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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 08:53:24 -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/t1225033035p1kql5znwvckul6.htm/, Retrieved Sat, 18 May 2024 19:52:30 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=18910, Retrieved Sat, 18 May 2024 19:52:30 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

182

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 R  D    [Univariate Explorative Data Analysis] [Q7] [2008-10-26 14:53:24] [6912578025c824de531bc660dd61b996] [Current]
F    D      [Univariate Explorative Data Analysis] [Q7] [2008-10-27 19:54:21] [b47fceb71c9525e79a89b5fc6d023d0e] 
-   PD        [Univariate Explorative Data Analysis] [Blog met lags] [2008-11-01 20:54:42] [ed2ba3b6182103c15c0ab511ae4e6284] 
-   PD        [Univariate Explorative Data Analysis] [Q7] [2008-11-03 18:43:02] [b47fceb71c9525e79a89b5fc6d023d0e] 
F    D      [Univariate Explorative Data Analysis] [] [2008-10-27 20:23:41] [fd59abe368d8219a006d49608e51987e] 
-   P         [Univariate Explorative Data Analysis] [Q7 Correctie] [2008-10-30 21:15:11] [547636b63517c1c2916a747d66b36ebf] 
- R             [Univariate Explorative Data Analysis] [Q7 waarde - gemid...] [2008-10-30 21:56:10] [547636b63517c1c2916a747d66b36ebf] 
-   PD      [Univariate Explorative Data Analysis] [] [2008-10-31 09:46:15] [a4ee3bef49b119f4bd2e925060c84f5e] 
-   PD      [Univariate Explorative Data Analysis] [tijdreeks autocor...] [2008-11-03 23:48:42] [82d201ca7b4e7cd2c6f885d29b5b6937] 
- RMPD      [Central Tendency] [central tendency] [2008-11-04 00:10:54] [82d201ca7b4e7cd2c6f885d29b5b6937] 

Feedback Forum

2008-11-04 00:16:16 [2b91075c702c6e89854c34747e80ec72] [reply] 
*assumptie 1: Om na te gaan of de dataset autocorrelatie bevat kan je best naar de lag plot kijken en de autocorrelatiefunctie i.p.v. de run sequence plot. Dit kan je instellen door het aantal lags in te vullen. De lag plot zoekt eigelijk het verband tussen het heden en het verleden. Voor het aantal lags kan je best 12 of 36(maximum) nemen.  
 
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/04/t1225756746fk22x43jopef3z6.htm 
 
In de eerste lagplot k=1 ligt de puntenwolk dicht bij  de rechte. In de 2de lagplot k=36 (samenvatting lag plot 3 jaar) ligt de puntenwolk al veel verder van de rechte af en kunnen we dus geen uitspraak doen over de toekomst.  
 
* assumptie 2: Is correct maar kon misschien iets uitgebreider worden uitgelegd. De normaalverdeling kan je inderdaad op drie wijzen aflezen. Via de histogram, de density plot en de normal QQ plot. De histogram en vooral de density plot tonen aan dat er niet echt een normaal verdeling is maar dat er veel meer waarnemingen zijn aan de linkerkant. Er zijn dus enkele afwijkingen rond de waarden 500 en 650. Doordat deze vrij groot zijn, zijn ze dus wel doorslaggevend.  Op de normal QQ plot is ook te zien dat er zich meer waarnemingen bevinden aan de linkerkant van de rechte. We kunnen dus stellen dat er hier geen sprake is van een normaalverdeling. 
 
* assumptie 3: Om na te gaan of de verdeling constant is moet je naar de run sequence plot kijken. Deze stijgt en is dus niet constant. Maar om een nog naukeuriger resultaat te verkrijgen moet je de central tendency van deze tijdreeksen berekenen. 
 
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/04/t12257575126azh90g7onyxpq6.htm 
 
Uit de grafieken van de Trimmed mean en de Winsored mean kunnen we afleiden dat het gemiddelde niet constant blijft maar eerder een stijgende trend aanneemt.  
 
* assumptie 4: Om na te gaan of de spreiding vast is moet je naar de Run sequence plot kijken. Je moet kijken naar de spreiding over de tijd heen. Deze grafiek moet je dan in midden delen en vergelijken of de schommelingen aan beide kanten ongeveer gelijk zijn. In het 1ste deel schommelt de reeks minder dan in het 2de deel van de grafiek. De spreiding is niet gelijk aan de linker en rechterkant. Over de tijd heen is er dus een verandering van de spreiding.

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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	3 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18910&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18910&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18910&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	3 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Descriptive Statistics
# observations	61
minimum	356.2
Q1	506.2
median	628.1
mean	612.645901639344
Q3	715.5
maximum	847.7

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 61 \tabularnewline
minimum & 356.2 \tabularnewline
Q1 & 506.2 \tabularnewline
median & 628.1 \tabularnewline
mean & 612.645901639344 \tabularnewline
Q3 & 715.5 \tabularnewline
maximum & 847.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18910&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]61[/C][/ROW]
[ROW][C]minimum[/C][C]356.2[/C][/ROW]
[ROW][C]Q1[/C][C]506.2[/C][/ROW]
[ROW][C]median[/C][C]628.1[/C][/ROW]
[ROW][C]mean[/C][C]612.645901639344[/C][/ROW]
[ROW][C]Q3[/C][C]715.5[/C][/ROW]
[ROW][C]maximum[/C][C]847.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18910&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18910&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	356.2
Q1	506.2
median	628.1
mean	612.645901639344
Q3	715.5
maximum	847.7

Figure 1

PNG link

Postscript link

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Figure 2

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Figure 3

PNG link

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Figure 4

PNG link

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Figure 5

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Figure 6

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Figure 7

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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')

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Dataset

Tables (Output of Computation)

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Input Parameters & R Code