143585
b-0.0108217064145584






Descriptive Statistics about e[t]
# observations60
minimum-0.072675400720077
Q1-0.0320781418586299
median0.00106823675984944
mean-1.72870248344341e-18
Q30.0339349371812192
maximum0.079794997183767

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -0.072675400720077 \tabularnewline
Q1 & -0.0320781418586299 \tabularnewline
median & 0.00106823675984944 \tabularnewline
mean & -1.72870248344341e-18 \tabularnewline
Q3 & 0.0339349371812192 \tabularnewline
maximum & 0.079794997183767 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52405&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]-0.072675400720077[/C][/ROW]
[ROW][C]Q1[/C][C]-0.0320781418586299[/C][/ROW]
[ROW][C]median[/C][C]0.00106823675984944[/C][/ROW]
[ROW][C]mean[/C][C]-1.72870248344341e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.0339349371812192[/C][/ROW]
[ROW][C]maximum[/C][C]0.079794997183767[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52405&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52405&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-0.072675400720077
Q1-0.0320781418586299
median0.00106823675984944
mean-1.72870248344341e-18
Q30.0339349371812192
maximum0.079794997183767


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')