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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 09:40:50 -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/t1257266589e21tq6o8a457l1a.htm/, Retrieved Wed, 01 May 2024 19:10:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53246, Retrieved Wed, 01 May 2024 19:10:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168

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] [] [2009-11-03 16:40:50] [17416e80e7873ecccac25c455c5f767e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
23531,56
21025
18961,29
21992,89
23164,84
28696,36
28425,96
25953,21
30310,81
32041
36328,36
36100
32978,56
30555,04
32580,25
38730,24
37558,44
38809
46785,69
49017,96
47480,41
52762,09
51710,76
41697,64
38651,56
39521,44
43056,25
36366,49
40642,56
44310,25
49952,25
50086,44
53453,44
59536
55084,09
62600,04
70596,49
82713,76
80258,89
87261,16
97531,29
111422,44
120895,29
146842,24
165730,41
171064,96
131551,29
103619,61
57312,36
36481
25504,09
26699,56
24837,76
27622,44
31222,89
39322,89
51166,44
46742,44
55648,81
51483,61
Dataseries Y:
Download CSV
Histogram 
9769,3456
9901,245025
10034,0289
9769,3456
10304,2801
10436,6656
10483,7121
10514,4516
10578,1225
10706,0409
10726,7449
10751,6161
10712,25
10706,0409
10701,9025
10708,1104
10801,4449
10793,1321
10899,36
10980,9441
10976,7529
11052,3169
11079,6676
11016,6016
10972,5625
11027,1001
11056,5225
11067,04
11187,2929
11189,4084
11291,1876
11263,5769
11261,4544
11357,1649
11329,4736
11350,7716
11470,41
11685,61
11750,56
11846,1456
12016,5444
12192,5764
12247,8489
12467,9556
12606,7984
12739,6369
12584,3524
12624,7696
12579,8656
12430,0201
12376,5625
12401,0496
12485,8276
12343,21
12394,3689
12376,5625
12329,8816
12314,3409
12389,9161
12325,4404

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=53246&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=53246&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53246&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]
c10642.9824770671
b0.0139208134315876

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 10642.9824770671 \tabularnewline
b & 0.0139208134315876 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53246&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]10642.9824770671[/C][/ROW]
[ROW][C]b[/C][C]0.0139208134315876[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53246&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53246&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]
c10642.9824770671
b0.0139208134315876






Descriptive Statistics about e[t]
# observations60
minimum-1201.21533358137
Q1-389.891449935572
median-164.853662285940
mean-7.55969361184346e-14
Q32.83895047565145
maximum1497.08329991431

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -1201.21533358137 \tabularnewline
Q1 & -389.891449935572 \tabularnewline
median & -164.853662285940 \tabularnewline
mean & -7.55969361184346e-14 \tabularnewline
Q3 & 2.83895047565145 \tabularnewline
maximum & 1497.08329991431 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53246&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]-1201.21533358137[/C][/ROW]
[ROW][C]Q1[/C][C]-389.891449935572[/C][/ROW]
[ROW][C]median[/C][C]-164.853662285940[/C][/ROW]
[ROW][C]mean[/C][C]-7.55969361184346e-14[/C][/ROW]
[ROW][C]Q3[/C][C]2.83895047565145[/C][/ROW]
[ROW][C]maximum[/C][C]1497.08329991431[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53246&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53246&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-1201.21533358137
Q1-389.891449935572
median-164.853662285940
mean-7.55969361184346e-14
Q32.83895047565145
maximum1497.08329991431


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')