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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 15:21:56 -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/t12566785943jn6s5hkdjj9su2.htm/, Retrieved Tue, 07 May 2024 14:39:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51255, Retrieved Tue, 07 May 2024 14:39:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsworkshop 4 deel 2
Estimated Impact165

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Explorative Data Analysis] [workshop 4 deel 2] [2009-10-27 21:21:56] [6198946fb53eb5eb18db46bb758f7fde] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.5291
1.5358
1.5355
1.5287
1.5334
1.5225
1.5135
1.5144
1.4913
1.4793
1.4663
1.4749
1.4745
1.4775
1.4678
1.4658
1.4572
1.4721
1.4624
1.4636
1.4649
1.465
1.4673
1.4679
1.4621
1.4674
1.4695
1.4964
1.5155
1.5411
1.5476
1.54
1.5474
1.5485
1.559
1.5544
1.5657
1.5734
1.567
1.5547
1.54
1.5192
1.527
1.5387
1.5431
1.5426
1.5216
1.5364
1.5469
1.5501
1.5494
1.5475
1.5448
1.5391
1.5578
1.5528
1.5496
1.549
1.5449
1.5479
Dataseries Y:
Download CSV
Histogram 
0.6348
0.634
0.62915
0.62168
0.61328
0.6089
0.60857
0.62672
0.62291
0.62393
0.61838
0.62012
0.61659
0.6116
0.61573
0.61407
0.62823
0.64405
0.6387
0.63633
0.63059
0.62994
0.63709
0.64217
0.65711
0.66977
0.68255
0.68902
0.71322
0.70224
0.70045
0.69919
0.69693
0.69763
0.69278
0.70196
0.69215
0.6769
0.67124
0.66532
0.67157
0.66428
0.66576
0.66942
0.6813
0.69144
0.69862
0.695
0.69867
0.68968
0.69233
0.68293
0.68399
0.66895
0.68756
0.68527
0.6776
0.68137
0.67933
0.67922

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51255&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51255&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51255&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Model: Y[t] = c + b X[t] + e[t]
c-0.191028651774352
b0.561232545674783

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -0.191028651774352 \tabularnewline
b & 0.561232545674783 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51255&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-0.191028651774352[/C][/ROW]
[ROW][C]b[/C][C]0.561232545674783[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51255&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51255&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]
c-0.191028651774352
b0.561232545674783






Descriptive Statistics about e[t]
# observations60
minimum-0.0562853337633594
Q1-0.0163029977000203
median0.00317996524274739
mean6.20954559676207e-19
Q30.0145165408334912
maximum0.0537007288042192

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -0.0562853337633594 \tabularnewline
Q1 & -0.0163029977000203 \tabularnewline
median & 0.00317996524274739 \tabularnewline
mean & 6.20954559676207e-19 \tabularnewline
Q3 & 0.0145165408334912 \tabularnewline
maximum & 0.0537007288042192 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51255&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]-0.0562853337633594[/C][/ROW]
[ROW][C]Q1[/C][C]-0.0163029977000203[/C][/ROW]
[ROW][C]median[/C][C]0.00317996524274739[/C][/ROW]
[ROW][C]mean[/C][C]6.20954559676207e-19[/C][/ROW]
[ROW][C]Q3[/C][C]0.0145165408334912[/C][/ROW]
[ROW][C]maximum[/C][C]0.0537007288042192[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51255&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51255&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-0.0562853337633594
Q1-0.0163029977000203
median0.00317996524274739
mean6.20954559676207e-19
Q30.0145165408334912
maximum0.0537007288042192


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')