Free Statistics

of Irreproducible Research!

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 16:27:16 -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/t12250601330n7afqtu7g1jee8.htm/, Retrieved Sun, 19 May 2024 13:05:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19080, Retrieved Sun, 19 May 2024 13:05:55 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [Werkloosheid Vrou...] [2008-10-12 15:02:59] [44ec60eb6065a3f81a5f756bd5af1faf]
F RMPD    [Univariate Explorative Data Analysis] [Werkloosheid Vrou...] [2008-10-26 22:27:16] [924502d03698cd41cacbcd1327858815] [Current]
Feedback Forum
2008-11-01 13:00:48 [Davy De Nef] [reply
De student onderzoekt de validiteit aan de hand van 4 assumpties.
1. Randomness
De lagplot vertoont een stijgende rechte.

2. Fixed distributions
Uit het histogram en de density plot kan de verdeling afleiden. Er is hier duidelijk geen sprake van een normaalverdeling!

3. Fixed location
Hiervoor gebruiken we de run sequence plot. Deze wordt door de student weergegeven op langere termijn. Op deze manier kan je zien dat er een licht stijgend verloop is. Er is dus geen sprake van een constante.

4. Fixed variation
We bekijken de run sequence plot. Links zien we een kleinere schommeling dan rechts. In het midden zien we een uitschieter.

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Dataseries X:
9
9.1
8.7
8.2
7.9
7.9
9.1
9.4
9.5
9.1
9
9.3
9.9
9.8
9.4
8.3
8
8.5
10.4
11.1
10.9
9.9
9.2
9.2
9.5
9.6
9.5
9.1
8.9
9
10.1
10.3
10.2
9.6
9.2
9.3
9.4
9.4
9.2
9
9
9
9.8
10
9.9
9.3
9
9
9.1
9.1
9.1
9.2
8.8
8.3
8.4
8.1
7.8
7.9
7.9
8




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=19080&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=19080&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19080&T=0

As an alternative you can also use a 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
minimum7.8
Q18.875
median9.1
mean9.14666666666667
Q39.5
maximum11.1

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 60 \tabularnewline
minimum & 7.8 \tabularnewline
Q1 & 8.875 \tabularnewline
median & 9.1 \tabularnewline
mean & 9.14666666666667 \tabularnewline
Q3 & 9.5 \tabularnewline
maximum & 11.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19080&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]7.8[/C][/ROW]
[ROW][C]Q1[/C][C]8.875[/C][/ROW]
[ROW][C]median[/C][C]9.1[/C][/ROW]
[ROW][C]mean[/C][C]9.14666666666667[/C][/ROW]
[ROW][C]Q3[/C][C]9.5[/C][/ROW]
[ROW][C]maximum[/C][C]11.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19080&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19080&T=1

As an alternative you can also use a QR Code:  

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

Descriptive Statistics
# observations60
minimum7.8
Q18.875
median9.1
mean9.14666666666667
Q39.5
maximum11.1



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)
qqline(x)
grid()
dev.off()
if (par2 > 0)
{
bitmap(file='lagplot1.png')
dum <- cbind(lag(x,k=1),x)
dum
dum1 <- dum[2:length(x),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main='Lag plot (k=1), lowess, and regression line')
lines(lowess(z))
abline(lm(z))
dev.off()
if (par2 > 1) {
bitmap(file='lagplotpar2.png')
dum <- cbind(lag(x,k=par2),x)
dum
dum1 <- dum[(par2+1):length(x),]
dum1
z <- as.data.frame(dum1)
z
mylagtitle <- 'Lag plot (k='
mylagtitle <- paste(mylagtitle,par2,sep='')
mylagtitle <- paste(mylagtitle,'), and lowess',sep='')
plot(z,main=mylagtitle)
lines(lowess(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')