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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 computationSat, 27 Nov 2010 14:31:27 +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/27/t1290868216nj6zgpbv5ouln0l.htm/, Retrieved Mon, 29 Apr 2024 13:38:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=102392, Retrieved Mon, 29 Apr 2024 13:38:06 +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] [opgave 7 oef 2] [2010-11-27 14:31:27] [3c84fba69796ffa9703fc49b6977555d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
102,8
106,3
103,7
106,9
104,3
105,4
96,2
95,7
95,9
93,6
94,7
94,5
96,6
96,7
98,9
102
105,2
106,4
99,3
96,4
93,1
95,6
93,3
96,7
105,6
105,2
107
104,9
104,5
105,2
99,7
100,2
98,5
98,4
97,1
98,4
100,6
111,3
119
117,8
108,8
109,3
103,5
103,7
110
105,5
110,4
106,7
110,2
105,2
108
108,1
107,2
106
99,4
100,2
100,3
100,8
99,5
100,2
103
111
120,5
109,5
106,6
105,5
103,9
104,9
104,8
99,6
97
95,4
99,3
103,9
107,4
107,4
111
113,2
108,5
113,3
113,8
105,3
107,5
109,4
118,9
119
115
124,1
120,5
117,7
117,1
118,1
119,6
118,8
124,9
124
124,9
121,7
121,6
125,1
127,9
129
130,1
130,3
127,9
124,1
125,7
129,2
129,2
132,6
131,5
131
125,8
127,2
127,3
127,5
122
118,4
118,3
115,5

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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=102392&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=102392&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=102392&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'RServer@AstonUniversity' @ vre.aston.ac.uk






Estimation Results of Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean108.95875109.831666666667110.1331250.9441857666289841.174375
median105.75106.95107.3750.958721512627891.62500000000001
midrange112.55112.85112.850.3095816795718530.299999999999997

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 108.95875 & 109.831666666667 & 110.133125 & 0.944185766628984 & 1.174375 \tabularnewline
median & 105.75 & 106.95 & 107.375 & 0.95872151262789 & 1.62500000000001 \tabularnewline
midrange & 112.55 & 112.85 & 112.85 & 0.309581679571853 & 0.299999999999997 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=102392&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]108.95875[/C][C]109.831666666667[/C][C]110.133125[/C][C]0.944185766628984[/C][C]1.174375[/C][/ROW]
[ROW][C]median[/C][C]105.75[/C][C]106.95[/C][C]107.375[/C][C]0.95872151262789[/C][C]1.62500000000001[/C][/ROW]
[ROW][C]midrange[/C][C]112.55[/C][C]112.85[/C][C]112.85[/C][C]0.309581679571853[/C][C]0.299999999999997[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=102392&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=102392&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
mean108.95875109.831666666667110.1331250.9441857666289841.174375
median105.75106.95107.3750.958721512627891.62500000000001
midrange112.55112.85112.850.3095816795718530.299999999999997


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')