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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

Mon, 27 Oct 2008 04:19:49 -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/t1225102840pfg0ljkpfkq65k5.htm/, Retrieved Sun, 19 May 2024 14:39:46 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19183, Retrieved Sun, 19 May 2024 14:39:46 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

132

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...] [2007-10-22 19:45:25] [b9964c45117f7aac638ab9056d451faa]
F    D    [Univariate Explorative Data Analysis] [q7 werkloosheid m...] [2008-10-27 10:19:49] [f24298b2e4c2a19d76cf4460ec5d2246] [Current]
F           [Univariate Explorative Data Analysis] [q7 autocorrolation ] [2008-10-27 10:41:40] [e43247bc0ab243a5af99ac7f55ba0b41] 
-   P       [Univariate Explorative Data Analysis] [q7] [2008-11-03 11:09:51] [e43247bc0ab243a5af99ac7f55ba0b41] 
- RMP       [Central Tendency] [q7] [2008-11-03 11:19:18] [e43247bc0ab243a5af99ac7f55ba0b41] 

Feedback Forum

2008-11-03 16:35:51 [Lindsay Heyndrickx] [reply] 
nr1: Als we hier de lags aanpassen krijgen we de autocorrelatiefunctie en die is hier veel duidelijker dan de run sequence plot. 
http://www.freestatistics.org/blog/date/2008/Nov/03/t1225710749odpfzks5w2udd9y.htm  
Hier zien we dat er wel correlatie is maar dat er geen seizoensgebondenheid is. 
 
nr2: Het is hier inderdaad niet helemaal perfect maar dit is geen reden om aan te nemen  dat hier geen normaalverdeling is. 
 
nr3: Hier is naar de foute grafiek gekeken. Hier had  de run sequence plot gebruikt moeten worden. Hier moet je kijken of het niveau constant blijft of niet. Dit kan je doen door de central tendancy hier te berekenen en te kijken of het gemiddelde constant blijft. Hier zie je dat het gemiddelde rond hetzelfde getal schommelt. 
 
nr4: Hier is niet naar de juiste grafiek gekeken. Hier had naar de run sequence plot gekeken moeten worden. Hier kunnen we zeggen dat dit helemaal geen vaste spreiding is. 
2008-11-03 20:34:03 [Jeroen Aerts] [reply] 
Bij assumption 3 en 4 is er naar de verkeerde grafiek gekeken. Hier moest naar de Run sequence plot gekeken worden. 
 
Haar eindconclusie is wel juist.

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Dataseries X:

Download CSV

Histogram

Boxplots

7.8
7.6
7.5
7.6
7.5
7.3
7.6
7.5
7.6
7.9
7.9
8.1
8.2
8.0
7.5
6.8
6.5
6.6
7.6
8.0
8.0
7.7
7.5
7.6
7.7
7.9
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.1
7.9
7.3
6.9
6.6
6.7
6.9
7.0
7.1
7.2
7.1
6.9
7.0
6.8
6.4
6.7
6.7
6.4
6.3
6.2
6.5
6.8
6.8
6.5
6.3
5.9
5.9
6.4
6.4

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=19183&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=19183&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19183&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	68
minimum	5.9
Q1	6.775
median	7.5
mean	7.25735294117647
Q3	7.7
maximum	8.2

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 68 \tabularnewline
minimum & 5.9 \tabularnewline
Q1 & 6.775 \tabularnewline
median & 7.5 \tabularnewline
mean & 7.25735294117647 \tabularnewline
Q3 & 7.7 \tabularnewline
maximum & 8.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19183&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]68[/C][/ROW]
[ROW][C]minimum[/C][C]5.9[/C][/ROW]
[ROW][C]Q1[/C][C]6.775[/C][/ROW]
[ROW][C]median[/C][C]7.5[/C][/ROW]
[ROW][C]mean[/C][C]7.25735294117647[/C][/ROW]
[ROW][C]Q3[/C][C]7.7[/C][/ROW]
[ROW][C]maximum[/C][C]8.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19183&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19183&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	68
minimum	5.9
Q1	6.775
median	7.5
mean	7.25735294117647
Q3	7.7
maximum	8.2

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