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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, 30 Oct 2009 07:22:16 -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/t1256909473r8fcku3oo3yzbkp.htm/, Retrieved Mon, 29 Apr 2024 00:16:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=52097, Retrieved Mon, 29 Apr 2024 00:16:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact157

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] [workshop 4,2,1] [2009-10-30 11:37:24] [35f0fff14d789f48983afb62e692bd0d]
-    D    [Bivariate Explorative Data Analysis] [workshop 4,2,3] [2009-10-30 13:22:16] [2210215221105fab636491031ce54076] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
65,61
64
56,25
54,76
59,29
62,41
59,29
50,41
38,44
33,64
37,21
47,61
53,29
51,84
37,21
33,64
37,21
39,69
46,24
46,24
42,25
38,44
39,69
40,96
43,56
57,76
40,96
46,24
49
47,61
50,41
51,84
50,41
49
47,61
44,89
43,56
47,61
53,29
62,41
67,24
67,24
67,24
65,61
62,41
59,29
59,29
57,76
56,25
56,25
50,41
56,25
56,25
60,84
60,84
60,84
57,76
56,25
59,29
65,61
64
Dataseries Y:
Download CSV
Histogram 
123,21
118,81
100
84,64
84,64
90,25
92,16
90,25
82,81
79,21
81
102,01
106,09
104,04
92,16
84,64
86,49
88,36
88,36
84,64
81
81
81
96,04
100
96,04
86,49
81
81
82,81
82,81
82,81
84,64
77,44
68,89
70,56
65,61
59,29
62,41
62,41
64
62,41
57,76
50,41
46,24
42,25
47,61
67,24
75,69
68,89
62,41
56,25
60,84
68,89
70,56
67,24
59,29
51,84
53,29
65,61
72,25

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 4 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52097&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52097&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52097&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 time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Model: Y[t] = c + b X[t] + e[t]
c106.105640002040
b-0.550209815951468

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 106.105640002040 \tabularnewline
b & -0.550209815951468 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52097&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]106.105640002040[/C][/ROW]
[ROW][C]b[/C][C]-0.550209815951468[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52097&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52097&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]
c106.105640002040
b-0.550209815951468






Descriptive Statistics about e[t]
# observations61
minimum-31.2337000142776
Q1-10.8467213639787
median-2.07087479955278
mean-8.23709270191282e-17
Q36.52766724951402
maximum53.2036260225357

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 61 \tabularnewline
minimum & -31.2337000142776 \tabularnewline
Q1 & -10.8467213639787 \tabularnewline
median & -2.07087479955278 \tabularnewline
mean & -8.23709270191282e-17 \tabularnewline
Q3 & 6.52766724951402 \tabularnewline
maximum & 53.2036260225357 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52097&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]-31.2337000142776[/C][/ROW]
[ROW][C]Q1[/C][C]-10.8467213639787[/C][/ROW]
[ROW][C]median[/C][C]-2.07087479955278[/C][/ROW]
[ROW][C]mean[/C][C]-8.23709270191282e-17[/C][/ROW]
[ROW][C]Q3[/C][C]6.52766724951402[/C][/ROW]
[ROW][C]maximum[/C][C]53.2036260225357[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52097&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52097&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-31.2337000142776
Q1-10.8467213639787
median-2.07087479955278
mean-8.23709270191282e-17
Q36.52766724951402
maximum53.2036260225357


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')