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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 computationMon, 09 Nov 2009 13:39:26 -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/09/t1257799946hmwr9mpiye30ucs.htm/, Retrieved Fri, 19 Apr 2024 03:09:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=55046, Retrieved Fri, 19 Apr 2024 03:09:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact195

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] [3/11/2009] [2009-11-02 22:01:32] [b98453cac15ba1066b407e146608df68]
-   PD    [Bivariate Explorative Data Analysis] [workshop 6] [2009-11-09 20:39:26] [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 
1.6891
1.7236
1.8072
1.7847
1.6813
1.6469
1.689
1.7169
1.8036
1.7955
1.7172
1.7348
1.7094
1.6963
1.6695
1.6537
1.6662
1.6793
1.7922
1.8045
1.7927
1.7831
1.7847
1.8076
1.8218
1.8112
1.795
1.7813
1.7866
1.7552
1.7184
1.7114
1.6967
1.6867
1.6337
1.6626
1.6374
1.626
1.637
1.6142
1.7033
1.7483
1.7135
1.7147
1.7396
1.7049
1.6867
1.7462
1.7147
1.667
1.6806
1.6738
1.6571
1.5875
1.6002
1.6144
1.6009
1.5937
1.603
1.5979

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'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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55046&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55046&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55046&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Model: Y[t] = c + b X[t] + e[t]
c3.40210897802926
b-1.11730902901076

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 3.40210897802926 \tabularnewline
b & -1.11730902901076 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55046&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]3.40210897802926[/C][/ROW]
[ROW][C]b[/C][C]-1.11730902901076[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55046&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55046&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]
c3.40210897802926
b-1.11730902901076






Descriptive Statistics about e[t]
# observations60
minimum-0.110657403305283
Q1-0.0455772982623618
median0.00824672351232822
mean1.02118292120978e-18
Q30.0385024773600421
maximum0.120719036016767

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -0.110657403305283 \tabularnewline
Q1 & -0.0455772982623618 \tabularnewline
median & 0.00824672351232822 \tabularnewline
mean & 1.02118292120978e-18 \tabularnewline
Q3 & 0.0385024773600421 \tabularnewline
maximum & 0.120719036016767 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55046&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]-0.110657403305283[/C][/ROW]
[ROW][C]Q1[/C][C]-0.0455772982623618[/C][/ROW]
[ROW][C]median[/C][C]0.00824672351232822[/C][/ROW]
[ROW][C]mean[/C][C]1.02118292120978e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.0385024773600421[/C][/ROW]
[ROW][C]maximum[/C][C]0.120719036016767[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55046&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55046&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-0.110657403305283
Q1-0.0455772982623618
median0.00824672351232822
mean1.02118292120978e-18
Q30.0385024773600421
maximum0.120719036016767


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')