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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 computationTue, 15 Nov 2011 06:43:12 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/15/t1321357417t72ri271cuyuqt9.htm/, Retrieved Thu, 28 Mar 2024 13:48:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=142763, Retrieved Thu, 28 Mar 2024 13:48:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Central Tendency] [Arabica Price in ...] [2008-01-06 21:28:17] [74be16979710d4c4e7c6647856088456]
- RM D  [Central Tendency] [central tendency ...] [2011-11-13 10:07:58] [74be16979710d4c4e7c6647856088456]
-    D    [Central Tendency] [Central Tendency ...] [2011-11-15 11:28:32] [74be16979710d4c4e7c6647856088456]
- R  D      [Central Tendency] [Central Tendency ...] [2011-11-15 11:40:55] [d9c77998677156eca5bd63e08beb400b]
- RMPD          [Blocked Bootstrap Plot - Central Tendency] [Blocked bootstrap...] [2011-11-15 11:43:12] [8432dc408001a08517818ba7ac24bdb0] [Current]
-    D            [Blocked Bootstrap Plot - Central Tendency] [Blocked bootstrap...] [2011-11-15 11:45:28] [d9c77998677156eca5bd63e08beb400b]
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Dataseries X:
1.544
1.544
1.524
1.524
1.524
1.524
1.558
1.558
1.558
1.558
1.558
1.558
1.536
1.536
1.536
1.536
1.536
1.536
1.536
1.536
1.536
1.536
1.559
1.559
1.559
1.591
1.591
1.591
1.591
1.591
1.624
1.624
1.587
1.587
1.587
1.572
1.572
1.572
1.636
1.636
1.636
1.660
1.660
1.660
1.660
1.660
1.660
1.682
1.682
1.682
1.682
1.682
1.658
1.658
1.658
1.616
1.616
1.639
1.639
1.639
1.639
1.606
1.606
1.606
1.606
1.606
1.606
1.569
1.569
1.569
1.571
1.596
1.596
1.596
1.643
1.643
1.643
1.643
1.643
1.643
1.643
1.599
1.599
1.599
1.599
1.586
1.586
1.586
1.586
1.586
1.618
1.618
1.657
1.657
1.657
1.657
1.657
1.645
1.645
1.645
1.585
1.585
1.593
1.593
1.593
1.593
1.627
1.627
1.627
1.627
1.627
1.590
1.590
1.590
1.590
1.590
1.590
1.590
1.607
1.607
1.607




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=142763&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=142763&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=142763&T=0

As an alternative you can also use a 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' @ jenkins.wessa.net







Estimation Results of Blocked Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean1.595297520661161.602495867768591.610202479338840.01082322465264450.0149049586776857
median1.5911.5961.6060.01330068044125570.0150000000000001
midrange1.5981.6031.6030.007232541414373030.00500000000000012

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 1.59529752066116 & 1.60249586776859 & 1.61020247933884 & 0.0108232246526445 & 0.0149049586776857 \tabularnewline
median & 1.591 & 1.596 & 1.606 & 0.0133006804412557 & 0.0150000000000001 \tabularnewline
midrange & 1.598 & 1.603 & 1.603 & 0.00723254141437303 & 0.00500000000000012 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=142763&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]1.59529752066116[/C][C]1.60249586776859[/C][C]1.61020247933884[/C][C]0.0108232246526445[/C][C]0.0149049586776857[/C][/ROW]
[ROW][C]median[/C][C]1.591[/C][C]1.596[/C][C]1.606[/C][C]0.0133006804412557[/C][C]0.0150000000000001[/C][/ROW]
[ROW][C]midrange[/C][C]1.598[/C][C]1.603[/C][C]1.603[/C][C]0.00723254141437303[/C][C]0.00500000000000012[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=142763&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=142763&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Estimation Results of Blocked Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean1.595297520661161.602495867768591.610202479338840.01082322465264450.0149049586776857
median1.5911.5961.6060.01330068044125570.0150000000000001
midrange1.5981.6031.6030.007232541414373030.00500000000000012



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')