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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_edabi.wasp
Title produced by softwareBivariate Explorative Data Analysis
Date of computationWed, 28 Oct 2009 06:19:32 -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/28/t1256732455091jlahmono0g2q.htm/, Retrieved Sun, 05 May 2024 22:25:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51343, Retrieved Sun, 05 May 2024 22:25:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact155

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Trivariate Scatterplots] [trivariate analysis] [2008-11-13 08:14:40] [3b5d63cebdc58ed6c519cdb5b6a36d46]
- RMPD    [Bivariate Explorative Data Analysis] [college Y=f(Z)] [2009-10-28 12:19:32] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-    D      [Bivariate Explorative Data Analysis] [WS5 berekening Y=...] [2009-10-29 14:37:50] [42ad1186d39724f834063794eac7cea3]
-  M          [Bivariate Explorative Data Analysis] [BDM2] [2009-11-03 11:53:00] [f5d341d4bbba73282fc6e80153a6d315]
-  M          [Bivariate Explorative Data Analysis] [TG2] [2009-11-03 11:59:56] [a21bac9c8d3d56fdec8be4e719e2c7ed]
-  M          [Bivariate Explorative Data Analysis] [P5] [2009-12-15 09:47:24] [f5d341d4bbba73282fc6e80153a6d315]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
11.836
11.85
11.897
12.082
11.936
11.928
12.646
12.747
12.447
12.445
12.257
12.878
13.69
13.665
13.78
13.608
13.375
13.376
13.918
14.304
13.877
14.543
14.291
14.788
15.241
15.265
15.322
15.175
14.817
14.579
15.247
15.385
14.891
14.766
14.42
14.85
15.117
15.352
15.099
15.291
15.208
14.995
15.454
15.251
14.975
14.005
13.55
13.422
13.848
13.376
13.038
12.974
12.554
11.971
12.916
12.757
11.924
11.693
11.382
11.821
Dataseries Y:
Download CSV
Histogram 
3.253
3.233
3.196
3.138
3.091
3.17
3.378
3.468
3.33
3.413
3.356
3.525
3.633
3.597
3.6
3.522
3.503
3.532
3.686
3.748
3.672
3.843
3.905
3.999
4.07
4.084
4.042
3.951
3.933
3.958
4.147
4.221
4.058
4.057
4.089
4.268
4.309
4.303
4.177
4.117
4.065
3.983
4.091
4.067
4.024
3.868
3.8
3.804
3.862
3.792
3.674
3.56
3.489
3.412
3.674
3.672
3.463
3.429
3.4
3.533

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51343&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]2 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=51343&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51343&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Model: Y[t] = c + b X[t] + e[t]
c0.43230284711819
b0.241211574298480

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 0.43230284711819 \tabularnewline
b & 0.241211574298480 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51343&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]0.43230284711819[/C][/ROW]
[ROW][C]b[/C][C]0.241211574298480[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51343&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51343&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]
c0.43230284711819
b0.241211574298480






Descriptive Statistics about e[t]
# observations60
minimum-0.220404197944842
Q1-0.101988358999389
median-0.0208135307503455
mean-6.2251274736876e-18
Q30.0933102095105914
maximum0.253705274549387

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -0.220404197944842 \tabularnewline
Q1 & -0.101988358999389 \tabularnewline
median & -0.0208135307503455 \tabularnewline
mean & -6.2251274736876e-18 \tabularnewline
Q3 & 0.0933102095105914 \tabularnewline
maximum & 0.253705274549387 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51343&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]-0.220404197944842[/C][/ROW]
[ROW][C]Q1[/C][C]-0.101988358999389[/C][/ROW]
[ROW][C]median[/C][C]-0.0208135307503455[/C][/ROW]
[ROW][C]mean[/C][C]-6.2251274736876e-18[/C][/ROW]
[ROW][C]Q3[/C][C]0.0933102095105914[/C][/ROW]
[ROW][C]maximum[/C][C]0.253705274549387[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51343&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51343&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-0.220404197944842
Q1-0.101988358999389
median-0.0208135307503455
mean-6.2251274736876e-18
Q30.0933102095105914
maximum0.253705274549387


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')