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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 03:15:48 -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/t1256289478hz2h0z6grqnlphk.htm/, Retrieved Wed, 01 May 2024 23:15:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=49857, Retrieved Wed, 01 May 2024 23:15:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact208

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]
- RMPD    [Bivariate Explorative Data Analysis] [WS4 Part2 Vraag1] [2009-10-23 09:15:48] [37de18e38c1490dd77c2b362ed87f3bb] [Current]
-   PD      [Bivariate Explorative Data Analysis] [WS4 Part2 Vraag2] [2009-10-23 09:20:24] [42ad1186d39724f834063794eac7cea3]
-   PD        [Bivariate Explorative Data Analysis] [WS4 Part2 Vraag3] [2009-10-23 09:26:27] [42ad1186d39724f834063794eac7cea3]
-   PD      [Bivariate Explorative Data Analysis] [WS4 Part2 Vraag2 TVD] [2009-10-23 09:31:37] [42ad1186d39724f834063794eac7cea3]
-   P         [Bivariate Explorative Data Analysis] [BDM 5] [2009-10-27 14:58:17] [f5d341d4bbba73282fc6e80153a6d315]
-   P         [Bivariate Explorative Data Analysis] [TG 5] [2009-10-27 15:06:55] [a21bac9c8d3d56fdec8be4e719e2c7ed]
-   PD      [Bivariate Explorative Data Analysis] [WS4 Part2 Vraag3 TVD] [2009-10-23 09:35:21] [42ad1186d39724f834063794eac7cea3]
-   P         [Bivariate Explorative Data Analysis] [BDM 6] [2009-10-27 14:59:24] [f5d341d4bbba73282fc6e80153a6d315]
-   P         [Bivariate Explorative Data Analysis] [TG 6] [2009-10-27 15:07:56] [a21bac9c8d3d56fdec8be4e719e2c7ed]
-   P       [Bivariate Explorative Data Analysis] [BDM 4] [2009-10-27 14:57:01] [f5d341d4bbba73282fc6e80153a6d315]
-  MP         [Bivariate Explorative Data Analysis] [tg12] [2009-11-10 14:39:34] [a21bac9c8d3d56fdec8be4e719e2c7ed]
- R P           [Bivariate Explorative Data Analysis] [ws6] [2009-11-15 22:36:29] [3fc64fd7a52ce121dfe13dba27bf6e5b]
-  MP         [Bivariate Explorative Data Analysis] [TVD12] [2009-11-10 22:34:49] [42ad1186d39724f834063794eac7cea3]
-   P           [Bivariate Explorative Data Analysis] [PA12] [2009-12-15 09:59:55] [a21bac9c8d3d56fdec8be4e719e2c7ed]
-  MPD        [Bivariate Explorative Data Analysis] [WS6 EDA] [2009-11-12 16:38:11] [445b292c553470d9fed8bc2796fd3a00]
-   P       [Bivariate Explorative Data Analysis] [TG 4] [2009-10-27 15:05:45] [a21bac9c8d3d56fdec8be4e719e2c7ed]
-  MP       [Bivariate Explorative Data Analysis] [P1] [2009-12-15 09:43:25] [f5d341d4bbba73282fc6e80153a6d315]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
89.3
90.3
91.1
90.1
86.7
85.1
83.4
82
80.4
81.9
93.8
94.8
92.3
87.5
83.2
82
80.3
81.8
85.1
84.2
84.4
84.5
93.3
93.2
100.3
111.4
114.9
109.5
109.9
105.8
110.8
108.8
116.1
109.8
113.8
113.8
117.4
119.5
122.6
120.7
119
126.1
133.9
138.1
140.4
148.2
148.2
155.9
171.1
171.9
188.8
214.9
228.5
220
225.4
220.7
219.7
232.1
223.5
218.9
Dataseries Y:
Download CSV
Histogram 
101.3
106.3
94
102.8
102
105.1
92.4
81.4
105.8
120.3
100.7
88.8
94.3
99.9
103.4
103.3
98.8
104.2
91.2
74.7
108.5
114.5
96.9
89.6
97.1
100.3
122.6
115.4
109
129.1
102.8
96.2
127.7
128.9
126.5
119.8
113.2
114.1
134.1
130
121.8
132.1
105.3
103
117.1
126.3
138.1
119.5
138
135.5
178.6
162.2
176.9
204.9
132.2
142.5
164.3
174.9
175.4
143

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=49857&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=49857&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=49857&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]
c59.3029920782513
b0.475417996747477

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 59.3029920782513 \tabularnewline
b & 0.475417996747477 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=49857&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]59.3029920782513[/C][/ROW]
[ROW][C]b[/C][C]0.475417996747477[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=49857&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=49857&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]
c59.3029920782513
b0.475417996747477






Descriptive Statistics about e[t]
# observations60
minimum-34.2622085451326
Q1-8.9009745889318
median0.604760225564558
mean-3.68917859224401e-17
Q38.4229156266954
maximum41.0050486373038

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -34.2622085451326 \tabularnewline
Q1 & -8.9009745889318 \tabularnewline
median & 0.604760225564558 \tabularnewline
mean & -3.68917859224401e-17 \tabularnewline
Q3 & 8.4229156266954 \tabularnewline
maximum & 41.0050486373038 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=49857&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]-34.2622085451326[/C][/ROW]
[ROW][C]Q1[/C][C]-8.9009745889318[/C][/ROW]
[ROW][C]median[/C][C]0.604760225564558[/C][/ROW]
[ROW][C]mean[/C][C]-3.68917859224401e-17[/C][/ROW]
[ROW][C]Q3[/C][C]8.4229156266954[/C][/ROW]
[ROW][C]maximum[/C][C]41.0050486373038[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=49857&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=49857&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-34.2622085451326
Q1-8.9009745889318
median0.604760225564558
mean-3.68917859224401e-17
Q38.4229156266954
maximum41.0050486373038


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = colombia ; par2 = www.ico.org ; par3 = Prices paid to growers in exporting Member countries in US cents per lb (Arabica, 1977/1 - 2006/12) ; par4 = usa ; par5 = www.ico.org ; par6 = Retail prices in importing Member countries in US cents per lb (Arabica, 1977/1 - 2006/12) ;
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')