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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 computationThu, 23 Oct 2008 04:41:45 -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/23/t1224759011vd0qfw7s9zv300j.htm/, Retrieved Fri, 17 May 2024 03:41:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=18455, Retrieved Fri, 17 May 2024 03:41:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact197

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...] [2007-10-21 18:26:46] [b9964c45117f7aac638ab9056d451faa]
F    D    [Univariate Explorative Data Analysis] [] [2008-10-23 10:41:45] [c60a842d48931bd392d024d8e9ef4583] [Current]
Feedback Forum
2008-11-02 21:00:50 [Bernard Femont] [reply] 
We merken op de “Run Sequence Plot” dat de productie van kleding een dalende trend vertoont in vergelijking met de totale productie. Dus kunnen we afleiden dat ze niet samen evolueren. Een goede interpretatie van de vraagstelling
2008-11-03 10:18:51 [Michael Van Spaandonck] [reply] 
Q3
Een korte maar juiste analyse van de student in het bijhorende document.

Q4
Er is sprake van sezoenaliteit, zoals in bijhorend document gezegd. Eventueel had dit nog gestaafd kunnen worden aan de hand van een autocorrelatiefunctie.
2008-11-04 07:36:52 [Evelyne Slegers] [reply] 
Het is inderdaad zo dat er geen constante is en dat het verloop dalend is.
2008-11-04 07:39:37 [Evelyne Slegers] [reply] 
Q4: Er is inderdaad een seizonaalpatroon maar dit is moeilijk te zien aan de hand van de autocorrelatie. Met de mean plot bekom je een nauwkeuriger resultaat.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0,989130435
0,919087137
0,925417076
0,925612053
1,066666667
0,851108765
1,030693069
0,989031079
0,913000978
0,792723264
0,978170478
0,987513007
0,909433962
0,883608147
0,82745098
0,8252149
1,023255814
0,815418024
1,026192703
0,914742451
0,807276303
0,739130435
0,98973306
0,972164948
0,853889943
0,856864654
0,775739042
0,789473684
0,931350114
0,73971079
0,885245902
0,842435094
0,818458418
0,72755418
0,923238696
0,922680412
0,883762201
0,818270165
0,771047228
0,825852783
0,924485126
0,755165289
0,874671341
0,815956482
0,799807507
0,712598425
0,832980973
0,910323253
0,869149952
0,779182879
0,750254842
0,75856014
0,920889988
0,743991641
0,816254417
0,769593957
0,784007353
0,683284457
0,850505051
0,900695134
0,868398268

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18455&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18455&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18455&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'George Udny Yule' @ 72.249.76.132






Descriptive Statistics
# observations61
minimum0.683284457
Q10.792723264
median0.853889943
mean0.86210009042623
Q30.922680412
maximum1.066666667

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 61 \tabularnewline
minimum & 0.683284457 \tabularnewline
Q1 & 0.792723264 \tabularnewline
median & 0.853889943 \tabularnewline
mean & 0.86210009042623 \tabularnewline
Q3 & 0.922680412 \tabularnewline
maximum & 1.066666667 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18455&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]61[/C][/ROW]
[ROW][C]minimum[/C][C]0.683284457[/C][/ROW]
[ROW][C]Q1[/C][C]0.792723264[/C][/ROW]
[ROW][C]median[/C][C]0.853889943[/C][/ROW]
[ROW][C]mean[/C][C]0.86210009042623[/C][/ROW]
[ROW][C]Q3[/C][C]0.922680412[/C][/ROW]
[ROW][C]maximum[/C][C]1.066666667[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18455&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18455&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
minimum0.683284457
Q10.792723264
median0.853889943
mean0.86210009042623
Q30.922680412
maximum1.066666667


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')