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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 12:33:35 -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/t1257968076pudjtjrxyju3kgd.htm/, Retrieved Fri, 26 Apr 2024 11:00:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=55826, Retrieved Fri, 26 Apr 2024 11:00:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact156

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 Bivariate EDA...] [2009-10-27 17:41:15] [1d635fe1113b56bab3f378c464a289bc]
-    D    [Bivariate Explorative Data Analysis] [WS304] [2009-10-29 11:36:59] [4a2be4899cba879e4eea9daa25281df8]
- RMPD      [Trivariate Scatterplots] [Workshop 5.1] [2009-11-11 19:06:58] [4a2be4899cba879e4eea9daa25281df8]
- RMPD          [Bivariate Explorative Data Analysis] [Workshop 5.5] [2009-11-11 19:33:35] [71c065898bd1c08eef04509b4bcee039] [Current]
- RMPD            [Pearson Correlation] [Workshop 5.6] [2009-11-11 19:36:06] [4a2be4899cba879e4eea9daa25281df8]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
79,8
88,8
106
102,6
96,4
94
86,8
88,1
96,9
95,3
90,6
106,8
73,4
88,2
105,6
98,2
101,1
94,4
90,9
92,5
107,3
92,5
102
107,3
78,5
93,9
108,1
109,6
105,9
94,9
98,8
98,2
111,9
99,1
102,5
110,8
85,8
97,6
106,1
116,1
107,1
93,5
103,9
106
103,4
111,2
103,3
109,8
88,8
92,5
109,9
109,6
93,6
88,1
83,9
85,5
95,2
86,4
84,4
95,2
Dataseries Y:
Download CSV
Histogram 
31.394
29.799
33.728
39.010
33.598
24.992
51.347
45.498
52.413
48.461
41.647
49.471
32.666
33.283
35.532
36.928
35.572
20.870
58.453
54.854
65.425
51.469
51.035
46.526
35.619
33.153
35.078
41.552
34.752
21.108
56.020
49.126
59.604
47.997
47.363
50.380
39.967
34.045
36.748
46.234
36.464
23.772
57.165
56.276
57.547
62.183
48.820
51.052
39.540
33.113
38.010
43.175
30.501
21.862
47.942
46.055
50.633
48.020
38.298
44.009

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=55826&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=55826&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55826&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]
c6.37721661281537
b0.369712875897769

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 6.37721661281537 \tabularnewline
b & 0.369712875897769 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55826&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]6.37721661281537[/C][/ROW]
[ROW][C]b[/C][C]0.369712875897769[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55826&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55826&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]
c6.37721661281537
b0.369712875897769






Descriptive Statistics about e[t]
# observations60
minimum-20.4081120975648
Q1-8.41702943766828
median0.597804232383483
mean3.16803874800264e-17
Q38.3152787738733
maximum19.3775918033540

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -20.4081120975648 \tabularnewline
Q1 & -8.41702943766828 \tabularnewline
median & 0.597804232383483 \tabularnewline
mean & 3.16803874800264e-17 \tabularnewline
Q3 & 8.3152787738733 \tabularnewline
maximum & 19.3775918033540 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55826&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]-20.4081120975648[/C][/ROW]
[ROW][C]Q1[/C][C]-8.41702943766828[/C][/ROW]
[ROW][C]median[/C][C]0.597804232383483[/C][/ROW]
[ROW][C]mean[/C][C]3.16803874800264e-17[/C][/ROW]
[ROW][C]Q3[/C][C]8.3152787738733[/C][/ROW]
[ROW][C]maximum[/C][C]19.3775918033540[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55826&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55826&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-20.4081120975648
Q1-8.41702943766828
median0.597804232383483
mean3.16803874800264e-17
Q38.3152787738733
maximum19.3775918033540


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')