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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationTue, 18 Aug 2009 12:43:29 -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/Aug/18/t125062105161m5aocm3mfswyr.htm/, Retrieved Mon, 06 May 2024 15:47:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=42870, Retrieved Mon, 06 May 2024 15:47:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact207

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [Opgave 6 Oefening...] [2008-12-03 17:26:25] [61fbc7ea9bd73829dc055ddb9178936d]
- RMPD    [Bootstrap Plot - Central Tendency] [dennis volkaerts ...] [2009-08-18 18:43:29] [9e20205489828c19845a9d736cd20362] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
17,23
17,36
17,39
17,29
17,28
17,4
17,51
17,54
17,64
17,65
17,5
17,37
17,56
17,49
17,61
17,79
17,83
17,56
17,95
18,09
18,38
18,38
18,44
18,84
19,01
19,06
19,06
18,97
18,98
19,41
19,55
19,64
19,71
19,48
19,48
19,41
19,25
19,14
19,21
19,3
19,53
19,14
19,16
19,24
19,38
19,27
19,27
19,07
19,15
19,24
19,36
19,57
19,59
19,36
19,46
19,65
19,46
19,51
19,64
19,64
19,69
19,28
19,67
19,65
19,6
19,53
19,64
19,67
19,81
19,73
19,87
19,97
20,12
19,94
20,31
20,13
20,22
20,38
20,44
20,34
20,14
19,97
19,82
19,98
20,12

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=42870&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]4 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=42870&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=42870&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Estimation Results of Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean18.923235294117619.052352941176519.10858823529410.1098230518292280.185352941176472
median19.2519.3619.410.1039591756608420.16
midrange18.8112518.83518.8350.02109840887704260.0237499999999997

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 18.9232352941176 & 19.0523529411765 & 19.1085882352941 & 0.109823051829228 & 0.185352941176472 \tabularnewline
median & 19.25 & 19.36 & 19.41 & 0.103959175660842 & 0.16 \tabularnewline
midrange & 18.81125 & 18.835 & 18.835 & 0.0210984088770426 & 0.0237499999999997 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=42870&T=1

[TABLE]
[ROW][C]Estimation Results of 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]18.9232352941176[/C][C]19.0523529411765[/C][C]19.1085882352941[/C][C]0.109823051829228[/C][C]0.185352941176472[/C][/ROW]
[ROW][C]median[/C][C]19.25[/C][C]19.36[/C][C]19.41[/C][C]0.103959175660842[/C][C]0.16[/C][/ROW]
[ROW][C]midrange[/C][C]18.81125[/C][C]18.835[/C][C]18.835[/C][C]0.0210984088770426[/C][C]0.0237499999999997[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=42870&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=42870&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 Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean18.923235294117619.052352941176519.10858823529410.1098230518292280.185352941176472
median19.2519.3619.410.1039591756608420.16
midrange18.8112518.83518.8350.02109840887704260.0237499999999997


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 50 ;
Parameters (R input):
par1 = 50 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
if (par1 < 10) par1 = 10
if (par1 > 5000) par1 = 5000
library(lattice)
library(boot)
boot.stat <- function(s,i)
{
s.mean <- mean(s[i])
s.median <- median(s[i])
s.midrange <- (max(s[i]) + min(s[i])) / 2
c(s.mean, s.median, s.midrange)
}
(r <- boot(x,boot.stat, R=par1, stype='i'))
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$t[,3]))
colnames(z) <- list('mean','median','midrange')
bitmap(file='plot7.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Bootstrap',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$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')