Free Statistics

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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 computationFri, 21 Nov 2008 03:41:47 -0700
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/Nov/21/t12272641577t5ipuveqgsxivz.htm/, Retrieved Mon, 20 May 2024 07:47:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=25106, Retrieved Mon, 20 May 2024 07:47:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact185
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Taak 6 - Q2 (1)] [2008-11-16 11:01:18] [46c5a5fbda57fdfa1d4ef48658f82a0c]
F    D    [Univariate Explorative Data Analysis] [Q2 (1)] [2008-11-21 10:41:47] [dbfa7caa6871c163dec68ca05d48bb00] [Current]
Feedback Forum
2008-11-25 22:24:20 [Glenn De Maeyer] [reply
We kunnen deze vraag ook oplossen door te kijken naar de link van vraag 1. (Link: http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/24/t1227517244av2qanxbpeaamu7.htm)
Vooreerst kijken we naar de 'adjusted R-squared', deze bedraagt 72%, wat wil zeggen dat we 72% van de schommelingen in het aantal verkeersslachtoffers per maand verklaren met het opgestelde model. Het resterende deel (28%) is te wijten aan uitzonderlijke invloeden (zoals weersomstandigheden, kettingbotsingen, enz.)Het model geeft dus een vrij goed beeld van de werkelijke situatie.
2008-11-30 15:17:14 [Evelyn Ongena] [reply
In deze vraag was het belangrijk te kijken naar inderdaad de adjusted R-Squared, waardoor we zo'n 72% van de schommelingen die bestaan in het aantal verkeerslachtoffers kunnen verklaren. Bovendien was het verstandig geweest van de student om ook de seizoenaliteit aan te duiden op de grafiek, dit door de steeds terugkomende pieken. En de student had ook moeten wijze op de plotse daling van het aantal sterfgevallen door het invoeren van de seatbeltlaw op dat moment. Verder was het ook belangrijk om erop te wijzen dat we hier niet te maken hebben met autocorrelatie... Dit is de eerste voorwaarde aan wat moet voldaan worden. De tweede voorwaarde had betrekking op het gemiddelde welke constant en nul moest zijn wat niet het geval was, is af te lezen op de grafiek. Bijgevolg heeft de studente haar conclusie wel juist.
2008-12-01 19:55:59 [4db2e62d895b4fb371d0fef3013b569f] [reply
Het moet duidelijk zijn dat 72% van de ongevallen toe te wijzen is aan dit model, de overige 28% vloeit voort uit andere elementen.

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Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 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=25106&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]5 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=25106&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25106&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 time5 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001







Descriptive Statistics
# observations192
minimum-333.6976091
Q1-105.826532175
median4.709752322
mean-8.0729257479187e-10
Q385.02414483
maximum414.6496829

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 192 \tabularnewline
minimum & -333.6976091 \tabularnewline
Q1 & -105.826532175 \tabularnewline
median & 4.709752322 \tabularnewline
mean & -8.0729257479187e-10 \tabularnewline
Q3 & 85.02414483 \tabularnewline
maximum & 414.6496829 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25106&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]192[/C][/ROW]
[ROW][C]minimum[/C][C]-333.6976091[/C][/ROW]
[ROW][C]Q1[/C][C]-105.826532175[/C][/ROW]
[ROW][C]median[/C][C]4.709752322[/C][/ROW]
[ROW][C]mean[/C][C]-8.0729257479187e-10[/C][/ROW]
[ROW][C]Q3[/C][C]85.02414483[/C][/ROW]
[ROW][C]maximum[/C][C]414.6496829[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25106&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25106&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
# observations192
minimum-333.6976091
Q1-105.826532175
median4.709752322
mean-8.0729257479187e-10
Q385.02414483
maximum414.6496829



Parameters (Session):
par1 = 0 ; par2 = 36 ;
Parameters (R input):
par1 = 0 ; par2 = 36 ;
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')