Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

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 16:17:06 -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/t1225059500fgvhke2g9sten9i.htm/, Retrieved Sun, 19 May 2024 14:57:21 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19072, Retrieved Sun, 19 May 2024 14:57:21 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

127

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

F     [Univariate Data Series] [Werkloosheid >25 ...] [2008-10-12 14:58:26] [44ec60eb6065a3f81a5f756bd5af1faf]
F RMPD    [Univariate Explorative Data Analysis] [Werloosheid >25 U...] [2008-10-26 22:17:06] [924502d03698cd41cacbcd1327858815] [Current]

Feedback Forum

2008-11-01 12:54:23 [Davy De Nef] [reply] 
De student past de 4 assumpties toe op zijn eigen reeksen. De 4 assumpties moeten kloppen om een geldige reeks te hebben. 
 
1. Randomness 
De rechte op de lagplot verloopt duidelijk stijgend. 
 
2. Fixed distribution 
Dit wordt bekeken via de histogram en de density plot. Deze geeft min of meer een normaalverdeling weer.  
Ook de normal QQ plot kan je in deze context bekijken. We zien dat de meeste punten vrij dicht bij de linieaire rechte liggen.  
 
3. Fixed location 
Hiervoor gebruiken we de run sequence plot. Deze wordt door de student weergegeven op langere termijn. Op deze manier kan je zien dat er een licht dalend verloop is. Er is dus geen sprake van een constante. 
 
4. Fixed variation 
We gebruiken hiervoor de run sequence plot. Zo kunnen we zien of de spreiding gelijk is of niet. We zien dat de linkerkant van de grafiek grotere schommelingen weergeeft dan de rechterkant. 
 
De conclusie die de student hier maakt, klopt. De linkerkant van de 'bell-shape' is te verwaarlozen.
2008-11-02 22:45:10 [Koen Van den Heuvel] [reply] 
Je zegt: 'De conclusie die de student hier maakt, klopt. De linkerkant van de 'bell-shape' is te verwaarlozen.' terwijl in mijn document duidelijk staat: 'Hoe mooier “bell-shaped” histogram en density-plot hoe beter, dit is niet het geval aangezien er aan de linkerzijde outliers te zien zijn.'

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

7
6.9
6.7
6.6
6.5
6.4
6.5
6.5
6.6
6.7
6.8
7.2
7.6
7.6
7.3
6.4
6.1
6.3
7.1
7.5
7.4
7.1
6.8
6.9
7.2
7.4
7.3
6.9
6.9
6.8
7.1
7.2
7.1
7
6.9
7
7.4
7.5
7.5
7.4
7.3
7
6.7
6.5
6.5
6.5
6.6
6.8
6.9
6.9
6.8
6.8
6.5
6.1
6
5.9
5.8
5.9
5.9
6.2

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19072&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19072&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19072&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	4 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

Descriptive Statistics
# observations	60
minimum	5.8
Q1	6.5
median	6.85
mean	6.81166666666667
Q3	7.125
maximum	7.6

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 60 \tabularnewline
minimum & 5.8 \tabularnewline
Q1 & 6.5 \tabularnewline
median & 6.85 \tabularnewline
mean & 6.81166666666667 \tabularnewline
Q3 & 7.125 \tabularnewline
maximum & 7.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19072&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]5.8[/C][/ROW]
[ROW][C]Q1[/C][C]6.5[/C][/ROW]
[ROW][C]median[/C][C]6.85[/C][/ROW]
[ROW][C]mean[/C][C]6.81166666666667[/C][/ROW]
[ROW][C]Q3[/C][C]7.125[/C][/ROW]
[ROW][C]maximum[/C][C]7.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19072&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19072&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	60
minimum	5.8
Q1	6.5
median	6.85
mean	6.81166666666667
Q3	7.125
maximum	7.6

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

Figure 7

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 0 ; par2 = 12 ;

Parameters (R input):

par1 = 0 ; par2 = 12 ;

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)
qqline(x)
grid()
dev.off()
if (par2 > 0)
{
bitmap(file='lagplot1.png')
dum <- cbind(lag(x,k=1),x)
dum
dum1 <- dum[2:length(x),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main='Lag plot (k=1), lowess, and regression line')
lines(lowess(z))
abline(lm(z))
dev.off()
if (par2 > 1) {
bitmap(file='lagplotpar2.png')
dum <- cbind(lag(x,k=par2),x)
dum
dum1 <- dum[(par2+1):length(x),]
dum1
z <- as.data.frame(dum1)
z
mylagtitle <- 'Lag plot (k='
mylagtitle <- paste(mylagtitle,par2,sep='')
mylagtitle <- paste(mylagtitle,'), and lowess',sep='')
plot(z,main=mylagtitle)
lines(lowess(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