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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 computationThu, 14 May 2009 12:33:11 -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/May/14/t12423260143j0c61fbpni6kkw.htm/, Retrieved Sun, 28 Apr 2024 21:45:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=40084, Retrieved Sun, 28 Apr 2024 21:45:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Mean Plot] [] [2009-05-07 16:38:34] [595bbfb6ab1e20d51262e8b831f4c453]
- RMPD  [(Partial) Autocorrelation Function] [] [2009-05-14 18:27:23] [96b01d8cb0304fe86f721affdc70b94f]
- RMP       [Bootstrap Plot - Central Tendency] [] [2009-05-14 18:33:11] [5ece983fa688b54e830000b964b580e8] [Current]
-             [Bootstrap Plot - Central Tendency] [] [2009-05-14 18:35:30] [96b01d8cb0304fe86f721affdc70b94f]
-               [Bootstrap Plot - Central Tendency] [] [2009-05-14 18:37:18] [96b01d8cb0304fe86f721affdc70b94f]
- RMPD          [Blocked Bootstrap Plot - Central Tendency] [] [2009-05-14 18:44:53] [96b01d8cb0304fe86f721affdc70b94f]
-   PD            [Blocked Bootstrap Plot - Central Tendency] [] [2009-05-14 18:47:09] [96b01d8cb0304fe86f721affdc70b94f]
-   P               [Blocked Bootstrap Plot - Central Tendency] [] [2009-05-14 18:49:15] [96b01d8cb0304fe86f721affdc70b94f]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3851.3
3851.8
3854.1
3858.4
3861.6
3856.3
3855.8
3860.4
3855.1
3839.5
3833
3833.6
3826.8
3818.2
3811.4
3806.8
3810.3
3818.2
3858.9
3867.8
3872.3
3873.3
3876.7
3882.6
3883.5
3882.2
3888.1
3893.7
3901.9
3914.3
3930.3
3948.3
3971.5
3990.1
3993
3998
4015.8
4041.2
4060.7
4076.7
4103
4125.3
4139.7
4146.7
4158
4155.1
4144.8
4148.2
4142.5
4142.1
4145.4
4146.3
4143.5
4149.2
4158.9
4166.1
4179.1
4194.4
4211.7
4226.3
4235.8
4243.6
4258.7
4278.2
4298
4315.1
4334.3
4356
4374
4395.5
4417.8
4432.8
4446.3

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=40084&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=40084&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=40084&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 Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean4024.192123287674044.327397260274059.8058219178123.656680306771435.6136986301367
median3976.154015.84072.779.018679811698196.5499999999997
midrange4119.84126.554127.86259.376556142957888.0625

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 4024.19212328767 & 4044.32739726027 & 4059.80582191781 & 23.6566803067714 & 35.6136986301367 \tabularnewline
median & 3976.15 & 4015.8 & 4072.7 & 79.0186798116981 & 96.5499999999997 \tabularnewline
midrange & 4119.8 & 4126.55 & 4127.8625 & 9.37655614295788 & 8.0625 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=40084&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]4024.19212328767[/C][C]4044.32739726027[/C][C]4059.80582191781[/C][C]23.6566803067714[/C][C]35.6136986301367[/C][/ROW]
[ROW][C]median[/C][C]3976.15[/C][C]4015.8[/C][C]4072.7[/C][C]79.0186798116981[/C][C]96.5499999999997[/C][/ROW]
[ROW][C]midrange[/C][C]4119.8[/C][C]4126.55[/C][C]4127.8625[/C][C]9.37655614295788[/C][C]8.0625[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=40084&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=40084&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
mean4024.192123287674044.327397260274059.8058219178123.656680306771435.6136986301367
median3976.154015.84072.779.018679811698196.5499999999997
midrange4119.84126.554127.86259.376556142957888.0625


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')