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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_edauni.wasp
Title produced by softwareUnivariate Explorative Data Analysis
Date of computationSun, 26 Oct 2008 12:56:43 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Oct/26/t1225047477am5tjstd7vxwzex.htm/, Retrieved Fri, 17 May 2024 03:19:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19021, Retrieved Fri, 17 May 2024 03:19:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147

Tree of Dependent Computations

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   PD    [Univariate Explorative Data Analysis] [Q7] [2008-10-26 18:56:43] [e81ac192d6ae6d77191d83851a692999] [Current]
Feedback Forum
2008-10-29 17:26:04 [Jan Van Riet] [reply] 
Voor assumption 1 moet je de autocorrelatie nagaan. Dit doen we door de autocorrelation function en de lag plot te onderzoeken.
Ik lees af dat er sprake is van autocorrelatie met seizonale betekenis (nl. om de lag).

In tegenstelling tot wat je beweert, is er wel een normaalverdeling. Ok, de grafie heeft geen perfecte belvorm, maar dit komt dicht genoeg in de buurt.

Ivm de 3e assumptie zou ik naar de Run Sequence plot kijken, om daarvan af te lezen dat deze op lange termijn zeker niet constant is (gaat op en neer).

Assumptie 4 kan wel beoordeeld worden, adhv de Run Sequence plot. Als we de grafiek doormidden delen zien we dat er min of meer een gelijke verdeling is, dus de spreiding is constant te noemen.

Je conclusie klopt wel, nl. deze tijdreeks voldoet niet, dit is geen geldig model omdat niet aan alle validiteitvoorwaarden is voldaan.
2008-10-30 01:04:49 [Gregory Van Overmeiren] [reply] 
De student hierboven heeft gelijk.
Kleine aanvulling: assumptie 2 => Hier gaan we kijken naar het Normal QQ Plot -> De punten wijken wat af van de rechte dwz dat we niet te maken hebben met een perfecte normaalverdeling, maar we liggen er wel dicht bij.
2008-11-03 09:28:14 [339a57d8a4d5d113e4804fc423e4a59e] [reply] 
Ass 1:
De student probeert deze assumptie te staven via de Run Suquence plot, maar dit is niet correct. Men moet deze assumptie controleren door naar de lagplot te kijken. Op de lagplot kan men zien dat elk punt onafhankelijk is van elkaar en dat er dus sprake is van autocorrelatie. In jouw geval is er wel degelijk autocorrelatie(Seizonaal).

Ass 3:
Er is hier geen 'fixed location'. Men kan zien dat de grafiek steeds stijgt en daalt.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,296364694
0,289012188
0,284750546
0,308461168
0,311197802
0,30529117
0,293051134
0,295099724
0,299855329
0,293829075
0,288573454
0,291966049
0,2963183
0,29200924
0,28480764
0,282794428
0,278180549
0,276553189
0,279449933
0,26945731
0,271973446
0,268878727
0,257315852
0,256300537
0,251716995
0,248027075
0,239819848
0,235972015
0,238589686
0,235190343
0,240847054
0,242726478
0,243078651
0,249048485
0,250492336
0,251108768
0,260807534
0,266405235
0,278845462
0,292498505
0,286193952
0,28362042
0,290952119
0,289686229
0,294248482
0,29239326
0,304865687
0,296759995
0,296725458
0,283560093
0,28887286
0,287995152
0,300349538
0,295216859
0,297463824
0,295403904
0,297420797
0,292759913
0,299713181
0,315840172

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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=19021&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=19021&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19021&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Descriptive Statistics
# observations60
minimum0.235190343
Q10.26500580975
median0.288284303
mean0.27977846415
Q30.29526362025
maximum0.315840172

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 60 \tabularnewline
minimum & 0.235190343 \tabularnewline
Q1 & 0.26500580975 \tabularnewline
median & 0.288284303 \tabularnewline
mean & 0.27977846415 \tabularnewline
Q3 & 0.29526362025 \tabularnewline
maximum & 0.315840172 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19021&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]0.235190343[/C][/ROW]
[ROW][C]Q1[/C][C]0.26500580975[/C][/ROW]
[ROW][C]median[/C][C]0.288284303[/C][/ROW]
[ROW][C]mean[/C][C]0.27977846415[/C][/ROW]
[ROW][C]Q3[/C][C]0.29526362025[/C][/ROW]
[ROW][C]maximum[/C][C]0.315840172[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19021&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19021&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Descriptive Statistics
# observations60
minimum0.235190343
Q10.26500580975
median0.288284303
mean0.27977846415
Q30.29526362025
maximum0.315840172


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 4 ;
Parameters (R input):
par1 = 0 ; par2 = 4 ;
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')