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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_edauni.wasp

Title produced by software

Univariate Explorative Data Analysis

Date of computation

Sun, 26 Oct 2008 09:15:56 -0600

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Oct/26/t1225034242dnaajrzh717aqyx.htm/, Retrieved Tue, 28 May 2024 22:49:54 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=18921, Retrieved Tue, 28 May 2024 22:49:54 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

clothing and overall economic growth

Estimated Impact

139

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

F     [Univariate Explorative Data Analysis] [Investigating Dis...] [2007-10-21 18:26:46] [b9964c45117f7aac638ab9056d451faa]
F    D    [Univariate Explorative Data Analysis] [Q3 ] [2008-10-26 15:15:56] [e4cb5a8878d0401c2e8d19a1768b515b] [Current]

Feedback Forum

2008-10-31 20:06:43 [Jan Van Riet] [reply] 
Deze uitleg is iets te onduidelijk. 
 
Het komt erop neer dat er hier een dalende trend op te merken is, het dus geen constante tijdreeks.  
De productie van kleding wijkt dus fundamenteel af van de economische groei.
2008-11-03 08:12:36 [Glenn De Maeyer] [reply] 
Indien Cl. Prod / Tot. Prod = constant dan betekent dit dat de LT-evolutie v/d totale productie overeenkomt met de LT trend v. Cl. prod. Aangezien de grafiek hier een dalend verloop vertoont hebben we hier dus te maken met totale productie die sneller groeit dan de productie van kleding.

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Dataseries X:

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Histogram

Boxplots

0,99
0,92
0,93
0,93
1,07
0,85
1,03
0,99
0,91
0,79
0,98
0,99
0,91
0,88
0,83
0,83
1,02
0,82
1,03
0,91
0,81
0,74
0,99
0,97
0,85
0,86
0,78
0,79
0,93
0,74
0,89
0,84
0,82
0,73
0,92
0,92
0,88
0,82
0,77
0,83
0,92
0,76
0,87
0,82
0,80
0,71
0,83
0,91
0,87
0,78
0,75
0,76
0,92
0,74
0,82
0,77
0,78
0,68
0,85
0,90
0,87

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18921&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]1 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=18921&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18921&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Descriptive Statistics
# observations	61
minimum	0.68
Q1	0.79
median	0.85
mean	0.862295081967213
Q3	0.92
maximum	1.07

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 61 \tabularnewline
minimum & 0.68 \tabularnewline
Q1 & 0.79 \tabularnewline
median & 0.85 \tabularnewline
mean & 0.862295081967213 \tabularnewline
Q3 & 0.92 \tabularnewline
maximum & 1.07 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18921&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]61[/C][/ROW]
[ROW][C]minimum[/C][C]0.68[/C][/ROW]
[ROW][C]Q1[/C][C]0.79[/C][/ROW]
[ROW][C]median[/C][C]0.85[/C][/ROW]
[ROW][C]mean[/C][C]0.862295081967213[/C][/ROW]
[ROW][C]Q3[/C][C]0.92[/C][/ROW]
[ROW][C]maximum[/C][C]1.07[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18921&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18921&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Descriptive Statistics
# observations	61
minimum	0.68
Q1	0.79
median	0.85
mean	0.862295081967213
Q3	0.92
maximum	1.07

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 0 ; par2 = 0 ;

Parameters (R input):

par1 = 0 ; par2 = 0 ;

R code (references can be found in the software module):

par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
x <- as.ts(x)
library(lattice)
bitmap(file='pic1.png')
plot(x,type='l',main='Run Sequence Plot',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic2.png')
hist(x)
grid()
dev.off()
bitmap(file='pic3.png')
if (par1 > 0)
{
densityplot(~x,col='black',main=paste('Density Plot   bw = ',par1),bw=par1)
} else {
densityplot(~x,col='black',main='Density Plot')
}
dev.off()
bitmap(file='pic4.png')
qqnorm(x)
grid()
dev.off()
if (par2 > 0)
{
bitmap(file='lagplot.png')
dum <- cbind(lag(x,k=1),x)
dum
dum1 <- dum[2:length(x),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Lag plot, lowess, and regression line'))
lines(lowess(z))
abline(lm(z))
dev.off()
bitmap(file='pic5.png')
acf(x,lag.max=par2,main='Autocorrelation Function')
grid()
dev.off()
}
summary(x)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Descriptive Statistics',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'# observations',header=TRUE)
a<-table.element(a,length(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'minimum',header=TRUE)
a<-table.element(a,min(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,quantile(x,0.25))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
a<-table.element(a,median(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
a<-table.element(a,mean(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,quantile(x,0.75))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'maximum',header=TRUE)
a<-table.element(a,max(x))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code