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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 computationThu, 29 Oct 2009 13:08:55 -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/29/t1256843439ubghzj5yoah5585.htm/, Retrieved Mon, 29 Apr 2024 04:17:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=52037, Retrieved Mon, 29 Apr 2024 04:17:52 +0000
QR Codes:

Original text written by user:Bivariate EDA Y=f(Z) en X=f(Z)
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact122

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] [Shw5: Bivariate E...] [2009-10-29 19:08:55] [7a39e26d7a09dd77604df90cb29f8d39] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.111303201
-0.371599129
-0.544498244
-0.672144477
-0.278753982
0.180265125
-0.223157237
-0.647930439
-0.115006714
0.474009056
0.444049469
0.142263723
-0.398007085
-0.306412935
0.14022632
-0.16357871
-0.355567414
-0.766507955
-0.723356535
-0.540373189
-0.442800262
-0.362274443
-0.506612614
-0.470776921
-0.671769894
-0.481586013
-0.435223289
-0.450030542
-0.28629111
-0.209552142
-0.233453609
-0.73921599
-0.789934939
-0.696006808
-0.694917718
-0.26050412
0.7801581
1.288611235
1.378888341
1.548015642
1.701326595
2.464384664
2.03982207
2.429835942
3.002296814
3.843909989
3.166523773
2.881455087
2.132925623
0.469808508
-0.58023403
-0.853988559
-0.448632611
-1.497842086
-1.275860125
-2.067516339
-3.015816436
-3.441007126
-3.601337506
Dataseries Y:
Download CSV
Histogram 
-1.069110747
-0.224600155
-0.519176561
-0.443841916
-0.891681921
-1.156458458
-1.006923284
-0.875826413
-0.540297967
0.171800008
0.591873748
0.389981437
0.673187551
1.247849664
0.778924303
0.678994652
0.484846026
-0.005963015
-0.059886436
0.354323765
-0.043744137
-2.324150774
-0.742260114
-0.928757841
0.452743329
0.698858699
1.179755043
1.546243351
-0.10570001
-0.32691026
0.362464223
0.651949789
0.551157845
0.446572065
0.494726378
0.755766464
1.09262198
0.863112479
0.364138006
0.637611953
0.988913294
-0.167260062
-0.154025582
-0.096528058
-0.45853115
-1.333579753
0.209242602
0.691360013
0.845796569
0.714135402
1.718736881
1.594872844
-2.013866339
-1.988010127
-1.266473304
-0.471055044
-0.459380319
-0.626904673
-1.931655944

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=52037&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=52037&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52037&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]
c-1.69490971281975e-11
b0.12935705251762

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -1.69490971281975e-11 \tabularnewline
b & 0.12935705251762 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52037&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-1.69490971281975e-11[/C][/ROW]
[ROW][C]b[/C][C]0.12935705251762[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52037&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52037&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-1.69490971281975e-11
b0.12935705251762






Descriptive Statistics about e[t]
# observations59
minimum-2.27728801983411
Q1-0.505733317913995
median0.0931901948070575
mean-1.46826700985749e-18
Q30.653351929290522
maximum1.79379424490817

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 59 \tabularnewline
minimum & -2.27728801983411 \tabularnewline
Q1 & -0.505733317913995 \tabularnewline
median & 0.0931901948070575 \tabularnewline
mean & -1.46826700985749e-18 \tabularnewline
Q3 & 0.653351929290522 \tabularnewline
maximum & 1.79379424490817 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=52037&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]59[/C][/ROW]
[ROW][C]minimum[/C][C]-2.27728801983411[/C][/ROW]
[ROW][C]Q1[/C][C]-0.505733317913995[/C][/ROW]
[ROW][C]median[/C][C]0.0931901948070575[/C][/ROW]
[ROW][C]mean[/C][C]-1.46826700985749e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.653351929290522[/C][/ROW]
[ROW][C]maximum[/C][C]1.79379424490817[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=52037&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=52037&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]
# observations59
minimum-2.27728801983411
Q1-0.505733317913995
median0.0931901948070575
mean-1.46826700985749e-18
Q30.653351929290522
maximum1.79379424490817


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')