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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, 23 Oct 2009 07:08:17 -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/23/t12563034190zmqrvy9la29gi5.htm/, Retrieved Thu, 02 May 2024 00:06:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=49969, Retrieved Thu, 02 May 2024 00:06:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsPart 2
Estimated Impact162

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]
-   PD  [Bivariate Data Series] [WS 4 1 PLOTS] [2009-10-23 11:13:05] [6e4e01d7eb22a9f33d58ebb35753a195]
- RMP     [Pearson Correlation] [ws 4 scatterplot] [2009-10-23 11:15:47] [6e4e01d7eb22a9f33d58ebb35753a195]
- RMPD        [Bivariate Explorative Data Analysis] [Workshop 4] [2009-10-23 13:08:17] [0bdf648420800d03e6dbfbd39fe2311c] [Current]
-    D          [Bivariate Explorative Data Analysis] [Workshop 4] [2009-10-28 16:29:35] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
33
39
45
46
45
45
49
50
54
59
58
56
48
50
52
53
55
43
42
38
41
41
39
34
27
15
14
31
41
43
46
42
45
45
40
35
36
38
39
32
24
21
12
29
36
31
28
30
38
27
40
40
44
47
45
42
38
46
37
41
40
33
34
36
36
38
42
35
25
24
22
27
17
30
30
34
37
36
33
33
33
37
40
35
37
43
42
33
39
40
37
44
42
43
40
30
30
31
18
24
22
26
28
23
17
12
9
19
21
18
18
15
24
18
19
30
33
35
36
47
46
Dataseries Y:
Download CSV
Histogram 
62
64
62
64
64
69
69
65
56
58
53
62
55
60
59
58
53
57
57
53
54
53
57
57
55
49
50
49
54
58
58
52
56
52
59
53
52
53
51
50
56
52
46
48
46
48
48
49
53
48
51
48
50
55
52
53
52
55
53
53
56
54
52
55
54
59
56
56
51
53
52
51
46
49
46
55
57
53
52
53
50
54
53
50
51
52
47
51
49
53
52
45
53
51
48
48
48
48
40
43
40
39
39
36
41
39
40
39
46
40
37
37
44
41
40
36
38
43
42
45
46

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=49969&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=49969&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=49969&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]
c36.0876640540603
b0.426549024778832

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 36.0876640540603 \tabularnewline
b & 0.426549024778832 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=49969&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]36.0876640540603[/C][/ROW]
[ROW][C]b[/C][C]0.426549024778832[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=49969&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=49969&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]
c36.0876640540603
b0.426549024778832






Descriptive Statistics about e[t]
# observations121
minimum-12.8841347974252
Q1-3.12131139211718
median-0.0310367478675531
mean2.04898081972401e-16
Q32.83621812823829
maximum13.7176298308923

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 121 \tabularnewline
minimum & -12.8841347974252 \tabularnewline
Q1 & -3.12131139211718 \tabularnewline
median & -0.0310367478675531 \tabularnewline
mean & 2.04898081972401e-16 \tabularnewline
Q3 & 2.83621812823829 \tabularnewline
maximum & 13.7176298308923 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=49969&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]121[/C][/ROW]
[ROW][C]minimum[/C][C]-12.8841347974252[/C][/ROW]
[ROW][C]Q1[/C][C]-3.12131139211718[/C][/ROW]
[ROW][C]median[/C][C]-0.0310367478675531[/C][/ROW]
[ROW][C]mean[/C][C]2.04898081972401e-16[/C][/ROW]
[ROW][C]Q3[/C][C]2.83621812823829[/C][/ROW]
[ROW][C]maximum[/C][C]13.7176298308923[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=49969&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=49969&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]
# observations121
minimum-12.8841347974252
Q1-3.12131139211718
median-0.0310367478675531
mean2.04898081972401e-16
Q32.83621812823829
maximum13.7176298308923


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 1 ;
Parameters (R input):
par1 = 0 ; par2 = 1 ;
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')