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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_bootstrapplot.wasp
Title produced by softwareBlocked Bootstrap Plot - Central Tendency
Date of computationSun, 02 Nov 2008 03:02:00 -0700
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/Nov/02/t1225620161wvitwexd4cqakge.htm/, Retrieved Mon, 20 May 2024 11:19:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20481, Retrieved Mon, 20 May 2024 11:19:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact206

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Blocked Bootstrap Plot - Central Tendency] [workshop 3] [2007-10-26 12:36:24] [e9ffc5de6f8a7be62f22b142b5b6b1a8]
F    D    [Blocked Bootstrap Plot - Central Tendency] [Q4: bootstrap] [2008-11-02 10:02:00] [54ae75b68e6a45c6d55fa4235827d5b3] [Current]
-           [Blocked Bootstrap Plot - Central Tendency] [Hypotheses testin...] [2008-11-02 18:09:55] [b943bd7078334192ff8343563ee31113]
-           [Blocked Bootstrap Plot - Central Tendency] [Hypotheses testin...] [2008-11-02 18:19:47] [b943bd7078334192ff8343563ee31113]
F R  D      [Blocked Bootstrap Plot - Central Tendency] [Q4] [2008-11-03 16:57:51] [7458e879e85b911182071700fff19fbd]
Feedback Forum
2008-11-05 15:53:04 [Ken Van den Heuvel] [reply] 
De midrange geeft welliswaar de kleinste spreiding, maar met een aantal outliers. We zullen dus moeten afwegen welke het beste is…rekeninghouden met spreiding EN outliers. Beoordelen naargelang de situatie is dus aangewezen.


De outliers zijn maw WEL van belang en dus niet irrelevant gezien ze de keuze van estimator beïnvloeden, en deze toch het resultaat zijn van een gemiddelde van de data.

2008-11-08 10:59:49 [Astrid Sniekers] [reply] 
Het antwoord is correct, buiten het feit dat ik zeg dat de outliers geen effect zouden hebben op het resultaat. Als gemiddelde kunnen we het beste de midrange nemen, omdat hier de spreiding/variantie het kleinst is. De midrange is echter zeer gevoelig voor outliers. De outliers wijken ver van het gemiddelde af en zijn zeker relevant! Omdat deze outliers een probleem zijn, zouden we ook kunnen kiezen voor de mean. Hier is de spreiding dan wel groter, maar omdat hier geen outliers zijn, kunnen we zekerder zijn van het gemiddelde. Meestal wordt het rekenkundig gemiddelde genomen.
2008-11-11 15:21:14 [Ellen Smolders] [reply] 
Uit het antwoord van de student kunnen we afleiden dat de student de bootstrap simulation goed geïnterpreteerd heeft.
Voor deze vraag moeten we zelf een keuze maken en realiseren dat outliers zeer relevant zijn!!! (dit in tegenstelling tot wat de student beweert)

- Wanneer we ons baseren op de midrange, dan zal het betrouwbaarheidsinterval zeer groot (doordat de mediaan het minst fluctureert) zijn en de spreiding het kleinst. Maar bij de midrange zijn er meer grote outliers, wat een risico met zich meebrengt.
- We kunnen ons ook baseren op de man, dit betekent dat het betrouwbaarheidsinterval kleiner is en de spreiding groter. Maar deze heeft de minste outliers.

Meestal baseren we ons op de midrange.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
109.20
88.60
94.30
98.30
86.40
80.60
104.10
108.20
93.40
71.90
94.10
94.90
96.40
91.10
84.40
86.40
88.00
75.10
109.70
103.00
82.10
68.00
96.40
94.30
90.00
88.00
76.10
82.50
81.40
66.50
97.20
94.10
80.70
70.50
87.80
89.50
99.60
84.20
75.10
92.00
80.80
73.10
99.80
90.00
83.10
72.40
78.80
87.30
91.00
80.10
73.60
86.40
74.50
71.20
92.40
81.50
85.30
69.90
84.20
90.70
100.30

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20481&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=20481&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20481&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 time6 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Estimation Results of Blocked Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean85.817213114754186.893442622950888.0864754098361.619960777614692.26926229508194
median86.487.3881.861981307311531.59999999999999
midrange88.188.188.850.989309282131440.75

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 85.8172131147541 & 86.8934426229508 & 88.086475409836 & 1.61996077761469 & 2.26926229508194 \tabularnewline
median & 86.4 & 87.3 & 88 & 1.86198130731153 & 1.59999999999999 \tabularnewline
midrange & 88.1 & 88.1 & 88.85 & 0.98930928213144 & 0.75 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20481&T=1

[TABLE]
[ROW][C]Estimation Results of Blocked Bootstrap[/C][/ROW]
[ROW][C]statistic[/C][C]Q1[/C][C]Estimate[/C][C]Q3[/C][C]S.D.[/C][C]IQR[/C][/ROW]
[ROW][C]mean[/C][C]85.8172131147541[/C][C]86.8934426229508[/C][C]88.086475409836[/C][C]1.61996077761469[/C][C]2.26926229508194[/C][/ROW]
[ROW][C]median[/C][C]86.4[/C][C]87.3[/C][C]88[/C][C]1.86198130731153[/C][C]1.59999999999999[/C][/ROW]
[ROW][C]midrange[/C][C]88.1[/C][C]88.1[/C][C]88.85[/C][C]0.98930928213144[/C][C]0.75[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=20481&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20481&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:

Estimation Results of Blocked Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean85.817213114754186.893442622950888.0864754098361.619960777614692.26926229508194
median86.487.3881.861981307311531.59999999999999
midrange88.188.188.850.989309282131440.75


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 500 ; par2 = 12 ;
Parameters (R input):
par1 = 500 ; par2 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
if (par1 < 10) par1 = 10
if (par1 > 5000) par1 = 5000
if (par2 < 3) par2 = 3
if (par2 > length(x)) par2 = length(x)
library(lattice)
library(boot)
boot.stat <- function(s)
{
s.mean <- mean(s)
s.median <- median(s)
s.midrange <- (max(s) + min(s)) / 2
c(s.mean, s.median, s.midrange)
}
(r <- tsboot(x, boot.stat, R=par1, l=12, sim='fixed'))
bitmap(file='plot1.png')
plot(r$t[,1],type='p',ylab='simulated values',main='Simulation of Mean')
grid()
dev.off()
bitmap(file='plot2.png')
plot(r$t[,2],type='p',ylab='simulated values',main='Simulation of Median')
grid()
dev.off()
bitmap(file='plot3.png')
plot(r$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange')
grid()
dev.off()
bitmap(file='plot4.png')
densityplot(~r$t[,1],col='black',main='Density Plot',xlab='mean')
dev.off()
bitmap(file='plot5.png')
densityplot(~r$t[,2],col='black',main='Density Plot',xlab='median')
dev.off()
bitmap(file='plot6.png')
densityplot(~r$t[,3],col='black',main='Density Plot',xlab='midrange')
dev.off()
z <- data.frame(cbind(r$t[,1],r$t[,2],r$t[,3]))
colnames(z) <- list('mean','median','midrange')
bitmap(file='plot7.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Blocked Bootstrap',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$t[,1],0.25)[[1]]
q3 <- quantile(r$t[,1],0.75)[[1]]
a<-table.element(a,q1)
a<-table.element(a,r$t0[1])
a<-table.element(a,q3)
a<-table.element(a,sqrt(var(r$t[,1])))
a<-table.element(a,q3-q1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
q1 <- quantile(r$t[,2],0.25)[[1]]
q3 <- quantile(r$t[,2],0.75)[[1]]
a<-table.element(a,q1)
a<-table.element(a,r$t0[2])
a<-table.element(a,q3)
a<-table.element(a,sqrt(var(r$t[,2])))
a<-table.element(a,q3-q1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'midrange',header=TRUE)
q1 <- quantile(r$t[,3],0.25)[[1]]
q3 <- quantile(r$t[,3],0.75)[[1]]
a<-table.element(a,q1)
a<-table.element(a,r$t0[3])
a<-table.element(a,q3)
a<-table.element(a,sqrt(var(r$t[,3])))
a<-table.element(a,q3-q1)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')