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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 computationWed, 28 Oct 2009 11:59:36 -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/t12567528373gkqiuo9twnwtf7.htm/, Retrieved Sun, 05 May 2024 22:12:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51671, Retrieved Sun, 05 May 2024 22:12:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact93

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [Inschrijvingen pe...] [2009-10-12 20:00:19] [a94022e7c2399c0f4d62eea578db3411]
- RMPD    [Bivariate Explorative Data Analysis] [WS4-2-BiEda] [2009-10-28 17:59:36] [30970b478e356ce7f8c2e9fca280b230] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
96.8
114.1
110.3
103.9
101.6
94.6
95.9
104.7
102.8
98.1
113.9
80.9
95.7
113.2
105.9
108.8
102.3
99
100.7
115.5
100.7
109.9
114.6
85.4
100.5
114.8
116.5
112.9
102
106
105.3
118.8
106.1
109.3
117.2
92.5
104.2
112.5
122.4
113.3
100
110.7
112.8
109.8
117.3
109.1
115.9
96
99.8
116.8
115.7
99.4
94.3
91
93.2
103.1
94.1
91.8
102.7
Dataseries Y:
Download CSV
Histogram 
39120
33702
25094
51442
45594
52518
48564
41745
49585
32747
33379
35645
37034
35681
20972
58552
54955
65540
51570
51145
46641
35704
33253
35193
41668
34865
21210
56126
49231
59723
48103
47472
50497
40059
34149
36860
46356
36577
23872
57276
56389
57657
62300
48929
51168
39636
33213
38127
43291
30600
21956
48033
46148
50736
48114
38390
44112
36287
30333

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=51671&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=51671&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51671&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]
c64112.8848760895
b-202.969486967982

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 64112.8848760895 \tabularnewline
b & -202.969486967982 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51671&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]64112.8848760895[/C][/ROW]
[ROW][C]b[/C][C]-202.969486967982[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51671&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51671&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]
c64112.8848760895
b-202.969486967982






Descriptive Statistics about e[t]
# observations59
minimum-21646.4162061802
Q1-7487.6515030296
median-901.456152402407
mean-2.79399855417531e-13
Q37538.95938339416
maximum21521.0943337407

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 59 \tabularnewline
minimum & -21646.4162061802 \tabularnewline
Q1 & -7487.6515030296 \tabularnewline
median & -901.456152402407 \tabularnewline
mean & -2.79399855417531e-13 \tabularnewline
Q3 & 7538.95938339416 \tabularnewline
maximum & 21521.0943337407 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51671&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]-21646.4162061802[/C][/ROW]
[ROW][C]Q1[/C][C]-7487.6515030296[/C][/ROW]
[ROW][C]median[/C][C]-901.456152402407[/C][/ROW]
[ROW][C]mean[/C][C]-2.79399855417531e-13[/C][/ROW]
[ROW][C]Q3[/C][C]7538.95938339416[/C][/ROW]
[ROW][C]maximum[/C][C]21521.0943337407[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51671&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51671&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-21646.4162061802
Q1-7487.6515030296
median-901.456152402407
mean-2.79399855417531e-13
Q37538.95938339416
maximum21521.0943337407


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')