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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, 11 Nov 2009 05:06:21 -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/11/t1257941306asjfqlatp3s8vov.htm/, Retrieved Thu, 25 Apr 2024 07:24:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=55516, Retrieved Thu, 25 Apr 2024 07:24:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact190

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] [] [2009-11-11 12:06:21] [c4328af89eba9af53ee195d6fed304d9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
5,9650
6,2050
6,7300
7,9400
6,1000
6,1700
5,8750
6,6400
6,7300
6,8400
7,2400
7,7150
6,3900
6,7000
7,2600
7,1050
6,8500
7,3800
7,0200
7,2500
6,8550
7,3700
7,5350
8,3150
9,0400
9,7700
9,8000
11,5850
13,9200
12,0500
11,2800
11,3550
12,7500
11,5800
12,3600
13,8500
13,0100
13,5800
14,2900
13,3350
13,4500
13,5300
12,4700
12,7700
12,1000
13,7750
14,3600
13,8600
14,9300
17,1900
20,1600
16,7400
16,6400
16,8600
17,5600
17,5900
13,5800
12,2800
10,0500
9,9100
Dataseries Y:
Download CSV
Histogram 
1111,92
1131,13
1144,94
1113,89
1107,30
1120,68
1140,84
1101,72
1104,24
1114,58
1130,20
1173,78
1211,92
1181,27
1203,60
1180,59
1156,85
1191,50
1191,33
1234,18
1220,33
1228,81
1207,01
1249,48
1248,29
1280,08
1280,66
1302,88
1310,61
1270,05
1270,06
1278,53
1303,80
1335,83
1377,76
1400,63
1418,03
1437,90
1406,80
1420,83
1482,37
1530,63
1504,66
1455,18
1473,96
1527,29
1545,79
1479,63
1467,97
1378,60
1330,45
1326,41
1385,97
1399,62
1276,69
1269,42
1287,83
1164,17
968,67
888,61

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55516&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]5 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=55516&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55516&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Model: Y[t] = c + b X[t] + e[t]
c995.028856470993
b25.7674948605789

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 995.028856470993 \tabularnewline
b & 25.7674948605789 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55516&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]995.028856470993[/C][/ROW]
[ROW][C]b[/C][C]25.7674948605789[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55516&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55516&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]
c995.028856470993
b25.7674948605789






Descriptive Statistics about e[t]
# observations60
minimum-361.77473053933
Q1-43.5544324778196
median4.39506200126425
mean-8.19714666514907e-16
Q349.600217624969
maximum188.310482617588

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -361.77473053933 \tabularnewline
Q1 & -43.5544324778196 \tabularnewline
median & 4.39506200126425 \tabularnewline
mean & -8.19714666514907e-16 \tabularnewline
Q3 & 49.600217624969 \tabularnewline
maximum & 188.310482617588 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55516&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]-361.77473053933[/C][/ROW]
[ROW][C]Q1[/C][C]-43.5544324778196[/C][/ROW]
[ROW][C]median[/C][C]4.39506200126425[/C][/ROW]
[ROW][C]mean[/C][C]-8.19714666514907e-16[/C][/ROW]
[ROW][C]Q3[/C][C]49.600217624969[/C][/ROW]
[ROW][C]maximum[/C][C]188.310482617588[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55516&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55516&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-361.77473053933
Q1-43.5544324778196
median4.39506200126425
mean-8.19714666514907e-16
Q349.600217624969
maximum188.310482617588


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')