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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 13:53:00 -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/t1225050851zv8dqjypdfjsk9o.htm/, Retrieved Sun, 19 May 2024 13:55:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19047, Retrieved Sun, 19 May 2024 13:55:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148

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] [4-plot Uitvoer VS] [2008-10-26 19:53:00] [21d7d81e7693ad6dde5aadefb1046611] [Current]
Feedback Forum
2008-10-29 14:43:50 [Nathalie Koulouris] [reply] 
De student heeft deze vraag correct beantwoord. In de Lag plot kunnen we zien dat de gegevens verdeeld zijn, we herkennen geen duidelijke structuur (cirkel, rechte, exp. functie…), d.w.z. dat de gegevens willekeurig zijn, er geen autocorrelatie is en er geen outliers zijn. Wanneer we naar de autocorrelation functie kijken, zien we dat er geen autocorrelatie is. Het model is dus geldig, aan alle voorwaarden is voldaan.

2008-10-31 14:19:33 [Matthieu Blondeau] [reply] 
Van 3 tot 27 is er een terugkerend patroon te zien. Het histogram en de density plot zijn volgens mij volkomen normaal. De punten in de QQ plot lopen bijna perfect over de rechte, behalve aan de uiteinden liggen de punten een beetje verder van de rechte. Er is geen duidelijke structuur terug te vinden in de lag plot dus er is geen autocorrelatie. Aan alle voorwaarden is voldaan.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
604,4
883,9
527,9
756,2
812,9
655,6
707,6
612,6
659,2
833,4
727,8
797,2
753
762
613,7
759,2
816,4
736,8
680,1
736,5
637,2
801,9
772,3
897,3
792,1
826,8
666,8
906,6
871,4
891
739,2
833,6
715,6
871,6
751,6
1005,5
681,2
837,3
674,7
806,3
860,2
689,8
691,6
682,6
800,1
1023,7
733,5
875,3
770,2
1005,7
982,3
742,9
974,2
822,3
773,2
750,9
708
690
652,8
620,7
461,9

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=19047&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=19047&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19047&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
minimum461.9
Q1689.8
median756.2
mean766.037704918033
Q3833.4
maximum1023.7

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 61 \tabularnewline
minimum & 461.9 \tabularnewline
Q1 & 689.8 \tabularnewline
median & 756.2 \tabularnewline
mean & 766.037704918033 \tabularnewline
Q3 & 833.4 \tabularnewline
maximum & 1023.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19047&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]61[/C][/ROW]
[ROW][C]minimum[/C][C]461.9[/C][/ROW]
[ROW][C]Q1[/C][C]689.8[/C][/ROW]
[ROW][C]median[/C][C]756.2[/C][/ROW]
[ROW][C]mean[/C][C]766.037704918033[/C][/ROW]
[ROW][C]Q3[/C][C]833.4[/C][/ROW]
[ROW][C]maximum[/C][C]1023.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19047&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19047&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
minimum461.9
Q1689.8
median756.2
mean766.037704918033
Q3833.4
maximum1023.7


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)
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')