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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 computationSun, 28 Nov 2010 16:59:02 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/28/t1290963465pl1dqvt6y42969a.htm/, Retrieved Fri, 03 May 2024 03:35:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=102662, Retrieved Fri, 03 May 2024 03:35:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W22
Estimated Impact109

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bootstrap Plot - Central Tendency] [Consumptieprijsin...] [2010-11-28 16:59:02] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
98,4
96,5
97,4
99,2
100,8
101,8
102,7
100
100,8
101,7
99
101,7
100,2
101,2
99,5
100,8
100,7
99,5
99,4
101,1
97,2
98,1
97,8
95,5
96,3
93,6
96,7
95,1
97,7
96,5
98,1
97,3
97
93,7
95,6
94,6
95,1
94,5
93,6
92,1
95,9
98,1
98,2
96,2
94,1
95
93,4
95,4
93,5
94,5
94,3
95,7
98,4
99,4
99,2
99
99,4
99,3
98,6
98,7
96
98,7
100,1
100
101,5
101,5
103,8
104,1
101
104,9
104,4
105,6
103,4
101,7
103,5
101,2
105,4
105,4
108,6
110,6
110,2
106,2
108,6
107,5
106,9
108,4
109,9
108,6
106,5
105,7
105,6
104,2
105,1
102,7
108,3
104,2
105,4
104,6
106,4
111
111,7
113,8
115,9
117,3
113,6
113,6
114,6
113,2
112,8
109,6
111,1
109,7
113
111
113,3
111,8
107,2
106,4
110
108,2
108,2

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=102662&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=102662&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=102662&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Estimation Results of Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean102.125619834711102.464462809917102.8223140495870.5563092049673320.696694214876032
median100.8101.2101.70.7897015711194780.900000000000006
midrange104.1625104.7104.70.7313818344442950.537499999999994

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 102.125619834711 & 102.464462809917 & 102.822314049587 & 0.556309204967332 & 0.696694214876032 \tabularnewline
median & 100.8 & 101.2 & 101.7 & 0.789701571119478 & 0.900000000000006 \tabularnewline
midrange & 104.1625 & 104.7 & 104.7 & 0.731381834444295 & 0.537499999999994 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=102662&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]102.125619834711[/C][C]102.464462809917[/C][C]102.822314049587[/C][C]0.556309204967332[/C][C]0.696694214876032[/C][/ROW]
[ROW][C]median[/C][C]100.8[/C][C]101.2[/C][C]101.7[/C][C]0.789701571119478[/C][C]0.900000000000006[/C][/ROW]
[ROW][C]midrange[/C][C]104.1625[/C][C]104.7[/C][C]104.7[/C][C]0.731381834444295[/C][C]0.537499999999994[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=102662&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=102662&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
mean102.125619834711102.464462809917102.8223140495870.5563092049673320.696694214876032
median100.8101.2101.70.7897015711194780.900000000000006
midrange104.1625104.7104.70.7313818344442950.537499999999994


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')