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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 computationSat, 19 Dec 2009 12:10:37 -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/Dec/19/t12612500676i5crzrja3ghzi2.htm/, Retrieved Sun, 05 May 2024 16:31:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=69734, Retrieved Sun, 05 May 2024 16:31:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsShwPaper; bivariate EDA analyse
Estimated Impact171

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
- RMPD  [Bivariate Explorative Data Analysis] [Ws4 part 1.1 s090...] [2009-10-27 21:56:53] [e0fc65a5811681d807296d590d5b45de]
-  M D      [Bivariate Explorative Data Analysis] [Paper; bivariate ...] [2009-12-19 19:10:37] [51108381f3361ca8af49c4f74052c840] [Current]
-    D        [Bivariate Explorative Data Analysis] [PAPER EDA analyse] [2010-11-24 17:56:00] [814f53995537cd15c528d8efbf1cf544]
- R PD        [Bivariate Explorative Data Analysis] [PAPER DMA Bivaria...] [2010-12-08 16:59:16] [74be16979710d4c4e7c6647856088456]
- RMPD        [] [PAPER DMA Bivaria...] [-0001-11-30 00:00:00] [74be16979710d4c4e7c6647856088456]
- RMPD          [Univariate Data Series] [Data Xt paper dma] [2010-12-09 15:27:56] [2099aacba481f75a7f949aa310cab952]
- RMPD          [Univariate Data Series] [Data Yt paper DMA] [2010-12-09 15:34:47] [2099aacba481f75a7f949aa310cab952]
- RMPD          [Univariate Data Series] [Yt Gemiddelde aan...] [2010-12-09 15:43:19] [2099aacba481f75a7f949aa310cab952]
- RMPD          [Univariate Data Series] [Xt Gemiddelde oli...] [2010-12-09 15:45:25] [2099aacba481f75a7f949aa310cab952]
- RMPD          [Bivariate Explorative Data Analysis] [Biveriate EDA Pap...] [2010-12-09 15:48:00] [2099aacba481f75a7f949aa310cab952]
-    D            [Bivariate Explorative Data Analysis] [] [2010-12-13 09:53:42] [1251ac2db27b84d4a3ba43449388906b]
-                   [Bivariate Explorative Data Analysis] [workshop 10: Mult...] [2010-12-14 08:27:59] [814f53995537cd15c528d8efbf1cf544]
- RMPD          [Multiple Regression] [Multiple Regressi...] [2010-12-09 15:50:37] [2099aacba481f75a7f949aa310cab952]
- RMPD        [] [PAPER DMA Multipl...] [-0001-11-30 00:00:00] [74be16979710d4c4e7c6647856088456]
- RMPD        [] [PAPER DMA Multipl...] [-0001-11-30 00:00:00] [74be16979710d4c4e7c6647856088456]
- RMPD        [Cross Correlation Function] [cross correlation...] [2010-12-08 19:50:23] [74be16979710d4c4e7c6647856088456]
-   PD          [Cross Correlation Function] [] [2010-12-09 09:22:26] [b98453cac15ba1066b407e146608df68]
-    D            [Cross Correlation Function] [workshop 10] [2010-12-12 14:17:25] [717f3d787904f94c39256c5c1fc72d4c]
-   PD            [Cross Correlation Function] [workshop 10] [2010-12-12 14:24:39] [717f3d787904f94c39256c5c1fc72d4c]
- R PD            [Cross Correlation Function] [CCF 3] [2010-12-14 17:53:52] [04d4386fa51dbd2ef12d0f1f80644886]
-    D            [Cross Correlation Function] [] [2010-12-19 19:14:52] [de55ccbf69577500a5f46ed42a101114]
-   PD          [Cross Correlation Function] [] [2010-12-09 09:25:48] [b98453cac15ba1066b407e146608df68]
- R P             [Cross Correlation Function] [PAPER DMA Cross C...] [2010-12-09 19:56:08] [2099aacba481f75a7f949aa310cab952]
-   P               [Cross Correlation Function] [Paper DMA Cross C...] [2010-12-15 17:50:43] [2099aacba481f75a7f949aa310cab952]
- RMPD            [Variance Reduction Matrix] [PAPER DMA Varianc...] [2010-12-09 20:08:14] [2099aacba481f75a7f949aa310cab952]
- RMPD            [Variance Reduction Matrix] [PAPER DMA VRM Olie] [2010-12-09 20:10:17] [2099aacba481f75a7f949aa310cab952]
-   PD            [Cross Correlation Function] [Cross validation ...] [2010-12-10 08:46:08] [aeb27d5c05332f2e597ad139ee63fbe4]
- RMPD              [Kendall tau Correlation Matrix] [WS10 Feedback Pea...] [2010-12-22 16:18:54] [abf4ff90b26c6b37be4a30063b404639]
- RMPD              [Kendall tau Correlation Matrix] [WS10 Feedback Ken...] [2010-12-22 16:20:27] [abf4ff90b26c6b37be4a30063b404639]
- RMPD              [Recursive Partitioning (Regression Trees)] [WS10 Feedback Rec...] [2010-12-22 16:40:24] [abf4ff90b26c6b37be4a30063b404639]
- R  D            [Cross Correlation Function] [Paper/ Cross Corr...] [2010-12-10 14:13:23] [d59201e34006b7e3f71c33fa566f42b3]
- R  D            [Cross Correlation Function] [CCF ] [2010-12-11 10:57:31] [04d4386fa51dbd2ef12d0f1f80644886]
-    D            [Cross Correlation Function] [Cross Correlation...] [2010-12-11 15:22:29] [62f7c80c4d96454bbd2b2b026ea9aad9]
- R  D            [Cross Correlation Function] [workshop 10] [2010-12-12 13:50:13] [717f3d787904f94c39256c5c1fc72d4c]
- R PD            [Cross Correlation Function] [CCF ] [2010-12-14 17:49:20] [04d4386fa51dbd2ef12d0f1f80644886]
- RMPD              [(Partial) Autocorrelation Function] [ACF aanvoerwaarde] [2010-12-16 11:01:16] [04d4386fa51dbd2ef12d0f1f80644886]
-   PD              [Cross Correlation Function] [CCF aanvoer en aa...] [2010-12-16 10:58:21] [04d4386fa51dbd2ef12d0f1f80644886]
-    D            [Cross Correlation Function] [] [2010-12-19 19:09:45] [de55ccbf69577500a5f46ed42a101114]
- RMPD          [Bivariate Granger Causality] [] [2010-12-09 09:27:32] [b98453cac15ba1066b407e146608df68]
-   PD            [Bivariate Granger Causality] [Bivariate Granger...] [2010-12-10 08:56:43] [aeb27d5c05332f2e597ad139ee63fbe4]
-   PD            [Bivariate Granger Causality] [workshop 10] [2010-12-12 14:35:01] [717f3d787904f94c39256c5c1fc72d4c]
-   PD            [Bivariate Granger Causality] [workshop 10] [2010-12-12 14:36:35] [717f3d787904f94c39256c5c1fc72d4c]
- R PD            [Bivariate Granger Causality] [granger] [2010-12-14 17:56:45] [04d4386fa51dbd2ef12d0f1f80644886]
-    D            [Bivariate Granger Causality] [] [2010-12-19 19:16:58] [de55ccbf69577500a5f46ed42a101114]
- RMPD          [Multiple Regression] [] [2010-12-09 09:37:57] [b98453cac15ba1066b407e146608df68]
-    D            [Multiple Regression] [Multiple Regressi...] [2010-12-10 09:09:44] [aeb27d5c05332f2e597ad139ee63fbe4]
-    D            [Multiple Regression] [workshop 10] [2010-12-12 20:47:37] [717f3d787904f94c39256c5c1fc72d4c]
- R P               [Multiple Regression] [Peer verbetering] [2010-12-17 17:37:09] [d6a5e6c1b0014d57cedb2bdfb4a7099f]
-    D            [Multiple Regression] [] [2010-12-22 19:51:26] [de55ccbf69577500a5f46ed42a101114]
-   PD          [Cross Correlation Function] [] [2010-12-09 09:43:43] [c2a9e95daa10045f9fd6252038bcb219]

[Truncated]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
103,1
103,1
103,3
103,5
103,3
103,5
103,8
103,9
103,9
104,2
104,6
104,9
105,2
105,2
105,6
105,6
106,2
106,3
106,4
106,9
107,2
107,3
107,3
107,4
107,55
107,87
108,37
108,38
107,92
108,03
108,14
108,3
108,64
108,66
109,04
109,03
109,03
109,54
109,75
109,83
109,65
109,82
109,95
110,12
110,15
110,2
109,99
110,14
110,14
110,81
110,97
110,99
109,73
109,81
110,02
110,18
110,21
110,25
110,36
110,51
110,64
110,95
111,18
111,19
111,69
111,7
111,83
111,77
111,73
112,01
111,86
112,04
Dataseries Y:
Download CSV
Histogram 
152,60
153,32
165,50
139,18
136,53
115,92
96,65
83,77
84,66
106,03
86,92
54,66
151,66
121,27
132,95
119,64
122,16
117,44
106,69
87,45
80,98
110,30
87,01
55,73
146,00
137,54
138,54
135,62
107,27
99,04
91,36
68,35
82,59
98,41
71,25
47,58
130,83
113,60
125,69
113,60
97,12
104,43
91,84
75,11
89,24
110,23
78,42
68,45
122,81
129,66
159,06
139,03
102,16
113,59
81,46
77,36
87,57
101,23
87,21
64,94
133,12
117,99
135,90
125,67
108,03
128,31
84,74
86,38
92,24
95,83
92,33
54,27

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=69734&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=69734&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69734&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]
c374.916985912493
b-2.48411738813970

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 374.916985912493 \tabularnewline
b & -2.48411738813970 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69734&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]374.916985912493[/C][/ROW]
[ROW][C]b[/C][C]-2.48411738813970[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69734&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69734&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]
c374.916985912493
b-2.48411738813970






Descriptive Statistics about e[t]
# observations72
minimum-59.6730718966392
Q1-20.6519983089707
median-0.507891089445255
mean1.69627043892599e-15
Q321.4002986242771
maximum59.8055206493687

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 72 \tabularnewline
minimum & -59.6730718966392 \tabularnewline
Q1 & -20.6519983089707 \tabularnewline
median & -0.507891089445255 \tabularnewline
mean & 1.69627043892599e-15 \tabularnewline
Q3 & 21.4002986242771 \tabularnewline
maximum & 59.8055206493687 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69734&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]72[/C][/ROW]
[ROW][C]minimum[/C][C]-59.6730718966392[/C][/ROW]
[ROW][C]Q1[/C][C]-20.6519983089707[/C][/ROW]
[ROW][C]median[/C][C]-0.507891089445255[/C][/ROW]
[ROW][C]mean[/C][C]1.69627043892599e-15[/C][/ROW]
[ROW][C]Q3[/C][C]21.4002986242771[/C][/ROW]
[ROW][C]maximum[/C][C]59.8055206493687[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69734&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69734&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]
# observations72
minimum-59.6730718966392
Q1-20.6519983089707
median-0.507891089445255
mean1.69627043892599e-15
Q321.4002986242771
maximum59.8055206493687


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')