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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 computationSun, 08 Nov 2009 04:19:51 -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/08/t12576792814pqec9vf021m4bq.htm/, Retrieved Sat, 04 May 2024 12:30:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=54515, Retrieved Sat, 04 May 2024 12:30:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact186

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] [Workshop5 Review ...] [2009-11-08 11:19:51] [5ed0eef5d4509bbfdac0ae6d87f3b4bf] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,024
0,924
0,104
-0,296
-0,196
-0,288
0,212
-0,188
-0,596
-0,196
-0,196
0,998
1,098
0,298
-0,232
0,268
0,268
-0,054
-0,654
-0,254
-0,04
-0,34
-0,44
0,748
0,348
0,148
-0,518
-0,418
-0,818
-0,97
-0,97
-0,97
-1,106
-1,106
-1,106
-1,234
-1,034
-0,234
-0,066
0,134
0,534
0,298
1,098
0,798
1,462
2,162
2,262
3,204
3,304
2,604
0,416
-0,084
-0,684
-0,548
-1,848
-1,748
-2,844
-3,644
-4,344
Dataseries Y:
Download CSV
Histogram 
2,396
2,396
1,116
1,116
1,116
0,968
0,968
0,968
2,516
2,516
2,516
0,602
0,602
0,602
3,432
3,432
3,432
2,314
2,314
2,314
0,18
0,18
0,18
-3,748
-3,748
-3,748
-0,502
-0,502
-0,502
-1,09
-1,09
-1,09
-3,074
-3,074
-3,074
-0,706
-0,706
-0,706
-1,614
-1,614
-1,614
-0,298
-0,298
-0,298
-1,482
-1,482
-1,482
1,616
1,616
1,616
-2,256
-2,256
-2,256
0,428
0,428
0,428
0,204
0,204
0,204

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=54515&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=54515&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54515&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]
c0.0228476854121161
b0.112673807528988

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 0.0228476854121161 \tabularnewline
b & 0.112673807528988 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54515&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]0.0228476854121161[/C][/ROW]
[ROW][C]b[/C][C]0.112673807528988[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54515&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54515&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]
c0.0228476854121161
b0.112673807528988






Descriptive Statistics about e[t]
# observations59
minimum-3.8551276934438
Q1-1.3164826531121
median0.206728789900638
mean1.95729731857248e-16
Q31.12087007124001
maximum3.43529263793461

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 59 \tabularnewline
minimum & -3.8551276934438 \tabularnewline
Q1 & -1.3164826531121 \tabularnewline
median & 0.206728789900638 \tabularnewline
mean & 1.95729731857248e-16 \tabularnewline
Q3 & 1.12087007124001 \tabularnewline
maximum & 3.43529263793461 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=54515&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]-3.8551276934438[/C][/ROW]
[ROW][C]Q1[/C][C]-1.3164826531121[/C][/ROW]
[ROW][C]median[/C][C]0.206728789900638[/C][/ROW]
[ROW][C]mean[/C][C]1.95729731857248e-16[/C][/ROW]
[ROW][C]Q3[/C][C]1.12087007124001[/C][/ROW]
[ROW][C]maximum[/C][C]3.43529263793461[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=54515&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=54515&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-3.8551276934438
Q1-1.3164826531121
median0.206728789900638
mean1.95729731857248e-16
Q31.12087007124001
maximum3.43529263793461


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')