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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 computationFri, 24 Oct 2008 07:12:07 -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/24/t1224861234k2oolop40y48cx4.htm/, Retrieved Tue, 28 May 2024 17:34:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=18636, Retrieved Tue, 28 May 2024 17:34:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact186

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] [Investigation Dis...] [2007-10-21 17:06:37] [b9964c45117f7aac638ab9056d451faa]
F   PD    [Univariate Explorative Data Analysis] [herberekening vra...] [2008-10-24 13:12:07] [3dc594a6c62226e1e98766c4d385bfaa] [Current]
F    D      [Univariate Explorative Data Analysis] [vraag 7] [2008-10-26 11:17:02] [c45c87b96bbf32ffc2144fc37d767b2e]
-   P         [Univariate Explorative Data Analysis] [verbetering taak 3] [2008-11-02 12:24:26] [c45c87b96bbf32ffc2144fc37d767b2e]
- RMPD      [Pearson Correlation] [correlatie rnvm -...] [2008-10-30 11:34:51] [28075c6928548bea087cb2be962cfe7e]
F RM D        [Kendall tau Correlation Matrix] [Q1] [2008-10-30 12:28:36] [28075c6928548bea087cb2be962cfe7e]
- RMPD      [Pearson Correlation] [correlatie rcf - rnr] [2008-10-30 11:37:41] [28075c6928548bea087cb2be962cfe7e]
- RMPD      [Pearson Correlation] [correlatie tussen...] [2008-10-30 11:41:51] [28075c6928548bea087cb2be962cfe7e]
- RMPD      [Pearson Correlation] [correlatie ren en...] [2008-10-30 11:44:13] [28075c6928548bea087cb2be962cfe7e]
F RMPD      [Star Plot] [q2] [2008-10-30 11:52:06] [28075c6928548bea087cb2be962cfe7e]
F RMPD      [Notched Boxplots] [notch boxplot Q3] [2008-10-30 12:09:20] [28075c6928548bea087cb2be962cfe7e]
F   P       [Univariate Explorative Data Analysis] [vraag 5] [2008-11-11 15:06:24] [c45c87b96bbf32ffc2144fc37d767b2e]
Feedback Forum
2008-10-31 12:09:51 [Michaël De Kuyer] [reply] 
Bij randomness had ik me ook nog moeten baseren op de lag plots. Zo had ik bij de eerste lag plot kunnen vaststellen dat als men 1 periode teruggaat (k=1), de puntenwolk niet rond de rechte ligt en dat men bijgevolg geen uitspraak kan doen over het heden als men 1 maand teruggaat. Bij de tweede lag plot (k=12) ziet men duidelijk dat er een positieve seizonale autocorrelatie is. Men kan dus uitspraak doen over de industriële productie als men 12 maanden teruggaat. Er is dus geen randomness.

De vaste verdeling is volledig juist geïnterpreteerd aan de hand van de juiste grafieken.

De analyse of er een vaste component is, is volledig fout. Ik had me inderdaad moeten baseren op de run sequense plot. Als ik dan het gemiddelde op lange termijn zou zien, had ik vastgesteld dat er een daling/achteruitgang was van het gemiddelde. Als het gemiddelde echter constant zou geweest zijn, had ik kunnen spreken van een vaste component.

De analyse van vaste variatie is eveneens foutief. Ik had me moeten controleren in welke mate de waarden schommelen. Als ik de run sequense plot in twee had gedeeld, dan had ik vastgesteld dat de waarden in het eerste deel sterk schommelen dan in het tweede deel. Deze voorwaarde was dus niet voldaan.

Na deze verbeteringen kan ik concluderen dat het model niet opgaat voor de industriële productie.
2008-11-03 22:35:55 [Martjin De Swert] [reply] 
Bij verhoging van de lag naar 36 wordt pas een seizonale autocorrelatie duidelijk, dit maakt het mogelijk om de maanden met elkaar te vergelijken.

Hier werd geen rekening bij gehouden bij de analyse van de student.

Als conclusie kunnen we dan ook stellen dat de tijdreeks niet random is want er is sprake van een seizonale autocorrelatie.

Voor de analyse van de aanwezigheid van een vaste component diende de student zich inderdaad te baseren op de run sequence plot.


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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
109.20
88.60
94.30
98.30
86.40
80.60
104.10
108.20
93.40
71.90
94.10
94.90
96.40
91.10
84.40
86.40
88.00
75.10
109.70
103.00
82.10
68.00
96.40
94.30
90.00
88.00
76.10
82.50
81.40
66.50
97.20
94.10
80.70
70.50
87.80
89.50
99.60
84.20
75.10
92.00
80.80
73.10
99.80
90.00
83.10
72.40
78.80
87.30
91.00
80.10
73.60
86.40
74.50
71.20
92.40
81.50
85.30
69.90
84.20
90.70
100.30

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'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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18636&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18636&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18636&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Descriptive Statistics
# observations61
minimum66.5
Q180.6
median87.3
mean86.8934426229508
Q394.1
maximum109.7

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 61 \tabularnewline
minimum & 66.5 \tabularnewline
Q1 & 80.6 \tabularnewline
median & 87.3 \tabularnewline
mean & 86.8934426229508 \tabularnewline
Q3 & 94.1 \tabularnewline
maximum & 109.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18636&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]61[/C][/ROW]
[ROW][C]minimum[/C][C]66.5[/C][/ROW]
[ROW][C]Q1[/C][C]80.6[/C][/ROW]
[ROW][C]median[/C][C]87.3[/C][/ROW]
[ROW][C]mean[/C][C]86.8934426229508[/C][/ROW]
[ROW][C]Q3[/C][C]94.1[/C][/ROW]
[ROW][C]maximum[/C][C]109.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18636&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18636&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
# observations61
minimum66.5
Q180.6
median87.3
mean86.8934426229508
Q394.1
maximum109.7


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')