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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 13:40:04 -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/t1256758882l3o2rqtcilhml34.htm/, Retrieved Mon, 06 May 2024 00:15:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51779, Retrieved Mon, 06 May 2024 00:15:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113

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] [Workshop 4, part 2,3] [2009-10-28 19:40:04] [852eae237d08746109043531619a60c9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
474,552
512
531,441
551,368
571,787
551,368
512
493,039
438,976
438,976
571,787
592,704
592,704
592,704
592,704
636,056
704,969
681,472
571,787
421,875
373,248
405,224
681,472
804,357
804,357
658,503
551,368
571,787
614,125
636,056
614,125
551,368
531,441
493,039
636,056
658,503
658,503
614,125
592,704
614,125
658,503
658,503
636,056
614,125
571,787
512
551,368
531,441
531,441
512
493,039
493,039
512
512
493,039
512
456,533
373,248
421,875
389,017
343
343
343
373,248
389,017
357,911
314,432
262,144
226,981
274,625
456,533
493,039
421,875
328,509
287,496
328,509
456,533
512
Dataseries Y:
Download CSV
Histogram 
1865409,391
2548895,896
1408694,561
860085,351
893056,347
1180932,193
1552836,312
1180932,193
1180932,193
1371330,631
559476,224
216000
1235376,017
979146,657
1462135,375
1291467,969
1006012,008
1121622,319
2668267,603
1736654,408
1006012,008
2936493,568
356400,829
618470,208
2352637
2548895,896
1638858,339
1201157,047
1829276,567
1976656,375
3268147,904
2014698,447
988047,936
2767587,264
517781,627
794022,776
2656741,625
1363938,029
1710777,536
1157625
1295029
2166720,184
3281379,256
1042590,744
2449456,192
2967360,453
543338,496
736314,327
2833148,375
2785366,143
2755776,808
1006012,008
1986121,593
1710777,536
2444008,923
1295029
1573037,747
3170044,709
924010,424
714516,984
2305199,161
2732256,792
2146689
1423828,125
1811386,459
1802485,313
1865409,391
2279122,496
1697936,057
2326203,125
949862,087
616295,051
2284322,013
2161700,757
746142,643
485587,656
327082,769
494913,671

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51779&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51779&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51779&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Model: Y[t] = c + b X[t] + e[t]
c2068834.49919048
b-879.269630482645

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 2068834.49919048 \tabularnewline
b & -879.269630482645 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51779&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]2068834.49919048[/C][/ROW]
[ROW][C]b[/C][C]-879.269630482645[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51779&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51779&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]
c2068834.49919048
b-879.269630482645






Descriptive Statistics about e[t]
# observations78
minimum-1340336.12797734
Q1-625357.861759494
median-129648.079173832
mean-2.84887555141886e-11
Q3528175.120819009
maximum1771809.48089579

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 78 \tabularnewline
minimum & -1340336.12797734 \tabularnewline
Q1 & -625357.861759494 \tabularnewline
median & -129648.079173832 \tabularnewline
mean & -2.84887555141886e-11 \tabularnewline
Q3 & 528175.120819009 \tabularnewline
maximum & 1771809.48089579 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51779&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]78[/C][/ROW]
[ROW][C]minimum[/C][C]-1340336.12797734[/C][/ROW]
[ROW][C]Q1[/C][C]-625357.861759494[/C][/ROW]
[ROW][C]median[/C][C]-129648.079173832[/C][/ROW]
[ROW][C]mean[/C][C]-2.84887555141886e-11[/C][/ROW]
[ROW][C]Q3[/C][C]528175.120819009[/C][/ROW]
[ROW][C]maximum[/C][C]1771809.48089579[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51779&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51779&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]
# observations78
minimum-1340336.12797734
Q1-625357.861759494
median-129648.079173832
mean-2.84887555141886e-11
Q3528175.120819009
maximum1771809.48089579


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')