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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 12:56:17 -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/t12250474316ourns0x6muwy61.htm/, Retrieved Sun, 19 May 2024 13:34:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19020, Retrieved Sun, 19 May 2024 13:34:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact145

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 18:56:17] [14a75ec03b2c0d8ddd8b141a7b1594fd] [Current]
-   PD      [Univariate Explorative Data Analysis] [verbetering Q7] [2008-10-30 16:30:24] [6816386b1f3c2f6c0c9f2aa1e5bc9362]
Feedback Forum
2008-10-30 16:39:42 [Kenny Simons] [reply] 
Om te zien of dit model juist is voor onze tijdreeks, moet onze tijdreeks aan 4 voorwaarden voldoen:
1) Random drawings (= een onafhankelijke toevallige trekking)
2) Een vaste verdeling
3) De verdeling moet een constante locatie / niveau hebben.
4) Er moet ook eenzelfde spreiding zijn (= dezelfde breedte)

Voor assumptie 1, heb ik de lags op 36 ingesteld omdat je zo meer kunt aflezen op de autocorrelation functie.
http://www.freestatistics.org/blog/index.php?v=date/2008/Oct/30/t1225384358sa6ee1ywarvg69v.htm
We kunnen dan zien bij de grafiek van de autocorrelatie functie, dat de autocorrelatie zeer groot is tot lag=12, na lag=12, valt alles binnen het betrouwbaarheidsinterval, de tijdreeks bevat dus wel correlatie, maar deze is niet seizoenaal.

Voor assumptie 2, zien we naar het histogram of naar het density plot. Hier zien we duidelijk dat er geen sprake is van een normaal verdeling. Ook bij het normal QQ plot vallen de punten niet op 1 rechte, dus is er geen sprake van een normaal verdeling.

Voor assumptie 3 moeten we naar het run sequence plot zien. Rond observatie 22 gaat de curve plotseling naar boven en blijft ze hoger. Het niveau is hier dus zeker niet constant, het niveau stijgt hier duidelijk.

Voor assumptie 4 zien we naar de spreiding van de curve bij het run sequence plot, ook deze spreiding is zeker niet constant.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,793120172
0,792404046
0,798733277
0,792472927
0,801583754
0,800779967
0,796457292
0,794236111
0,786619647
0,7909731
0,784395274
0,795456132
0,805038314
0,799336464
0,807777892
0,809105332
0,809935895
0,809730841
0,810919757
0,800497147
0,785970882
0,826728976
0,827939046
0,831805591
0,833033335
0,835898213
0,833649002
0,835830216
0,83329087
0,83497378
0,823182477
0,827437378
0,824557069
0,827703169
0,829068243
0,827612041
0,831136868
0,833317282
0,825606365
0,833879102
0,82990246
0,82831348
0,829989621
0,832772975
0,826000887
0,827872063
0,828840441
0,828814107
0,832422325
0,840413025
0,832671665
0,830154726
0,837680443
0,822135395
0,833747382
0,834151327
0,823301409
0,828700587
0,834911097
0,83463772

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19020&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19020&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19020&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 time3 seconds
R Server'George Udny Yule' @ 72.249.76.132






Descriptive Statistics
# observations60
minimum0.784395274
Q10.804174674
median0.827657605
mean0.81932710635
Q30.8326969925
maximum0.840413025

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 60 \tabularnewline
minimum & 0.784395274 \tabularnewline
Q1 & 0.804174674 \tabularnewline
median & 0.827657605 \tabularnewline
mean & 0.81932710635 \tabularnewline
Q3 & 0.8326969925 \tabularnewline
maximum & 0.840413025 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19020&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]0.784395274[/C][/ROW]
[ROW][C]Q1[/C][C]0.804174674[/C][/ROW]
[ROW][C]median[/C][C]0.827657605[/C][/ROW]
[ROW][C]mean[/C][C]0.81932710635[/C][/ROW]
[ROW][C]Q3[/C][C]0.8326969925[/C][/ROW]
[ROW][C]maximum[/C][C]0.840413025[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19020&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19020&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
minimum0.784395274
Q10.804174674
median0.827657605
mean0.81932710635
Q30.8326969925
maximum0.840413025


Figures (Output of Computation)

Input Parameters & R Code

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)
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')