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Author's title

Author

*Unverified author*

R Software Module

rwasp_edauni.wasp

Title produced by software

Univariate Explorative Data Analysis

Date of computation

Mon, 27 Oct 2008 11:38:10 -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/27/t122512917273hnhye5ai5346d.htm/, Retrieved Sun, 19 May 2024 13:05:05 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19315, Retrieved Sun, 19 May 2024 13:05:05 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

129

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

F       [Univariate Explorative Data Analysis] [Univariate explor...] [2008-10-27 17:38:10] [81dc0ee785f23261ccd6abf7aef76c2a] [Current]

Feedback Forum

2008-11-04 07:42:00 [Bonifer Spillemaeckers] [reply] 
Voor assumptie 1 ben ik gaan kijken naar de grafiek van autocorrelation function. Hier zien we dat tot lag 12 de verticale lijnen boven het interval liggen. Na lag 12 vallen deze lijnen binnen het interval. Er is dus sprake van autocorrelatie.  
 
Voor assumptie 2 ben ik gaan kijken naar het histogram of naar het density plot. zien we naar het histogram of naar het density plot. Als we kijken naar het histogram en het density plot kunnen we hier toch min of meer een normaalverdeling vaststellen. We kunnen ook kijken naar het Q-Q plot. Als de punten op een lineaire rechte liggen, hebben we te maken met een normaalverdeling. Dit is hier het geval. 
 
Voor assumptie 3 gaan we kijken naar het run sequence plot. We zien of de grafiek niet te hard fluctueert op lange termijn. Dit is hier niet het geval. We kunnen wel bemerken dat er een stijgende trend zichtbaar is.  
 
Voor assumptie 4 kijken we ook weer naar het run sequence plot. We moeten hier kijken naar de spreiding van de reeks. We kunnen zien dat de grafiek mooi op en neer gaat, zonder echte uitschieters.  
 
Dit model voldoet niet aan alle voorwaarden. Enkel de 1ste assumptie voldoet niet aan het model.  
 
http://www.freestatistics.org/blog/date/2008/Nov/04/t12257833758a2tgoszlax3w2l.htm

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Dataseries X:

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 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 & 5 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=19315&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19315&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19315&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	5 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

Descriptive Statistics
# observations	60
minimum	11703.7
Q1	16234.925
median	17785.2
mean	17788.375415
Q3	19445.775
maximum	22238.5

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 60 \tabularnewline
minimum & 11703.7 \tabularnewline
Q1 & 16234.925 \tabularnewline
median & 17785.2 \tabularnewline
mean & 17788.375415 \tabularnewline
Q3 & 19445.775 \tabularnewline
maximum & 22238.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19315&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]11703.7[/C][/ROW]
[ROW][C]Q1[/C][C]16234.925[/C][/ROW]
[ROW][C]median[/C][C]17785.2[/C][/ROW]
[ROW][C]mean[/C][C]17788.375415[/C][/ROW]
[ROW][C]Q3[/C][C]19445.775[/C][/ROW]
[ROW][C]maximum[/C][C]22238.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19315&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19315&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	11703.7
Q1	16234.925
median	17785.2
mean	17788.375415
Q3	19445.775
maximum	22238.5

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

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Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

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Figure 6

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PDF link

Figure 7

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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)
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