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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 computationTue, 27 Oct 2009 14:04:56 -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/27/t125667397479cwv1igr1xzmao.htm/, Retrieved Tue, 07 May 2024 18:05:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51207, Retrieved Tue, 07 May 2024 18:05:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordslny
Estimated Impact92

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] [ln y] [2009-10-27 20:04:56] [950726a732ba3ca782ecb1a5307d0f6f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12002.4
15525.5
14247.9
15000.7
14931.4
13333.7
14711.2
17197.3
14985.2
14734.4
15937.8
13028.1
13836.8
16677.5
15130
17504
16979.9
16012.5
16247.7
19268.2
15533
16803.3
17396.1
15155.4
15692.4
18063.7
17568.6
18154.3
15467.4
16956.1
16854
19396.4
16457.6
17284.5
18395.3
16938.7
16414.3
18173.4
19919.7
19623.8
17228.1
18730.3
19039.1
19413.3
20013.6
17917.2
21270.3
18766.1
16790.8
19960.6
19586.7
17179
14964.9
13918.5
14401.3
15994.6
14521.1
13746.5
15956
14332.2
Dataseries Y:
Download CSV
Histogram 
9.482814894
9.779391507
9.735837836
9.759547896
9.694259234
9.646632087
9.71459445
9.873790941
9.760482858
9.712641768
9.806998678
9.652060261
9.633134462
9.829894832
9.773612325
9.849485329
9.751955109
9.773652178
9.779533012
9.943270608
9.756019494
9.851362333
9.929769146
9.745798119
9.73476708
9.917138947
9.90740985
9.898520236
9.741686013
9.885471229
9.853456619
9.984652707
9.843519902
9.897137433
9.974328049
9.873378799
9.819306711
9.93978573
10.01091942
9.979012378
9.770801517
9.958198251
9.964159232
9.970819045
10.00537605
9.918893273
10.0633462
9.964798997
9.819458995
10.02727033
9.999447521
9.821338879
9.687828423
9.68269748
9.70568884
9.784129527
9.701970944
9.65901183
9.78595886
9.704969538

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=51207&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=51207&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51207&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]
c8.86981504750691
b5.7461137261855e-05

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 8.86981504750691 \tabularnewline
b & 5.7461137261855e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51207&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]8.86981504750691[/C][/ROW]
[ROW][C]b[/C][C]5.7461137261855e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51207&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51207&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]
c8.86981504750691
b5.7461137261855e-05






Descriptive Statistics about e[t]
# observations60
minimum-0.0935443030994866
Q1-0.015445719510545
median0.00296504204740409
mean-2.64543918364884e-18
Q30.0179815639368595
maximum0.0603544085721288

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -0.0935443030994866 \tabularnewline
Q1 & -0.015445719510545 \tabularnewline
median & 0.00296504204740409 \tabularnewline
mean & -2.64543918364884e-18 \tabularnewline
Q3 & 0.0179815639368595 \tabularnewline
maximum & 0.0603544085721288 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51207&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]-0.0935443030994866[/C][/ROW]
[ROW][C]Q1[/C][C]-0.015445719510545[/C][/ROW]
[ROW][C]median[/C][C]0.00296504204740409[/C][/ROW]
[ROW][C]mean[/C][C]-2.64543918364884e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.0179815639368595[/C][/ROW]
[ROW][C]maximum[/C][C]0.0603544085721288[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51207&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51207&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-0.0935443030994866
Q1-0.015445719510545
median0.00296504204740409
mean-2.64543918364884e-18
Q30.0179815639368595
maximum0.0603544085721288


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')