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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_meanplot.wasp
Title produced by softwareMean Plot
Date of computationThu, 30 Oct 2008 08:47:33 -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/30/t1225378606jrgkkb47u3lyyv4.htm/, Retrieved Sun, 19 May 2024 16:31:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20074, Retrieved Sun, 19 May 2024 16:31:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact174

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Blocked Bootstrap Plot - Central Tendency] [Taak 5 deel 2 Q1 ...] [2008-10-30 13:01:20] [6fea0e9a9b3b29a63badf2c274e82506]
F RM D  [Star Plot] [Task 5 Deel 2 Q2 ...] [2008-10-30 14:15:09] [819b576fab25b35cfda70f80599828ec]
F RM D      [Mean Plot] [Taak 5 deel 2 Q3 ...] [2008-10-30 14:47:33] [286e96bd53289970f8e5f25a93fb50b3] [Current]
Feedback Forum
2008-11-07 13:11:49 [Tom Ardies] [reply] 
Je hebt heel veel berekeningen gedaan die eigenlijk al werden samengevat door de link in het voorbeeld van de student.

Je moet gewoon de boxplots met elkaar vergelijken en zien dewelke het beste is. De laatste heeft de hoogste mediaan en de kleinste spreiding van het betrouwbaarheidsinterval.

http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/03/t12257410488hv8anut5aw9t0h.htm
2008-11-08 14:32:02 [Mehmet Yilmaz] [reply] 
Je had al de berekeningen simpel kunnen samenvatten met een Notched Box Plot grafiek.

Hieronder de link:

http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/08/t122615454707c4ehk5coko5dt.htm

Bij de 2 laatste variabelen (zie tabel) is het rendement meer dan 3,8%, we zullen uiteindelijk toch de laatste kiezen want hier heb je nog meer winst dan bij de voorlaatste variabele.
2008-11-10 12:55:41 [Kevin Neelen] [reply] 
Er is een verkeerde methode gebruikt. Hier zou een notched boxplot gebruikt moeten zijn geweest. Ik heb zelf even een computation gemaakt: http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/10/t1226321296pqffxjo3ers3iff.htm
Als we deze grafiek bestuderen, stellen we vast dat de laatste boxplot(IND10_UT90) de beste investering is, aangezien hier de mediaan er het hoogste ligt.
2008-11-10 21:31:56 [Michael Van Spaandonck] [reply] 
Zoals bovenstaande studenten zeggen is dit niet de juiste methode voor het oplossen van de vraag.
Notched box plots geven hier een beter beeld. Hieronder de link naar de herberekening en bespreking:
http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/10/t1226352561v2rjhe46qc74wym.htm


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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100,00
100,39
100,15
100,21
100,03
99,58
99,40
99,77
100,41
100,12
99,83
99,73
98,74
98,44
98,79
99,60
99,82
99,85
100,01
100,28
100,63
101,14
101,51
102,41
102,46
102,09
101,99
101,52
102,44
103,42
103,63
103,28
103,98
103,56
103,42
103,92
103,81
103,09
102,60
102,77
102,60
102,88
102,17
101,85
101,66
101,91
102,13
102,71
103,17
102,89
102,94
103,33
103,75
104,11
104,77
104,62
105,00
105,74
105,94
106,37
106,65
107,08
106,77
107,21
107,34
107,12
106,86
106,92
106,95
107,23
106,94
106,62
105,94
105,91
106,52
106,85
107,22
107,28
107,86
107,68
108,07
107,87
107,65
108,16
108,60
108,92
109,66
109,87
109,54
109,06

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20074&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]3 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=20074&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20074&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 time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 500 ; par2 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
(n <- length(x))
(np <- floor(n / par1))
arr <- array(NA,dim=c(par1,np+1))
darr <- array(NA,dim=c(par1,np+1))
ari <- array(0,dim=par1)
dx <- diff(x)
j <- 0
for (i in 1:n)
{
j = j + 1
ari[j] = ari[j] + 1
arr[j,ari[j]] <- x[i]
darr[j,ari[j]] <- dx[i]
if (j == par1) j = 0
}
ari
arr
darr
arr.mean <- array(NA,dim=par1)
arr.median <- array(NA,dim=par1)
arr.midrange <- array(NA,dim=par1)
for (j in 1:par1)
{
arr.mean[j] <- mean(arr[j,],na.rm=TRUE)
arr.median[j] <- median(arr[j,],na.rm=TRUE)
arr.midrange[j] <- (quantile(arr[j,],0.75,na.rm=TRUE) + quantile(arr[j,],0.25,na.rm=TRUE)) / 2
}
overall.mean <- mean(x)
overall.median <- median(x)
overall.midrange <- (quantile(x,0.75) + quantile(x,0.25)) / 2
bitmap(file='plot1.png')
plot(arr.mean,type='b',ylab='mean',main='Mean Plot',xlab='Periodic Index')
mtext(paste('#blocks = ',np))
abline(overall.mean,0)
dev.off()
bitmap(file='plot2.png')
plot(arr.median,type='b',ylab='median',main='Median Plot',xlab='Periodic Index')
mtext(paste('#blocks = ',np))
abline(overall.median,0)
dev.off()
bitmap(file='plot3.png')
plot(arr.midrange,type='b',ylab='midrange',main='Midrange Plot',xlab='Periodic Index')
mtext(paste('#blocks = ',np))
abline(overall.midrange,0)
dev.off()
bitmap(file='plot4.png')
z <- data.frame(t(arr))
names(z) <- c(1:par1)
(boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Periodic Subseries'))
dev.off()
bitmap(file='plot4b.png')
z <- data.frame(t(darr))
names(z) <- c(1:par1)
(boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Differenced Periodic Subseries'))
dev.off()
bitmap(file='plot5.png')
z <- data.frame(arr)
names(z) <- c(1:np)
(boxplot(z,notch=TRUE,col='grey',xlab='Block Index',ylab='Value',main='Notched Box Plots - Sequential Blocks'))
dev.off()
bitmap(file='plot6.png')
z <- data.frame(cbind(arr.mean,arr.median,arr.midrange))
names(z) <- list('mean','median','midrange')
(boxplot(z,notch=TRUE,col='grey',ylab='Overall Central Tendency',main='Notched Box Plots'))
dev.off()