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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 09:46:16 -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/t1225036036wcwc8sdl3hk8st0.htm/, Retrieved Sun, 19 May 2024 13:21:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=18949, Retrieved Sun, 19 May 2024 13:21:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact124

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] [Univariate Explor...] [2008-10-26 15:46:16] [07b7cf1321bc38017c2c7efcf91ca696] [Current]
Feedback Forum
2008-11-03 16:49:19 [Lindsay Heyndrickx] [reply] 
nr 1: Als we naar de autocorrelatie kijken zien we een seizoensgebonden correlatie. Om de drie maand komt dezelfde trend terug.
nr2: Er is hier inderdaad een kleine afwijking maar dit is niet zo erg. Hierdoor is nog steeds geen reden om aan te nemen dat er geen vaste spreiding is.
nr3: Hier moet je kijken naar de run sequence plot om te kijken of het niveau constant blijft. Je moet hier ook de central tendancy toepassen om te kijken of het gemiddelde constant blijft. Als dit constant blijft heb je een vaste locatie.
nr4: je moet hier naar de run sequence plot kijken of er een vaste spreiding is .
2008-11-03 23:11:58 [Bart Haemels] [reply] 
Assumptie 1: Ik had naar de autocorrelation moeten zien en daar zou ik gemerkt hebben dat er een 3 maandelijks terugkerend patroon is.
Assumptie 2: Dit doe ik correct
Assumptie 3: Ik had hier moeten kijken naar de Run Sequency Plot, daaruit had ik kunnen aflijden dat er een stijgend verloop is.
Assumptie 4: Er is een redelijk gelijke sprijding, iets meer naar de linker kant.
Zo had ik kunnen concluderen dat dit geen gepast model was.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0118.3
127.3
112.3
114.9
108.2
105.4
122.1
113.5
110
125.3
114.3
115.6
127.1
123
122.2
126.4
112.7
105.8
120.9
116.3
115.7
127.9
108.3
121.1
128.6
123.1
127.7
126.6
118.4
110
129.6
115.8
125.9
128.4
114
125.6
128.5
136.6
133.1
124.6
123.5
117.2
135.5
124.8
127.8
133.1
125.7
128.4
131.9
146.3
140.6
129.5
132.4
125.9
126.9
135.8
129.5
130.2
133.8
123.3

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=18949&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=18949&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18949&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
# observations60
minimum105.4
Q1116.175
median125.45
mean123.553333333333
Q3128.525
maximum146.3

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 60 \tabularnewline
minimum & 105.4 \tabularnewline
Q1 & 116.175 \tabularnewline
median & 125.45 \tabularnewline
mean & 123.553333333333 \tabularnewline
Q3 & 128.525 \tabularnewline
maximum & 146.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18949&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]105.4[/C][/ROW]
[ROW][C]Q1[/C][C]116.175[/C][/ROW]
[ROW][C]median[/C][C]125.45[/C][/ROW]
[ROW][C]mean[/C][C]123.553333333333[/C][/ROW]
[ROW][C]Q3[/C][C]128.525[/C][/ROW]
[ROW][C]maximum[/C][C]146.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18949&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18949&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
minimum105.4
Q1116.175
median125.45
mean123.553333333333
Q3128.525
maximum146.3


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')