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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 14:02:09 -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/t1256673842998fk56ponubyde.htm/, Retrieved Tue, 07 May 2024 06:59:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51203, Retrieved Tue, 07 May 2024 06:59:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Gemiddelde renden...] [2008-10-13 19:42:56] [86c69698417c3ad89592e76776a2c65b]
-   PD  [Univariate Data Series] [Werkloosheid vana...] [2009-10-11 19:32:57] [5c968c05ca472afa314d272082b56b09]
-   P     [Univariate Data Series] [WS3, part 2] [2009-10-18 13:50:42] [5c968c05ca472afa314d272082b56b09]
- RMPD        [Bivariate Explorative Data Analysis] [WS4, Part2.3 ln...] [2009-10-27 20:02:09] [b8ce264f75295a954feffaf60221d1b0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
363
364
363
358
357
357
380
378
376
380
379
384
392
394
392
396
392
396
419
421
420
418
410
418
426
428
430
424
423
427
441
449
452
462
455
461
461
463
462
456
455
456
472
472
471
465
459
465
468
467
463
460
462
461
476
476
471
453
443
442
444
438
427
424
416
406
431
434
418
412
404
409
Dataseries Y:
Download CSV
Histogram 
6,165417854
6,163314804
6,152732695
6,133398043
6,120297419
6,12249281
6,248042875
6,263398263
6,259581464
6,251903883
6,232448017
6,238324625
6,251903883
6,248042875
6,234410726
6,232448017
6,216606101
6,228511004
6,343880434
6,363028104
6,359573869
6,336825731
6,304448802
6,318968114
6,33150185
6,329720906
6,318968114
6,298949247
6,285998095
6,29710932
6,386879319
6,415096959
6,418364936
6,415096959
6,386879319
6,388561406
6,381816017
6,378426184
6,369900983
6,350885717
6,340359304
6,343880434
6,431331082
6,444131257
6,442540166
6,416732283
6,388561406
6,391917113
6,385194399
6,380122537
6,363028104
6,352629396
6,350885717
6,350885717
6,429719478
6,439350371
6,429719478
6,376726948
6,338594078
6,32256524
6,329720906
6,308098442
6,276643489
6,265301213
6,23636959
6,212606096
6,318968114
6,336825731
6,295266001
6,267200549
6,234410726
6,242223265

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51203&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]4 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=51203&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51203&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 time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Model: Y[t] = c + b X[t] + e[t]
c5.37759449978982
b0.00219207923986511

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 5.37759449978982 \tabularnewline
b & 0.00219207923986511 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51203&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]5.37759449978982[/C][/ROW]
[ROW][C]b[/C][C]0.00219207923986511[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51203&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51203&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]
c5.37759449978982
b0.00219207923986511






Descriptive Statistics about e[t]
# observations72
minimum-0.0549725751750605
Q1-0.0218617968076754
median-0.00420338574939561
mean2.02158530078026e-19
Q30.0198861010115827
maximum0.0625682442269624

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 72 \tabularnewline
minimum & -0.0549725751750605 \tabularnewline
Q1 & -0.0218617968076754 \tabularnewline
median & -0.00420338574939561 \tabularnewline
mean & 2.02158530078026e-19 \tabularnewline
Q3 & 0.0198861010115827 \tabularnewline
maximum & 0.0625682442269624 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51203&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]72[/C][/ROW]
[ROW][C]minimum[/C][C]-0.0549725751750605[/C][/ROW]
[ROW][C]Q1[/C][C]-0.0218617968076754[/C][/ROW]
[ROW][C]median[/C][C]-0.00420338574939561[/C][/ROW]
[ROW][C]mean[/C][C]2.02158530078026e-19[/C][/ROW]
[ROW][C]Q3[/C][C]0.0198861010115827[/C][/ROW]
[ROW][C]maximum[/C][C]0.0625682442269624[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51203&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51203&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]
# observations72
minimum-0.0549725751750605
Q1-0.0218617968076754
median-0.00420338574939561
mean2.02158530078026e-19
Q30.0198861010115827
maximum0.0625682442269624


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 50 ; par2 = 50 ; par3 = 0 ; par4 = 0 ; par5 = 0 ; par6 = Y ; par7 = Y ;
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')