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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 07:27:07 -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/t1224854890ysbz8xksck6hehl.htm/, Retrieved Thu, 06 Jun 2024 15:07:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=18604, Retrieved Thu, 06 Jun 2024 15:07:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact234

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    D    [Univariate Explorative Data Analysis] [Reproduce Q2] [2008-10-24 13:27:07] [5e9e099b83e50415d7642e10d74756e4] [Current]
F    D      [Univariate Explorative Data Analysis] [Q7 reeks: Het aan...] [2008-10-27 17:01:24] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
- RMP         [Central Tendency] [Q9 Central Tenden...] [2008-10-27 17:51:46] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
- RMP           [Harrell-Davis Quantiles] [Q9 Harrell-Davis ...] [2008-10-27 18:01:08] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
-   P         [Univariate Explorative Data Analysis] [Feedback Q7 Lags] [2008-11-03 18:03:18] [d32f94eec6fe2d8c421bd223368a5ced]
-   P       [Univariate Explorative Data Analysis] [Feedback Q2] [2008-11-03 09:47:38] [d32f94eec6fe2d8c421bd223368a5ced]
-   P         [Univariate Explorative Data Analysis] [Feedback2 Q2] [2008-11-03 09:56:48] [d32f94eec6fe2d8c421bd223368a5ced]
- RMP       [Central Tendency] [Q2 verbetering, a...] [2008-11-03 19:29:23] [8e4e5f204c24e6d05647858dae308d17]
- R PD      [Univariate Explorative Data Analysis] [Paper H3 Mannen U...] [2008-12-13 12:29:40] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
- R PD      [Univariate Explorative Data Analysis] [Paper H3 Vrouwen ...] [2008-12-13 12:33:06] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
- RMPD      [Variance Reduction Matrix] [Paper H5 Mannen VRM] [2008-12-13 13:41:01] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
- RMPD      [Standard Deviation-Mean Plot] [Paper H5 Mannen S...] [2008-12-13 13:44:05] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
- RMPD      [Spectral Analysis] [Paper H5 Mannen S...] [2008-12-13 13:52:00] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
- RMPD      [(Partial) Autocorrelation Function] [Paper H5 Mannen (...] [2008-12-13 14:12:34] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
-   P         [(Partial) Autocorrelation Function] [Paper H5 Mannen c...] [2008-12-13 14:28:40] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
- RMP         [ARIMA Backward Selection] [Paper H6 Mannen A...] [2008-12-13 16:00:00] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
- RMP           [ARIMA Forecasting] [Paper H6 Mannen A...] [2008-12-22 19:57:49] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
- RMP           [ARIMA Forecasting] [Paper H6 Mannen A...] [2008-12-22 20:35:43] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
-   PD          [ARIMA Backward Selection] [Paper H6 Vrouwen ...] [2008-12-22 21:30:00] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
- RMPD          [ARIMA Forecasting] [Paper H6 Vrouwen ...] [2008-12-22 21:38:56] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
Feedback Forum
2008-11-03 10:18:54 [Evelien Blockx] [reply] 
Assumptie 1: Om te kijken of er autocorrelatie is, moet je de twee laatste grafieken zichtbaar maken. Dit kan je doen door het aantal lags in te stellen. Hieronder vind je de link waarop ik het aantal lags ingesteld heb op 12.

http://www.freestatistics.org/blog/date/2008/Nov/03/t1225705717o7pt0wa1vz8bbql.htm

De autocorrelaties moeten dichtbij nul liggen (randomness). Als de randomness geldt, zou de lag plot structuurloos moeten zijn. Je ziet dat de rechte lijn bijna horizontaal loopt en de punten gespreid liggen. De autocorrelatie is dichtbij nul.

Maar wanneer je daarna naar de laatste grafiek kijkt (autocorrelation function), merk je wel een positieve seizoenale autocorrelatie. Dit wordt nog duidelijker als je het aantal lags op 36 instelt.

http://www.freestatistics.org/blog/date/2008/Nov/03/t1225706333otuo916yb5aktn1.htm

Op de autocorrelation function zie je dat de 12de en 24ste autocorrelatie zeer groot is. Dat wijst op seizoenale autocorrelatie. De tijdreeks is dus bijgevolg niet random, er is seizoenale autocorrelatie.

Assumptie 2: Deze assumptie werd correct getest met histogram en density plot. Hier mag je wel besluiten dat het min of meer normaal verdeeld is.

Assumptie 3: Hiervoor moet je de run sequence plot gebruiken. Als “fixed location” geldt, dan is de run sequence plot vlak en niet-fluctuerend op LT. Als je kijkt naar de lange termijn trend, merk je dat er een kleine achteruitgang is, het niveau is niet helemaal constant. Er is dus een vermoeden dat “fixed location” hier niet geldt, al is het moeilijk te zien.

Assumptie 4: Hiervoor gebruik je de run sequence plot. Als “fixed variation” geldt, dan is de verticale wijdte in de run sequence plot ongeveer hetzelfde over de hele horizontale as. Je ziet dat de spreiding in het eerste gedeelte groter is dan in het tweede gedeelte van de grafiek. De spreiding verandert.

Het model is dus niet geldig.
2008-11-03 19:35:44 [Niels Herremans] [reply] 
aanvulling op assumption 3: Je moet inderdaad kijken naar de run sequence plot maar omdat het niet altijd even duidelijk is kan je ook central tendency gebruiken. Bij central tendendy kan je dan bij robustness gaan kijken of het gemiddelde constant is.

http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/03/t12257406805v0u6z8n64661gy.htm

In deze situatie is het moeilijk te zien of het nu al dan niet constant is maar we merken toch een zeer licht dalende trend.

Post a new message

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 time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18604&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]2 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=18604&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18604&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






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=18604&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=18604&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18604&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 = 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')