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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, 03 Nov 2009 11:35:20 -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/03/t12572733853kdqh6pn464dowt.htm/, Retrieved Wed, 01 May 2024 20:22:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53318, Retrieved Wed, 01 May 2024 20:22:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsSDHW, DSHW
Estimated Impact178

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Trivariate Scatterplots] [WS 5 1] [2009-10-28 22:35:45] [6e4e01d7eb22a9f33d58ebb35753a195]
- RMPD    [Bivariate Explorative Data Analysis] [DSHW-WS5-NewBivar...] [2009-11-03 18:35:20] [36295456a56d4c7dcc9b9537ce63463b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
44,175
45,105
38,715
34,85
36,105
38,675
39,13
38,675
34,85
34,425
30,415
36,55
36,975
37,845
37,825
37,38
39,525
42,195
43,065
42,57
42,075
38,595
32,4
29,93
27,135
27,945
29,2
29,625
30,415
31,6
30,8
28,835
30
27,335
22,68
25,875
24,455
22,05
21,35
20,65
23,4
25,915
25,205
21,42
18,24
15,555
18,525
30,415
31,995
27,375
19,665
16,83
20,355
29,645
35,6
37,2
34,265
30,295
31,45
38,475
40,255
Dataseries Y:
Download CSV
Histogram 
73,005
71,145
58,725
48,79
49,385
53,125
54,61
53,125
47,97
45,765
45,425
58,91
61,335
60,465
53,975
49,98
51,425
53,505
53,505
51,17
48,875
47,725
46
53,71
54,675
53,055
48,4
45,425
45,425
46,8
46,8
46,215
47,6
42,735
36,36
38,625
35,405
31,15
32,55
32,55
34,2
33,945
30,885
26,18
22,72
19,825
23,725
38,115
43,055
37,875
32,085
28,05
31,395
38,885
41,2
39,6
34,265
28,835
29,97
39,285
43,575

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=53318&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=53318&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53318&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]
c6.2397184973082
b1.19768618431006

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 6.2397184973082 \tabularnewline
b & 1.19768618431006 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53318&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]6.2397184973082[/C][/ROW]
[ROW][C]b[/C][C]1.19768618431006[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53318&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53318&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]
c6.2397184973082
b1.19768618431006






Descriptive Statistics about e[t]
# observations61
minimum-13.9369489938596
Q1-4.55234379309867
median0.564768324500236
mean5.32389478586259e-16
Q32.95675884253963
maximum15.9360668914383

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 61 \tabularnewline
minimum & -13.9369489938596 \tabularnewline
Q1 & -4.55234379309867 \tabularnewline
median & 0.564768324500236 \tabularnewline
mean & 5.32389478586259e-16 \tabularnewline
Q3 & 2.95675884253963 \tabularnewline
maximum & 15.9360668914383 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53318&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]61[/C][/ROW]
[ROW][C]minimum[/C][C]-13.9369489938596[/C][/ROW]
[ROW][C]Q1[/C][C]-4.55234379309867[/C][/ROW]
[ROW][C]median[/C][C]0.564768324500236[/C][/ROW]
[ROW][C]mean[/C][C]5.32389478586259e-16[/C][/ROW]
[ROW][C]Q3[/C][C]2.95675884253963[/C][/ROW]
[ROW][C]maximum[/C][C]15.9360668914383[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53318&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53318&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]
# observations61
minimum-13.9369489938596
Q1-4.55234379309867
median0.564768324500236
mean5.32389478586259e-16
Q32.95675884253963
maximum15.9360668914383


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')