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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 computationMon, 27 Oct 2008 10:46:27 -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/27/t1225126031ungd24dgycx494b.htm/, Retrieved Sun, 19 May 2024 14:12:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19252, Retrieved Sun, 19 May 2024 14:12:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact140
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Univariate Explorative Data Analysis] [Univariate Explor...] [2008-10-27 16:46:27] [b5110a3ab194da7214bdf478e0a05dbd] [Current]
-   P     [Univariate Explorative Data Analysis] [] [2008-11-03 18:55:33] [af90f76a5211a482a7c35f2c76d2fd61]
-   P     [Univariate Explorative Data Analysis] [Investigating Dis...] [2008-11-03 20:15:26] [38f43994ada0e6172896e12525dcc585]
Feedback Forum
2008-11-03 19:06:04 [Elias Van Deun] [reply
Assumptie 1: We berekenen de Autocorrelation Function door het aantal lags te veranderen:
http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/03/t12257385608t9dxa38qmz59is.htm
hierop zien we dat er autocorrelatie is.

Assumptie 2: Er is ook de normal QQ plot die je kan gebruiken. Hierop zien we dat de puntjes dicht bij de rechte liggen. Er is ongeveer een normale verdeling.

Assumptie 3: Hiervoor gebruiken we de run sequence plot. We kunnen afleiden dat de constante op langere termijn daalt.

Assumptie 4: We gebruiken nogmaals de run sequence plot. We delen de grafiek in 2 helften. We stellen vast dat de linker helft een grotere spreiding heeft dan de rechter.

Post a new message
Dataseries X:
8.3
8.2
8
7.9
7.6
7.6
8.2
8.3
8.4
8.4
8.4
8.6
8.9
8.8
8.3
7.5
7.2
7.5
8.8
9.3
9.3
8.7
8.2
8.3
8.5
8.6
8.6
8.2
8.1
8
8.6
8.7
8.8
8.5
8.4
8.5
8.7
8.7
8.6
8.5
8.3
8.1
8.2
8.1
8.1
7.9
7.9
7.9
8
8
7.9
8
7.7
7.2
7.5
7.3
7
7
7
7.2
7.3
7.1
6.8
6.6
6.2




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19252&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19252&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19252&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Descriptive Statistics
# observations65
minimum6.2
Q17.6
median8.2
mean8.04615384615385
Q38.5
maximum9.3

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 65 \tabularnewline
minimum & 6.2 \tabularnewline
Q1 & 7.6 \tabularnewline
median & 8.2 \tabularnewline
mean & 8.04615384615385 \tabularnewline
Q3 & 8.5 \tabularnewline
maximum & 9.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19252&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]65[/C][/ROW]
[ROW][C]minimum[/C][C]6.2[/C][/ROW]
[ROW][C]Q1[/C][C]7.6[/C][/ROW]
[ROW][C]median[/C][C]8.2[/C][/ROW]
[ROW][C]mean[/C][C]8.04615384615385[/C][/ROW]
[ROW][C]Q3[/C][C]8.5[/C][/ROW]
[ROW][C]maximum[/C][C]9.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19252&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19252&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
# observations65
minimum6.2
Q17.6
median8.2
mean8.04615384615385
Q38.5
maximum9.3



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)
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')