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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, 04 Nov 2009 08:23:45 -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/04/t1257348330nkxmmgt73ade41s.htm/, Retrieved Mon, 29 Apr 2024 11:59:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=53635, Retrieved Mon, 29 Apr 2024 11:59:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128

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 ...] [2009-11-04 14:28:47] [8cf9233b7464ea02e32be3b30fdac052]
-    D  [Bivariate Explorative Data Analysis] [WS 5: Bivariate E...] [2009-11-04 15:17:33] [8cf9233b7464ea02e32be3b30fdac052]
-    D      [Bivariate Explorative Data Analysis] [WS 5: Bivariate E...] [2009-11-04 15:23:45] [b9056af0304697100f456398102f1287] [Current]
-    D        [Bivariate Explorative Data Analysis] [WS 5: Bivariate E...] [2009-11-04 15:57:59] [b97b96148b0223bc16666763988dc147]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
109,44073
111,43416
148,43580
157,43416
156,43087
144,44567
134,43909
130,43251
125,43416
122,43909
117,44731
112,44073
107,43909
108,44567
144,44567
152,44895
152,45060
142,43745
132,43909
127,44731
120,44731
118,44731
112,43745
109,44402
106,44731
107,44238
139,44073
145,43909
144,43251
129,42923
118,43416
111,43580
112,42923
106,43087
100,43087
97,43087
90,42265
88,42265
119,42265
125,42101
119,42429
110,43745
101,44895
100,45224
100,45882
96,46046
90,47361
88,46868
79,48512
82,49663
111,49827
115,49005
112,49170
104,48019
100,45389
102,44567
104,43580
104,43251
103,41114
102,41279
Dataseries Y:
Download CSV
Histogram 
412,06293
416,05528
430,05719
438,05528
441,05145
451,06867
444,06102
450,05337
450,05528
452,06102
451,07058
445,06293
444,06102
445,06867
461,06867
461,07249
460,07441
454,05910
448,06102
454,07058
457,07058
456,07058
452,05910
449,06676
451,07058
450,06484
465,06293
465,06102
460,05337
442,04954
432,05528
431,05719
433,04954
427,05145
416,05145
413,05145
405,04189
395,04189
420,04189
423,03997
407,04380
401,05910
393,07249
398,07632
401,08397
395,08589
387,10119
386,09545
374,11458
379,12797
402,12988
402,12032
390,12223
386,10884
386,07823
398,06867
408,05719
413,05337
417,02850
419,03041

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53635&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53635&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53635&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'George Udny Yule' @ 72.249.76.132






Model: Y[t] = c + b X[t] + e[t]
c308.744311369004
b1.01401295708544

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 308.744311369004 \tabularnewline
b & 1.01401295708544 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53635&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]308.744311369004[/C][/ROW]
[ROW][C]b[/C][C]1.01401295708544[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53635&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53635&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]
c308.744311369004
b1.01401295708544






Descriptive Statistics about e[t]
# observations60
minimum-32.690122733573
Q1-12.9794012260326
median-0.0624509054974372
mean1.72391036630988e-15
Q310.3216138461719
maximum34.3873170441047

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -32.690122733573 \tabularnewline
Q1 & -12.9794012260326 \tabularnewline
median & -0.0624509054974372 \tabularnewline
mean & 1.72391036630988e-15 \tabularnewline
Q3 & 10.3216138461719 \tabularnewline
maximum & 34.3873170441047 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=53635&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]-32.690122733573[/C][/ROW]
[ROW][C]Q1[/C][C]-12.9794012260326[/C][/ROW]
[ROW][C]median[/C][C]-0.0624509054974372[/C][/ROW]
[ROW][C]mean[/C][C]1.72391036630988e-15[/C][/ROW]
[ROW][C]Q3[/C][C]10.3216138461719[/C][/ROW]
[ROW][C]maximum[/C][C]34.3873170441047[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=53635&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=53635&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-32.690122733573
Q1-12.9794012260326
median-0.0624509054974372
mean1.72391036630988e-15
Q310.3216138461719
maximum34.3873170441047


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')