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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_bootstrapplot.wasp
Title produced by softwareBlocked Bootstrap Plot - Central Tendency
Date of computationWed, 03 Jun 2009 00:30:22 -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/2009/Jun/03/t1244010783j10wnhh9zretg5p.htm/, Retrieved Sun, 12 May 2024 02:11:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=41404, Retrieved Sun, 12 May 2024 02:11:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsblocked bootstrap plot datareeks jonge werkzoekenden onder de 25 jaar in Vlaanderen
Estimated Impact143

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] [blocked bootstrap...] [2009-06-03 06:30:22] [bddbb8640adbf7d76f1766fd0c9aa6ca] [Current]
- RMPD    [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2009-06-03 07:10:10] [af2e14e160e92cc9abbae03c4f4c84fa]
- RMPD    [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2009-06-03 07:25:35] [af2e14e160e92cc9abbae03c4f4c84fa]
- RMP     [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2009-06-03 07:42:52] [af2e14e160e92cc9abbae03c4f4c84fa]
-   P       [(Partial) Autocorrelation Function] [(Partial) Autocor...] [2009-06-03 07:46:35] [af2e14e160e92cc9abbae03c4f4c84fa]
- RMPD      [Variability] [spreidingsmaten s...] [2009-06-03 08:08:49] [af2e14e160e92cc9abbae03c4f4c84fa]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
51772
48439
45716
43851
41622
45180
72550
77681
71177
63390
57386
56765
55772
53605
50338
47314
44596
47029
72490
78086
71058
63276
56918
55170
52980
50466
48553
46307
43796
45642
70765
75685
69220
62898
56011
54148
46626
46018
42408
42483
40113
41381
62348
63611
58389
46175
40555
37909
37866
34418
31736
29533
27604
30575
51345
52455
43367
37077
33016
33117
32279
30369
28983
27864
24591
29528
46549
47932
41584
37295
34666
36773
39591
39833

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=41404&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=41404&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=41404&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
mean45333.851351351448048.837837837849940.12837837843367.258890436214606.27702702703
median43823.54624147252.1253064.511819513643428.625
midrange5113651338.5523931634.210622806581257

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 45333.8513513514 & 48048.8378378378 & 49940.1283783784 & 3367.25889043621 & 4606.27702702703 \tabularnewline
median & 43823.5 & 46241 & 47252.125 & 3064.51181951364 & 3428.625 \tabularnewline
midrange & 51136 & 51338.5 & 52393 & 1634.21062280658 & 1257 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=41404&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]45333.8513513514[/C][C]48048.8378378378[/C][C]49940.1283783784[/C][C]3367.25889043621[/C][C]4606.27702702703[/C][/ROW]
[ROW][C]median[/C][C]43823.5[/C][C]46241[/C][C]47252.125[/C][C]3064.51181951364[/C][C]3428.625[/C][/ROW]
[ROW][C]midrange[/C][C]51136[/C][C]51338.5[/C][C]52393[/C][C]1634.21062280658[/C][C]1257[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=41404&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=41404&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
mean45333.851351351448048.837837837849940.12837837843367.258890436214606.27702702703
median43823.54624147252.1253064.511819513643428.625
midrange5113651338.5523931634.210622806581257


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 50 ; par2 = 12 ;
Parameters (R input):
par1 = 50 ; 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')