FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationThu, 06 Nov 2008 15:29:33 -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/06/t12260107942uj9c9c4s3n7z58.htm/, Retrieved Mon, 20 May 2024 10:08:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22437, Retrieved Mon, 20 May 2024 10:08:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact171

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] [] [2008-11-06 22:29:33] [905ab864170ee411eb174f5362414698] [Current]
-           [Blocked Bootstrap Plot - Central Tendency] [Blocked Bootstrap...] [2008-11-07 08:21:44] [17bd4671b42d569d890f7246b2ee4ecc]
Feedback Forum
2008-11-11 13:18:41 [Liese Drijkoningen] [reply] 
De stundet heeft al enkele belangrijke zaken aangehaald, maar ik denk dat ik hier nog een aanvulling op kan doen.
We moeten dus nagaan wat de beste schatter is. Als je naar de 'simulation' grafieken kijkt, zie je bij het gemiddelde geen vreband (randonverdeling), bij de mediaan een zwak verband (een patroon wordt zichtbaar) en bij de midrange een sterk verband. Daarom zou je kunnen zeggen dat de midrange de beste schatter is omdat deze de kleinste spreiding heeft. Maar er zijn wel veel outliers aanwezig. Het risico dat je neemt heeft dus een grote impact indien het fout gaat. Bij het gemiddelde is het risico kleiner, want er zijn weinig outliers, maar de schatting zal minder nauwkeurig zijn. Je moet daarom afwegen wat het belangrijskte is en zo kiezen elke schatter je gebruikt.
2008-11-11 16:18:10 [Peter Van Doninck] [reply] 
De student is er zich van bewust dat het gemiddelde de beste spreiding geeft en bovendien ook het meeste aanleunt bij de normaalverdeling. De midrange echter vertoont de kleinste spreiding, maar er moet rekening gehouden worden met de kost van eventuele outliers, wat hier niet exact vermeld word. In de praktijk wordt echter meestal het gemiddelde gebruikt.
2008-11-12 09:51:47 [76d4ba45ca37c54e4ca3ca97939a2cd4] [reply] 
Q4: De uitleg die je bij deze vraag geeft is zeer volledig en correct.

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 time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22437&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22437&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22437&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'Gwilym Jenkins' @ 72.249.127.135






Estimation Results of Blocked Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean85.79139344262386.893442622950887.88811475409841.679103740107652.09672131147542
median86.487.3881.927394442940351.59999999999999
midrange88.188.188.851.109840746668610.75

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 85.791393442623 & 86.8934426229508 & 87.8881147540984 & 1.67910374010765 & 2.09672131147542 \tabularnewline
median & 86.4 & 87.3 & 88 & 1.92739444294035 & 1.59999999999999 \tabularnewline
midrange & 88.1 & 88.1 & 88.85 & 1.10984074666861 & 0.75 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22437&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]85.791393442623[/C][C]86.8934426229508[/C][C]87.8881147540984[/C][C]1.67910374010765[/C][C]2.09672131147542[/C][/ROW]
[ROW][C]median[/C][C]86.4[/C][C]87.3[/C][C]88[/C][C]1.92739444294035[/C][C]1.59999999999999[/C][/ROW]
[ROW][C]midrange[/C][C]88.1[/C][C]88.1[/C][C]88.85[/C][C]1.10984074666861[/C][C]0.75[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22437&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22437&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
mean85.79139344262386.893442622950887.88811475409841.679103740107652.09672131147542
median86.487.3881.927394442940351.59999999999999
midrange88.188.188.851.109840746668610.75


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')