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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, 04 Nov 2009 20:11:20 -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/05/t1257390910mannuboyenx9xpd.htm/, Retrieved Mon, 29 Apr 2024 10:24:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53879, Retrieved Mon, 29 Apr 2024 10:24:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWS 5
Estimated Impact179

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [SHWWS2/1] [2009-10-12 16:52:29] [9717cb857c153ca3061376906953b329]
-   PD  [Univariate Data Series] [Aantal niet-werke...] [2009-10-24 19:05:50] [9717cb857c153ca3061376906953b329]
-  MPD    [Univariate Data Series] [Aantal inschrijvi...] [2009-11-05 03:01:41] [9717cb857c153ca3061376906953b329]
- RMPD        [Bivariate Explorative Data Analysis] [WS 5] [2009-11-05 03:11:20] [52b85b290d6f50b0921ad6729b8a5af2] [Current]
-    D          [Bivariate Explorative Data Analysis] [WS 5] [2009-11-05 03:39:48] [9717cb857c153ca3061376906953b329]
- RMPD          [Trivariate Scatterplots] [WS 5] [2009-11-05 04:15:15] [9717cb857c153ca3061376906953b329]
- RMP             [Partial Correlation] [WS 5] [2009-11-05 04:24:39] [9717cb857c153ca3061376906953b329]
- R  D          [Bivariate Explorative Data Analysis] [] [2011-11-22 17:33:37] [fbaf17a8836493f6de0f4e0e997711e1]
- R  D          [Bivariate Explorative Data Analysis] [] [2011-11-22 17:44:12] [fbaf17a8836493f6de0f4e0e997711e1]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4138
4634
3996
4308
4143
4429
5219
4929
5755
5592
4163
4962
5208
4755
4491
5732
5731
5040
6102
4904
5369
5578
4619
4731
5011
5299
4146
4625
4736
4219
5116
4205
4121
5103
4300
4578
3809
5526
4247
3830
4394
4826
4409
4569
4106
4794
3914
3793
4405
4022
4100
4788
3163
3585
3903
4178
3863
4187
4178
4354
Dataseries Y:
Download CSV
Histogram 
20366
22782
19169
13807
29743
25591
29096
26482
22405
27044
17970
18730
19684
19785
18479
10698
31956
29506
34506
27165
26736
23691
18157
17328
18205
20995
17382
9367
31124
26551
30651
25859
25100
25778
20418
18688
20424
24776
19814
12738
31566
30111
30019
31934
25826
26835
20205
17789
20520
22518
15572
11509
25447
24090
27786
26195
20516
22759
19028
16971

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=53879&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=53879&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53879&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]
c13111.6301352442
b2.10687881237168

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 13111.6301352442 \tabularnewline
b & 2.10687881237168 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53879&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]13111.6301352442[/C][/ROW]
[ROW][C]b[/C][C]2.10687881237168[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53879&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53879&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]
c13111.6301352442
b2.10687881237168






Descriptive Statistics about e[t]
# observations60
minimum-14490.2594877587
Q1-3951.6553079311
median-402.81904167128
mean8.18308384017049e-14
Q34117.85164253496
maximum9196.7443631946

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -14490.2594877587 \tabularnewline
Q1 & -3951.6553079311 \tabularnewline
median & -402.81904167128 \tabularnewline
mean & 8.18308384017049e-14 \tabularnewline
Q3 & 4117.85164253496 \tabularnewline
maximum & 9196.7443631946 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53879&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]-14490.2594877587[/C][/ROW]
[ROW][C]Q1[/C][C]-3951.6553079311[/C][/ROW]
[ROW][C]median[/C][C]-402.81904167128[/C][/ROW]
[ROW][C]mean[/C][C]8.18308384017049e-14[/C][/ROW]
[ROW][C]Q3[/C][C]4117.85164253496[/C][/ROW]
[ROW][C]maximum[/C][C]9196.7443631946[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53879&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53879&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-14490.2594877587
Q1-3951.6553079311
median-402.81904167128
mean8.18308384017049e-14
Q34117.85164253496
maximum9196.7443631946


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')