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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 10:53:15 -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/t1258912435knvheyybo7s9nld.htm/, Retrieved Sat, 27 Apr 2024 15:24:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58673, Retrieved Sat, 27 Apr 2024 15:24:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact169

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] [005293453b571dbccb80b45226e44173]
-    D        [Bivariate Explorative Data Analysis] [WS 5 Bivariate EDA 1] [2009-11-22 17:53:15] [b02b8a83db8a631da1ab9c106b4cdcf2] [Current]
-    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 
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
Dataseries Y:
Download CSV
Histogram 
272.433
268.361
268.586
264.768
269.974
304.744
309.365
308.347
298.427
289.231
291.975
294.912
293.488
290.555
284.736
281.818
287.854
316.263
325.412
326.011
328.282
317.480
317.539
313.737
312.276
309.391
302.950
300.316
304.035
333.476
337.698
335.932
323.931
313.927
314.485
313.218
309.664
302.963
298.989
298.423
301.631
329.765
335.083
327.616
309.119
295.916
291.413
291.542
284.678
276.475
272.566
264.981
263.290
296.806
303.598
286.994
276.427
266.424
267.153
268.381
262.522
255.542
253.158
243.803
250.741
280.445
285.257
270.976
261.076
255.603
260.376
263.903
264.291
263.276
262.572
256.167
264.221
293.860
300.713

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=58673&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=58673&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58673&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]
c342.488691254421
b-0.496932561329373

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 342.488691254421 \tabularnewline
b & -0.496932561329373 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58673&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]342.488691254421[/C][/ROW]
[ROW][C]b[/C][C]-0.496932561329373[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58673&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58673&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]
c342.488691254421
b-0.496932561329373






Descriptive Statistics about e[t]
# observations79
minimum-44.470348813386
Q1-21.3545971784614
median0.283570668546407
mean2.84316786007596e-15
Q318.0084735300585
maximum49.6960746880645

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 79 \tabularnewline
minimum & -44.470348813386 \tabularnewline
Q1 & -21.3545971784614 \tabularnewline
median & 0.283570668546407 \tabularnewline
mean & 2.84316786007596e-15 \tabularnewline
Q3 & 18.0084735300585 \tabularnewline
maximum & 49.6960746880645 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58673&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]-44.470348813386[/C][/ROW]
[ROW][C]Q1[/C][C]-21.3545971784614[/C][/ROW]
[ROW][C]median[/C][C]0.283570668546407[/C][/ROW]
[ROW][C]mean[/C][C]2.84316786007596e-15[/C][/ROW]
[ROW][C]Q3[/C][C]18.0084735300585[/C][/ROW]
[ROW][C]maximum[/C][C]49.6960746880645[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58673&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58673&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-44.470348813386
Q1-21.3545971784614
median0.283570668546407
mean2.84316786007596e-15
Q318.0084735300585
maximum49.6960746880645


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')