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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, 05 Dec 2009 07:12:28 -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/05/t1260022703kyiayjwzcwyvjig.htm/, Retrieved Tue, 30 Apr 2024 03:11:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64259, Retrieved Tue, 30 Apr 2024 03:11:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsJSSHWPAP11
Estimated Impact103

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] [Besluit] [2009-10-29 14:53:44] [214e6e00abbde49700521a7ef1d30da2]
-  M D    [Bivariate Explorative Data Analysis] [Bivariate EDA res...] [2009-12-05 14:12:28] [c8fd62404619100d8e91184019148412] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
64,42592896
47,69064634
61,95277662
69,48479846
66,46888521
72,11049407
75,08901594
51,9287114
74,0142332
70,04903748
61,37521144
54,3296681
52,12640731
44,72803771
43,39112469
55,16936356
56,31634195
60,38853759
46,72545061
53,26821152
60,80579823
51,78432011
41,53551598
37,18268971
43,88575516
44,40186375
50,43666803
42,19860296
52,12382022
60,64808078
42,37779853
39,76284198
67,37262435
52,22823305
46,60512454
54,40742862
37,18527681
42,77616814
56,51960273
52,31634195
40,54625504
48,2149069
52,60771163
42,56773317
44,51960273
39,83503763
64,54366795
55,07568979
35,35373332
45,55440701
41,06495073
41,3296681
59,84060251
47,51960273
52,35373332
52,12382022
39,80838532
48,20158074
43,60512454
40,0168203
Dataseries Y:
Download CSV
Histogram 
0,169087676
-0,973912324
-0,195912324
3,597087676
1,985087676
-1,740912324
2,351087676
-5,182912324
4,311087676
-0,247912324
4,116087676
3,221087676
-3,128912324
-2,453912324
6,728087676
1,687087676
0,688087676
-2,350912324
-0,532912324
2,714087676
1,803087676
-5,104912324
0,650087676
-4,779912324
1,001087676
0,182087676
2,623087676
-3,167912324
3,792087676
7,348087676
-1,804912324
-2,012912324
3,037087676
-4,884912324
-1,467912324
-5,521912324
-0,700912324
5,520087676
1,038087676
-3,311912324
-4,895912324
-1,417912324
-0,388912324
-0,987912324
2,038087676
0,948087676
1,025087676
-2,181912324
0,208087676
-3,520912324
-0,635912324
-0,778912324
4,244087676
-2,961912324
2,208087676
-2,207912324
-2,117912324
0,049087676
-2,467912324
2,390087676

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=64259&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=64259&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64259&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]
c-4.847062876889
b0.0932541431397675

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -4.847062876889 \tabularnewline
b & 0.0932541431397675 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64259&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-4.847062876889[/C][/ROW]
[ROW][C]b[/C][C]0.0932541431397675[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64259&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64259&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]
c-4.847062876889
b0.0932541431397675






Descriptive Statistics about e[t]
# observations60
minimum-5.74856758350717
Q1-2.00531222103000
median0.0762987028149353
mean6.6967554187188e-18
Q31.80980263682098
maximum7.52874840005224

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -5.74856758350717 \tabularnewline
Q1 & -2.00531222103000 \tabularnewline
median & 0.0762987028149353 \tabularnewline
mean & 6.6967554187188e-18 \tabularnewline
Q3 & 1.80980263682098 \tabularnewline
maximum & 7.52874840005224 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64259&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]-5.74856758350717[/C][/ROW]
[ROW][C]Q1[/C][C]-2.00531222103000[/C][/ROW]
[ROW][C]median[/C][C]0.0762987028149353[/C][/ROW]
[ROW][C]mean[/C][C]6.6967554187188e-18[/C][/ROW]
[ROW][C]Q3[/C][C]1.80980263682098[/C][/ROW]
[ROW][C]maximum[/C][C]7.52874840005224[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64259&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64259&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-5.74856758350717
Q1-2.00531222103000
median0.0762987028149353
mean6.6967554187188e-18
Q31.80980263682098
maximum7.52874840005224


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')