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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_edauni.wasp

Title produced by software

Univariate Explorative Data Analysis

Date of computation

Fri, 24 Oct 2008 04:34:50 -0600

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Oct/24/t1224844549zb6lq3x5ig3tojh.htm/, Retrieved Sun, 19 May 2024 13:34:59 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=18581, Retrieved Sun, 19 May 2024 13:34:59 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

194

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

F     [Tukey lambda PPCC Plot] [Tukey lambda tot ...] [2008-10-24 09:02:28] [e1a46c1dcfccb0cb690f79a1a409b517]
F RMPD    [Univariate Explorative Data Analysis] [unvariate explor ...] [2008-10-24 10:34:50] [bda7fba231d49184c6a1b627868bbb81] [Current]

Feedback Forum

2008-11-03 09:51:43 [Stéphanie Claes] [reply] 
Deze vraag werd correct opgelost. Er is dus geen constante verhouding.
2008-11-03 09:54:32 [Stéphanie Claes] [reply] 
Voor Q4 kijken we opnieuw naar de run sequence en zien we een grote piek gevolgd door een kleine, er is een zeker seizonaal patroon maar het is moeilijk te zien. Maar zoals eerder gezegd is er seizonaliteit door autocorrelatie.

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Dataseries X:

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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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 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=18581&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=18581&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18581&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Descriptive Statistics
# observations	61
minimum	0.683284457
Q1	0.792723264
median	0.853889943
mean	0.86210009042623
Q3	0.922680412
maximum	1.066666667

\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=18581&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=18581&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18581&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
# observations	61
minimum	0.683284457
Q1	0.792723264
median	0.853889943
mean	0.86210009042623
Q3	0.922680412
maximum	1.066666667

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code