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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 computationSat, 01 Nov 2008 06:09:27 -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/Nov/01/t12255427007kqyk89oq3fzju5.htm/, Retrieved Tue, 14 May 2024 08:37:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20378, Retrieved Tue, 14 May 2024 08:37:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact208

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] [Testing Hypothese...] [2008-11-01 12:09:27] [dafd615cb3e0decc017580d68ecea30a] [Current]
F           [Blocked Bootstrap Plot - Central Tendency] [T1 - Q4] [2008-11-03 18:52:56] [b187fac1a1b0cb3920f54366df47fea3]
F             [Blocked Bootstrap Plot - Central Tendency] [q4] [2008-11-03 22:56:26] [b641c14ac36cb6fee377f3b099dcac19]
-             [Blocked Bootstrap Plot - Central Tendency] [] [2008-11-09 18:51:01] [888addc516c3b812dd7be4bd54caa358]
-             [Blocked Bootstrap Plot - Central Tendency] [] [2008-11-09 18:51:01] [888addc516c3b812dd7be4bd54caa358]
Feedback Forum
2008-11-09 14:16:50 [Bob Leysen] [reply] 
De grafieken zijn correct.

De density plot gaat over 500 random observaties. De median heeft een klein betrouwbaarheidsinterval, maar bij de midrange is het nog kleiner.
De mediaan van de midrange blijft zeer kort bij elkaar. De midrange heeft wel serieuze outliers. De midrange vertoont de kleinste spreiding en is de beste benadering. Hoe groter de spreiding, hoe groter de variantie. Alle punten zijn rekenkundige gemiddelden, en niet van een dataset.
2008-11-10 22:35:18 [Ilknur Günes] [reply] 
Bootstrapping: gem. Dataset  500 x opnieuw
Telkens 1 eruit nemen en een andere terugleggen ( er bestaat dan natuurlijk de kans dat je hetzelfde terug neemt)
Simulation of mean: Alle punten zijn alle berekende gemiddelden, door elkaar.
Simulation of median: meer een patroon
Imulation of midrange: duidelijk patroon
Hoe minder variatie, hoe nauwkeuriger
Midrange als gemiddelde nemen omdat daar de variatie het kleinst is
Maar: daar zijn wel heel veel outliers!!! Je hebt een gemiddelde waarvan de getrouwheidsinterval zeer klein is, maar als je er buiten zit, zit je er wel extreem buiten. Je moet maw zelf een overweging doen. Dwz dat de mean ook goed kan zijn. Het heeft een groter getrouwheidsinterval, maar de outliers zijn minder extreem.
De punten op de grafiek zij gemiddelden, dus je kan ze niet vinden in je dataset
Outliers zijn dus WEL relevant! .. ze bepalen de keuze, MAAR het gaat over gemiddelden
2008-11-11 08:28:01 [Jeroen Michel] [reply] 
Samengevat en eenvoudiger tot woorden, zit er in mijn besluit (in het word document een fout). Het is duidelijk dat hier sprake is van rekenkundige gemiddelden. De mediaan en midrange zijn hier ook weergegeven, maar zeggen hier weinig. Belangrijkste opmerking binnen de analyse die ik heb gemaakt is te stellen dat de outliers hier wel van belang zijn!

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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=20378&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=20378&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20378&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
mean85.63360655737786.893442622950887.9942622950821.737210532827512.36065573770493
median86.487.3882.017952731471401.59999999999999
midrange87.8588.188.851.043425542972501

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 85.633606557377 & 86.8934426229508 & 87.994262295082 & 1.73721053282751 & 2.36065573770493 \tabularnewline
median & 86.4 & 87.3 & 88 & 2.01795273147140 & 1.59999999999999 \tabularnewline
midrange & 87.85 & 88.1 & 88.85 & 1.04342554297250 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20378&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]85.633606557377[/C][C]86.8934426229508[/C][C]87.994262295082[/C][C]1.73721053282751[/C][C]2.36065573770493[/C][/ROW]
[ROW][C]median[/C][C]86.4[/C][C]87.3[/C][C]88[/C][C]2.01795273147140[/C][C]1.59999999999999[/C][/ROW]
[ROW][C]midrange[/C][C]87.85[/C][C]88.1[/C][C]88.85[/C][C]1.04342554297250[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=20378&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20378&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
mean85.63360655737786.893442622950887.9942622950821.737210532827512.36065573770493
median86.487.3882.017952731471401.59999999999999
midrange87.8588.188.851.043425542972501


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')