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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 10:10:19 -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/t12567467311ohj557cdzh35pz.htm/, Retrieved Mon, 06 May 2024 08:07:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51509, Retrieved Mon, 06 May 2024 08:07:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact88

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
- RMPD    [Bivariate Explorative Data Analysis] [EDA part 2 worksh...] [2009-10-28 16:10:19] [9a3898f49d4e2f0208d1968305d88f0a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3977.7
3983.4
4152.9
4286.1
4348.1
3949.3
4166.7
4217.9
4528.2
4232.2
4470.9
5121.2
4170.8
4398.6
4491.4
4251.8
4901.9
4745.2
4666.9
4210.4
5273.6
4095.3
4610.1
4718.1
4185.5
4314.7
4422.6
5059.2
5043.6
4436.6
4922.6
4454.8
5058.7
4768.9
5171.8
4989.3
5202.1
4838.4
4876.5
5875.5
5717.9
4778.8
6195.9
4625.4
5549.8
6397.6
5856.7
6343.8
6615.5
5904.6
6861
6553.5
5481
5435.3
5278
4671.8
4891.5
4241.6
4152.1
4484.4
4124.7
Dataseries Y:
Download CSV
Histogram 
3956.2
3142.7
3884.3
3892.2
3613
3730.5
3481.3
3649.5
4215.2
4066.6
4196.8
4536.6
4441.6
3548.3
4735.9
4130.6
4356.2
4159.6
3988
4167.8
4902.2
3909.4
4697.6
4308.9
4420.4
3544.2
4433
4479.7
4533.2
4237.5
4207.4
4394
5148.4
4202.2
4682.5
4884.3
5288.9
4505.2
4611.5
5104
4586.6
4529.3
4504.1
4604.9
4795.4
5391.1
5213.9
5415
5990.3
4241.8
5677.6
5164.2
3962.3
4011
3310.3
3837.3
4145.3
3796.7
3849.6
4285
4189.6

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'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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51509&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51509&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51509&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'George Udny Yule' @ 72.249.76.132






Model: Y[t] = c + b X[t] + e[t]
c1538.6271090769
b0.577774022625357

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 1538.6271090769 \tabularnewline
b & 0.577774022625357 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51509&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]1538.6271090769[/C][/ROW]
[ROW][C]b[/C][C]0.577774022625357[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51509&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51509&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]
c1538.6271090769
b0.577774022625357






Descriptive Statistics about e[t]
# observations61
minimum-1277.81840049353
Q1-192.613403444615
median60.2965616709584
mean-5.42644253757361e-15
Q3229.606391601044
maximum744.63464782373

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 61 \tabularnewline
minimum & -1277.81840049353 \tabularnewline
Q1 & -192.613403444615 \tabularnewline
median & 60.2965616709584 \tabularnewline
mean & -5.42644253757361e-15 \tabularnewline
Q3 & 229.606391601044 \tabularnewline
maximum & 744.63464782373 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51509&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]61[/C][/ROW]
[ROW][C]minimum[/C][C]-1277.81840049353[/C][/ROW]
[ROW][C]Q1[/C][C]-192.613403444615[/C][/ROW]
[ROW][C]median[/C][C]60.2965616709584[/C][/ROW]
[ROW][C]mean[/C][C]-5.42644253757361e-15[/C][/ROW]
[ROW][C]Q3[/C][C]229.606391601044[/C][/ROW]
[ROW][C]maximum[/C][C]744.63464782373[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51509&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51509&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]
# observations61
minimum-1277.81840049353
Q1-192.613403444615
median60.2965616709584
mean-5.42644253757361e-15
Q3229.606391601044
maximum744.63464782373


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')