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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 computationWed, 28 Oct 2009 11:35:45 -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/28/t1256751388h67zouaigl9ohvp.htm/, Retrieved Mon, 06 May 2024 03:44:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51635, Retrieved Mon, 06 May 2024 03:44:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact116

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] [] [2009-10-25 17:21:06] [badc6a9acdc45286bea7f74742e15a21]
-   PD    [Bivariate Data Series] [] [2009-10-25 17:43:31] [badc6a9acdc45286bea7f74742e15a21]
-   PD      [Bivariate Data Series] [] [2009-10-28 17:21:09] [badc6a9acdc45286bea7f74742e15a21]
-   PD        [Bivariate Data Series] [] [2009-10-28 17:33:36] [badc6a9acdc45286bea7f74742e15a21]
- RM D            [Bivariate Explorative Data Analysis] [] [2009-10-28 17:35:45] [0545e25c765ce26b196961216dc11e13] [Current]
- RM                [Pearson Correlation] [] [2009-10-28 17:39:19] [badc6a9acdc45286bea7f74742e15a21]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4.8841
5.0625
5.0625
6.0025
6.25
6.25
6.9696
7.5625
8.5849
9
10.0489
10.5625
11.4921
12.25
12.25
13.3225
14.0625
14.0625
15.21
16
16
16
16
16
16
16
16
16
16
16
16
17.4724
18.0625
18.0625
15.7609
11.6964
7.5625
5.3361
4
2.7556
1.7161
1.1881
1
1
1
1
Dataseries Y:
Download CSV
Histogram 
1.96
1.44
1
2.89
5.76
4
4.41
4
3.24
7.29
5.29
3.61
4
5.29
7.84
5.76
5.29
7.29
7.29
8.41
9
4.84
5.29
7.84
7.84
7.84
4.84
6.76
7.84
6.25
5.76
5.29
3.61
2.89
4
4.41
2.89
3.24
3.24
3.24
1.69
1.69
1.69
1.44
1.96
4.84
8.41
9.61
12.25
12.96
19.36
16.81
26.01
33.64
34.81
29.16
30.25
23.04
10.24
7.29
4.41
3.61
0.36
0.49
0.04
1
2.89
0.49
1

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'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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51635&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51635&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51635&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Model: Y[t] = c + b X[t] + e[t]
c0.652932863451816
b0.805180470525073

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 0.652932863451816 \tabularnewline
b & 0.805180470525073 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51635&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]0.652932863451816[/C][/ROW]
[ROW][C]b[/C][C]0.805180470525073[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51635&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51635&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]
c0.652932863451816
b0.805180470525073






Descriptive Statistics about e[t]
# observations69
minimum-12.0958203918530
Q1-2.51168816803071
median-0.26365474555211
mean5.18849663565427e-17
Q32.15468919576648
maximum20.104179608147

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 69 \tabularnewline
minimum & -12.0958203918530 \tabularnewline
Q1 & -2.51168816803071 \tabularnewline
median & -0.26365474555211 \tabularnewline
mean & 5.18849663565427e-17 \tabularnewline
Q3 & 2.15468919576648 \tabularnewline
maximum & 20.104179608147 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51635&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]69[/C][/ROW]
[ROW][C]minimum[/C][C]-12.0958203918530[/C][/ROW]
[ROW][C]Q1[/C][C]-2.51168816803071[/C][/ROW]
[ROW][C]median[/C][C]-0.26365474555211[/C][/ROW]
[ROW][C]mean[/C][C]5.18849663565427e-17[/C][/ROW]
[ROW][C]Q3[/C][C]2.15468919576648[/C][/ROW]
[ROW][C]maximum[/C][C]20.104179608147[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51635&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51635&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]
# observations69
minimum-12.0958203918530
Q1-2.51168816803071
median-0.26365474555211
mean5.18849663565427e-17
Q32.15468919576648
maximum20.104179608147


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
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')