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 computationSun, 26 Oct 2008 11:47:31 -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/t1225043388imkvxnufx69k9jg.htm/, Retrieved Sun, 19 May 2024 14:58:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=18991, Retrieved Sun, 19 May 2024 14:58:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact158
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] [Univariate Explor...] [2008-10-24 12:02:31] [e1a46c1dcfccb0cb690f79a1a409b517]
-   PD    [Univariate Explorative Data Analysis] [UEDA - Vlaams gew...] [2008-10-26 09:55:38] [46c5a5fbda57fdfa1d4ef48658f82a0c]
F    D        [Univariate Explorative Data Analysis] [4 plot België] [2008-10-26 17:47:31] [b23db733701c4d62df5e228d507c1c6a] [Current]
- RM            [Central Tendency] [taak 3 task 3 ver...] [2008-10-28 19:26:19] [46c5a5fbda57fdfa1d4ef48658f82a0c]
- RMP           [Tukey lambda PPCC Plot] [Feedback Lambda] [2008-11-04 09:03:43] [b635de6fc42b001d22cbe6e730fec936]
Feedback Forum
2008-10-28 20:00:37 [Ken Van den Heuvel] [reply
Q7: assumptie 3&4.

http://www.freestatistics.org/blog/index.php?v=date/2008/Oct/28/t12252220375g6wbdb5zmbex4e.htm

Assumptie 3:

Ik had in feite moeten kijken of de mean plot (winsorized) hetzelfde verloop kent gedurende heel de periode. Dit blijkt niet het geval te zijn. De locatie zal dus niet vast zijn.

Assumptie 4:
Als we de mean aftrekken van de tijdsreeks houden we error component over. De mean van deze component zou 0 moeten zijn. Uit de gegeven herberekening blijkt dit ongeveer te kloppen (winsorized mean ligt binnen het betrouwbaarheidsinterval). De variatie lijkt dus vast.

2008-11-04 09:02:19 [Bas van Keken] [reply
Wat betekent die term bijassumptiereeks precies voor uw datareeks? In dit geval stelt het handboek dat er daardoor een hoge voorspelbaarheid is van de reeks.
2008-11-04 09:08:09 [Bas van Keken] [reply
Als u stelt dat er een vrij goede distributie is moet u dit kunnen staven. De berekening die u bewering bekrachtigt staat hieronder:
http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/04/t1225789604jtflp1kfzrklfvq.htm
  2008-11-04 09:09:37 [Bas van Keken] [reply
Ik zie wel dat u dit in Q8 hebt opgelost, echter trekt u de conclusie al eerder.

Post a new message
Dataseries X:
565464
547344
554788
562325
560854
555332
543599
536662
542722
593530
610763
612613
611324
594167
595454
590865
589379
584428
573100
567456
569028
620735
628884
628232
612117
595404
597141
593408
590072
579799
574205
572775
572942
619567
625809
619916
587625
565742
557274
560576
548854
531673
525919
511038
498662
555362
564591
541657
527070
509846
514258
516922
507561
492622
490243
469357
477580
528379
533590
517945




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18991&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18991&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18991&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 time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Descriptive Statistics
# observations60
minimum469357
Q1533110.75
median565027.5
mean562109.15
Q3593438.5
maximum628884

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics \tabularnewline
# observations & 60 \tabularnewline
minimum & 469357 \tabularnewline
Q1 & 533110.75 \tabularnewline
median & 565027.5 \tabularnewline
mean & 562109.15 \tabularnewline
Q3 & 593438.5 \tabularnewline
maximum & 628884 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=18991&T=1

[TABLE]
[ROW][C]Descriptive Statistics[/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]469357[/C][/ROW]
[ROW][C]Q1[/C][C]533110.75[/C][/ROW]
[ROW][C]median[/C][C]565027.5[/C][/ROW]
[ROW][C]mean[/C][C]562109.15[/C][/ROW]
[ROW][C]Q3[/C][C]593438.5[/C][/ROW]
[ROW][C]maximum[/C][C]628884[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=18991&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=18991&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
# observations60
minimum469357
Q1533110.75
median565027.5
mean562109.15
Q3593438.5
maximum628884



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')