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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, 22 Nov 2009 10:49:18 -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/22/t12589121917wkgegbvvkcuw9l.htm/, Retrieved Sat, 27 Apr 2024 23:57:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58671, Retrieved Sat, 27 Apr 2024 23:57:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact159

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] [Ws 5 bivariate X ...] [2009-11-04 15:39:55] [62d3ced7fb1c10c35a82e9cb1d0d0e2b]
-    D  [Bivariate Explorative Data Analysis] [Ws 5 bivariate X ...] [2009-11-04 16:22:44] [62d3ced7fb1c10c35a82e9cb1d0d0e2b]
-    D      [Bivariate Explorative Data Analysis] [WS 5 bivariate EDA] [2009-11-22 17:49:18] [b02b8a83db8a631da1ab9c106b4cdcf2] [Current]
-    D        [Bivariate Explorative Data Analysis] [WS 5 Bivariate EDA 1] [2009-11-22 17:53:15] [005293453b571dbccb80b45226e44173]
-    D          [Bivariate Explorative Data Analysis] [WS 5 Bivariate EDA 2] [2009-11-22 18:01:47] [005293453b571dbccb80b45226e44173]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
244.576
241.572
240.541
236.089
236.997
264.579
270.349
269.645
267.037
258.113
262.813
267.413
267.366
264.777
258.863
254.844
254.868
277.267
285.351
286.602
283.042
276.687
277.915
277.128
277.103
275.037
270.150
267.140
264.993
287.259
291.186
292.300
288.186
281.477
282.656
280.190
280.408
276.836
275.216
274.352
271.311
289.802
290.726
292.300
278.506
269.826
265.861
269.034
264.176
255.198
253.353
246.057
235.372
258.556
260.993
254.663
250.643
243.422
247.105
248.541
245.039
237.080
237.085
225.554
226.839
247.934
248.333
246.969
245.098
246.263
255.765
264.319
268.347
273.046
273.963
267.430
271.993
292.710
295.881
Dataseries Y:
Download CSV
Histogram 
97
105,4
102,7
98,1
104,5
87,4
89,9
109,8
111,7
98,6
96,9
95,1
97
112,7
102,9
97,4
111,4
87,4
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
82,6
89,1

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=58671&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=58671&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58671&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]
c150.938455346336
b-0.179364480603179

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 150.938455346336 \tabularnewline
b & -0.179364480603179 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58671&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]150.938455346336[/C][/ROW]
[ROW][C]b[/C][C]-0.179364480603179[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58671&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58671&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]
c150.938455346336
b-0.179364480603179






Descriptive Statistics about e[t]
# observations79
minimum-18.5143940127474
Q1-6.76533683565546
median0.316859088612809
mean7.58974458527372e-16
Q37.2326607017098
maximum16.4179961654866

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 79 \tabularnewline
minimum & -18.5143940127474 \tabularnewline
Q1 & -6.76533683565546 \tabularnewline
median & 0.316859088612809 \tabularnewline
mean & 7.58974458527372e-16 \tabularnewline
Q3 & 7.2326607017098 \tabularnewline
maximum & 16.4179961654866 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58671&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]79[/C][/ROW]
[ROW][C]minimum[/C][C]-18.5143940127474[/C][/ROW]
[ROW][C]Q1[/C][C]-6.76533683565546[/C][/ROW]
[ROW][C]median[/C][C]0.316859088612809[/C][/ROW]
[ROW][C]mean[/C][C]7.58974458527372e-16[/C][/ROW]
[ROW][C]Q3[/C][C]7.2326607017098[/C][/ROW]
[ROW][C]maximum[/C][C]16.4179961654866[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58671&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58671&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]
# observations79
minimum-18.5143940127474
Q1-6.76533683565546
median0.316859088612809
mean7.58974458527372e-16
Q37.2326607017098
maximum16.4179961654866


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')