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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 computationSat, 25 Oct 2008 08:47:25 -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/25/t1224946079cxl3s0d9vn67nmh.htm/, Retrieved Sun, 19 May 2024 13:05:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=18758, Retrieved Sun, 19 May 2024 13:05:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168

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    D    [Univariate Explorative Data Analysis] [] [2008-10-25 14:47:25] [6d40a467de0f28bd2350f82ac9522c51] [Current]
F    D      [Univariate Explorative Data Analysis] [] [2008-10-27 17:04:00] [077ffec662d24c06be4c491541a44245]
-   PD        [Univariate Explorative Data Analysis] [distributions tas...] [2008-10-28 18:12:04] [077ffec662d24c06be4c491541a44245]
Feedback Forum
2008-10-29 14:39:55 [Siem Van Opstal] [reply] 
Voor sommige assumpties kan je beter andere technieken gebruiken. Voor assumptie 1 moet je ook naar de lag plot kijken. om de grafieken te voorschijn te krijgen, moet je het aantal lag's invullen. Nu is daar niets ingevuld en krijg je de grafieken niet te zien.

Assumptie 2 kunnen we ook van het qq plot aflezen, de gegevens verlopen ongeveer als een rechte en ook het histogram verloopt ongeveer als een normaalverdeling.

Voor assumptie 3 kijken we naar het sequence plot. Om constant te zijn zou deze niet mogen flucturen. We zien dat het wel sterk fluctueert.

Voor assumptie 4 kijken we op run sequence plot naar de spreiding over de tijd heen. We kunnen duidelijke aflezen dat er geen gelijke spreiding is.

We kunnen concluderen dat er niet aan al de voorwaarden voldaan wordt, het model is dus niet geldig.
2008-10-30 14:27:32 [Christy Masson] [reply] 
ik ga akkoord met de student voor mij
2008-10-30 15:45:00 [2df1bcd103d52957f4a39bd4617794c8] [reply] 
Het model is niet geldig want er werd niet aan alle voorwaarden voldaan.
assumptie 1: Er is geen autocorrelatie
assumptie 2: we noteren een normaalverdeling
assumptie 3: we stellen schommelingen vast
assumptie 4: spreiding is niet gelijk over de tijd heen

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
299,63
305,945
382,252
348,846
335,367
373,617
312,612
312,232
337,161
331,476
350,103
345,127
297,256
295,979
361,007
321,803
354,937
349,432
290,979
349,576
327,625
349,377
336,777
339,134
323,321
318,86
373,583
333,03
408,556
414,646
291,514
348,857
349,368
375,765
364,136
349,53
348,167
332,856
360,551
346,969
392,815
372,02
371,027
342,672
367,343
390,786
343,785
362,6
349,468
340,624
369,536
407,782
392,239
404,824
373,669
344,902
396,7
398,911
366,009
392,484

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18758&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18758&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18758&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Descriptive Statistics
# observations60
minimum290.979
Q1334.78275
median349.4045
mean352.135916666667
Q3372.41075
maximum414.646

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 60 \tabularnewline
minimum & 290.979 \tabularnewline
Q1 & 334.78275 \tabularnewline
median & 349.4045 \tabularnewline
mean & 352.135916666667 \tabularnewline
Q3 & 372.41075 \tabularnewline
maximum & 414.646 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18758&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]290.979[/C][/ROW]
[ROW][C]Q1[/C][C]334.78275[/C][/ROW]
[ROW][C]median[/C][C]349.4045[/C][/ROW]
[ROW][C]mean[/C][C]352.135916666667[/C][/ROW]
[ROW][C]Q3[/C][C]372.41075[/C][/ROW]
[ROW][C]maximum[/C][C]414.646[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18758&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18758&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
minimum290.979
Q1334.78275
median349.4045
mean352.135916666667
Q3372.41075
maximum414.646


Figures (Output of Computation)

Input Parameters & R Code

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)
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')